Public report of D4Vinci/Scrapling, published 25 October 2026. It has the same score, grades and number of
findings as the full report. The details of security findings (where they are in the code, which known
vulnerabilities and which passwords or keys were found) are left out, so nobody can use them against the project.
Public
Executive reportMeasured under the Code Assurance Index, an open standard for code quality, using its published scoring rules (rubric version 2026.10.5). Watchdog signed this survey and filed it in the standard's public register, where anyone can look it up by its survey ID. Check this survey →
Signed and filed in the standard's public register, 25 October 2026, 00:06 UTC · Survey ID cd_bcd3f5a4f2ec445d9e1d54385b728c58
D4Vinci/Scrapling
Measured 25 October 2026, 00:03 UTC, at code version 82180a1
No regression since the last survey
A regression is a drop of 0.5 points or more in the score, or of 2.5 or more out of 10 in a dimension, since the last survey.
CAI scoreAdequate
CriticalWeakAdequateStrongExemplary
No change since the last survey
The score is built from these lenses. Each lens grades one area of the code, such as how easy it is to change or how well it is protected. The weakest lens counts most, so one weak area pulls the score down instead of being averaged away.
A small codebase: 14,556 lines of product code in 2 projects, tests not counted · Estimated rebuild effort: about 0.3 person-years · Weakest lens: Production Readiness, 57
Findings by how serious they are
33 critical91 serious37 minor
15 dimensions are left out of the score. For each one, Watchdog either couldn't measure it in this code, or found no evidence of the safeguard it looks for, such as a tested backup restore. They count neither for nor against the score, and Limitations lists each one with its reason.
This survey was produced by
Watchdog
Producer
Canine Development
Analyzer
Watchdog engine 1.0.0
Measured
25 October 2026, 00:03 UTC
A measurement, not a certificate. The Code Assurance Index records a number
together with the evidence it was worked out from, so anyone can recalculate it, but it doesn't certify or guarantee
the code. The standard was written by Canine Development, which also builds Watchdog. It is open to any tool that
implements it, and today Watchdog is the only one that does.
The score is deterministic, which means the same code always gets the same score. Watchdog works it out with tools that follow published rules, and an AI model never decides it, so anyone who applies the same rules to the same code gets the same number. How it is done is explained under Methodology ▸
54of 58 dimensions measured by tools. The other 4 are advisory notes from an AI model, which explain the code but don't count towards the score.
148of 161 findings point to an exact file and line, so you can open each one in the code. The other 13 concern the repository as a whole, such as missing documentation.
58dimensions across 6 lenses, read from 14,556 lines of product code in 2 projects.
D4Vinci/Scrapling scores 64 out of 100 and is rated Adequate, while four of its six lenses score higher still: Performance at 100, rated Exemplary, Architecture at 86 and Security and Compliance at 71, both rated Adequate, and Maturity at 75, rated Strong. The project hasn't declared a stable release yet, so its lenses are rated against a lower bar than a released product's, while the score itself is worked out as for any repository.
The score is lower than those four because the weakest lens counts most, and here that is Production Readiness, which scores 57 and is rated Adequate. Its Security & performance tooling dimension scores 0 out of 10, and until that reaches 4, Production Readiness can't be rated higher than Adequate. The first actions are to add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing and to make sure a person approves each release, and if Production Readiness rose to 70, the score would rise from 64 to about 69.
33 findings are critical: 31 in Static Analysis (SAST) and two in IaC & Container Security.
Watchdog couldn't measure 14 dimensions in this code, among them Code Coverage, Navigability and Dependency Vulnerabilities, so they are left out of the score. The product code is 14,556 lines, which would take an estimated 0.3 person-years, around €42,000, to rebuild.
A lens is capped at Adequate while one of its dimensions scores below 4 out of 10, however well the rest of the lens does. That way a serious problem in one place always shows in the lens's rating.
How the score is built: what each lens counts forThe lenses don't count equally. The weakest lens counts most, the next weakest a little less, and so on, so a weak area pulls the score down instead of being averaged away. In the bar below, each part's width shows how much that lens counts and its colour shows its rating. Improving the widest part moves the score most, and as that lens improves, it counts for less, because another lens becomes the weakest.
Watchdog compares every finding with the previous survey's. A finding that has only moved to another line in the same file counts as the same finding, so only new findings appear here. Advisory notes from the AI model are left out, because the model words them afresh in every survey.
Fixed since the last survey: 26 findings
These 26 critical or serious findings were in the previous survey, on 10 October 2026, and are gone from this one.
€14,000 to €69,000, for 230 to 731 hours of work, about 0.3 person-years for one engineer
Difficulty of the problem
Standard, judged from the kind of software and how it is built. Code that solves a harder problem, such as payments or medical bookings, costs more per line to write.
Quality factor
×0.9 at a score of 64. Better code takes more work to write, so the factor is ×1.0 at a score of 70, lower below it and higher above it.
Size
Small: 14,556 lines of product code
How the estimate is made: Watchdog prices the hours a team would need to write this code again, at €60 to €95 an hour. Declarations and boilerplate are priced at a fast rate and logic at a slower one, and the logic is then multiplied by the difficulty of the problem and by the quality factor. The estimate can be about 30% off either way, and it moves most with the hourly rate and the difficulty. For this code, every line is priced as straight logic, which keeps the estimate on the low side.
Top priorities
The three actions that raise the score most for the work they take. The full ranked list is in the Roadmap.
1
Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing.
The release job names an environment, but its approval rules live in the code host's settings, where Watchdog can't see them. Check that a reviewer must approve each release, or publish releases as drafts first, so a bad build can be stopped before anyone downloads it.
Each conclusion below combines several dimensions into one point that no single dimension shows, and lists the measurements it rests on.
Where the investment is most exposed · Value at risk
This code would cost about €42,000 to build again, about 0.3 person-years of work. Its weakest lens is Production Readiness, at 57 (Adequate). Production Readiness is about whether the product is ready to run in production and to change without breaking. Because the weakest lens counts most in the score, work on Production Readiness also moves the score most.
Based on: the rebuild estimate · the Production Readiness grade · how much each lens counts
What to do: Start with the Production Readiness actions in Top priorities.
If you do one thing first · First action
Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing. Of all the actions in this report, it raises the score most for the work it takes: about 6 points for medium effort. The next survey will show the change in the score.
Based on: the ranked actions in Top priorities, by score gain and effort
Architecture: which parts of the code depend on which
The code is divided into modules, groups of code that each handle one part of the product, and a module depends on another when it uses code from it. The table lists the modules both down the side and across the top, in an order where each module should only use modules listed above it. A number in a cell means the module on that row uses the module in that column, and it counts the pairs of building blocks (types) that connect them. The one thing to look for is a mark above the diagonal line. It means two or more modules use each other in a loop (a dependency cycle), so none of them can be changed, tested or released on its own.
54 modules, with 39 dependencies between them. Every module uses only modules listed above it, so there are no loops.
The table shows the 40 modules with the most connections; 14 more are left out to keep it readable.
Which modules use which. The module on each row uses the module in each column, and the number counts the pairs of types that connect them. A cell above the diagonal is part of a loop.
scrapling.core.ai.server uses scrapling.core.ai._network_formatting, so a change to scrapling.core.ai._network_formatting can break scrapling.core.ai.server, while a change to scrapling.core.ai.server can't break scrapling.core.ai._network_formatting.
Position
Below the diagonal: scrapling.core.ai.server uses a module listed above it, which is the healthy direction.
scrapling.engines._browsers uses scrapling.engines._browsers._validators, so a change to scrapling.engines._browsers._validators can break scrapling.engines._browsers, while a change to scrapling.engines._browsers can't break scrapling.engines._browsers._validators.
Position
Below the diagonal: scrapling.engines._browsers uses a module listed above it, which is the healthy direction.
scrapling.engines._browsers._base uses scrapling.engines._browsers._validators, so a change to scrapling.engines._browsers._validators can break scrapling.engines._browsers._base, while a change to scrapling.engines._browsers._base can't break scrapling.engines._browsers._validators.
Position
Below the diagonal: scrapling.engines._browsers._base uses a module listed above it, which is the healthy direction.
scrapling.parser uses scrapling.core.custom_types, so a change to scrapling.core.custom_types can break scrapling.parser, while a change to scrapling.parser can't break scrapling.core.custom_types.
Position
Below the diagonal: scrapling.parser uses a module listed above it, which is the healthy direction.
scrapling.parser uses scrapling.core.mixins, so a change to scrapling.core.mixins can break scrapling.parser, while a change to scrapling.parser can't break scrapling.core.mixins.
Position
Below the diagonal: scrapling.parser uses a module listed above it, which is the healthy direction.
scrapling.spiders.scheduler uses scrapling.spiders.request, so a change to scrapling.spiders.request can break scrapling.spiders.scheduler, while a change to scrapling.spiders.scheduler can't break scrapling.spiders.request.
Position
Below the diagonal: scrapling.spiders.scheduler uses a module listed above it, which is the healthy direction.
1 pair of types, where a type in scrapling.core uses a type in scrapling.parser.
The types involved
scrapling.core.shell → scrapling.parser.Selector
Direction
scrapling.core uses scrapling.parser, so a change to scrapling.parser can break scrapling.core, while a change to scrapling.core can't break scrapling.parser.
Position
Below the diagonal: scrapling.core uses a module listed above it, which is the healthy direction.
scrapling.core.shell uses scrapling.core.custom_types, so a change to scrapling.core.custom_types can break scrapling.core.shell, while a change to scrapling.core.shell can't break scrapling.core.custom_types.
Position
Below the diagonal: scrapling.core.shell uses a module listed above it, which is the healthy direction.
scrapling.core.shell uses scrapling.parser, so a change to scrapling.parser can break scrapling.core.shell, while a change to scrapling.core.shell can't break scrapling.parser.
Position
Below the diagonal: scrapling.core.shell uses a module listed above it, which is the healthy direction.
scrapling.engines.toolbelt.custom uses scrapling.parser, so a change to scrapling.parser can break scrapling.engines.toolbelt.custom, while a change to scrapling.engines.toolbelt.custom can't break scrapling.parser.
Position
Below the diagonal: scrapling.engines.toolbelt.custom uses a module listed above it, which is the healthy direction.
scrapling.core.ai uses scrapling.core.ai._network_formatting, so a change to scrapling.core.ai._network_formatting can break scrapling.core.ai, while a change to scrapling.core.ai can't break scrapling.core.ai._network_formatting.
Position
Below the diagonal: scrapling.core.ai uses a module listed above it, which is the healthy direction.
scrapling.core.ai uses scrapling.core.ai.server, so a change to scrapling.core.ai.server can break scrapling.core.ai, while a change to scrapling.core.ai can't break scrapling.core.ai.server.
Position
Below the diagonal: scrapling.core.ai uses a module listed above it, which is the healthy direction.
scrapling.core.ai uses scrapling.engines.toolbelt.custom, so a change to scrapling.engines.toolbelt.custom can break scrapling.core.ai, while a change to scrapling.core.ai can't break scrapling.engines.toolbelt.custom.
Position
Below the diagonal: scrapling.core.ai uses a module listed above it, which is the healthy direction.
scrapling.engines._browsers._network uses scrapling.engines.toolbelt.custom, so a change to scrapling.engines.toolbelt.custom can break scrapling.engines._browsers._network, while a change to scrapling.engines._browsers._network can't break scrapling.engines.toolbelt.custom.
Position
Below the diagonal: scrapling.engines._browsers._network uses a module listed above it, which is the healthy direction.
scrapling.engines._browsers._stealth uses scrapling.engines._browsers._base, so a change to scrapling.engines._browsers._base can break scrapling.engines._browsers._stealth, while a change to scrapling.engines._browsers._stealth can't break scrapling.engines._browsers._base.
Position
Below the diagonal: scrapling.engines._browsers._stealth uses a module listed above it, which is the healthy direction.
scrapling.engines._browsers._stealth uses scrapling.engines.toolbelt.custom, so a change to scrapling.engines.toolbelt.custom can break scrapling.engines._browsers._stealth, while a change to scrapling.engines._browsers._stealth can't break scrapling.engines.toolbelt.custom.
Position
Below the diagonal: scrapling.engines._browsers._stealth uses a module listed above it, which is the healthy direction.
scrapling.engines.static uses scrapling.engines.toolbelt.custom, so a change to scrapling.engines.toolbelt.custom can break scrapling.engines.static, while a change to scrapling.engines.static can't break scrapling.engines.toolbelt.custom.
Position
Below the diagonal: scrapling.engines.static uses a module listed above it, which is the healthy direction.
scrapling.engines.toolbelt.convertor uses scrapling.engines.toolbelt.custom, so a change to scrapling.engines.toolbelt.custom can break scrapling.engines.toolbelt.convertor, while a change to scrapling.engines.toolbelt.convertor can't break scrapling.engines.toolbelt.custom.
Position
Below the diagonal: scrapling.engines.toolbelt.convertor uses a module listed above it, which is the healthy direction.
scrapling.fetchers.requests uses scrapling.engines.toolbelt.custom, so a change to scrapling.engines.toolbelt.custom can break scrapling.fetchers.requests, while a change to scrapling.fetchers.requests can't break scrapling.engines.toolbelt.custom.
Position
Below the diagonal: scrapling.fetchers.requests uses a module listed above it, which is the healthy direction.
scrapling.fetchers.stealth_chrome uses scrapling.engines.toolbelt.custom, so a change to scrapling.engines.toolbelt.custom can break scrapling.fetchers.stealth_chrome, while a change to scrapling.fetchers.stealth_chrome can't break scrapling.engines.toolbelt.custom.
Position
Below the diagonal: scrapling.fetchers.stealth_chrome uses a module listed above it, which is the healthy direction.
scrapling.integrations uses scrapling.engines.toolbelt.custom, so a change to scrapling.engines.toolbelt.custom can break scrapling.integrations, while a change to scrapling.integrations can't break scrapling.engines.toolbelt.custom.
Position
Below the diagonal: scrapling.integrations uses a module listed above it, which is the healthy direction.
scrapling.spiders.cache uses scrapling.engines.toolbelt.custom, so a change to scrapling.engines.toolbelt.custom can break scrapling.spiders.cache, while a change to scrapling.spiders.cache can't break scrapling.engines.toolbelt.custom.
Position
Below the diagonal: scrapling.spiders.cache uses a module listed above it, which is the healthy direction.
scrapling.spiders.session uses scrapling.spiders.request, so a change to scrapling.spiders.request can break scrapling.spiders.session, while a change to scrapling.spiders.session can't break scrapling.spiders.request.
Position
Below the diagonal: scrapling.spiders.session uses a module listed above it, which is the healthy direction.
scrapling.spiders.session uses scrapling.engines.toolbelt.custom, so a change to scrapling.engines.toolbelt.custom can break scrapling.spiders.session, while a change to scrapling.spiders.session can't break scrapling.engines.toolbelt.custom.
Position
Below the diagonal: scrapling.spiders.session uses a module listed above it, which is the healthy direction.
scrapling.spiders.engine uses scrapling.spiders.request, so a change to scrapling.spiders.request can break scrapling.spiders.engine, while a change to scrapling.spiders.engine can't break scrapling.spiders.request.
Position
Below the diagonal: scrapling.spiders.engine uses a module listed above it, which is the healthy direction.
scrapling.spiders.engine uses scrapling.spiders.result, so a change to scrapling.spiders.result can break scrapling.spiders.engine, while a change to scrapling.spiders.engine can't break scrapling.spiders.result.
Position
Below the diagonal: scrapling.spiders.engine uses a module listed above it, which is the healthy direction.
scrapling.spiders.engine uses scrapling.engines.toolbelt.custom, so a change to scrapling.engines.toolbelt.custom can break scrapling.spiders.engine, while a change to scrapling.spiders.engine can't break scrapling.engines.toolbelt.custom.
Position
Below the diagonal: scrapling.spiders.engine uses a module listed above it, which is the healthy direction.
scrapling.spiders.engine uses scrapling.spiders.session, so a change to scrapling.spiders.session can break scrapling.spiders.engine, while a change to scrapling.spiders.engine can't break scrapling.spiders.session.
Position
Below the diagonal: scrapling.spiders.engine uses a module listed above it, which is the healthy direction.
scrapling.spiders.spider uses scrapling.spiders.request, so a change to scrapling.spiders.request can break scrapling.spiders.spider, while a change to scrapling.spiders.spider can't break scrapling.spiders.request.
Position
Below the diagonal: scrapling.spiders.spider uses a module listed above it, which is the healthy direction.
scrapling.spiders.spider uses scrapling.spiders.result, so a change to scrapling.spiders.result can break scrapling.spiders.spider, while a change to scrapling.spiders.spider can't break scrapling.spiders.result.
Position
Below the diagonal: scrapling.spiders.spider uses a module listed above it, which is the healthy direction.
scrapling.spiders.spider uses scrapling.spiders.session, so a change to scrapling.spiders.session can break scrapling.spiders.spider, while a change to scrapling.spiders.spider can't break scrapling.spiders.session.
Position
Below the diagonal: scrapling.spiders.spider uses a module listed above it, which is the healthy direction.
agent-skill.Scrapling-Skill.examples.04_spider uses scrapling.engines.toolbelt.custom, so a change to scrapling.engines.toolbelt.custom can break agent-skill.Scrapling-Skill.examples.04_spider, while a change to agent-skill.Scrapling-Skill.examples.04_spider can't break scrapling.engines.toolbelt.custom.
Position
Below the diagonal: agent-skill.Scrapling-Skill.examples.04_spider uses a module listed above it, which is the healthy direction.
agent-skill.Scrapling-Skill.examples.04_spider uses scrapling.spiders.spider, so a change to scrapling.spiders.spider can break agent-skill.Scrapling-Skill.examples.04_spider, while a change to agent-skill.Scrapling-Skill.examples.04_spider can't break scrapling.spiders.spider.
Position
Below the diagonal: agent-skill.Scrapling-Skill.examples.04_spider uses a module listed above it, which is the healthy direction.
scrapling.spiders.templates.crawler uses scrapling.spiders.links, so a change to scrapling.spiders.links can break scrapling.spiders.templates.crawler, while a change to scrapling.spiders.templates.crawler can't break scrapling.spiders.links.
Position
Below the diagonal: scrapling.spiders.templates.crawler uses a module listed above it, which is the healthy direction.
scrapling.spiders.templates.crawler uses scrapling.spiders.spider, so a change to scrapling.spiders.spider can break scrapling.spiders.templates.crawler, while a change to scrapling.spiders.templates.crawler can't break scrapling.spiders.spider.
