Public report of google/langextract, 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 report Measured 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, 07:52 UTC · Survey ID cd_99287d7db16e43a38296b78a2e3d6aea

google/langextract

Measured 25 October 2026, 07:50 UTC, at code version 3a68580

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 score 70out of 100 Adequate
CriticalWeakAdequateStrongExemplary
Up 2 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.

Architecture 84 Adequate Code Health 80 Adequate Security and Compliance 70 Adequate Production Readiness 69 Adequate Maturity 67 Adequate

A small codebase: 15,094 lines of product code in 1 project, tests not counted · Estimated rebuild effort: about 0.3 person-years · Weakest lens: Maturity, 67

Findings by how serious they are

36 critical 86 serious 21 minor

17 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, 07:50 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 ▸

51of 51 dimensions measured by tools.
130of 143 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.
51dimensions across 5 lenses, read from 15,094 lines of product code in 1 project.
Chapters

Executive summary

google/langextract falls short of our standard for production, in part because the project keeps no record of its design decisions. It scores 70 out of 100 and is rated Adequate, and all 36 of its critical findings are security problems in the build pipeline and in its servers and containers.

Maturity, at 67, is the weakest lens by a small margin, and the weakest lens counts most in the score. The best first step is recording design decisions, worth about 4 points. Adding automated security scanning is another step, worth about 2 points.

Twenty pipeline tools are referenced by a changeable name, so the build pipeline can be tampered with. Eleven pipeline steps install packages that can change without warning. Pinning each tool to an exact version and pinning what the pipeline installs would close both risks. Passing outside input through environment variables would fix the two pipeline steps that put outside input straight into a shell command, and limiting each secret to its own step would deal with the two secrets exposed to more of the pipeline than needed.

The product code is 15,094 lines, about €48,000 to rebuild.

Code Health 80 Adequate, capped by Cognitive Complexity Architecture 84 Adequate, capped by Project Cohesion Maturity 67 Adequate, capped by Architecture documentation Production Readiness 69 Adequate, capped by Security & performance tooling Security and Compliance 70 Adequate, capped by Static Analysis (SAST)

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 for The 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.
Maturity 67 · counts for 47%Production Readiness 69 · counts for 26%Security and Compliance 70 · counts for 14%Code Health 80 · counts for 8%Architecture 84 · counts for 4%

The weakest lenses score close to each other, so improving any of them moves the score.

Code composition: what the lines of code are
Product code 41%Tests 59%
New since the last survey: 6 findings

These 6 critical or serious findings weren't in the previous survey, on 11 October 2026.

  • Cyclomatic Complexity · pr_security_check.parse_diff (cyclomatic 19) .github/scripts/pr_security_check.py
  • Cyclomatic Complexity · pr_security_check._check_credential_egress (cyclomatic 16) .github/scripts/pr_security_check.py
  • Cyclomatic Complexity · Resolver.extract_ordered_extractions (cyclomatic 16) langextract/resolver.py
  • Cognitive Complexity · pr_security_check.parse_diff (cognitive 18) .github/scripts/pr_security_check.py
  • Cognitive Complexity · pr_security_check._check_credential_egress (cognitive 16) .github/scripts/pr_security_check.py
  • God Classes · FileTooLong: scripts/pr_security_check.py .github/scripts/pr_security_check.py

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.

Rebuild cost: about €48,000 · Estimate
Cost to rebuild€16,000 to €80,000, for 267 to 846 hours of work, about 0.3 person-years for one engineer
Difficulty of the problemStandard, 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×1.0 at a score of 70. 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.
SizeSmall: 15,094 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
Resolve the 1 No ADRs found finding(s) in ADR Quality.
Raises the score by about 4 points · Low effort · ADR Quality
2
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.
Raises the score by about 3 points · Medium effort · Architecture documentation
3
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.
Raises the score by about 2 points · Medium effort · Security & performance tooling

Diagnosis: what the measurements show together

Each conclusion below combines several dimensions into one point that no single dimension shows, and lists the measurements it rests on.

If you do one thing first · First action
Resolve the 1 No ADRs found finding(s) in ADR Quality. Of all the actions in this report, it raises the score most for the work it takes: about 4 points for low effort, because it clears the 1 ADR Quality findings. The next survey will show the change in the score.
Based on: the ranked actions in Top priorities, by score gain and effort
What hard-to-read code costs each year · Cost
Four dimensions measure how easy the code is to work in: two measure how tangled its logic is, one how much of it is copied, and one how well each part sticks to a single job. In this codebase they average 7.0 out of 10. Code at that level is slower to change than clean code, and Watchdog estimates the extra work at 3% to 6% on every change. The history of the code shows how much it changes: about 9,653 lines over the last 90 days, which is about 39,148 lines a year. At that pace, the extra work comes to an estimated 2.5 to 15.1 engineer-days a year, and it grows as more code changes.
How this is estimated: The number of changed lines is measured from the history of the code. The rest rests on two assumptions of Watchdog's own: code scoring 8 out of 10 or more adds no extra work, and each point below 8 adds 2.5% to 5.6% to every change; and an engineer changes 150 to 400 lines in a day. The result is a range for planning, not a measurement.
Based on: Cyclomatic Complexity · Cognitive Complexity · Code Duplication · Cohesion (LCOM4) · the code's change history
What to do: Start with the files listed under Churn × Complexity Hotspots, which change often and are hard to read, because that is where the extra work is paid most often.

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.

42 modules, with 50 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; 2 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.
uses →1 (global)2 .github.scripts.pr_security_check3 benchmarks.config4 docs._static5 examples.custom_provider_plugin6 examples.ollama7 langextract._compat8 langextract._compat.inference9 langextract.core.base_model10 langextract.core.exceptions11 langextract.core.format_handler12 langextract.core.types13 langextract.factory14 langextract.prompt_validation15 langextract.providers.builtin_registry16 langextract.providers.gemini_batch17 langextract.providers.openai_batch18 langextract.providers.router19 .github.scripts20 benchmarks.benchmark21 examples.custom_provider_plugin.langextract_provider_example.provider22 langextract.core.schema23 langextract.core.tokenizer24 langextract.io25 langextract.providers26 langextract.providers.gemini27 langextract.providers.ollama28 langextract.providers.openai29 examples.custom_provider_plugin.langextract_provider_example.schema30 langextract.core31 langextract.core.data32 langextract.providers.schemas.gemini33 langextract.providers.schemas.openai34 langextract.chunking35 langextract.prompting36 langextract.resolver37 langextract.visualization38 benchmarks39 langextract40 langextract.annotation
1 (global)
2 .github.scripts.pr_security_check
3 benchmarks.config
4 docs._static
5 examples.custom_provider_plugin
6 examples.ollama
7 langextract._compat
8 langextract._compat.inference
9 langextract.core.base_model
10 langextract.core.exceptions
11 langextract.core.format_handler
12 langextract.core.types
13 langextract.factory
14 langextract.prompt_validation
15 langextract.providers.builtin_registry
16 langextract.providers.gemini_batch
17 langextract.providers.openai_batch
18 langextract.providers.router
19 .github.scripts1
20 benchmarks.benchmark1
21 examples.custom_provider_plugin.langextract_provider_example.provider1
22 langextract.core.schema11
23 langextract.core.tokenizer1
24 langextract.io1
25 langextract.providers11
26 langextract.providers.gemini11
27 langextract.providers.ollama11
28 langextract.providers.openai111
29 examples.custom_provider_plugin.langextract_provider_example.schema1
30 langextract.core114
31 langextract.core.data2
32 langextract.providers.schemas.gemini11
33 langextract.providers.schemas.openai11
34 langextract.chunking151
35 langextract.prompting11
36 langextract.resolver111
37 langextract.visualization1
38 benchmarks1111
39 langextract11133311
40 langextract.annotation1111
Uses a module listed above itPart of a loopThe module itself
1 (global)2 .github.scripts.pr_security_check3 benchmarks.config4 docs._static5 examples.custom_provider_plugin6 examples.ollama7 langextract._compat8 langextract._compat.inference9 langextract.core.base_model10 langextract.core.exceptions11 langextract.core.format_handler12 langextract.core.types13 langextract.factory14 langextract.prompt_validation15 langextract.providers.builtin_registry16 langextract.providers.gemini_batch17 langextract.providers.openai_batch18 langextract.providers.router19 .github.scripts20 benchmarks.benchmark21 examples.custom_provider_plugin.langextract_provider_example.provider22 langextract.core.schema23 langextract.core.tokenizer24 langextract.io25 langextract.providers26 langextract.providers.gemini27 langextract.providers.ollama28 langextract.providers.openai29 examples.custom_provider_plugin.langextract_provider_example.schema30 langextract.core31 langextract.core.data32 langextract.providers.schemas.gemini33 langextract.providers.schemas.openai34 langextract.chunking35 langextract.prompting36 langextract.resolver37 langextract.visualization38 benchmarks39 langextract40 langextract.annotation(global)1.github.scripts.pr_security_check2benchmarks.config3docs._static4examples.custom_provider_plugin5examples.ollama6langextract._compat7langextract._compat.inference8langextract.core.base_model9langextract.core.exceptions10langextract.core.format_handler11langextract.core.types12langextract.factory13langextract.prompt_validation14langextract.providers.builtin_registry15langextract.providers.gemini_batch16langextract.providers.openai_batch17langextract.providers.router18.github.scripts19benchmarks.benchmark20examples.custom_provider_plugin.langextract_provider_example.provider21langextract.core.schema22langextract.core.tokenizer23langextract.io24langextract.providers25langextract.providers.gemini26langextract.providers.ollama27langextract.providers.openai28examples.custom_provider_plugin.langextract_provider_example.schema29langextract.core30langextract.core.data31langextract.providers.schemas.gemini32langextract.providers.schemas.openai33langextract.chunking34langextract.prompting35langextract.resolver36langextract.visualization37benchmarks38langextract39langextract.annotation4011111111111111111114211111511111111111111333111111+2 more modules (most-connected shown)