Position
Below the diagonal: scrapling.spiders.templates.crawler uses a module listed above it, which is the healthy direction.
scrapling.spiders.templates.feed uses scrapling.spiders.spider, so a change to scrapling.spiders.spider can break scrapling.spiders.templates.feed, while a change to scrapling.spiders.templates.feed can't break scrapling.spiders.spider.
Position
Below the diagonal: scrapling.spiders.templates.feed uses a module listed above it, which is the healthy direction.
scrapling.spiders.templates.shopify uses scrapling.spiders.spider, so a change to scrapling.spiders.spider can break scrapling.spiders.templates.shopify, while a change to scrapling.spiders.templates.shopify can't break scrapling.spiders.spider.
Position
Below the diagonal: scrapling.spiders.templates.shopify uses a module listed above it, which is the healthy direction.
scrapling.spiders.templates.sitemap uses scrapling.spiders.spider, so a change to scrapling.spiders.spider can break scrapling.spiders.templates.sitemap, while a change to scrapling.spiders.templates.sitemap can't break scrapling.spiders.spider.
Position
Below the diagonal: scrapling.spiders.templates.sitemap uses a module listed above it, which is the healthy direction.
scrapling.spiders.templates.site_to_markdown uses scrapling.spiders.templates.crawler, so a change to scrapling.spiders.templates.crawler can break scrapling.spiders.templates.site_to_markdown, while a change to scrapling.spiders.templates.site_to_markdown can't break scrapling.spiders.templates.crawler.
Position
Below the diagonal: scrapling.spiders.templates.site_to_markdown uses a module listed above it, which is the healthy direction.
At a glance — Code Health · 63% · Adequate · gated by D2, X4 ·
Findings mapped to OWASP categories; the specific CVEs/secrets are in the Security dimension cards below and findings.md (redacted only on the public version of this report).
OWASP category
Findings
Severity
A05:2025 — Injection
36
High / Critical
A03:2025 — Software Supply Chain Failures
5
Medium
A02:2025 — Security Misconfiguration
4
High / Critical
Roadmap
First, integrate static security analysis into the CI pipeline to block builds containing vulnerabilities and ensure every release requires explicit human approval or is published as a draft to prevent bad deployments. Next, refactor the most complex functions to reduce cyclomatic complexity and replace interpolated log messages with structured logging to improve observability. Finally, address the missing architectural decision record to complete the documentation baseline.
Ranked by impact ÷ effort. "Helps" is the estimated gain on the 0–100 health score.
Do this
Helps
Effort
Dimension
Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing.
Make sure a person approves each release. The release job names an environment, but its approval rules live in the code host's settings, where Watchdog can't see them. Check that a reviewer must approve each release, or publish releases as drafts first, so a bad build can be stopped before anyone downloads it.
Bring the 19 bodies over 15 down to 15 or less in Cyclomatic Complexity — start with StealthySession._cloudflare_solver (cyclomatic 36), AsyncStealthySession._cloudflare_solver (cyclomatic 36), CurlParser.parse (cyclomatic 33).
Every finding carries one of four grades. Three say how serious it is. The fourth says this
survey could not settle it — and it is a grade, not a gap.
Critical — 33
A definite problem that already costs you something and drags the score down: a
missing authorisation check, a dependency with a known exploit, a build that does not reproduce. Failure here
tends to cause failures elsewhere.
Serious — 91
Likely wrong, but not failing yet. It degrades
the codebase over a longer horizon and can cause failures elsewhere — not urgent this week, not something to
carry for two years either.
Minor — 37
Recorded, with no effect on how the codebase functions.
Present so the survey is complete, not because it needs doing.
Could not be resolved — 15
Something this survey could not settle
from the outside, and which could be critical or serious. Either a control was required and no
positive evidence of it exists in the repository — a backup job that nothing shows was ever restored from proves
nothing about restores — or our own analysis could not run over that part of the tree. This is not a clean
result. These are excluded from the score rather than awarded a pass, so the number on the cover neither
rewards nor penalises them: if you act on this survey without resolving them, you carry that risk yourself. Each
one is named under Limitations.
Methodology & how to trust this report
Watchdog is a deep, periodic assessment — run each sprint, monthly, or quarterly, taking the time to go wider and deeper than a quick check and surfacing in one coherent report what you'd otherwise piece together from a dozen separate tools. It scores deterministically: the same commit yields the same score, every run. 54 of 58 evaluated dimensions are computed purely by tools and static analysis (confidence 1.0); 4 documentation/naming judgement(s) are LLM-assisted and labelled advisory. Overall confidence is 1.0 — the weighted average across measured dimensions; it falls as more of the score leans on LLM-assisted judgement and rises when it's fully tool-backed.
Every figure here is one of three kinds, and we label which: ✓ Measured — a deterministic fact (LoC, complexity, coverage); ~ Modeled — an estimate from a stated model (cost, effort, value-at-risk), always a range with its assumptions, never a precise fact; ◐ Advisory — an LLM prose judgement. We never present a modelled estimate as if it were measured. Perfect or absent scores carry their provenance too (ADR-0011): ✓ Tool-verified means the property itself was measured across the surface; ○ Nothing flagged means the probes came back clean — a claim bounded by what a repository can show; ⊘ Not evidenced means a working control (a tested restore, an automated rollback) showed no positive evidence — absence of evidence is not evidence of a control, so it's excluded from the score rather than awarded a spurious 10; ◐ Sampled · advisory marks an LLM verdict over a bounded sample — advisory, never a deterministic measurement.
What we checked — 58 dimensions across the health lenses
Each chip is a dimension scored from real signals across architecture, testing, dependencies, security & compliance, documentation, git-history and code quality — in one coherent pass. A surface report typically covers a handful.
How to trust any code-health report — three questions
Can you open the finding? Real findings cite a repo-relative file and line you can open at the cited line — never an absolute scratch path. Here, 148 of 161 do; the remainder are repo-wide signals — a dimension-level measurement, not a single line. (Every path in this report is repo-relative by construction: paths are normalized at the producer and the report is rejected if any rooted path leaks through.)
Is there a tool behind the number? Every score below names the method that produced it — Roslyn, git, a scanner, or (for a handful of documentation/naming dimensions) an LLM labelled sampled · advisory — not a narrative.
Does re-running give the same result? Run it again on the same commit and the score — and this report, byte for byte — is identical. A report whose numbers move between runs is describing the run, not the code.
This report answers yes to all three. That's the bar to hold any assessment to.
Tools & methods
The actual versions used this run (captured at analysis time) — re-run on the same commit for the identical score.
Method
Backs
Version
Evaluator
Roslyn static analysis
Complexity, cohesion, coupling, dead code, API surface, layering
What ran differently this time — a tool absent, degraded, or that fell back to an estimate. Named openly, not folded silently into the scores. A degraded run also records its exact cause in diagnostics.md.
D8 Code Coverage — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. Coverage NOT MEASURED: test source is present (.py) but the built-in coverage collector has no runner for this repository's ecosystem — so this suite was never executed by it. Not scored — this is a gap in the analyzer's language coverage, not a defect in the repo. To have real coverage read, produce a coverage report in a standard format (`coverage run -m pytest` then `coverage xml`) and commit it — a hosted scan measures a clone of the repository, so a report that exists only in a working tree, a CI runner's or your own, never reaches it; the artefact is commonly gitignored, so `git add -f` that one file (or un-ignore its path) and commit it alongside the code it measures. You can widen what we reach: optional: produce a coverage report in a standard format (`coverage run -m pytest` then `coverage xml`) and commit it — a hosted scan measures a clone of the repository, so a report that exists only in a working tree, a CI runner's or your own, never reaches it; the artefact is commonly gitignored, so `git add -f` that one file (or un-ignore its path) and commit it alongside the code it measures — then the real number is read on the next scan.
D12 Dependency Hygiene — not measured this run — This is a true statement about the repository that carries nothing for its owner to act on, so it is reported here rather than as a defect in their code. This repository's 6 shipped Python distribution(s) across 3 manifest(s) WERE read; what went unanswered is the currency half, because it grades an EXACT pin against the newest release and every declaration here is a floor or a range — which installs the newest release it admits and so cannot be behind one. So pypi.org was never asked. This is a true statement about the repository, and the healthy one, NOT a gap on our side: no reader we could write would find a currency question here.
D16 Bus Factor — not measured this run — This is a true statement about the repository that carries nothing for its owner to act on, so it is reported here rather than as a defect in their code. Single-maintainer repository — bus factor is not applicable (44 contributor(s) across 1780 commit(s) sampled, automation and bot accounts excluded). One of them holds 94% of the history; the other 43 hold 0.1% each on average, below the 5% at which there is somebody to hand the work to. That is a single maintainer with drive-by contributors, not a team whose knowledge has concentrated — so the bus factor is not applicable and there is nothing here for the owner to act on.
D27 Navigability — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. Navigability is taken over call sites whose callee resolved. For C# that population includes calls into referenced assemblies; for this language only calls declared inside the repository count, so the abstention threshold reads the repository's use of its standard library as a resolution failure. That is a limit of this analyzer, not a build problem in this repository.
D30 Dependency Vulnerabilities — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. Osv: osv-scanner exited 1 with no findings — dependency CVEs were not measured. 'osv-scanner' exited 1 and produced no findings, and that exit code has no documented repo-side meaning — so this run measured nothing, and nothing here is a statement about the repository.
D43 Malicious Dependencies — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. Osv: osv-scanner exited 1 with no findings — dependency CVEs were not measured. 'osv-scanner' exited 1 and produced no findings, and that exit code has no documented repo-side meaning — so this run measured nothing, and nothing here is a statement about the repository.
AX6 Interface segregation — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. This check is computed over the public interfaces this run's compilations declare, and none was loaded, so it had nothing to read. That is a gap in this analyzer's language reach — not a finding that the repository is free of what this check looks for.
AXR1 Runtime accessibility — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. Render strategy: boot via REDACTED (REDACTED). docker build failed (exit 1) — DEPRECATED: The legacy builder is deprecated and will be removed in a future release.
Install the buildx component to build images with BuildKit:
https://docs.docker.com/go/bui…; runtime evidence skipped.
C1 Data Protection — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. These personal data controls are read from declarative annotations, request middleware, entity/column names and guard methods in a C# source model, and none was loaded on this run, so there was nothing to gather. That is a gap in this analyzer's language reach — not a finding that the repository lacks personal data controls.
C3 Audit Trail — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. 'sqlite3' is imported (scrapling/core/storage.py:5), but no entity, schema, table or model declaration this check reads was found beside it, so whether personal data is stored could not be decided.
C4 Data Retention — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. 'sqlite3' is imported (scrapling/core/storage.py:5), but no entity, schema, table or model declaration this check reads was found beside it, so whether personal data is stored could not be decided.
C5 Data-Subject Rights — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. 'sqlite3' is imported (scrapling/core/storage.py:5), but no entity, schema, table or model declaration this check reads was found beside it, so whether personal data is stored could not be decided.
GD1 Unfinished & placeholder code — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. This check reads C# syntax, F#, JavaScript/TypeScript, Dart, Ruby and Scala source only, and this repository's Python is most of its product and is not read yet, so it had nothing of this repository's product to read. That is a gap in this analyzer's language reach — not a finding that the repository is free of what this check looks for.
IC1 Incompleteness & stubs — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. This check reads C# syntax, JavaScript/TypeScript, Java, Dart, Ruby and Scala source only, and this repository's Python is most of its product and is not read yet, so it had nothing of this repository's product to read. That is a gap in this analyzer's language reach — not a finding that the repository is free of what this check looks for.
P5 DR & Backup — not measured this run — This is a true statement about the repository that carries nothing for its owner to act on, so it is reported here rather than as a defect in their code. No backup/snapshot/replication config, RTO/RPO or restore-procedure documentation was found — and no production persistence was detected either (no data-access packages, no data-store services, no database resources), so there is nothing in this repository whose loss a DR control would recover. If this system's data lives in a platform or ops repo we can't see, that's where the DR evidence belongs.
S1 Web-Security Posture — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. These web-security controls are read from declarative annotations, request middleware, entity/column names and guard methods in a C# source model, and none was loaded on this run, so there was nothing to gather. That is a gap in this analyzer's language reach — not a finding that the repository lacks web-security controls.
X5 Nullable reference types — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. This check reads C# syntax and MSBuild project configuration, TypeScript source and its tsconfig files, Java source and its package and module declarations, PHP source and its PHPStan and Psalm configuration, Dart source and its pubspec language version, Ruby source and its Sorbet and Steep configuration, Scala source and its build's explicit-nulls, WartRemover and Scalafix settings, and Kotlin source only, and no C# was loaded, no TypeScript was found, and this repository's Python is not read yet, so it had nothing of this repository's product to read. That is a gap in this analyzer's language reach — not a finding that the repository is free of what this check looks for.
Repo exclusion declarations (.gitattributes linguist-generated/vendored, .editorconfig generated_code): none declared — every source file was scored.
Limitations & what we did not check
Watchdog assesses the repository exactly as committed, and only the repository. By design it does not reach outside the source tree: the live cloud account, the running CI/CD pipeline, the host's branch-protection and approval rules, the production configuration, or a restore actually exercised against a backup are all out of scope. That boundary is a feature, not a gap — a repo-relative, deterministic scan re-runs identically on any commit and every finding opens at a real file and line, where a live audit can neither be reproduced nor traced. The visible consequence is that controls which leave no in-repo evidence are reported as "not evidenced" and excluded from the score rather than awarded a number a static scan cannot justify.
Per-dimension blind spots
For each dimension that was measured, what a static, repo-only scan structurally cannot see — the honest edge of the measurement, not a failure of it.
D1 Cyclomatic Complexity: Cyclomatic complexity counts branches statically — it cannot tell an essential decision tree from accidental tangle, nor see complexity that lives in data or configuration (large switch-case token tables, DSL lexers/parsers, data-as-code rule tables) rather than control flow: a tokenizer's many single-character cases read as high complexity though each branch is trivial.
D2 Cognitive Complexity: Cognitive-complexity heuristics approximate how hard code is to follow; genuine domain difficulty and well-named intent that eases reading are not captured.
D3 God Classes: "God class" is sized by members and responsibilities visible in the type — a deliberately broad facade over a coherent subsystem can read the same as an accidental grab-bag. For front-end JS the file-length check is cohesion-aware (a single-responsibility module — one class/IIFE — earns a 3× threshold), but cohesion is approximated from top-level declarations, not true dependency structure.
D4 Code Duplication: Duplication is token-similarity — an in-process token-stream comparison over sliding windows, with type-aware normalization — so it finds copy-paste, not semantic duplication expressed differently. Committed machine-written code (scaffolded migrations, designer/codegen output, protobuf/OpenAPI stubs, model snapshots) is EXCLUDED — its repetition is the tool's, not the team's — so the score reflects hand-written duplication only.
D6 Cohesion (LCOM4): LCOM4 cohesion is syntactic — it infers connectivity from which methods touch which fields/methods by name, not from real runtime behaviour or intent.
D9 Test Distribution: The test-pyramid shape is inferred from project/folder naming and references, with a single test host bucketed per-file by its path tier and content signals — a suite that names tiers unconventionally and gives no per-file signal can still be mis-bucketed.
D11 Test Reliability: Flakiness is inferred from history/markers — Watchdog runs the suite once (for coverage), not the repeated runs under varied conditions that reveal nondeterminism, so a flaky test never recorded as failing is invisible here.
D13 Secret Scanning: Secret detection is signature- and entropy-based on the current tree — a secret that does not match a known pattern, or one already rotated, will not be flagged (a clean scan is "nothing matched", not "no secrets exist").
D14 License Compliance: License compatibility is checked against declared package metadata and a policy — mislabelled or missing license metadata, and obligations that depend on how you distribute, are not resolved here.
D15 Churn × Complexity Hotspots: Churn hotspots come from git history — a freshly imported or squashed repository has no churn signal, and recent rewrites can mask a historically risky file.
D17 Explicit Debt: Acknowledged-debt signals (TODO/FIXME, suppressions, dead code) are textual — undocumented debt that nobody marked, and debt that lives in design rather than annotations, is invisible. Committed machine-written code (scaffolded migrations, designer/codegen output, generated stubs) is excluded — it is never the team's dead code to delete.
D19 Documentation Quality: Documentation quality is judged by an LLM over a bounded sample of docs — it reads what is written, not whether the docs match the running system, and it is advisory, not a measurement. Its critique rows are drawn from a closed category vocabulary and each row means the same thing in every run, so two scans can be compared row by row; the SET that fires is still a sample, and does not repeat exactly. Measured on one frozen input, six scans at one engine SHA: 2-5 critique rows per scan, 8 distinct rows across the six, 3 of those 8 seen in only one scan. So a D19 row is evidence about the documentation, but a COUNT of D19 rows is not a quantity — never read a change in it as an improvement or a regression.
D20 ADR Quality: ADR quality is an LLM read of the decision records present — it cannot know about decisions made and never recorded, and its verdict is sampled and advisory.
D21 Naming Consistency: Naming quality is an LLM judgement over a bounded sample — it assesses clarity/consistency of the names it sees, not domain-correctness, and is advisory.
D22 Internal API Consistency: API-surface coherence is an LLM judgement over a sample of the public surface — consistency of intent across the whole API is approximated, not exhaustively verified.
D26 Project Cohesion: Project focus is sized from members/namespaces per project — a project that is broad by deliberate design reads the same as one that has sprawled.
D28 Secrets (history): Secret-history scanning sweeps the git log for known patterns — a secret that predates the available history, or never matched a signature, is not found (clean means "nothing matched in the history we can see").
D29 Static Analysis (SAST): SAST findings are pattern-based (semgrep) — it finds classes of bug it has rules for; logic flaws, auth/authorization gaps and issues needing runtime context are out of reach (and clean means "no rule matched").
D31 IaC & Container Security: IaC scanning checks Dockerfiles/Terraform/Kubernetes against best-practice rules — it cannot see the live cloud account, runtime configuration, or drift between the committed config and what is actually deployed.
D34 Knowledge Freshness: Freshness is decayed commit RECENCY, not comprehension — code read often but rarely committed reads as orphaned, and stable code that genuinely needs no changes is penalised the same as forgotten code; bot/squash commits distort it like the bus factor.
D35 Change Coupling: Change coupling is co-change in COMMITS — files split across separate commits, or coupled only through a shared config/build step, read as uncoupled, and a sweeping commit (rename/format) is excluded so it doesn't couple everything. It shows that files change together, not WHY: a high coupling can be a healthy cohesive pair as readily as a hidden leak.
D44 Platform End-of-Life: The support table is FROZEN, so it goes out of date by losing RECALL: a release that ended support after the table was written is missed until the table is refreshed, and this dimension reading 10 is not evidence that a platform is current. Only platforms the repository DECLARES in a place this pass reads are seen — a runtime named only in a REDACTED (D31's subject), in a CI workflow (D29's), or in a file this pass does not parse (go.mod, a Gemfile ruby directive) is invisible here, which is why a repository declaring none of them abstains rather than scoring. Only frameworks with a PUBLISHED support policy are tracked: React, Flask and Express publish none, so their age cannot be judged and their absence from a report is not a statement that they are supported.
AX10 Code composition: Role is inferred from namespace/folder convention, not semantics — a domain concept living in a folder named "Services" reads as application, and the split is lines-of-code, not business value. The business-logic-share score is a SOFT, FLOORED signal: it contributes to the Architecture lens but is floored at the Critical gate, so an infrastructure-heavy design (a gateway, an ETL, a driver) is legitimately low without being nuked to zero.
M4 Documentation accuracy: Onboarding quality is an LLM read of the docs/setup present — it cannot run the onboarding or measure how long a real new joiner takes; the verdict is sampled and advisory.
P4 Deployment & Rollback: Approval/branch-protection rules live in repository settings the scan cannot see — only their in-repo evidence (config files, workflows) is checked, so a control enforced purely in the host's settings reads as "not evidenced".
P6 Release Hygiene: Rollback/observability controls are inferred from repo artefacts (pipelines, dashboards-as-code) — controls configured in external tooling, with no in-repo trace, cannot be credited.
The LLM boundary
LLM-set scores this run (4): D19, D21, D22, M4 (model: Local LLM). For these, a model reads a bounded sample and sets the numeric score; each names its own sample and method on its card. They are sampled and advisory by design: they vary at the margins between runs and are never a deterministic measurement. Every other score in this report is tool-computed at confidence 1.0.
What it measures: How tangled the control flow is — methods with many branches are hard to test and change.
Method: Cyclomatic complexity per method (1 + decision points), computed exhaustively across production source; test projects separated by convention. Deterministic.
+ 14 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Bring the 19 bodies over 15 down to 15 or less in Cyclomatic Complexity — start with StealthySession._cloudflare_solver (cyclomatic 36), AsyncStealthySession._cloudflare_solver (cyclomatic 36), CurlParser.parse (cyclomatic 33). — This score is capped by its worst body, so a finding fixed alone moves it by almost nothing — the next one down takes its place. Refactoring these 19 together lifts Cyclomatic Complexity from 7.2 to about 10.0/10, projected with the scoring formula itself and assuming each lands exactly at 15.
Enforce Cyclomatic Complexity in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d1_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: How hard the code is for a person to follow, beyond raw branching.
Method: Cognitive complexity per method (Sonar-style nesting-penalized score), computed exhaustively over production code, excluding test projects. Deterministic.
+ 26 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Bring the 3 bodies over 60 down to 60 or less in Cognitive Complexity — start with StealthySession._cloudflare_solver (cognitive 98), AsyncStealthySession._cloudflare_solver (cognitive 98), _browser_actions._run_actions (cognitive 87). — This score is capped by its worst body, so a finding fixed alone moves it by almost nothing — the next one down takes its place. Refactoring these 3 together lifts Cognitive Complexity from 3.5 to about 4.7/10, projected with the scoring formula itself and assuming each lands exactly at 60; a cleaner split scores higher.
Enforce Cognitive Complexity in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d2_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D3 · God Classes8.8 / 10Strong✓ Tool-verified
What it measures: Over-large classes that try to do too much ("god classes").
Method: God-class detection by line and method-count thresholds per logical type (partial classes unified), filtered for generated code and registration/contract false positives. Deterministic.
Resolve the 4 FileTooLong finding(s) in God Classes — start with server.py, parser.py, cli.py. — One of this dimension's main actionable groups (4 warning-level).
Resolve the 1 TooManyMethods finding(s) in God Classes — start with parser.py. — One of this dimension's main actionable groups (1 warning-level).
Enforce God Classes in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d3_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Copy-pasted code that should be shared instead.
Method: Code duplication via token-stream sliding windows with type-aware normalization (locals masked, type names preserved), density-scored per KLoC of production code. Deterministic.
11 duplicated block group(s) detected. A further 2 rows report members as variants of one another; they aggregate block groups already counted above and are not themselves counted.
+ 7 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Resolve the 2 Duplicated block (6 lines × 2) finding(s) in Code Duplication — start with static.py, __init__.py. — One of this dimension's main actionable groups (2 warning-level).
Resolve the 1 Near-duplicate member pair (69 shared lines) finding(s) in Code Duplication — start with _stealth.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 Near-duplicate member pair (35 shared lines) finding(s) in Code Duplication — start with static.py. — One of this dimension's main actionable groups (1 warning-level).
Enforce Code Duplication in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Verified — provenance only; does not change the score.
Detailed fixes: d4_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether a class's methods are focused on a single responsibility.
Method: LCOM4 cohesion per production class with at least two methods: connected components of methods sharing state or calls, computed syntactically. Deterministic, not a proxy.
Coverage: Exhaustive · type-level: LCOM4 cohesion computed over every production class — the population is all types, not a name convention.
D9 · Test Distribution10.0 / 10Exemplary✓ Tool-verified
What it measures: Whether the test suite has a healthy mix of unit / integration / end-to-end tests.
Method: Test projects classified (Unit/Integration/BDD/E2E) from compiled metadata; test methods counted exhaustively across projects with placement-agnostic disk fallback. Deterministic.
1329 test methods: 1329 unit, 0 integration, 0 BDD, 0 e2e. The Python suite contributes 1329 test function(s) across 89 file(s) declaring at least one — every `def test…` in a file pytest or unittest would collect, which is those frameworks' own definition of a case; a parametrize table counts once, so this is a floor. Its tier split is read from file names and paths only.
✓ On the Gold path — maintain.
Detailed fixes: d9_recommendation.md.
Do you agree with this assessment?
D11 · Test Reliability10.0 / 10Exemplary✓ Tool-verified
What it measures: Whether the tests pass reliably, with no flakiness.
Method: Suite re-run N times within tiered wall-clock budgets (unit to e2e); tests failing non-deterministically across runs flagged; guarded tests retried when #if guards detected.
What it measures: Whether any secrets (keys, tokens, passwords) have leaked into the code.
Method: In-process native secret scanner (entropy plus signature patterns) across all tracked files; no external tool. A clean result is a measured 10, not no-data zero. Deterministic.
What it measures: Whether the licenses of third-party packages are compatible with your policy.
Method: Third-party package licenses resolved from declared package metadata and checked against the configured policy (allow/deny/copyleft). Deterministic; clean = no incompatible license found at metadata depth.
0 of 6 shipped Python distribution(s) use a banned license. Licences were resolved from PyPI over the distributions a consumer installs — this repository's 6 declared runtime requirement(s) closed transitively over each distribution's published `requires_dist` (0 reached that way). Requirements it states ONLY under an extra, a PEP 735 dependency group, a Poetry dev group or a dev-named requirements file are excluded: pip does not install any of them for a consumer. ★ This repository commits no dependency lockfile that this pass reads, so each licence is the one PyPI publishes for the distribution's CURRENT release rather than for a pinned version. This repository publishes itself under BSD, which is its own choice and is not judged here.
What it measures: Files that change often and are also complex — the riskiest hotspots.
Method: Per production file churn times cyclomatic complexity over a rolling window, computed from git and Roslyn/JS/Razor analysis. Exhaustive, deterministic per commit date.
Top hotspots: scrapling/engines/_browsers/_stealth.py (9×36=324); scrapling/engines/_browsers/_base.py (8×20=160); scrapling/engines/toolbelt/convertor.py (6×26=156) Repeated repair below the complexity floor: scrapling/spiders/templates/sitemap.py (3 of 4 changes were fixes); scrapling/spiders/request.py (3 of 3 changes were fixes)
Resolve the 6 Hotspot finding(s) in Churn × Complexity Hotspots — start with _stealth.py, _base.py, convertor.py. — One of this dimension's main actionable groups (6 warning-level).
Resolve the 2 Repeated repair finding(s) in Churn × Complexity Hotspots — start with sitemap.py, request.py. — One of this dimension's main actionable groups (2 warning-level).
Detailed fixes: d15_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Acknowledged debt left in the code — TODOs, dead code, suppressed warnings.
Method: Roslyn syntactic debt markers (suppressions/TODO/FIXME/HACK/empty-catch/commented-code/Obsolete) plus SymbolFinder dead-code analysis; weighted-debt-per-KLoC density deducted 2.0x per unit. Deterministic, exhaustive.
0 deducted task-comment markers across 14556 LoC (0.0/KLoC) → score 10.0. Task comments only: this repository's language is read without a compiler, so D17's suppression, dead-code and commented-out-code arms did not run and this score counts fewer marker kinds than a .NET repository's would.
What it measures: Whether the project's documentation is clear, complete, and useful.
Method: Judged by language model at low temperature (0.0-0.1) on a deterministic doc sample (READMEs plus first 25 architecture docs), with two-pass stability filtering. Advisory, sampled.
Scrapling's documentation is clear and complete for a modern web scraping library: the repository root README (Scrapling) gives an overview of what the project does, installation, usage, and contribution guidance; each language-specific README (agent-skill/README.md, agent-skill/Scrapling-Skill/examples/README.md, docs/README_AR.md through docs/README_FR.md) documents its directory rather than the repository as a whole but is internally consistent for that tier. The architecture/design documentation set at 392 markdown files is well-organized and fully referenced in the outline (e.g., 'High-Performance & battle-tested Architecture', 'AI Features'). Scrapling's documentation is comprehensive and well-organized: a single root README (docs/index.md) gives an overview of what the project does, installation, usage, contribution guidance, and licensing; three language-specific READMEs (JP/PT_BR/RU) each describe their directory's purpose with equivalent install/usage content plus regional badges; a dedicated benchmarks document compares Scrapling against other libraries on HTML parsing, text extraction, and element similarity; a changelog doc lists every release with breaking changes and migration guidance; and a donate page explains sponsorship tiers. The READMEs are clipped mid-outline but the visible sections (overview, installation, usage) are complete for each document. Scrapling's documentation is comprehensive and well-structured: the READMEs cover migration to v0.5 (with a detailed checklist), an overview of parsing, HTTP requests, and stealth browsers, a dedicated AI/agent skill guide with installation for skills.sh, Clawhub, and manual download, a building-RAG-systems tutorial on crawling into Markdown, a full MCP server setup guide including one-shot tools, session management, browser state, authentication, and examples, plus an API reference for custom types, fetchers, and the MCP server response model. The documentation is clear, complete, and well-organized.
Documentation: no project overview · ×2README.md
✓ On the Gold path — maintain.
Detailed fixes: d19_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D20 · ADR Quality0.0 / 10Critical✓ Tool-verified
What it measures: Whether architecture decisions are recorded well (context, decision, consequences).
Method: Per-ADR judgment by language model at low temperature with two-pass stability; confidence is share of ADRs evaluated; enforcement-field presence detected deterministically. Advisory.
What it measures: Whether names — types, methods, variables — are clear and consistent.
Method: Judged by language model at low temperature (0.0-0.1) on a deterministic random symbol sample (fixed size, not exhaustive), with disclosed confidence band. Advisory, sampled.
0 naming inconsistencies across 0 sampled symbols.
✓ On the Gold path — maintain.
Detailed fixes: d21_recommendation.md.
Do you agree with this assessment?
D22 · Internal API ConsistencyWeak◐ Sampled · advisory
What it measures: Whether the internal API surface is consistent and coherent.
Method: Judged by language model at low temperature over a sample of the public API surface (IsPackable or .Contracts types). Sampled, advisory; confidence discounted by model uncertainty.
4 API inconsistencies across a 400-member sample of 127 exposed types.
Redundant HTTP verb methods vs generic fetch. The CLI exposes specific methods for GET, POST, PUT, and DELETE, but also exposes a generic `fetch` method. This creates confusion about which method to use for standard HTTP requests versus complex browser-based fetching.
Overlapping functionality between browser fetch methods. `browser_fetch` and `browser_fetch_once` appear to perform similar HTTP-like extraction tasks within a browser context, while `session_make_request` handles HTTP requests within a session. The distinction between 'fetch' and 'make_request' in the context of a browser session is unclear and likely redundant.
Inconsistent naming for state persistence. The session objects use `save_state`/`load_state`, while the MCP server uses `browser_save_state`/`browser_load_state`. This inconsistency extends to other methods like `close` vs `close_session`.
Duplicate configuration types. `PlaywrightConfig` and `_fetch_params` contain nearly identical fields (e.g., `timeout`, `wait`, `proxy`, `headers`), as do `StealthConfig` and `StealthFetchParams`. This duplication increases maintenance burden and risk of drift.
What to do
Resolve the 1 Redundant HTTP verb methods vs generic fetch. The CLI exposes specific… finding(s) in Internal API Consistency. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 Overlapping functionality between browser fetch methods. `browser_fetch`… finding(s) in Internal API Consistency. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 Inconsistent naming for state persistence. The session objects use… finding(s) in Internal API Consistency. — One of this dimension's main actionable groups (1 warning-level).
Detailed fixes: d22_recommendation.md · top locations in Appendix A, every location in findings.md.
1 of 2 build units (Python) flagged as possibly oversized/incoherent.
Projects may be oversized for their cohesion
What to do
Resolve the 1 Projects may be oversized for their cohesion finding(s) in Project Cohesion. — One of this dimension's main actionable groups (1 recommendation-level).
Detailed fixes: d26_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether any secrets were ever committed — scanned across the full git history, not just now.
Method: Secret scan via TWO gitleaks detect passes in an isolated checkout — the full git history, then a second --no-git pass over the working tree as it stands — merged and de-duplicated by (rule, file, line); each match flagged High. Both invocations are recorded in the audit trail. Exhaustive; when the tool is absent, or when its output cannot be parsed into the expected shape, the dimension is WITHHELD as an explicit measurement gap on our side — unscored and excluded from the lens, never a hedged middling score.
What it measures: Real static-analysis (SAST) findings — likely security bugs in the code, any language.
Method: Polyglot static analysis via semgrep across the repo using the pinned, image-baked p/security-audit + p/owasp-top-ten rulesets (no scan-time registry fetch); severity rules (ERROR/WARNING/INFO) map to a full-band severity-weighted score. Exhaustive, deterministic; degrades on parse failure.
Coverage: semgrep pattern rules over all files — exhaustive for the rule set, blind to classes of bug without a rule (clean = no rule matched).
36 finding(s): 0 critical, 31 high, 5 medium, 0 low. 20 unpinned-GitHub-Actions row(s) are reported here but scored by D36 (supply-chain provenance), which measures that posture as `pinned_actions` — one pinning decision is charged once, not once per lens. semgrep hit a parse error in 2 file(s) — `docs/overrides/main.html` (lines 1–3, lines 7–10, lines 12–27, …), `zensical.de.yml` — so no absence of findings in the named regions is evidence of anything; rows reported elsewhere in those files are real. Fix the syntax error (or exclude the file deliberately) and re-scan to cover them. Separately, one or more rules could not re-parse an embedded snippet in 3 file(s) (e.g. a workflow `run:` block read as shell). Those files WERE scanned and their other rows are unaffected; only those rules' view of those snippets is missing.
REDACTED
REDACTED
REDACTED
REDACTED
REDACTED
+ 4 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Resolve the 5 REDACTED finding(s) in Static Analysis (SAST) — start with REDACTED (2), REDACTED (2), REDACTED. — One of this dimension's main actionable groups (5 issue-level).
Resolve the 4 REDACTED finding(s) in Static Analysis (SAST) — start with REDACTED (3), REDACTED. — One of this dimension's main actionable groups (4 issue-level).
No action in Static Analysis (SAST) — all 20 REDACTED finding(s) are reported here at file:line but scored by D36 (supply-chain provenance), so none is charged to this dimension. — One of this dimension's main actionable groups (20 issue-level, 0 of them charged here).
Detailed fixes: d29_recommendation.md · top locations in Appendix A, every location in findings.md.
Resolve the 2 High IaC finding(s) in IaC & Container Security — start with REDACTED (2). — One of this dimension's main actionable groups (2 issue-level).
Resolve the 1 Medium IaC finding(s) in IaC & Container Security — start with REDACTED. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 Low IaC finding(s) in IaC & Container Security — start with REDACTED. — One of this dimension's main actionable groups (1 recommendation-level).
Detailed fixes: d31_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether anyone still has living knowledge of each file, or it has been orphaned — last understood long ago by someone now gone quiet. The sibling of the bus factor: D16 asks who owns it, D34 asks whether anyone still knows it.
Method: File orphaning as total living-knowledge decay below one focused-commit's worth within a year, computed per-file from the D16 decay model. Exhaustive, deterministic over fixed history.
Every significant source file has living knowledge — recently and meaningfully worked. Counted over 44 of the 61 production source files in this repository: the rest are under the ~2,400-byte size floor this dimension measures over.
What it measures: Whether files that change together actually belong together — pairs that repeatedly co-change in git history despite having no explicit code dependency, surfacing the hidden/logical coupling (and boundaries in the wrong place) a static scan can't see.
Method: Pairwise co-occurrence over the per-commit file sets in git history (production source only — tests and generated dropped): Degree-of-Coupling = shared ÷ min individual revisions, reported above noise floors (each file ≥10 revisions, ≥5 shared commits, ≥50% strength); sweeping commits excluded. Deterministic over fixed history.
Coverage: Population: PRODUCTION source files only — test and generated files are dropped before pairing, so a class co-changing with its own test (trivially ~100%) can't drown the real production↔production coupling. Pairs ranked by Degree-of-Coupling. A non-source file is never a coupling PARTICIPANT either: documentation, schemas, config and data files are dropped with the rest, so a code↔docs pair — a command and the reference page that restates it — is not reported however strongly the two co-change; nor is coupling that runs THROUGH a build step or config file.
What it measures: Whether the build pipeline provides supply-chain integrity — generated provenance/attestation, signed artifacts (cosign/sigstore), an SBOM, and pinned build actions. Presence of the configuration, not a runtime guarantee.
Method: Supply-chain provenance/signing read deterministically from CI/build config (.github/workflows, .gitlab-ci.yml, azure-pipelines, Jenkinsfile, .circleci) + the release surface: four signals — generated provenance/attestation (SLSA/in-toto/actions-attest), artifact signing (cosign/sigstore/gitsign), an SBOM (syft/sbom-action/*.spdx.json/*.cdx.json), and SHA-pinned build actions — scored 10·present/denom. NotApplicable without a build pipeline. Detects configuration presence, not runtime enforcement.