At a glance — Code Health · 80% · Adequate · gated by D2 ·

The Good
D3 · Python God Classes 8.8 D4 · Python Code Duplication 9.7 D17 · Python Explicit Debt 9.9 X1 · Repo Async correctness 10.0 X12 · Repo Unreachable branch 10.0 X13 · Repo Undrained process stream 10.0 X19 · Repo Unrestored process-global state 10.0 X21 · Repo Side-effecting pattern guard 10.0 X22 · Repo Contradicted release guard 10.0 X25 · Repo Inert configuration knob 10.0 X26 · Repo Unsynchronised callback handoff 10.0 X27 · Repo Collection changed while being enumerated 10.0 X28 · Repo Index access outside its own emptiness guard 10.0 X30 · Repo Support guard that admits what it rejects 10.0 X4 · Repo Structured logging 10.0
The Bad
D1 · Python Cyclomatic Complexity 6.6 X23 · Repo Unguarded diagnostic materialisation 5.4 X3 · Repo Exception handling 6.4
The Ugly
D2 · Python Cognitive Complexity 2.4

At a glance — Architecture · 84% · Adequate · gated by D26 ·

The Good
D6 · Python Cohesion (LCOM4) 9.1 D35 · Python Change Coupling 10.0 AX10 · Repo Code composition 9.8 AX3 · Repo Project dependency cycles 10.0 AX4 · Repo Dependency direction 10.0
The Ugly
D26 · Python Project Cohesion 0.0

At a glance — Maturity · 67% · Adequate · gated by M2 ·

The Good
D15 · Python Churn × Complexity Hotspots 9.5 D19 · Python Documentation Quality 10.0 D34 · Python Knowledge Freshness 9.7 M1 · Repo Documentation (README) 9.3 M3 · Repo Folder & project structure 8.0 M4 · Repo Documentation accuracy 10.0
The Ugly
D20 · Python ADR Quality 0.0 M2 · Repo Architecture documentation 2.0

At a glance — Readiness · 69% · Adequate · gated by P3 ·

The Good
D9 · Python Test Distribution 10.0 D11 · Python Test Reliability 10.0 D13 · Python Secret Scanning 10.0 D14 · Python License Compliance 10.0 P1 · Repo CI/CD gates 10.0 P10 · Repo Library API & versioning 10.0 P4 · Repo Deployment & Rollback 10.0
The Bad
P6 · Repo Release Hygiene 5.0
The Ugly
P3 · Repo Security & performance tooling 3.0

At a glance — Security · 70% · Adequate · gated by D29 ·

The Good
D28 · Python Secrets (history) 10.0 D31 · Python IaC & Container Security 9.5 D44 · Python Platform End-of-Life 10.0 X14 · Repo Bypassable address classification 10.0 X15 · Repo Unvalidated length from an untrusted reader 10.0 X17 · Repo Uncapped recursion over a caller-supplied document 10.0 X24 · Repo Document value interpolated into markup unescaped 10.0
The Bad
D36 · Python Supply-chain Provenance & Signing 5.0
The Ugly
D29 · Python Static Analysis (SAST) 0.7

Security & Compliance — OWASP Top-10 mapping

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 categoryFindingsSeverity
A05:2025 — Injection38High / Critical
A03:2025 — Software Supply Chain Failures5Medium
A02:2025 — Security Misconfiguration3High / Critical

Roadmap

First, establish a formal process for recording significant design decisions by creating Architecture Decision Records in a dedicated folder. Next, integrate static analysis tools into the CI pipeline to automatically block security regressions, while simultaneously fixing the identified unpinned package installations in your CI workflows. Finally, maintain a changelog to clearly document what is shipped in each release, ensuring transparency and hygiene in your delivery process.