Resolve the 1 REDACTED finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 REDACTED finding(s) in Supply-chain Provenance & Signing — start with REDACTED. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 REDACTED finding(s) in Supply-chain Provenance & Signing — start with REDACTED. — One of this dimension's main actionable groups (1 warning-level).
Detailed fixes: d36_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether anyone still ships security patches for the platform this repository RUNS ON — the runtime it pins and the framework majors its own constraints hold it to. Separate from D12 because the question differs: a current Django on an end-of-life Python is perfectly up to date and completely unsupported, and the fix is a migration rather than a version bump. What the repository says it merely SUPPORTS is never charged.
Method: End-of-life PLATFORM read from the repository's own declarations and graded against a FROZEN, dated table of vendor support dates — no network, no feed, no API, so this dimension answers identically inside a closed scan fence. Two subjects: a RUNTIME the project pins (a single or all-end-of-life TargetFramework, a .nvmrc or .python-version, a requires-python CAP) and a FRAMEWORK major a dependency constraint cannot move off (a caret, tilde or exact version; `vue@^2.7.16` pins Vue 2). A FLOOR is deliberately never charged — `requires-python = ">=3.8"` states what a package SUPPORTS, not what it runs on — and a multi-target project is charged only when EVERY target is out of support. Runtime 4.0/product capped 8.0, framework 1.5 capped 4.5. The table is safe to freeze because a statement about support that ended in the past cannot become false: it loses recall as it ages, never precision, and a test asserts every entry predates the freeze date. Disjoint from D31 (a container image's OS layer) and D29 (the toolchain a CI workflow installs). Abstains when the repository declares no platform this pass reads — never scores it clean.
0 end-of-life runtime(s) and 0 end-of-life framework(s), read from 1 platform declaration(s) and 6 dependency declaration(s). This dimension reads what the repository says about ITSELF — a pinned target framework, a version file, a capped requires-python, a Rust toolchain pin, a framework major a constraint cannot move off. A FLOOR is deliberately never charged: `requires-python = ">=3.8"` states what the package SUPPORTS, not what it runs on, and a well-maintained library declares exactly that while running its own CI on a current release. The end-of-life facts are FROZEN and dated, so this dimension needs no network and answers identically inside a closed scan fence; as the table ages it loses recall and never precision, because a statement about support that ended in the past cannot become false. The OS layer of a container image is D31's question and the toolchain a CI workflow installs is D29's; this row is neither.
Other · Architecture — How the codebase splits by code ROLE — domain, application, infrastructure, test, generated. The significance map behind the knowledge/coupling weighting, and a DDD signal in its own right: a thin domain core under fat infrastructure is the anemic-domain smell, quantified. How each file's role is decided, because the split is only as good as that: a generated name or a build-output tree makes it Generated, a test project makes it Test, and otherwise the file's NAMESPACE and PATH words are matched against fixed vocabularies in a fixed ORDER — domain, then infrastructure, then application — so a file whose words hit two layers is counted under the earlier one. A production file matching none of them counts as application, so that share reads 'application or unclassified' rather than a measured application layer. Roles come from naming convention, never from what the code does. On this repository the split was taken from the source tree on disk rather than from a loaded .NET workspace, so a file's role is decided by its PATH segments alone — no declared namespace was available to add to the evidence — and generated output is excluded from the census entirely rather than counted as a generated share.
Method: Roslyn line-count by code ROLE: every source file classified Domain/Application/Infrastructure/Test/Generated by namespace + path convention (the shared CodeRoleClassifier), then significant lines summed per role. Deterministic; the advisory score is the business-logic (domain+application) share of production code.
Coverage: Population: ALL source files, each bucketed into ONE of five roles (Domain/Application/Infrastructure/Test/Generated) by namespace + path convention — a file whose layer isn't named in the convention falls to Application (the neutral default), and the split is line-count, not semantic depth or business value.
What to do
The domain core is a small share of production code, but most of the rest matched no layer vocabulary at all — so this is not yet an anemic-domain finding. The namespace/path convention could not place that code, which makes the composition above a statement about the naming, not about the design. Name the layers (or check that the repository's conventions differ from the ones this check knows) before reading a thin domain into it.
Other · Architecture — Whether the project-reference graph is acyclic (cycles block independent build/deploy and signal eroding boundaries).
Method: Project reference cycles via elementary-DFS over real .csproj references, using the engine shared with D5/D7; cyclic versus acyclic. Exhaustive, deterministic.
Other · Architecture — Whether dependencies point inward (Domain ← Application ← Infrastructure/Web) — the clean-architecture dependency rule, checked across the project graph.
Method: Layer violations by name-segment inference (Domain/Core to Application to Infrastructure/Web) over the project-reference graph. Exhaustive over all projects, deterministic.
Maturity · Maturity — Whether the repo and its projects have a README, and whether it's substantive and current.
Method: Filesystem scan: README presence, word count, and headings for depth; git history for staleness. Exhaustive across root and project dirs, deterministic.
Maturity · Maturity — Whether key decisions (ADRs) and the high-level shape (C4/diagrams) are written down.
Method: Filesystem scan: ADR folder/naming conventions or content, plus Mermaid/PlantUML/C4/architecture.md discovery. Exhaustive, deterministic.
No Architecture Decision Records found — no conventional ADR directory, no numbered `NNNN-title` documents in any markup this check reads, and nothing ADR-shaped by content. Design rationale recorded elsewhere (a design-notes tree, a mailing list, pull-request discussion) is not visible to this check and is not re-findable per decision, so a future maintainer cannot ask why one choice was made and get an answer.
What to do
Record your significant design decisions. Write one short document per decision, giving its date, the situation, the decision and its consequences, and keep them together with your other design documents. A docs/adr folder with each file named by number and title is the easiest place to find them.
Maturity · Maturity — Whether the repo is organised deliberately — src/test separation and consistent project naming.
Method: Filesystem scan: src/test folder separation and namespace-prefix consistency (majority RootNamespace agreement). Exhaustive across projects, deterministic.
Production code isn't grouped under a src/ folder — it's spread across several top-level directories, so there's no one place that says 'this is the product'.
What to do
Group production code under src/ (or split deliberately, e.g. backend/ + frontend/) so production and tooling code aren't mixed at the root.
Maturity · Maturity — Whether the README actually describes the code that exists (LLM-judged, advisory).
Method: Judged by language model at low temperature: README accuracy versus actual projects, within a disclosed tolerance. Advisory, not a measured number.
docs/spiders/architecture.md documents the environment variable `ROBOTSTXT_OBEY`, but no code, script or configuration file names it. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/spiders/architecture.md:102
docs/locales/ar/spiders/architecture.md documents the environment variable `ROBOTSTXT_OBEY`, but no code, script or configuration file names it. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/locales/ar/spiders/architecture.md:100
docs/locales/de/spiders/architecture.md documents the environment variable `ROBOTSTXT_OBEY`, but no code, script or configuration file names it. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/locales/de/spiders/architecture.md:102
docs/locales/es/spiders/architecture.md documents the environment variable `ROBOTSTXT_OBEY`, but no code, script or configuration file names it. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/locales/es/spiders/architecture.md:100
What to do
Reconcile the README with reality: docs/spiders/architecture.md documents the environment variable `ROBOTSTXT_OBEY`, but no code, script or configuration file names it; docs/locales/ar/spiders/architecture.md documents the environment variable `ROBOTSTXT_OBEY`, but no code, script or configuration file names it; docs/locales/de/spiders/architecture.md documents the environment variable `ROBOTSTXT_OBEY`, but no code, script or configuration file names it; docs/locales/es/spiders/architecture.md documents the environment variable `ROBOTSTXT_OBEY`, but no code, script or configuration file names it.
Readiness · Readiness — Whether an automated pipeline builds and tests every change.
Method: Filesystem scan: CI workflow files (.github/workflows, .gitlab-ci.yml, etc.) for build and test stages. Exhaustive, deterministic.
Do you agree with this assessment?
P10 · Library API & versioning10.0 / 10Exemplary○ Nothing flagged
Readiness · Readiness — For a library: a deliberate (small) public API surface and explicit semantic versioning so consumers can depend on it safely.
Method: Roslyn scan: public API surface area and semantic-versioning markers (SemVer attributes, changelog entries) for libraries; off .NET, a library is the ecosystem's publication act (an npm package that is not private and names an entry point, a PyPI distribution with a build system, a Rust library crate, a Maven/Gradle module that publishes, a Go module with no package main, a gemspec, a Composer library, a SwiftPM library product, a pub.dev or Hex package), its surface is the share of types the language model records as public (Rust, Swift, Java, Kotlin, Go, Dart; not measured where the model records no type visibility or, as in TypeScript, only module-level export), and its version is read from the manifest, a semver CHANGELOG, release tooling or semver git tags. Exhaustive, deterministic.
Do you agree with this assessment?
P2 · Observability7.0 / 10Strong✓ Tool-verified
Readiness · Readiness — Whether the code is diagnosable in production — structured logging, tracing/metrics, health checks.
Readiness · Readiness — Whether SAST, secret/dependency scanning and performance benchmarking are wired in (presence, not runtime).
Method: Filesystem scan: SAST configuration, dependency-update automation, secret scanning, and a benchmark harness or benchmark step — in this repository's own ecosystem. Exhaustive, deterministic.
No static application security testing detected. For this repository's stack, add bandit, `semgrep --config=p/python`, or CodeQL's python pack as a CI step. What was searched, so you can tell an absence from a miss: the 15968 CI workflow file(s) in this repository, and the scanner and linter configuration checked in beside them. A scan that runs outside CI, one configured in your forge's web UI rather than in a committed file, or a tool whose name is none of those this check carries, is not seen — if that is your case the row is wrong, and saying so is more useful than adding a second scanner.
What to do
Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing.
Enable Dependabot/Renovate or a dependency-review gate.
Add gitleaks/trufflehog in CI to block PRs that introduce committed secrets.
Readiness · Readiness — Whether releases are automated and safely reversible (probes, rolling updates, approval gates) — from manifests/pipeline files, not the live environment.
Method: Filesystem scan: deployment manifests/IaC (K8s YAML, Helm, Terraform) for rolling updates, probes, approval gates, migration hooks. Exhaustive, deterministic.
What to do
Make sure a person approves each release. The release job names an environment, but its approval rules live in the code host's settings, where Watchdog can't see them. Check that a reviewer must approve each release, or publish releases as drafts first, so a bad build can be stopped before anyone downloads it.
Readiness · Performance — Whether asynchronous code stays responsive — it avoids sync-over-async blocking (a .NET .Wait()/.GetAwaiter().GetResult(), a time.sleep or blocking HTTP call inside a Python coroutine, a *Sync call inside an async JavaScript function, block_on inside a Rust async fn, runBlocking inside a Kotlin suspend function, block() inside a Reactor publisher) that stalls a thread or event loop and risks deadlock, and, where the code is a reusable library on .NET, awaits with ConfigureAwait(false) so it never captures and stalls its caller's context.
Method: Production-source scan: sync-over-async blocking counted everywhere — .Wait()/.GetAwaiter().GetResult() in .NET; off .NET, read from the language model, a blocking call inside an async function (Python, TS/JS, Rust, Kotlin, Swift, Dart) or inside a Java method returning a Reactor Mono/Flux or a Scala method returning a Future or effect — and, for a .NET library with ≥5 awaits, the share of awaits using ConfigureAwait(false). Deterministic, syntax/text detection.
Other · Code Health — Whether the code avoids sync-over-async (deadlock-prone blocking on tasks) and async void.
Method: Roslyn syntax scan: async methods scanned for .Wait()/.GetAwaiter().GetResult() and async-void outside event handlers. Deterministic, hard fact per invocation.
Other · Code Health — Whether any branch is dead by construction — a switch arm whose label can never equal a case-normalised subject, or an `else if` whose predicate the arm above has already swallowed.
Method: Roslyn syntax + semantics: switch labels compared against the subject's own case normaliser, and if/else-if chains checked for a literal an earlier arm's containment test already swallows. Deterministic, provable per finding. Advisory.
Do you agree with this assessment?
X13 · Undrained process stream10.0 / 10Exemplary○ Nothing flagged
Other · Code Health — Whether a child process that has BOTH standard streams redirected drains both — reading one to the end while the other is never read deadlocks once the child fills the unread pipe.
Method: Roslyn syntax + semantics: ProcessStartInfo launches with both streams redirected, checked for a drain of each stream across the enclosing type. On a repository with no .NET source it reads JavaScript/TypeScript off the engine’s own token stream (test, vendored and minified paths not) with the same rule: a `spawn` imported from `child_process` whose stdout and stderr are both pipes (no options, no `stdio`, or `stdio` of `'pipe'`), bound to a local that never leaves its scope, where exactly one of the two streams is read, the other never, and the child’s `close`/`exit` (or the read stream’s end) is awaited; a shell redirect in the call’s arguments or a `kill` of the child suppresses it. The same rule reads Python’s `subprocess.Popen`, Dart’s `Process.start`, Ruby’s `Open3.popen3`, and Scala’s JDK process starts (`new ProcessBuilder(…).start()` with no `redirectErrorStream`/`redirectError`/`redirectOutput`/`inheritIO`, `Runtime.getRuntime.exec`) where one of `getInputStream`/`getErrorStream` is read and the other never while `waitFor()` is called or the read stream is read to its end. Deterministic, provable per finding. Advisory.
Other · Security — Whether a hand-rolled public/private IP check can be walked past — a method that unwraps IPv4-mapped IPv6 but returns the opposite verdict for the same host written as IPv4-compatible, 6to4 or NAT64.
Method: Roslyn syntax + semantics: methods that unwrap IPv4-mapped IPv6 and hand-roll IPv4 range carve-outs, checked for whether the IPv6 branch also accounts for the IPv4-compatible, 6to4 and NAT64 embeddings. Deterministic, provable per finding. Advisory.
Do you agree with this assessment?
X15 · Unvalidated length from an untrusted reader10.0 / 10Exemplary○ Nothing flagged
Other · Security — Whether a length read out of the stream being parsed is bounded before it is allocated or read — an unchecked count taken from the input lets the input choose the allocation.
Method: Roslyn syntax + semantics: integer lengths read from a BinaryReader and spent on a bulk read or an array allocation, checked for any comparison or bounding call on the value anywhere in the method. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether a loop that shortens a string until it fits a length budget has a floor — one with none grinds the value down to the empty string, or past it into a negative-length `Substring`.
Method: Roslyn syntax + semantics: while/do loops whose body's only effect on a string is to drop its last character, checked for whether anything — a direct comparison on the length, a body guard, a break — bounds that length below. On a repository with no .NET source it reads JavaScript/TypeScript off the engine’s own token stream (test, vendored and minified paths not) with the same rule: a `while`/`do` loop whose body’s one assignment to a value is `x = x.slice(0, -1)` or `x = x.slice|substring|substr(0, x.length - 1)`, driven by a condition that reads `x.length` only as a term of a larger expression — never compared directly, never tested for truthiness, and with no other read of `x` — and whose body has no `break`, `return`, `throw` or `if` naming `x`. Deterministic, provable per finding. Advisory.
Do you agree with this assessment?
X17 · Uncapped recursion over a caller-supplied document10.0 / 10Exemplary○ Nothing flagged
Other · Security — Whether a walk that recurses through a JSON/XML tree handed in by its caller bounds how deep it will go — an uncapped walk lets the document's nesting choose the stack depth, and the resulting StackOverflowException cannot be caught.
Method: Roslyn syntax + semantics: methods that take a JSON/XML document node and call themselves with a child of it, reachable from an externally-callable member of the same type that accepts a document, checked for any depth parameter, descent counter or threaded arithmetic anywhere in the walk. On a repository with no .NET source it reads TypeScript off the engine’s own token stream (test, vendored, generated and minified paths and `.d.ts` not) with the same rule: a function or method with a parameter typed as a document value — `unknown`, `any`, `object`, `Record<string, unknown|any>`, a JSON alias (`JsonValue`, `JSONObject`, …) or a DOM node global the file does not rebind — that calls itself (bare, or through `this` for a method, or hands itself to a call over the value as in `value.map(walk)`) with something it took out of that value and never through an ancestor accessor such as `closest()` or `parentElement`, that is exported or reached from an exported function (a public method of the same exported class) taking such a value, and that names no depth, level, nesting, recursion, remaining or budget anywhere and threads no `+`/`-` arithmetic through a self-call. Plain JavaScript is not read: with no annotation nothing tells a parsed document from a tree the code built itself. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether a type's disposal matches what it OWNS — releasing what it created, leaving alone what it was handed, and not declaring a finalizer for state that has nothing unmanaged to finalize.
Method: Roslyn syntax + semantics: every assignment to a disposable field is read to decide whether the type CREATED the value or was handed it, and the type's disposal is checked against that answer — an injected interface it disposes, a value it constructed and never releases, a finalizer on a type holding nothing unmanaged, and a disposable local whose every reference is a plain member read. A value handed to a container that disposes its contents (a parent control's `Controls` collection, a component `IContainer`) is released by that container and is not reported; generated code is out of population. On a repository with no .NET source the same ownership questions are read in JavaScript/TypeScript off the engine’s own token stream (test, vendored, generated and minified paths not): a class declaring `dispose()`, `[Symbol.dispose]()` or `[Symbol.asyncDispose]()` that disposes a field it was handed through a constructor parameter typed as a repository interface or resolved by a dependency-injection container; that assigns a field only ever from `new X(…)` of a disposable class and neither releases it anywhere in the class nor names it in its disposal member or a method that member calls, nor hands it to anything else; and a `const`/`let` local built from literals only whose every reference opens a statement operating on a non-release member of it. A class is disposable when every repository declaration of its name declares or inherits a disposal member, or when it is a documented library disposable (`vscode` EventEmitter, CancellationTokenSource and Disposable; three.js geometries, materials, textures, render targets, renderers, controls and composers). The finalizer arm has no JavaScript counterpart: a class cannot declare one. Dart is read for the owned-and-never-released arm: a Flutter `State` (or a class declaring `dispose()`/`close()`) whose library-private field is written only with direct creations of a framework controller, `FocusNode`, `StreamController` or periodic `Timer` (or a `.listen(…)` into a field declared `StreamSubscription`), which nothing in the class or its library releases (`dispose`/`close`/`cancel`) and which is never handed on except as a named argument. Ruby is read for the owned-and-never-released arm and the abandoned-local arm: a class declaring an instance `close`, `dispose`, `shutdown`, `stop`, `disconnect` or `terminate` whose instance variable is written only with a blockless core descriptor creation (`File.open`/`new`, `Tempfile`, `Dir.open`, `IO.popen`, the `socket` and `openssl` socket classes) that no method of the class closes and that is never handed on (passed, returned, stored, exposed by `attr_reader`, delegated) — a class whose superclass or mixin lies outside the repository is not judged — and a blockless creation read through and dropped (`File.open(path).read`) or held in a local that is never closed, returned, passed or stored (a `&:read` block-pass, an `autoclose: false` borrow and a `fileno` hand-over are not abandonment). PHP is read for the same two arms: a class (not a trait, and not one using a trait) declaring an instance `close`, `dispose`, `shutdown`, `stop`, `disconnect` or `terminate` whose `private` property (or a `final` class's `protected` one) is written only with a direct core `fopen`, `tmpfile`, `popen`, `proc_open`, `fsockopen`, `stream_socket_client`/`server` or `opendir` that nothing in the class closes (`fclose`, `pclose`, `closedir`, `unset`, a reset to `null`) and that is never handed on, and a handle held in a function's local, or read through as the argument of a reader, that is never closed, returned, passed, captured or stored; a `php://` stream, `curl_init`, `SplFileObject`, `PDO` and a function the repository declares under the core name are not read. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether a method that temporarily changes state belonging to the whole process — the working directory, an environment variable — puts it back on EVERY path: a restore reached only when nothing throws leaks the change to the rest of the process.
Method: Roslyn syntax + semantics: method bodies that write the process working directory or an environment variable and write it back in the same body, checked for whether that restore sits in a `finally`/`catch` or only on the straight-line path. On a repository with no .NET source the same rule reads production JavaScript/TypeScript off the engine’s own token stream (test, vendored and minified paths not): `process.chdir`, `process.env.NAME =`/`["NAME"] =` and `delete process.env.NAME`, and Deno’s `Deno.chdir`/`Deno.env.set`/`Deno.env.delete`, paired per function body (a nested function or arrow is its own body, and module top-level code is none), where the last write puts back a local the body captured from the same global or deletes a variable the first write set, with at least one statement between them; a write in a `catch`/`finally` of that body silences it. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether a `when` guard is free of side effects — a guard that increments a counter or assigns while deciding whether its arm matches applies that change during PATTERN MATCHING, on an arm that may not be selected, and skips it entirely when a short-circuit to its left answers first.
Method: Roslyn syntax: `when` guards on case labels and switch-expression arms, read for a mutation (`++`/`--`/assignment) sitting in a position the guard's own `&&`/`||`/`??`/`?:`/`?.` can skip. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether a method that TAKES a lock or semaphore and gives it back from a flag-guarded `finally` returns the value that flag implies — reporting success while the guard hands the primitive back admits a second caller the exclusion was there to keep out, and reporting failure while the guard keeps it leaves nothing to ever give it back.
Method: Roslyn syntax: `try` statements whose `finally` releases a synchronisation primitive under a bare local-bool guard, where the method also TOOK that same primitive before the `try`, checked for a `return` of a bool literal whose value disagrees with the flag state the method's own straight-line assignments put it in. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether the work a diagnostic log line costs is paid only when that line is wanted — C# evaluates a call's arguments BEFORE the call, so a trace/debug message joined or projected out of a collection is built in full on every pass, and then discarded by a sink the shipped configuration leaves switched off.
Method: Roslyn syntax: log calls at a diagnostic level (a `Log`-prefixed method naming Trace/Debug/Verbose, or a bare `Debug`/`Trace`/`Verbose` on a receiver named for a logger), whose argument list is read for a call whose cost scales with a sequence — a LINQ operator, a materialisation, `string.Join`, a serializer — with no enclosing level check or conditional-compilation region. On a repository with no .NET source it reads JavaScript/TypeScript off the engine’s own token stream (test, vendored and minified paths not) with the same rule: a `debug`/`trace`/`verbose` call on a receiver named for a logger, or a `log`-prefixed method naming the level, whose argument calls an array operator (`map`/`filter`/`reduce`/`sort`/…), `Array.from`, `Object.keys/values/entries`, `JSON.stringify`/`util.inspect` over anything but a literal, or an array `join` — outside any arrow or function passed as an argument, which the logger calls only when the level is on — with no enclosing `if`, `&&` or `?:` whose condition names a level, a level string, or the `NODE_ENV`/`__DEV__`/`DEV` build switch. Deterministic, provable per finding. Advisory.
`debug` at line 248 is called with an argument built by `list(cookies.keys())` (line 248), whose cost grows with the collection it walks. Python evaluates a call's arguments BEFORE entering the method, so that join/projection runs in full every time this line is reached — and then the level check inside the logger discards the string it produced, because debug is a level a shipped configuration normally leaves off. Nothing here decides beforehand whether the message is wanted: no `isEnabledFor(logging.DEBUG)` test, no debug flag, no `lambda` the logger calls only when the level is on. — scrapling/core/shell.py:248
`debug` at line 171 is called with an argument built by `pprint.pformat(processed_result)` (line 171), whose cost grows with the collection it walks. Python evaluates a call's arguments BEFORE entering the method, so that join/projection runs in full every time this line is reached — and then the level check inside the logger discards the string it produced, because debug is a level a shipped configuration normally leaves off. Nothing here decides beforehand whether the message is wanted: no `isEnabledFor(logging.DEBUG)` test, no debug flag, no `lambda` the logger calls only when the level is on. — scrapling/spiders/engine.py:171
What to do
In Python, put the construction behind the logger's own check (`if logger.isEnabledFor(logging.DEBUG):`), or pass the collection itself with a `%s` placeholder (`logger.debug("Items %s", items)`) so the logging module renders it only when the record is emitted. The placeholder alone is not the fix when the argument is still built first: it defers the formatting, not a `json.dumps`, a `join` or a comprehension in the argument, and an f-string defers nothing at all.
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X24 · Document value interpolated into markup unescaped10.0 / 10Exemplary○ Nothing flagged
Other · Security — Whether text read out of the document being converted is escaped before it is written into generated markup — a value the document's author chose, interpolated into an attribute the surrounding literal delimits, can close that attribute and open another.
Method: Roslyn semantic model over the whole compilation: a string-typed `Value`/`InnerText`/`InnerXml`/`Text` member declared inside `DocumentFormat.OpenXml` or `System.Xml` is a taint SOURCE, propagated through assignments, returns, arguments, tuple elements and string composition to its transitive closure, then read at interpolated-string holes that sit in a markup position the surrounding literal itself delimits. Escaper/encoder calls and enclosing validator conditions cut the flow. Flow- and container-insensitive by construction. A second arm needs no provenance at all and reports a type that CONTRADICTS ITSELF — the same expression escaped at one delimited markup hole and interpolated raw at another hole in the same markup position of the same type, which the type's own escaping proves is a defect without knowing where the value came from. On a repository with no .NET source it reads JavaScript/TypeScript off the token stream with the same rule: a DOM read of raw document text (`getAttribute`, `textContent`, `innerText`, `nodeValue`) is the source, propagated through local bindings and string composition, and judged at template-literal and concatenation holes in the same two delimited markup positions; escapers and validating conditions cut it, and documentation-site, test, vendored and minified scripts are not read. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether a value the caller is invited to supply is the value the type actually uses — a constructor parameter stored in a private field that nothing ever reads while the default it was given is spelled out a second time at the site that should have read it, a keyed lookup that falls back to a different setting than the one its key names while the same type falls back to the matching one for that same key, or a culture-sensitive parse given no format provider by a type that feeds its own settable culture to the same kind of parse elsewhere, or a number an entry point writes to standard output under the host's culture while the same entry point formats with the invariant culture elsewhere. Either way, every caller who supplies a value silently gets something else.
Method: Roslyn syntax: private instance fields of a non-partial type assigned in a constructor from one of its own parameters with a `??` fallback, checked for whether anything in the type body reads the field and whether that same fallback expression is spelled out again outside the constructor; and `??` fallbacks onto a member access from a lookup call carrying exactly one string literal, grouped by that key across the type and checked for a fallback member whose folded name disagrees with the key while a sibling site for the same key agrees with it. On a repository with no .NET source the first two arms read JavaScript/TypeScript off the engine’s own token stream (tests included, bundles and vendored paths not): a `#x`, `private` or `private` parameter-property instance field filled in the constructor from a parameter (or one member of one) through `??`/`||` or a parameter default, never read anywhere in the file by name, whose constructed default is spelled again in the class body; and `lookup("key") ?? s.member` grouped by key per class, or per module outside every class. The culture arm has no JavaScript counterpart: its parses take no locale. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether a value handed from a callback to the body that waits on it crosses on something built to be crossed — a `Queue<T>`/`List<T>`/`Dictionary<K,V>` written inside an event handler and read back outside it is mutated by two flows at once, and the semaphore or completion source beside it orders how MANY items exist while leaving the collection's own head, tail and backing array unprotected.
Method: Roslyn syntax: method, accessor, local-function and lambda bodies that declare BOTH a non-thread-safe generic collection (`Queue`/`Stack`/`List`/`Dictionary`/`HashSet`/`Sorted*`/`LinkedList`) and a synchronisation primitive (`SemaphoreSlim`/`TaskCompletionSource`/`ManualResetEvent(Slim)`/`AutoResetEvent`/`CountdownEvent`) as locals, then read for a `+=`-registered lambda that raises that primitive while the body outside every lambda waits on it — and, in that scope, a mutating call on the collection inside the lambda paired with a mention of it outside. Any `lock` in the scope abstains it. On a repository with no .NET source the same handoff is read in Java off the engine’s own token stream (test source sets, vendored and demonstration paths not): an `ArrayList`/`LinkedList`/`ArrayDeque`/`PriorityQueue`/`Hash*`/`LinkedHash*`/`Tree*` local and a `CountDownLatch`/`Semaphore`/`CompletableFuture` local, a lambda or anonymous class that raises the primitive and mutates the collection, and a wait outside it; any `synchronized` or `lock()` abstains the body. Because each of those primitives orders what the callback wrote before raising it, only a touch that provably overlaps the callback is convicted: one after the registration and before the next wait, or one in a loop registered-before, waiting on every pass and not declaring the collection. Deterministic, provable per finding. Advisory.
Do you agree with this assessment?
X27 · Collection changed while being enumerated10.0 / 10Exemplary○ Nothing flagged
Other · Code Health — Whether a `for` loop leaves the `dict`, `set` or `deque` it is walking alone — CPython's iterators over those check that the collection has not changed size since the loop began, so a body that removes from (or empties) the very collection the loop is walking makes the next step raise `RuntimeError: dictionary changed size during iteration` and the remaining items are never seen.
Method: Roslyn syntax + semantics: `foreach` statements whose body calls a structural mutator (`Add`/`Remove`/`Clear`/`Insert`/…) on the very expression the loop is enumerating. Two arms. ARM A — the source is a concrete fragile BCL collection, or a live `Keys`/`Values` view over one, and the mutator resolves to that same collection's own member; concurrent and immutable collections and arrays are outside the population by construction, since their enumerators survive a structural change. ARM B — the source is an argument-less accessor CALL on a receiver whose body is in source: the accessor must return a stored field VERBATIM and a sibling member must structurally change that same field, both read off the implementations rather than from the members' names. A mutation the loop provably exits immediately after (`break`/`return`/`throw`/`goto`), or one written inside a nested loop or a lambda, is counted and never reported. Deterministic, provable per finding. Advisory.
Do you agree with this assessment?
X28 · Index access outside its own emptiness guard10.0 / 10Exemplary○ Nothing flagged
Other · Code Health — Whether a condition that tests a value for emptiness indexes that same value only where the test holds — an `||` written one parenthesis too far to the left leaves an index access outside the guard beside it, so the empty case the guard exists to anticipate reaches the index and throws.
Method: Roslyn syntax only, no semantic model: the OUTERMOST `&&`/`||` of every boolean condition, read for a symbol the condition tests for emptiness (`string.IsNullOrEmpty`/`IsNullOrWhiteSpace`, a `Length`/`Count` comparison against a literal, `Any()`, a `Length`/`Count` pattern, or a comparison against `""`) and ALSO indexes. Each `symbol[...]` access is placed by a boolean-reachability walk from the access up to the outermost connective: an access is COVERED when some enclosing step has it in the right operand and the left operand, under the truth value that step forces, proves the symbol non-empty — a recursion over `&&`/`||` whose true- and false-directions are asymmetric. A finding needs BOTH an uncovered access and a covered one on the same symbol in the same condition, which is the agreeing twin that separates a misplaced parenthesis from an unrelated length test. Bare index accesses with no emptiness test in the condition are neither counted nor reported; a non-identifier receiver and a lambda nested inside the condition are outside the population. Deterministic, provable per finding. Advisory.
Do you agree with this assessment?
X29 · Per-element action decided by a fixed element10.0 / 10Exemplary○ Nothing flagged
Other · Code Health — Whether a decision taken once per element is taken ABOUT that element — a test inside a counted loop that reads a fixed subscript of the very collection its guarded statement indexes by the loop variable applies element zero's answer to all of them, so the elements that differ from it are all handled wrongly, and in the same direction.
Method: Roslyn syntax only, no semantic model: every `for` statement declaring exactly ONE loop variable, and every `if` inside its body that is not under a nested loop or a lambda. A site enters the population when the `if`’s condition never mentions the loop variable while the statement it guards indexes some collection by that variable ALONE (`c[i]`; `c[i + 1]` and `c[i, j]` are outside it). A finding additionally needs the AGREEING TWIN at the same-collection grain: the condition must read THAT SAME collection at a subscript that does not move — written into the condition, or reached through a local declared BEFORE the loop, so an alias bound inside the body is not followed. Both collection expressions must be simple identifiers. On a repository with no .NET source the same rule reads JavaScript/TypeScript off the engine’s own token stream (tests included, bundles and vendored paths not): a `for (let|var|const x = …; …; …)` with one declarator and a braced body, an alias followed only when it is declared before the loop in a block that encloses it and never assigned inside the loop. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether exceptions are handled rather than silently swallowed or rethrown with lost stack traces.
Method: Roslyn syntax scan: every catch clause counted; empty catches and bare rethrows flagged. Population is all catch clauses, not estimated. Deterministic, hard fact.
An empty catch block silently discards the error — failures vanish with no log and no rethrow. Log it, handle it, or don't catch it. (×2) — scrapling/engines/_browsers/_base.py:180, scrapling/engines/_browsers/_base.py:406
What to do
Swallowed exception (empty catch)
Do you agree with this assessment?
X30 · Support guard that admits what it rejects10.0 / 10Exemplary○ Nothing flagged
Other · Code Health — Whether a guard written as a NEGATED `||` says what its author meant — `!(a || b || x != k)` is `!a && !b && x == k` by De Morgan, so a bail-out that mixes capabilities the code needs with a fault it refuses turns inside out: it fires only where the capabilities are ABSENT, and lets every value the fault term names walk straight into the body that cannot handle it.
Method: Roslyn syntax only, no semantic model: every logical-not whose operand is a parenthesised `||` chain of two or more disjuncts, flattened (a left-nested `a || b || c` read once would see `(a || b)` as one disjunct). A site enters the population on that shape alone. A finding additionally needs the disjuncts to DISAGREE in polarity: at least one bare boolean read — an identifier or member access, never an invocation, which is a predicate rather than a capability flag — and at least one `x != <constant>`, the only form that negates into an exact-value pin (`== null` negates into a looser requirement and is outside the fault set). Consistently-polarised disjunctions, all-fault or all-capability, are counted and never reported; a negated `&&` is outside the population entirely. No same-receiver gate: it was measured to cost a real defect and remove no false positive. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether log calls use message templates (queryable) rather than interpolated strings.
Method: Roslyn syntax scan: every log call-site counted; interpolated-string first-argument violations flagged. Population is all log calls, not estimated. On a repository with no .NET source it reads JavaScript/TypeScript off the engine’s own token stream (test, vendored and minified paths not) with the same rule: a `trace`/`debug`/`info`/`warn`/`error`/`fatal`/`verbose`/`silly`/`http`/`log` call on a receiver named for a logger, in a package whose own or an enclosing `package.json` declares a logger that carries values as fields (pino, pino-http, nestjs-pino, Fastify, winston, bunyan, tslog, LogTape, roarr), whose first deciding argument is a template literal with substitutions; an object-literal fields argument is stepped over, a plain string message clears the call. Python is read the same way: a `debug`/`info`/`warning`/`error`/`exception`/`critical`/`log` call on a logger in a file that imports the standard library’s `logging` or structlog (loguru, which formats at the call, is not judged), whose message is a whole f-string with a non-constant field; a `%`-formatted, `.format()`-ed or concatenated message is not convicted, as in C#. PHP is read the same way: a PSR-3 level call (`emergency`…`debug`, `log`) on a logger (`$logger`, `$this->logger`, Laravel’s `Log::`, `logger()`, `\Drupal::logger()`) in a package whose `composer.json` requires a PSR-3 logger or a framework that hands one out (psr/log, Monolog, Laravel, Symfony, Drupal, CakePHP), or a file that imports one, whose message is a double-quoted string interpolating a variable; a single-quoted message clears the call, and `sprintf` or `.` concatenation is not convicted, as in C#. Dart is read the same way: a `trace`/`debug`/`info`/`warn`/`error`/`fatal` call on Sentry’s structured logger (`Sentry.logger`) whose message is a whole string literal interpolating a value (`$name`, `${…}`); the `Sentry.logger.fmt` template API clears the call. Dart that logs only through loggers with one message and no fields (package:logging, package:logger, talker, dart:developer) is not applicable rather than a reach gap. Ruby is read the same way: a `trace`/`debug`/`info`/`warn`/`error`/`fatal` call on the console gem’s `Console` (declared by a Gemfile, gemspec or Gemfile.lock, or required) or, where SemanticLogger, Ougai or dry-logger is declared or required, on a receiver named for a logger (`logger`, `@logger`, `Rails.logger`, `log`), whose first deciding argument is, as the whole argument, a double-quoted string interpolating a non-constant value (`#{…}`, `#@ivar`); a plain literal clears the call, keyword arguments are the fields, and a block, `%`-format or `+` concatenation is not convicted. Ruby that logs only through the standard Logger (Rails.logger) is not applicable rather than a reach gap. Deterministic.