Ranked by impact ÷ effort. "Helps" is the estimated gain on the 0–100 health score.

Do thisHelpsEffortDimension
Resolve the 1 No ADRs found finding(s) in ADR Quality.+3.7 ptsLowADR Quality
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.+3.3 ptsMediumArchitecture documentation
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.+2.1 ptsMediumSecurity & performance tooling
Keep a changelog (e.g. Keep-a-Changelog) recording what shipped in each release.+1.7 ptsMediumRelease Hygiene
Resolve the 11 REDACTED finding(s) in Static Analysis (SAST) — start with REDACTED (7), REDACTED (2), REDACTED (2).+1.5 ptsMediumStatic Analysis (SAST)
Resolve the 1 Projects may be oversized for their cohesion finding(s) in Project Cohesion.+0.7 ptsLowProject Cohesion
Group production code under src/ (or split deliberately, e.g. backend/ + frontend/) so production and tooling code aren't mixed at the root.+1.4 ptsMediumFolder & project structure
Resolve the 2 REDACTED finding(s) in Static Analysis (SAST) — start with REDACTED, REDACTED.+0.6 ptsLowStatic Analysis (SAST)

File quality

Per-file score 0–10 — a quality signature. Of 38 files carrying findings, judged against the Production bar: 0% slop · 79% mixed · 21% near-clean.

FileScoreBandWorst signal
REDACTED4.1MixedIaC & Container Security: High IaC: REDACTED
REDACTED4.4MixedStatic Analysis (SAST): High: REDACTED
REDACTED4.5MixedStatic Analysis (SAST): High: REDACTED
REDACTED4.6MixedStatic Analysis (SAST): High: REDACTED
REDACTED4.6MixedStatic Analysis (SAST): Medium: REDACTED
REDACTED4.8MixedStatic Analysis (SAST): High: REDACTED
REDACTED5.1MixedStatic Analysis (SAST): High: REDACTED
REDACTED5.8MixedStatic Analysis (SAST): High: REDACTED
REDACTED5.8MixedStatic Analysis (SAST): High: REDACTED
langextract/extraction.py6.0MixedExplicit Debt: TodoComment
langextract/resolver.py6.0MixedExplicit Debt: TodoComment
langextract/core/tokenizer.py6.0MixedExplicit Debt: TodoComment
langextract/providers/gemini.py7.0MixedCyclomatic Complexity: GeminiLanguageModel.infer (cyclomatic 23)
.github/scripts/pr_security_check.py7.1MixedCyclomatic Complexity: pr_security_check.parse_diff (cyclomatic 19)
langextract/providers/gemini_batch.py7.2MixedCyclomatic Complexity: gemini_batch.infer_batch (cyclomatic 19)
langextract/providers/ollama.py7.2MixedCognitive Complexity: OllamaLanguageModel.__init__ (cognitive 17)
REDACTED7.2MixedStatic Analysis (SAST): High: REDACTED
REDACTED7.2MixedStatic Analysis (SAST): High: REDACTED
REDACTED7.2MixedStatic Analysis (SAST): High: REDACTED
REDACTED7.2MixedStatic Analysis (SAST): High: REDACTED

How the grades work

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 — 36

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 — 86

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 — 21

Recorded, with no effect on how the codebase functions. Present so the survey is complete, not because it needs doing.

Could not be resolved — 17

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. 51 of 51 evaluated dimensions are computed purely by tools and static analysis (confidence 1.0); none rely on LLM judgement. Overall confidence is 0.9 — 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 — 51 dimensions across the health lenses
D1D2D3D4D6D9D11D13D14D15D17D19D20D26D28D29D31D34D35D36D44AX10AX3AX4M1M2M3M4P1P10P3P4P6X1X12X13X14X15X17X19X21X22X23X24X25X26X27X28X3X30X4

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
  1. 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, 130 of 143 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.)
  2. 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.
  3. 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.