`error` at line 130 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.error("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.error("order moved", order_id=order_id, status=status)` with structlog. — scrapling/core/shell.py:130
`error` at line 135 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.error("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.error("order moved", order_id=order_id, status=status)` with structlog. — scrapling/core/shell.py:135
`error` at line 199 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.error("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.error("order moved", order_id=order_id, status=status)` with structlog. — scrapling/core/shell.py:199
`error` at line 214 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.error("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.error("order moved", order_id=order_id, status=status)` with structlog. — scrapling/core/shell.py:214
`debug` at line 248 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.debug("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.debug("order moved", order_id=order_id, status=status)` with structlog. — scrapling/core/shell.py:248
`error` at line 250 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.error("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.error("order moved", order_id=order_id, status=status)` with structlog. — scrapling/core/shell.py:250
`warning` at line 264 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.warning("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.warning("order moved", order_id=order_id, status=status)` with structlog. — scrapling/core/shell.py:264
`warning` at line 281 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.warning("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.warning("order moved", order_id=order_id, status=status)` with structlog. — scrapling/core/shell.py:281
`warning` at line 302 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.warning("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.warning("order moved", order_id=order_id, status=status)` with structlog. — scrapling/core/shell.py:302
`debug` at line 331 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.debug("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.debug("order moved", order_id=order_id, status=status)` with structlog. — scrapling/core/shell.py:331
`error` at line 371 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.error("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.error("order moved", order_id=order_id, status=status)` with structlog. — scrapling/core/shell.py:371
`error` at line 374 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.error("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.error("order moved", order_id=order_id, status=status)` with structlog. — scrapling/core/shell.py:374
`error` at line 428 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.error("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.error("order moved", order_id=order_id, status=status)` with structlog. — scrapling/core/shell.py:428
`error` at line 430 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.error("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.error("order moved", order_id=order_id, status=status)` with structlog. — scrapling/core/shell.py:430
`warning` at line 463 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.warning("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.warning("order moved", order_id=order_id, status=status)` with structlog. — scrapling/core/shell.py:463
`info` at line 480 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.info("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.info("order moved", order_id=order_id, status=status)` with structlog. — scrapling/core/shell.py:480
`info` at line 481 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.info("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.info("order moved", order_id=order_id, status=status)` with structlog. — scrapling/core/shell.py:481
`info` at line 482 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.info("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.info("order moved", order_id=order_id, status=status)` with structlog. — scrapling/core/shell.py:482
`info` at line 599 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.info("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.info("order moved", order_id=order_id, status=status)` with structlog. — scrapling/core/shell.py:599
`error` at line 603 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.error("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.error("order moved", order_id=order_id, status=status)` with structlog. — scrapling/core/shell.py:603
The score is the rank-weighted fold of these lenses (worst-heaviest), each including its meta-dimensions; a lens with a Critical contributor is capped at Fair (its band reads "gated by …") and is never the strongest area however high its average.
Not evidenced — 4 control(s) we could not find positive evidence for
These checks grade a working control, and the repository shows no evidence of one. That is deliberately not scored as a zero: a repository cannot show an ops runbook, a database TTL or an infrastructure-side audit log, so absence of evidence here is not evidence the control is missing. It is also not a statement that the check is irrelevant to this codebase — the thing it grades applies; we just could not see it. Excluded from the score either way.
C3 Audit Trail — Not assessed: this repository stores data through 'sqlite3' (scrapling/core/storage.py:5) but declares no persisted model this check reads (Django, SQLAlchemy, SQLModel, Peewee, Tortoise and MongoEngine model fields, Alembic and Django migration columns, CREATE TABLE and ALTER TABLE … ADD columns in SQL files). Personal data may be written through raw SQL or a hosted backend, so whether its changes need an audit trail could not be established, and the trail's absence is not scored.
C4 Data Retention — Not assessed: this repository stores data through 'sqlite3' (scrapling/core/storage.py:5) but declares no persisted model this check reads (Django, SQLAlchemy, SQLModel, Peewee, Tortoise and mongoengine model fields, Alembic and Django migration columns, SQL schemas). Personal data may be written through raw SQL or a hosted backend, so whether a retention limit applies here could not be established, and its absence is not scored.
C5 Data-Subject Rights — Not assessed: this repository stores data through 'sqlite3' (scrapling/core/storage.py:5) but declares no persisted model this check reads (Django, SQLAlchemy, SQLModel, Peewee, Tortoise and mongoengine model fields, Alembic and Django migration columns, CREATE TABLE and ALTER TABLE … ADD columns in SQL files). Personal data may be written through raw SQL or a hosted backend, so whether data-subject rights apply here could not be established, and their absence is not scored.
P5 DR & Backup — not evidenced — repo shows no backup/RTO/RPO controls; absence of evidence is not evidence of a working control
Not included — 58 check(s) not relevant to this codebase
These checks had nothing to measure here (no tests, no git history, the codebase is small, or the architecture style doesn't apply), so they're omitted above rather than scored low.
AC1 Text alternatives — Frontend below the scale floor (24 DOM element(s) < 25) — too little surface to assess accessibility.
AC2 Forms & labels — Frontend below the scale floor (24 DOM element(s) < 25) — too little surface to assess accessibility.
AC3 Page structure — Frontend below the scale floor (24 DOM element(s) < 25) — too little surface to assess accessibility.
AC4 Keyboard semantics — Frontend below the scale floor (24 DOM element(s) < 25) — too little surface to assess accessibility.
AC5 ARIA correctness — Frontend below the scale floor (24 DOM element(s) < 25) — too little surface to assess accessibility.
AC6 Visual & motion safety — Frontend below the scale floor (24 DOM element(s) < 25) — too little surface to assess accessibility.
AC7 A11y enforcement — Frontend below the scale floor (24 DOM element(s) < 25) — too little surface to assess accessibility.
AX1 Captive dependencies — Not applicable: this repository's Python imports no dependency-injection container and defines no container of its own — nothing that both registers and resolves bindings, and nothing that names two lifetimes — so nothing holds one lifetime's instance while handing out another's.
AX2 Stateful singletons — No container singleton was found, so there is no shared instance for concurrent requests to race on. No function in this Python code is served by a threaded web framework (a Flask, Bottle or FastAPI route, a Django or Pyramid view), so no module is shared between request threads. TypeScript/JavaScript runs each process's requests on one event loop, so no two requests write a shared object at the same instant (interleaving across an await is a different defect).
AX5 Architecture & structure — not assessed — architecture style/structure is computed from a project graph (projects, types, module namespaces) that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
AX6 Interface segregation — not assessed — interface segregation is computed over a type surface that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
AX7 Slice cohesion — not applicable — not a vertical-slice architecture
AX8 Test isolation — no test/production split to check
AX9 CQS / query purity — no CQRS query handlers detected — query purity is not applicable to this codebase
AXB2 Runtime readiness — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
AXR1 Runtime accessibility — docker build failed (exit 1) — DEPRECATED: The legacy builder is deprecated and will be removed in a future release.
Install the buildx component to build images with BuildKit:
https://docs.docker.com/go/bui…; runtime evidence skipped This is a statement about this run, not a statement about your application: nothing here says the surface is inaccessible, only that it was never rendered.
C1 Data Protection — Not assessed: these personal data controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks personal data controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
C2 Access Controls — No access-control surface detected in the analyzed source — this repository registers no route and starts no server another machine can reach (no Flask/FastAPI/Starlette route, Django urlpatterns or class-based view, Tornado handler). Access control is therefore N/A here — a library or command-line tool is authorized by the application that serves it, not by itself. If this codebase grows request handlers, the dimension reactivates and a default-deny posture is expected then.
D10 Test Quality — ~21375 lines of test source are present (.py) but no test cases reached the test census for this repository — no test root we could resolve declared them, or the files we read declare no cases in a test framework we recognise — so skipped/assertion-free tests couldn't be counted. Not scored — this is a gap in the analyzer, not a finding about this repository.
D12 Dependency Hygiene — Dependency currency not established — no declaration carries an exact pin to grade
D16 Bus Factor — single-maintainer repository — bus factor is not applicable
D18 Solution Shape — D18 scores the shape of a .NET solution; this repository has no .NET solution or project files, so the dimension does not apply.
D23 Boundary Type-Coupling — No bounded-context organisation was detected either — neither a context-shaped layout, nor 2+ sibling source directories each declaring an aggregate root, nor 2+ projects each declaring an entity of their own. Declaring this codebase's bounded contexts (≥2) would let cross-boundary type coupling be assessed. Declare them in `.codehealth/config.yaml` at the repository root (create it if absent), mapping each context name to the module-path or namespace prefixes that belong to it — e.g. `architecture:` → `contexts:` → `Billing: ["src/billing", "Acme.Billing"]`, `Catalog: ["src/catalog", "Acme.Catalog"]`.
D24 Comment Value — No inline comments to assess — comment value is not applicable here.
D25 ADR Conformance — no ADRs to check
D27 Navigability — D27 counts a call into the standard library or a dependency as unresolved for this repository's language (.py), so too few call sites counted as resolved to assess navigability. Not scored: this is a gap in the analyzer, not a verdict about this repository.
D30 Dependency Vulnerabilities — Scanner failed to run — not a clean result
D32 Data Compliance (PII/GDPR) — No personal data was found crossing a boundary the PII/GDPR ruleset checks — nothing written to a log or console sink, placed in a URL or query string, or persisted to browser storage. That is a clean result for the LEAK surface only: this ruleset detects personal data escaping, it does not inventory the personal data a repository holds, so it is not evidence that this repository has no personal-data surface. The personal-data map (Appendix C) and the C1-C5 compliance cards are what speak to that.
D37 Vulnerability-disclosure Policy — No vulnerability-disclosure policy file found (SECURITY.md/.markdown/.rst/.txt at root or under .github/.forgejo/.gitea/docs, .well-known/security.txt). A coordinated-disclosure policy may live off-repo, so this is not evidenced rather than failed.
D39 IL Efficiency — D39 measures the IL emitted by a .NET build; this repository has no .NET solution or project files, so the dimension does not apply.
D40 Network Egress Confinement — No Kubernetes/orchestration workloads found in the repository manifests; network egress policy is a cluster-native control that may live at the platform/firewall layer, so there is nothing to assess here.
D41 Kernel & Syscall Confinement — No Kubernetes/orchestration workloads found in the repository manifests; seccomp/AppArmor/SELinux confinement is a workload-level control, so there is nothing to assess here.
D42 Runtime Threat Enforcement — No Kubernetes/orchestration workloads found in the repository manifests; runtime threat-detection and admission-control policy are cluster-level controls, so there is nothing to assess here.
D43 Malicious Dependencies — Scanner failed to run — not a clean result
D5 Coupling — Not applicable — this Python build ships 1 production module(s), so there is no coupling BETWEEN modules to measure. (Its test and non-production modules are not part of the shipped graph.)
D7 Architectural Integrity — no checkable ADRs, and no project-reference graph for the cycle pass to read — so this dimension makes no claim about dependency cycles in either direction (where this repository's language has an import-cycle lens, cycles are reported there). Architectural integrity not assessed
D8 Code Coverage — Coverage not included — suite not readable by the collector
DM1 Domain Modelling — not scored — this repository shows only 1 of the 3 signals this lens looks for (14 value object(s))
ED1 Event-Driven — not scored — this repository shows none of the 3 signals this lens looks for
ED5 Idempotency — no mutating command handlers or message consumers detected — idempotency check not applicable
ES1 Event Sourcing — not scored — this repository shows none of the 3 signals this lens looks for
GD1 Unfinished & placeholder code — no source files were read — this check reads C#, F#, JavaScript/TypeScript, Dart, Ruby and Scala, and none was read for this repository's product. That is a limit of the analyzer, not a finding about your code.
IC1 Incompleteness & stubs — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
P12 CI test-gate honesty — Reported, not scored — and nothing was matched here. The coverage check applies to any stack, and the automatic-re-run check to any GitHub-Actions workflow, but the checks for excluded tests, skipped tests and sleep-based synchronisation currently recognise only some ecosystems' test-runner idioms, so on a repository built with another stack the zeros below mean 'not checked', not 'clean'.
P7 Outbound HTTP resilience — not applicable — no HTTP server, API framework or worker entry point was found in the JavaScript/TypeScript, Python source, so there is no service whose uptime a failing dependency could take down
P8 Schema migrations — no ORM, schema-migration tool or schema auto-create was found in this repository's dependency manifests or source, so there is no database schema for this check to judge
P9 Domain vs controller coverage — no coverage report found on disk — produce a coverage report in a standard format (`coverage run -m pytest` then `coverage xml`) and commit it — a hosted scan measures a clone of the repository, so a report that exists only in a working tree, a CI runner's or your own, never reaches it; the artefact is commonly gitignored, so `git add -f` that one file (or un-ignore its path) and commit it alongside the code it measures, or wire coverage collection into CI, to enable this cross-layer check
PF1 Benchmark discipline — Not applicable: no benchmark suite was found. This check searched for a pytest-benchmark or pytest-codspeed dependency with tests taking the `benchmark` fixture, an asv.conf.json with its `time_`/`mem_`/`peakmem_` benchmarks, or a pyperf Runner, and for a `*benchmark*` script that this repository's CI runs. Benchmarks are credited as a bonus, so their absence is neither scored nor deducted.
PF2 Allocation hygiene — Not applicable: Python runs on a garbage-collected runtime that gives a program no allocation-control idiom to choose on a hot path — no pools, stack allocation or value types — so allocation awareness is not something this code can be rated on.
S1 Web-Security Posture — Not assessed: these web-security controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks web-security controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
X10 Duplicated predicate — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
X2 Cancellation propagation — no C# was loaded; this repository's Python was read, and no request in it can be cancelled by a caller — a Python request takes no token: asyncio cancels the task and trio or anyio the scope that made it, from outside the call, and requests, httpx, aiohttp and gRPC take a timeout, not a cancellation
X20 Mistyped argument guard — This check asks whether an argument guard reports an EMPTY value as a NULL one, which needs a language that throws a null-specific argument exception — .NET's ArgumentNullException, the JVM's NullPointerException, Dart's ArgumentError.notNull. This repository contains none of those languages: the ones it is written in have a single exception for a bad argument, so there is no pair of exceptions to confuse and nothing here for it to assess. Not a gap in the analyzer and not a finding about your code.
X32 Type resolved by simple name across every loaded assembly — This check is about how a .NET program searches the assemblies loaded into its process for a type, and this repository contains no .NET source, so there is nothing here for it to assess. Not a gap in the analyzer and not a finding about your code.
X5 Nullable reference types — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
X6 Hand-rolled structured-format parsing — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
X7 Silent fallback defaults — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
X9 Subsumed condition operand — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
Appendix A — Findings (grouped)
The findings behind the scores, grouped by severity, then by dimension and kind. The high-severity issues are enumerated in full below; items per group are capped at 25 with any overflow stated explicitly per group, never silently truncated. The complete machine-readable list of every finding (all severities) is the companion findings.md in this report's bundle.
Hotspot: scrapling/engines/_browsers/_stealth.py scrapling/engines/_browsers/_stealth.py:114— scrapling/engines/_browsers/_stealth.py changed 9 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 36 in StealthySession._cloudflare_solver at line 114. 4 of those changes were fix/bug commits, and the other 5 changed it for other reasons — this file is under both repair and feature pressure. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-24..2026-10-22, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-24 23:43:40 +03:00' --until='2026-10-22 23:43:40 +03:00' --full-history --no-merges -- scrapling/engines/_browsers/_stealth.py`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: scrapling/engines/_browsers/_base.py scrapling/engines/_browsers/_base.py:509— scrapling/engines/_browsers/_base.py changed 8 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 20 in BaseSessionMixin.__generate_options__ at line 509. 2 of those changes were fix/bug commits, and the other 6 changed it for other reasons — this file is under both repair and feature pressure. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-24..2026-10-22, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-24 23:43:40 +03:00' --until='2026-10-22 23:43:40 +03:00' --full-history --no-merges -- scrapling/engines/_browsers/_base.py`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: scrapling/engines/toolbelt/convertor.py scrapling/engines/toolbelt/convertor.py:157— scrapling/engines/toolbelt/convertor.py changed 6 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 26 in ResponseFactory.from_playwright_response at line 157. 2 of those changes were fix/bug commits, and the other 4 changed it for other reasons — this file is under both repair and feature pressure. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-24..2026-10-22, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-24 23:43:40 +03:00' --until='2026-10-22 23:43:40 +03:00' --full-history --no-merges -- scrapling/engines/toolbelt/convertor.py`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: scrapling/parser.py scrapling/parser.py:701— scrapling/parser.py changed 4 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 30 in Selector.find_all at line 701. 4 of those changes were fix/bug commits, so the churn is repair rather than feature work. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-24..2026-10-22, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-24 23:43:40 +03:00' --until='2026-10-22 23:43:40 +03:00' --full-history --no-merges -- scrapling/parser.py`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: scrapling/spiders/engine.py scrapling/spiders/engine.py:186— scrapling/spiders/engine.py changed 5 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 22 in CrawlerEngine._process_request at line 186. 3 of those changes were fix/bug commits, so the churn is repair rather than feature work. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-24..2026-10-22, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-24 23:43:40 +03:00' --until='2026-10-22 23:43:40 +03:00' --full-history --no-merges -- scrapling/spiders/engine.py`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: scrapling/spiders/links.py scrapling/spiders/links.py:226— scrapling/spiders/links.py changed 2 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 16 in LinkExtractor.extract at line 226. 2 of those changes were fix/bug commits, so the churn is repair rather than feature work. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-24..2026-10-22, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-24 23:43:40 +03:00' --until='2026-10-22 23:43:40 +03:00' --full-history --no-merges -- scrapling/spiders/links.py`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
FileTooLong: ai/server.py scrapling/core/ai/server.py— FileTooLong — 950 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 450 over it, 1.90× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: scrapling/parser.py scrapling/parser.py— FileTooLong — 865 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 365 over it, 1.73× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: scrapling/cli.py scrapling/cli.py— FileTooLong — 535 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 35 over it, 1.07× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: core/shell.py scrapling/core/shell.py— FileTooLong — 509 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 9 over it, 1.02× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
Repeated repair: scrapling/spiders/templates/sitemap.py scrapling/spiders/templates/sitemap.py:111— scrapling/spiders/templates/sitemap.py changed 4 times in last 90 days and 3 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 10 (its worst body is SitemapSpider._sm_body at line 111), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix(spiders): route sitemap URLs to parse() instead of the sitemap parser (#458)”; “fix(spiders): ignore a leading byte-order mark in robots.txt and CSV feeds (#453)”; “fix(spiders): skip non-element nodes in sitemap XML instead of crashing on comments (#450)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-24..2026-10-22, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-24 23:43:40 +03:00' --until='2026-10-22 23:43:40 +03:00' --full-history --no-merges -- scrapling/spiders/templates/sitemap.py`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Repeated repair: scrapling/spiders/request.py scrapling/spiders/request.py:72— scrapling/spiders/request.py changed 3 times in last 90 days and 3 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 14 (its worst body is Request.update_fingerprint at line 72), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix(spiders): preserve falsy JSON bodies in request fingerprints (#480)”; “fix: make request fingerprint independent of header order (#466)”; “fix(spiders): include params in the request fingerprint (#448)”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-24..2026-10-22, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-24 23:43:40 +03:00' --until='2026-10-22 23:43:40 +03:00' --full-history --no-merges -- scrapling/spiders/request.py`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Duplicated block (6 lines × 2) scrapling/engines/static.py:736— scrapling/engines/static.py:736-741 | scrapling/engines/static.py:762-767 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (6 lines × 2) scrapling/__init__.py:27— scrapling/__init__.py:27-32 | scrapling/fetchers/__init__.py:31-36 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Swallowed exception (empty catch) scrapling/engines/_browsers/_base.py:180— An empty catch block silently discards the error — failures vanish with no log and no rethrow. Log it, handle it, or don't catch it.
Swallowed exception (empty catch) scrapling/engines/_browsers/_base.py:406— An empty catch block silently discards the error — failures vanish with no log and no rethrow. Log it, handle it, or don't catch it.
StealthySession._cloudflare_solver (cyclomatic 36) scrapling/engines/_browsers/_stealth.py:114— StealthySession._cloudflare_solver has cyclomatic complexity 36 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top. This shape REPEATS in the file: one other method here (AsyncStealthySession._cloudflare_solver) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared. This file is where this pass's cyclomatic complexity CONCENTRATES: scrapling/engines/_browsers/_stealth.py holds 4 of the 19 methods over the threshold — including the worst — and 50 of the 154 points over it (32%), 2.3× the next-largest file (scrapling/engines/toolbelt/convertor.py at 22). No single row can show this, because each is measured only against the threshold: reducing this one file moves this dimension further than any other file in the repository.
AsyncStealthySession._cloudflare_solver (cyclomatic 36) scrapling/engines/_browsers/_stealth.py:431— AsyncStealthySession._cloudflare_solver has cyclomatic complexity 36 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top. This shape REPEATS in the file: one other method here (StealthySession._cloudflare_solver) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared. This file is where this pass's cyclomatic complexity CONCENTRATES: scrapling/engines/_browsers/_stealth.py holds 4 of the 19 methods over the threshold — including the worst — and 50 of the 154 points over it (32%), 2.3× the next-largest file (scrapling/engines/toolbelt/convertor.py at 22). No single row can show this, because each is measured only against the threshold: reducing this one file moves this dimension further than any other file in the repository.
CurlParser.parse (cyclomatic 33) scrapling/core/shell.py:191— CurlParser.parse has cyclomatic complexity 33 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
_browser_actions._run_actions (cyclomatic 32) scrapling/core/ai/_browser_actions.py:168— _browser_actions._run_actions has cyclomatic complexity 32 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
Selector.find_all (cyclomatic 30) scrapling/parser.py:701— Selector.find_all has cyclomatic complexity 30 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
ResponseFactory.from_playwright_response (cyclomatic 26) scrapling/engines/toolbelt/convertor.py:157— ResponseFactory.from_playwright_response has cyclomatic complexity 26 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top. This shape REPEATS in the file: one other method here (ResponseFactory.from_async_playwright_response) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared.
ResponseFactory.from_async_playwright_response (cyclomatic 26) scrapling/engines/toolbelt/convertor.py:332— ResponseFactory.from_async_playwright_response has cyclomatic complexity 26 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top. This shape REPEATS in the file: one other method here (ResponseFactory.from_playwright_response) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared.
CrawlerEngine._process_request (cyclomatic 22) scrapling/spiders/engine.py:186— CrawlerEngine._process_request has cyclomatic complexity 22 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
BaseSessionMixin.__generate_options__ (cyclomatic 20) scrapling/engines/_browsers/_base.py:509— BaseSessionMixin.__generate_options__ has cyclomatic complexity 20 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
PlaywrightConfig.__post_init__ (cyclomatic 20) scrapling/engines/_browsers/_validators.py:98— PlaywrightConfig.__post_init__ has cyclomatic complexity 20 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
CrawlerEngine.crawl (cyclomatic 20) scrapling/spiders/engine.py:356— CrawlerEngine.crawl has cyclomatic complexity 20 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
StealthySession.fetch (cyclomatic 19) scrapling/engines/_browsers/_stealth.py:231— StealthySession.fetch has cyclomatic complexity 19 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top. This shape REPEATS in the file: one other method here (AsyncStealthySession.fetch) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared. This file is where this pass's cyclomatic complexity CONCENTRATES: scrapling/engines/_browsers/_stealth.py holds 4 of the 19 methods over the threshold — including the worst — and 50 of the 154 points over it (32%), 2.3× the next-largest file (scrapling/engines/toolbelt/convertor.py at 22). No single row can show this, because each is measured only against the threshold: reducing this one file moves this dimension further than any other file in the repository.
AsyncStealthySession.fetch (cyclomatic 19) scrapling/engines/_browsers/_stealth.py:553— AsyncStealthySession.fetch has cyclomatic complexity 19 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top. This shape REPEATS in the file: one other method here (StealthySession.fetch) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared. This file is where this pass's cyclomatic complexity CONCENTRATES: scrapling/engines/_browsers/_stealth.py holds 4 of the 19 methods over the threshold — including the worst — and 50 of the 154 points over it (32%), 2.3× the next-largest file (scrapling/engines/toolbelt/convertor.py at 22). No single row can show this, because each is measured only against the threshold: reducing this one file moves this dimension further than any other file in the repository.
Selector.xpath (cyclomatic 18) scrapling/parser.py:631— Selector.xpath has cyclomatic complexity 18 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
SelectorsGeneration._general_selection (cyclomatic 17) scrapling/core/mixins.py:15— SelectorsGeneration._general_selection has cyclomatic complexity 17 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
LinkExtractor._url_passes (cyclomatic 17) scrapling/spiders/links.py:283— LinkExtractor._url_passes has cyclomatic complexity 17 (threshold 15). To reduce it, name the conditions: bind each compound test to a well-named local or a small predicate function, so the body reads as a sequence of named decisions rather than a chain of operators.
_SyncSessionLogic._make_request (cyclomatic 16) scrapling/engines/static.py:241— _SyncSessionLogic._make_request has cyclomatic complexity 16 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top. This shape REPEATS in the file: one other method here (_ASyncSessionLogic._make_request) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared.
_ASyncSessionLogic._make_request (cyclomatic 16) scrapling/engines/static.py:457— _ASyncSessionLogic._make_request has cyclomatic complexity 16 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top. This shape REPEATS in the file: one other method here (_SyncSessionLogic._make_request) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared.
LinkExtractor.extract (cyclomatic 16) scrapling/spiders/links.py:226— LinkExtractor.extract has cyclomatic complexity 16 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
StealthySession._cloudflare_solver (cognitive 98) scrapling/engines/_browsers/_stealth.py:114— StealthySession._cloudflare_solver has cognitive complexity 98 (threshold 15). Drivers by points: if/else 25 (72 pts), loops 5 (17 pts), boolean chains 6, error handling 1 (3 pts) (nesting depth added 61). To reduce it, flatten the nesting: this score is depth rather than breadth — most of its points come from checks stacked inside one another, so the work sits several levels in. Invert each enclosing check into an early exit (a return, or the language's equivalent) so the happy path stays at one level, and where a level cannot be exited early, lift the block it encloses into its own named function. This shape REPEATS in the file: one other method here (AsyncStealthySession._cloudflare_solver) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared. This file is where this pass's cognitive complexity CONCENTRATES: scrapling/engines/_browsers/_stealth.py holds 4 of the 31 methods over the threshold — including the worst — and 214 of the 585 points over it (37%), 3× the next-largest file (scrapling/core/ai/_browser_actions.py at 72). No single row can show this, because each is measured only against the threshold: reducing this one file moves this dimension further than any other file in the repository.
AsyncStealthySession._cloudflare_solver (cognitive 98) scrapling/engines/_browsers/_stealth.py:431— AsyncStealthySession._cloudflare_solver has cognitive complexity 98 (threshold 15). Drivers by points: if/else 25 (72 pts), loops 5 (17 pts), boolean chains 6, error handling 1 (3 pts) (nesting depth added 61). To reduce it, flatten the nesting: this score is depth rather than breadth — most of its points come from checks stacked inside one another, so the work sits several levels in. Invert each enclosing check into an early exit (a return, or the language's equivalent) so the happy path stays at one level, and where a level cannot be exited early, lift the block it encloses into its own named function. This shape REPEATS in the file: one other method here (StealthySession._cloudflare_solver) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared. This file is where this pass's cognitive complexity CONCENTRATES: scrapling/engines/_browsers/_stealth.py holds 4 of the 31 methods over the threshold — including the worst — and 214 of the 585 points over it (37%), 3× the next-largest file (scrapling/core/ai/_browser_actions.py at 72). No single row can show this, because each is measured only against the threshold: reducing this one file moves this dimension further than any other file in the repository.
_browser_actions._run_actions (cognitive 87) scrapling/core/ai/_browser_actions.py:168— _browser_actions._run_actions has cognitive complexity 87 (threshold 15). Drivers by points: if/else 19 (52 pts), error handling 3 (13 pts), ternaries 2 (10 pts), loops 3 (9 pts), boolean chains 3 (nesting depth added 57). To reduce it, flatten the nesting: this score is depth rather than breadth — most of its points come from checks stacked inside one another, so the work sits several levels in. Invert each enclosing check into an early exit (a return, or the language's equivalent) so the happy path stays at one level, and where a level cannot be exited early, lift the block it encloses into its own named function.
SelectorsGeneration._general_selection (cognitive 50) scrapling/core/mixins.py:15— SelectorsGeneration._general_selection has cognitive complexity 50 (threshold 15). Drivers by points: if/else 11 (29 pts), ternaries 4 (15 pts), loops 2 (5 pts), boolean chains 1 (nesting depth added 32). To reduce it, flatten the nesting: this score is depth rather than breadth — most of its points come from checks stacked inside one another, so the work sits several levels in. Invert each enclosing check into an early exit (a return, or the language's equivalent) so the happy path stays at one level, and where a level cannot be exited early, lift the block it encloses into its own named function.
Selector.find_all (cognitive 47) scrapling/parser.py:701— Selector.find_all has cognitive complexity 47 (threshold 15). Drivers by points: if/else 15 (26 pts), loops 8 (15 pts), boolean chains 6 (nesting depth added 18). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CurlParser.parse (cognitive 40) scrapling/core/shell.py:191— CurlParser.parse has cognitive complexity 40 (threshold 15). Drivers by points: if/else 12 (16 pts), error handling 9 (15 pts), ternaries 2 (5 pts), boolean chains 2, loops 1 (2 pts) (nesting depth added 14). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
StealthySession.fetch (cognitive 39) scrapling/engines/_browsers/_stealth.py:231— StealthySession.fetch has cognitive complexity 39 (threshold 15). Drivers by points: if/else 12 (23 pts), error handling 4 (11 pts), boolean chains 2, ternaries 2, loops 1 (nesting depth added 18). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body. This shape REPEATS in the file: one other method here (AsyncStealthySession.fetch) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared. This file is where this pass's cognitive complexity CONCENTRATES: scrapling/engines/_browsers/_stealth.py holds 4 of the 31 methods over the threshold — including the worst — and 214 of the 585 points over it (37%), 3× the next-largest file (scrapling/core/ai/_browser_actions.py at 72). No single row can show this, because each is measured only against the threshold: reducing this one file moves this dimension further than any other file in the repository.
AsyncStealthySession.fetch (cognitive 39) scrapling/engines/_browsers/_stealth.py:553— AsyncStealthySession.fetch has cognitive complexity 39 (threshold 15). Drivers by points: if/else 12 (23 pts), error handling 4 (11 pts), boolean chains 2, ternaries 2, loops 1 (nesting depth added 18). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body. This shape REPEATS in the file: one other method here (StealthySession.fetch) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared. This file is where this pass's cognitive complexity CONCENTRATES: scrapling/engines/_browsers/_stealth.py holds 4 of the 31 methods over the threshold — including the worst — and 214 of the 585 points over it (37%), 3× the next-largest file (scrapling/core/ai/_browser_actions.py at 72). No single row can show this, because each is measured only against the threshold: reducing this one file moves this dimension further than any other file in the repository.
ResponseFactory.from_playwright_response (cognitive 37) scrapling/engines/toolbelt/convertor.py:157— ResponseFactory.from_playwright_response has cognitive complexity 37 (threshold 15). Drivers by points: if/else 10 (17 pts), boolean chains 8, error handling 4 (7 pts), ternaries 5 (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body. This shape REPEATS in the file: one other method here (ResponseFactory.from_async_playwright_response) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared.
ResponseFactory.from_async_playwright_response (cognitive 37) scrapling/engines/toolbelt/convertor.py:332— ResponseFactory.from_async_playwright_response has cognitive complexity 37 (threshold 15). Drivers by points: if/else 10 (17 pts), boolean chains 8, error handling 4 (7 pts), ternaries 5 (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body. This shape REPEATS in the file: one other method here (ResponseFactory.from_playwright_response) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared.
CrawlerEngine.crawl (cognitive 36) scrapling/spiders/engine.py:356— CrawlerEngine.crawl has cognitive complexity 36 (threshold 15). Drivers by points: if/else 15 (29 pts), boolean chains 3, loops 2 (3 pts), ternaries 1 (nesting depth added 15). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
LinkExtractor.extract (cognitive 33) scrapling/spiders/links.py:226— LinkExtractor.extract has cognitive complexity 33 (threshold 15). Drivers by points: if/else 9 (22 pts), loops 4 (7 pts), error handling 1 (3 pts), boolean chains 1 (nesting depth added 18). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
BaseSessionMixin.__generate_options__ (cognitive 32) scrapling/engines/_browsers/_base.py:509— BaseSessionMixin.__generate_options__ has cognitive complexity 32 (threshold 15). Drivers by points: if/else 11 (17 pts), ternaries 4 (10 pts), boolean chains 5 (nesting depth added 12). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
Selector.find_by_text (cognitive 27) scrapling/parser.py:1103— Selector.find_by_text has cognitive complexity 27 (threshold 15). Drivers by points: if/else 10 (24 pts), loops 1 (2 pts), boolean chains 1 (nesting depth added 15). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CrawlerEngine._process_request (cognitive 27) scrapling/spiders/engine.py:186— CrawlerEngine._process_request has cognitive complexity 27 (threshold 15). Drivers by points: if/else 15 (18 pts), boolean chains 6, ternaries 1 (2 pts), error handling 1 (nesting depth added 4). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
_SyncSessionLogic._make_request (cognitive 25) scrapling/engines/static.py:241— _SyncSessionLogic._make_request has cognitive complexity 25 (threshold 15). Drivers by points: if/else 9 (15 pts), boolean chains 6, error handling 1 (2 pts), loops 1, ternaries 1 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body. This shape REPEATS in the file: one other method here (_ASyncSessionLogic._make_request) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared.
_ASyncSessionLogic._make_request (cognitive 25) scrapling/engines/static.py:457— _ASyncSessionLogic._make_request has cognitive complexity 25 (threshold 15). Drivers by points: if/else 9 (15 pts), boolean chains 6, error handling 1 (2 pts), loops 1, ternaries 1 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body. This shape REPEATS in the file: one other method here (_SyncSessionLogic._make_request) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared.
Selector.__init__ (cognitive 25) scrapling/parser.py:92— Selector.__init__ has cognitive complexity 25 (threshold 15). Drivers by points: if/else 12 (21 pts), boolean chains 4 (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
PlaywrightConfig.__post_init__ (cognitive 24) scrapling/engines/_browsers/_validators.py:98— PlaywrightConfig.__post_init__ has cognitive complexity 24 (threshold 15). Drivers by points: if/else 17 (21 pts), boolean chains 3 (nesting depth added 4). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
Selector.xpath (cognitive 23) scrapling/parser.py:631— Selector.xpath has cognitive complexity 23 (threshold 15). Drivers by points: if/else 8 (16 pts), boolean chains 6, error handling 1 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
_shell._ParseHeaders (cognitive 22) scrapling/core/utils/_shell.py:19— _shell._ParseHeaders has cognitive complexity 22 (threshold 15). Drivers by points: if/else 8 (16 pts), error handling 1 (5 pts), loops 1 (nesting depth added 12). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
SessionManager.fetch (cognitive 19) scrapling/spiders/session.py:103— SessionManager.fetch has cognitive complexity 19 (threshold 15). Drivers by points: if/else 8 (17 pts), boolean chains 1, ternaries 1 (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
SitemapSpider._sm_body (cognitive 19) scrapling/spiders/templates/sitemap.py:111— SitemapSpider._sm_body has cognitive complexity 19 (threshold 15). Drivers by points: if/else 4 (10 pts), loops 2 (6 pts), error handling 2, boolean chains 1 (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
ScraplingMCPServer.browser_screenshot (cognitive 18) scrapling/core/ai/server.py:586— ScraplingMCPServer.browser_screenshot has cognitive complexity 18 (threshold 15). Drivers by points: boolean chains 6, error handling 2 (5 pts), if/else 5, ternaries 1 (2 pts) (nesting depth added 4). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CurlParser.convert2fetcher (cognitive 18) scrapling/core/shell.py:346— CurlParser.convert2fetcher has cognitive complexity 18 (threshold 15). Drivers by points: if/else 7 (12 pts), error handling 1 (3 pts), ternaries 1 (2 pts), boolean chains 1 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
Convertor._extract_content (cognitive 18) scrapling/core/shell.py:659— Convertor._extract_content has cognitive complexity 18 (threshold 15). Drivers by points: loops 2 (6 pts), if/else 3 (4 pts), boolean chains 3, match/switch 1 (3 pts), ternaries 1 (2 pts) (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
Selector.__calculate_similarity_score (cognitive 18) scrapling/parser.py:814— Selector.__calculate_similarity_score has cognitive complexity 18 (threshold 15). Drivers by points: if/else 6 (10 pts), boolean chains 6, loops 1, ternaries 1 (nesting depth added 4). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CrawlerEngine._run_callbacks (cognitive 18) scrapling/spiders/engine.py:155— CrawlerEngine._run_callbacks has cognitive complexity 18 (threshold 15). Drivers by points: if/else 7 (15 pts), error handling 1, loops 1, ternaries 1 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
LinkExtractor._url_passes (cognitive 18) scrapling/spiders/links.py:283— LinkExtractor._url_passes has cognitive complexity 18 (threshold 15). Drivers by points: boolean chains 9, if/else 7 (9 pts) (nesting depth added 2). To reduce it, name the conditions: bind each compound test to a well-named local or a small predicate function, so the body reads as a sequence of named decisions rather than a chain of operators.