MethodBacksVersionEvaluator
Roslyn static analysisComplexity, cohesion, coupling, dead code, API surface, layering5.3.0✓ deterministic
Native secret scannerHardcoded secrets / credentials1.0.0✓ deterministic
Watchdog duplication detector (in-process)Code duplication1.0.0✓ deterministic
Coverage (coverlet / dotnet-coverage)Line & branch coverage10.0.400✓ deterministic
NuGet / dotnetOutdated, vulnerable & deprecated dependencies10.0.400✓ deterministic
git / LibGit2SharpChurn hotspots, knowledge concentration, history2.43.0 · 0.31.0✓ deterministic
gitleaks · semgrep · trivy · checkovSecrets in history, SAST, CVEs, IaC & container, PII / GDPR1.86.0 · 0.69.3 · 3.2.533✓ deterministic
LLM (sampled · advisory)Documentation quality, ADR conformance, naming — sampled over a bounded sample; advisory, never a deterministic measurement—◐ LLM · sampled · advisory

Every finding is locatable in findings.md. Run 01a1720a-04f8-704c-b5a9-611d16be4938.

The exact command behind every deep-scan dimension — tool, version, invocation and retained raw output — is in Appendix B — Reproduction & audit trail.

Run transparency — what happened this run

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 17 shipped Python distribution(s) across 1 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 (28 contributor(s) across 196 commit(s) sampled, automation and bot accounts excluded). One of them holds 84% of the history; the other 27 hold 0.6% 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.
  • D19 Documentation Quality — measured, with a gap in what it reached — Watchdog measured this, but not all of it. What it did not reach is a gap on our side — a collector, parser or image we have not built yet — so the numbers on that dimension cover less than the repository, and the part left out is not evidence that it would have passed. Each ecosystem's manifests (what makes a folder runnable or a published package) need their own proof before D19 judges its units. Until then those folders' READMEs are judged only as documents (boilerplate, structure), never counted as complete.
  • D21 Naming Consistency — 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. D21's deterministic naming rules are gated on C# first (docs/plans/deterministic-engine.md, P5): each other language is switched on only after its own concept suite and A/B gate pass. Until then its declarations carry no naming verdict this run — this says nothing about whether its names are consistent.
  • D22 Internal API Consistency — 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. D22's deterministic call-shape rules are gated on C# first (docs/plans/deterministic-engine.md, P5): each other language is switched on only after its own concept suite and A/B gate pass. This says nothing about whether that surface is consistent.
  • D24 Comment Value — 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's reader does not cover the language this repository's product is written in, so it had nothing of the product to read. That is a gap in this analyzer's language reach — not a finding about this repository.
  • 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.
  • AX1 Captive 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. This check reads Microsoft.Extensions.DependencyInjection registrations in C# and Spring beans in Java/Kotlin only, and this repository defines its own container in langextract/providers/router.py, whose lifetimes are not modelled 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.
  • 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 DockerCompose (examples/ollama/docker-compose.yml). compose up failed (exit 18 — an image could not be pulled) — ollama Pulling ollama Error Get "https://registry-1.docker.io/v2/": Forbidden Error response from daemon: Get "https://registry-1.docker.io/v2/": Forbidden; the earlier `compose pull` step reported: langextract Skipped - No image to be pulled ollama Pulling ollama Error Get "https://registry-1.docker.io/v2/": Forbidden Error response from daemon: Get "https://registry-1.docker.io/v2/": Forbidden; the runtime sandbox reaches registries only through the in-fence pull-through mirror, so an image the mirror does not carry cannot be fetched — this is a limit of our sandbox, not of your stack; 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.
  • 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 no C#, F#, JavaScript/TypeScript, Dart, Ruby or Scala 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.
  • 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 no C#, JavaScript/TypeScript, Java, Dart, Ruby or Scala 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.
  • M4 Documentation accuracy — measured, with a gap in what it reached — Watchdog measured this, but not all of it. What it did not reach is a gap on our side — a collector, parser or image we have not built yet — so the numbers on that dimension cover less than the repository, and the part left out is not evidence that it would have passed. M4 checks only the claim kinds adjudicated at or above 95 % precision: links and versions (PREREGISTRATION version 9). Paths, command arguments, installs, environment variables, settings, projects, packages, routes, cd targets, infrastructure products and removed modules are read but not checked, and none of them is reported or counted.
  • 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.
  • X16 Unfloored truncation loop — 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 JavaScript/TypeScript, Java, PHP, Dart, Ruby, Scala and Kotlin source only, and no C# was loaded and no JavaScript/TypeScript, Java, PHP, Dart, Ruby, Scala or Kotlin was found in this repository, 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.
  • X18 Disposal-pattern correctness — 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 JavaScript/TypeScript, Java, Kotlin, Dart, Ruby, Scala and PHP source only, and no C# was loaded and no JavaScript/TypeScript, Java, Kotlin, Dart, Ruby, Scala or PHP was found in this repository, 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.
  • X29 Per-element action decided by a fixed element — 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 JavaScript/TypeScript, Java, Rust, Dart, Ruby, Scala and Kotlin source only, and no C# was loaded and no JavaScript/TypeScript, Java, Rust, Dart, Ruby, Scala or Kotlin was found in this repository, 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.
  • 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 publishes two things only: a runnable or packaged unit with no example, and a multi-project root README that maps none of its parts. It does not judge clarity, and its other checks (overview, licence, boilerplate, structure, empty sections, XML-doc coverage) are withheld after adjudication: their candidates are counted in the card's gaps, not shown as rows. A false statement in a document is M4's; units are .NET projects only.
  • 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.
  • 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.