Selector.relocate (cognitive 17) scrapling/parser.py:522— Selector.relocate has cognitive complexity 17 (threshold 15). Drivers by points: if/else 6 (11 pts), loops 2 (5 pts), boolean chains 1 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
NetworkRecorder.search (cognitive 16) scrapling/engines/_browsers/_network.py:86— NetworkRecorder.search has cognitive complexity 16 (threshold 15). Drivers by points: boolean chains 8, if/else 3 (5 pts), error handling 1, loops 1, ternaries 1 (nesting depth added 2). To reduce it, name the conditions: bind each compound test to a well-named local or a small predicate function, so the body reads as a sequence of named decisions rather than a chain of operators.
D22 · Internal API Consistency· Redundant HTTP verb methods vs generic fetch. The CLI exposes specific methods for GET, POST, PUT, and DELETE, but also exposes a generic `fetch` method. This creates confusion about which method to use for standard HTTP requests versus complex browser-based fetching. · ×1
Redundant HTTP verb methods vs generic fetch. The CLI exposes specific methods for GET, POST, PUT, and DELETE, but also exposes a generic `fetch` method. This creates confusion about which method to use for standard HTTP requests versus complex browser-based fetching. — Remove `cli.get`, `cli.post`, `cli.put`, and `cli.delete`. Rely on `cli.fetch` for all HTTP operations, or remove `cli.fetch` if the specific verbs are preferred, but do not mix both patterns. (signatures: cli.get(url, ...) | cli.post(url, ...) | cli.put(url, ...) | cli.delete(url, ...) | cli.fetch(url, ...))
D22 · Internal API Consistency· Overlapping functionality between browser fetch methods. `browser_fetch` and `browser_fetch_once` appear to perform similar HTTP-like extraction tasks within a browser context, while `session_make_request` handles HTTP requests within a session. The distinction between 'fetch' and 'make_request' in the context of a browser session is unclear and likely redundant. · ×1
Overlapping functionality between browser fetch methods. `browser_fetch` and `browser_fetch_once` appear to perform similar HTTP-like extraction tasks within a browser context, while `session_make_request` handles HTTP requests within a session. The distinction between 'fetch' and 'make_request' in the context of a browser session is unclear and likely redundant. — Unify these into a single `request` or `fetch` method on the session object, or clearly distinguish between 'browser navigation/fetch' and 'HTTP session request' with non-overlapping names and signatures. (signatures: ScraplingMCPServer.browser_fetch(url, ...) | ScraplingMCPServer.browser_fetch_once(url, ...) | ScraplingMCPServer.session_make_request(url, ...))
D22 · Internal API Consistency· Inconsistent naming for state persistence. The session objects use `save_state`/`load_state`, while the MCP server uses `browser_save_state`/`browser_load_state`. This inconsistency extends to other methods like `close` vs `close_session`. · ×1
Inconsistent naming for state persistence. The session objects use `save_state`/`load_state`, while the MCP server uses `browser_save_state`/`browser_load_state`. This inconsistency extends to other methods like `close` vs `close_session`. — Standardize on `save_state`/`load_state` for all session-like objects, or prefix all with `browser_` if they are strictly browser-specific, but keep the verb-noun structure consistent across the API. (signatures: SyncSession.save_state(path) | AsyncSession.save_state(path) | ScraplingMCPServer.browser_save_state(session_id, path))
D22 · Internal API Consistency· Duplicate configuration types. `PlaywrightConfig` and `_fetch_params` contain nearly identical fields (e.g., `timeout`, `wait`, `proxy`, `headers`), as do `StealthConfig` and `StealthFetchParams`. This duplication increases maintenance burden and risk of drift. · ×1
Duplicate configuration types. `PlaywrightConfig` and `_fetch_params` contain nearly identical fields (e.g., `timeout`, `wait`, `proxy`, `headers`), as do `StealthConfig` and `StealthFetchParams`. This duplication increases maintenance burden and risk of drift. — Consolidate these into a single `FetchConfig` or `BrowserConfig` type that inherits or composes common settings, removing the redundant `_fetch_params` and specific `*FetchParams` types if they are just wrappers. (signatures: PlaywrightConfig | StealthConfig | _fetch_params)
TooManyMethods: Selector scrapling/parser.py:77— TooManyMethods — 41 methods. The bar is 30 methods; this is 11 over it, 1.37× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
Near-duplicate member pair (69 shared lines) scrapling/engines/_browsers/_stealth.py:232— scrapling/engines/_browsers/_stealth.py:232-342 | scrapling/engines/_browsers/_stealth.py:554-665 — These two members are variants of one another: 69 of their lines are already reported as duplicated blocks below, spread through both bodies rather than gathered into one. Read them as a single construct written twice. The repair is at the members' grain — factor the shared pipeline into one implementation the two call with their differences as parameters or as an injected step, or, where the difference is systematic (sync against async, one transport against another), generate one from the other. Extracting the individual blocks below is not the same fix: it leaves the two bodies in place and the next edit still has to be made twice.
Near-duplicate member pair (35 shared lines) scrapling/engines/static.py:242— scrapling/engines/static.py:242-296 | scrapling/engines/static.py:458-515 — These two members are variants of one another: 35 of their lines are already reported as duplicated blocks below, spread through both bodies rather than gathered into one. Read them as a single construct written twice. The repair is at the members' grain — factor the shared pipeline into one implementation the two call with their differences as parameters or as an injected step, or, where the difference is systematic (sync against async, one transport against another), generate one from the other. Extracting the individual blocks below is not the same fix: it leaves the two bodies in place and the next edit still has to be made twice.
Duplicated block (39–40 lines × 2) scrapling/engines/_browsers/_stealth.py:232— scrapling/engines/_browsers/_stealth.py:232-270 | scrapling/engines/_browsers/_stealth.py:554-593 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `scrapling/engines/_browsers/_stealth.py:232` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (20 lines × 2) scrapling/engines/_browsers/_stealth.py:315— scrapling/engines/_browsers/_stealth.py:315-334 | scrapling/engines/_browsers/_stealth.py:638-657 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `scrapling/engines/_browsers/_stealth.py:315` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (15–16 lines × 2) scrapling/engines/static.py:259— scrapling/engines/static.py:259-273 | scrapling/engines/static.py:477-492 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (14 lines × 2) scrapling/core/ai/server.py:697— scrapling/core/ai/server.py:697-710 | scrapling/core/ai/server.py:946-959 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `scrapling/core/ai/server.py:697` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (12–13 lines × 2) scrapling/engines/_browsers/_stealth.py:133— scrapling/engines/_browsers/_stealth.py:133-145 | scrapling/engines/_browsers/_stealth.py:451-462 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (12 lines × 2) scrapling/engines/static.py:276— scrapling/engines/static.py:276-287 | scrapling/engines/static.py:494-505 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (11 lines × 2) scrapling/engines/toolbelt/convertor.py:199— scrapling/engines/toolbelt/convertor.py:199-209 | scrapling/engines/toolbelt/convertor.py:375-385 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (9–10 lines × 2) scrapling/engines/_browsers/_stealth.py:272— scrapling/engines/_browsers/_stealth.py:272-281 | scrapling/engines/_browsers/_stealth.py:596-604 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `scrapling/engines/_browsers/_stealth.py:596` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (8 lines × 2) scrapling/engines/static.py:242— scrapling/engines/static.py:242-249 | scrapling/engines/static.py:458-465 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Interpolated log message defeats structured logging scrapling/core/shell.py:130— `error` at line 130 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.error("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.error("order moved", order_id=order_id, status=status)` with structlog.
Interpolated log message defeats structured logging scrapling/core/shell.py:135— `error` at line 135 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.error("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.error("order moved", order_id=order_id, status=status)` with structlog.
Interpolated log message defeats structured logging scrapling/core/shell.py:199— `error` at line 199 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.error("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.error("order moved", order_id=order_id, status=status)` with structlog.
Interpolated log message defeats structured logging scrapling/core/shell.py:214— `error` at line 214 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.error("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.error("order moved", order_id=order_id, status=status)` with structlog.
Interpolated log message defeats structured logging scrapling/core/shell.py:248— `debug` at line 248 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.debug("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.debug("order moved", order_id=order_id, status=status)` with structlog.
Interpolated log message defeats structured logging scrapling/core/shell.py:250— `error` at line 250 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.error("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.error("order moved", order_id=order_id, status=status)` with structlog.
Interpolated log message defeats structured logging scrapling/core/shell.py:264— `warning` at line 264 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.warning("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.warning("order moved", order_id=order_id, status=status)` with structlog.
Interpolated log message defeats structured logging scrapling/core/shell.py:281— `warning` at line 281 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.warning("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.warning("order moved", order_id=order_id, status=status)` with structlog.
Interpolated log message defeats structured logging scrapling/core/shell.py:302— `warning` at line 302 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.warning("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.warning("order moved", order_id=order_id, status=status)` with structlog.
Interpolated log message defeats structured logging scrapling/core/shell.py:331— `debug` at line 331 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.debug("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.debug("order moved", order_id=order_id, status=status)` with structlog.
Interpolated log message defeats structured logging scrapling/core/shell.py:371— `error` at line 371 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.error("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.error("order moved", order_id=order_id, status=status)` with structlog.
Interpolated log message defeats structured logging scrapling/core/shell.py:374— `error` at line 374 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.error("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.error("order moved", order_id=order_id, status=status)` with structlog.
Interpolated log message defeats structured logging scrapling/core/shell.py:428— `error` at line 428 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.error("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.error("order moved", order_id=order_id, status=status)` with structlog.
Interpolated log message defeats structured logging scrapling/core/shell.py:430— `error` at line 430 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.error("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.error("order moved", order_id=order_id, status=status)` with structlog.
Interpolated log message defeats structured logging scrapling/core/shell.py:463— `warning` at line 463 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.warning("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.warning("order moved", order_id=order_id, status=status)` with structlog.
Interpolated log message defeats structured logging scrapling/core/shell.py:480— `info` at line 480 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.info("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.info("order moved", order_id=order_id, status=status)` with structlog.
Interpolated log message defeats structured logging scrapling/core/shell.py:481— `info` at line 481 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.info("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.info("order moved", order_id=order_id, status=status)` with structlog.
Interpolated log message defeats structured logging scrapling/core/shell.py:482— `info` at line 482 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.info("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.info("order moved", order_id=order_id, status=status)` with structlog.
Interpolated log message defeats structured logging scrapling/core/shell.py:599— `info` at line 599 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.info("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.info("order moved", order_id=order_id, status=status)` with structlog.
Interpolated log message defeats structured logging scrapling/core/shell.py:603— `error` at line 603 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.error("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.error("order moved", order_id=order_id, status=status)` with structlog.
README/code drift docs/spiders/architecture.md:102— docs/spiders/architecture.md documents the environment variable `ROBOTSTXT_OBEY`, but no code, script or configuration file names it. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/locales/ar/spiders/architecture.md:100— docs/locales/ar/spiders/architecture.md documents the environment variable `ROBOTSTXT_OBEY`, but no code, script or configuration file names it. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/locales/de/spiders/architecture.md:102— docs/locales/de/spiders/architecture.md documents the environment variable `ROBOTSTXT_OBEY`, but no code, script or configuration file names it. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/locales/es/spiders/architecture.md:100— docs/locales/es/spiders/architecture.md documents the environment variable `ROBOTSTXT_OBEY`, but no code, script or configuration file names it. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
Documentation: no project overview README.md— The repository root README begins with a project banner, logo, and tagline but does not state what Scrapling is or what it does for readers who skim the top. State the core value proposition (e.g. 'Effortless web scraping for modern websites') so new visitors can find it without reading the entire README.
Documentation: contradicts the code docs/requirements.txt— The requirements file lists zensical>=0.0.70 but Scrapling's latest release (and the referenced docs) uses a newer version of zensical. Update the requirements to match the current library version.
X23 · Unguarded diagnostic materialisation· Diagnostic log message is built whether or not it is wanted · ×2
Diagnostic log message is built whether or not it is wanted scrapling/core/shell.py:248— `debug` at line 248 is called with an argument built by `list(cookies.keys())` (line 248), whose cost grows with the collection it walks. Python evaluates a call's arguments BEFORE entering the method, so that join/projection runs in full every time this line is reached — and then the level check inside the logger discards the string it produced, because debug is a level a shipped configuration normally leaves off. Nothing here decides beforehand whether the message is wanted: no `isEnabledFor(logging.DEBUG)` test, no debug flag, no `lambda` the logger calls only when the level is on.
Diagnostic log message is built whether or not it is wanted scrapling/spiders/engine.py:171— `debug` at line 171 is called with an argument built by `pprint.pformat(processed_result)` (line 171), whose cost grows with the collection it walks. Python evaluates a call's arguments BEFORE entering the method, so that join/projection runs in full every time this line is reached — and then the level check inside the logger discards the string it produced, because debug is a level a shipped configuration normally leaves off. Nothing here decides beforehand whether the message is wanted: no `isEnabledFor(logging.DEBUG)` test, no debug flag, no `lambda` the logger calls only when the level is on.
No ADRs found — No ADRs found. No recognised ADR directory (`docs/adr/`, `docs/decisions/`, `adr/`, `docs/rfcs/`, an `ADR0001/` folder, or their siblings) exists anywhere in this tree. What was searched, so you can tell an empty log from a search that missed one: every directory under the tree (build output, dependencies and VCS metadata excepted), for a document that is either any non-index page inside a recognised ADR directory, whatever its name and however deeply nested (`docs/adr/use-postgres.md`, `docs/adr/2024/0001-x.md`); or a file anywhere whose name is ADR-shaped (`0001-use-postgres.md`, `adr-012-caching.md`); or, when neither turned anything up, a document carrying the decision-record signature (an "Architecture Decision Record" heading, or Status / Context / Decision / Consequences as section headings). A decision log that clears none of these — unnumbered files outside any recognised directory, without those headings — is not seen by this check and this row is then wrong. If that is your case, say so rather than renaming anything; otherwise, consider recording architectural decisions in `docs/adr/`.
D26 · Project Cohesion· Projects may be oversized for their cohesion · ×1
Projects may be oversized for their cohesion — 1 of 2 project(s) overshoot their size bounds, lowering Project Cohesion to 0.0/10. The most over is `(repository root)` (14556 LoC, 130 module-visible types across 13 directories). Review these for cohesion — draw the boundary inside the module first (group each responsibility into its own package or directory and keep the cross-boundary members non-public), since splitting a published package moves types between packages and breaks consumers.
No ADRs — No Architecture Decision Records found — no conventional ADR directory, no numbered `NNNN-title` documents in any markup this check reads, and nothing ADR-shaped by content. Design rationale recorded elsewhere (a design-notes tree, a mailing list, pull-request discussion) is not visible to this check and is not re-findable per decision, so a future maintainer cannot ask why one choice was made and get an answer.
No src/ separation — Production code isn't grouped under a src/ folder — it's spread across several top-level directories, so there's no one place that says 'this is the product'.
No SAST — No static application security testing detected. For this repository's stack, add bandit, `semgrep --config=p/python`, or CodeQL's python pack as a CI step. What was searched, so you can tell an absence from a miss: the 15968 CI workflow file(s) in this repository, and the scanner and linter configuration checked in beside them. A scan that runs outside CI, one configured in your forge's web UI rather than in a committed file, or a tool whose name is none of those this check carries, is not seen — if that is your case the row is wrong, and saying so is more useful than adding a second scanner.
Dependency hygiene PARTLY measured — Python dependencies read, no exact pin to grade for currency — This repository's Python dependencies were read: 6 shipped distribution(s) across 3 manifest(s) (21 development or optional declaration(s) excluded, because a consumer does not install them). Whether any of them is behind its current release is the dependency-CURRENCY question, and no declaration here carries an EXACT pin to ask it about — every one is a floor or a range, which installs the newest release it admits and therefore cannot be behind one. That is a healthy declaration style, NOT a defect and NOT a gap in this analyzer; there is simply no currency question to answer. This is NOT a finding that these dependencies are current or healthy. Lockfiles are deliberately not read (poetry.lock, uv.lock, Pipfile.lock, pdm.lock): the four disagree about dev/optional membership, and misreading one would charge a test-only package a consumer never receives. Known CVEs in the same dependency graph are a separate question, reported under D30.
Appendix B — Reproduction & audit trail
Every external tool invocation behind a deep-scan dimension — the tool, its captured version, the exact command, how many findings it yielded, and a link to the retained raw output. To reproduce any finding: check out the same commit and run the command shown (repo-relative — never an absolute scratch path). The complete raw scanner output is retained verbatim under artifacts/raw/ (indexed in artifacts/raw/index.json); per-invocation exit codes and wall-clock durations are in sidecar.json — kept out of this table so the rendered report stays byte-identical across runs of the same commit.
semgrep: not applicable — No personal data was found crossing a boundary the PII/GDPR ruleset checks — nothing written to a log or console sink, placed in a URL or query string, or persisted to browser storage. That is a clean result for the LEAK surface only: this ruleset detects personal data escaping, it does not inventory the personal data a repository holds, so it is not evidence that this repository has no personal-data surface. The personal-data map (Appendix C) and the C1-C5 compliance cards are what speak to that.
disclosure: not applicable — No vulnerability-disclosure policy file found (SECURITY.md/.markdown/.rst/.txt at root or under .github/.forgejo/.gitea/docs, .well-known/security.txt). A coordinated-disclosure policy may live off-repo, so this is not evidenced rather than failed.
runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; network egress policy is a cluster-native control that may live at the platform/firewall layer, so there is nothing to assess here.
runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; seccomp/AppArmor/SELinux confinement is a workload-level control, so there is nothing to assess here.
runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; runtime threat-detection and admission-control policy are cluster-level controls, so there is nothing to assess here.
Run 01a1705e-556a-7fd1-ae1c-d7fd9ff9e260 · every finding is also locatable in findings.md, and the complete scoring record (with exit codes + durations) in sidecar.json.
Issues: 33 · Warnings: 91 · Recommendations: 36 · Info: 1 — Appendix A · all findings · full markdown report.
Generated by Watchdog — deterministic code-health analysis. 25-10-2026 @ 00:03 UTC.
Downloadable artifacts
Machine-readable and reproducible from this commit + frozen rubric — drop them straight into a contract appendix, a CRA dossier, or a downstream SCA / VEX tool.