Dimensions

D1 · Cyclomatic Complexity6.6 / 10Adequate✓ Tool-verified

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.

Maturity: Documented → Verified → Prevented · effective 6.6 / 10 · rule-coverage 100% · ceiling Prevented

16 method(s) exceeded the cyclomatic complexity threshold of 15; the worst was extraction.extract at 49.

extraction.extract (cyclomatic 49)langextract/extraction.py:45
WordAligner.align_extractions (cyclomatic 30)langextract/resolver.py:828
validate_community_providers.validate (cyclomatic 28)scripts/validate_community_providers.py:70
FormatHandler.parse_output (cyclomatic 25)langextract/core/format_handler.py:169
openai_batch._infer_batch_one_job (cyclomatic 25)langextract/providers/openai_batch.py:411

+ 11 more group(s) — more in Appendix A; the complete list is findings.md.

What to do

  1. Bring the 1 body over 30 down to 30 or less in Cyclomatic Complexity — start with extraction.extract (cyclomatic 49). — Refactoring it lifts Cyclomatic Complexity from 6.6 to about 7.5/10.
  2. 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.

D2 · Cognitive Complexity2.4 / 10Critical✓ Tool-verified

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.

Maturity: Documented → Verified → Prevented · effective 2.4 / 10 · rule-coverage 100% · ceiling Prevented

36 method(s) exceeded the cognitive complexity threshold of 15; the worst was extraction.extract at 75.

extraction.extract (cognitive 75)langextract/extraction.py:45
UnicodeTokenizer.tokenize (cognitive 52)langextract/core/tokenizer.py:336
WordAligner.align_extractions (cognitive 49)langextract/resolver.py:828
GeminiLanguageModel.infer (cognitive 48)langextract/providers/gemini.py:490
Resolver.extract_ordered_extractions (cognitive 40)langextract/resolver.py:467

+ 31 more group(s) — more in Appendix A; the complete list is findings.md.

What to do

  1. Bring the 1 body over 60 down to 60 or less in Cognitive Complexity — start with extraction.extract (cognitive 75). — Refactoring it lifts Cognitive Complexity from 2.4 to about 3.2/10.
  2. 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.

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.

Maturity: Documented → Verified → Prevented · effective 8.8 / 10 · rule-coverage 100% · ceiling Prevented

4 over-large unit(s) detected — types, modules or files that carry too much.

FileTooLong: langextract/resolver.py · ×4langextract/resolver.py

What to do

  1. Resolve the 4 FileTooLong finding(s) in God Classes — start with resolver.py, pr_security_check.py, gemini_batch.py. — One of this dimension's main actionable groups (4 warning-level).
  2. 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.

D4 · Code Duplication9.7 / 10Stronggated by 10 serious findings✓ Tool-verified

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.

Maturity: Documented → Verified → Prevented · effective 9.7 / 10 · rule-coverage 100% · ceiling Verified

9 duplicated block group(s) detected. One further row reports members as variants of one another; it aggregates block groups already counted above and is not itself counted. 1 of the 10 are in trees this repository does not ship — vendored, example/demo, project-template, fixture and benchmark code — and are ranked below the shipped groups rather than excluded from them: the duplication there is real and is still counted in this dimension's score. The dimensions that publish a production-file census leave those trees out of theirs, so this count is deliberately drawn over the wider population.

Duplicated block (15 lines × 2) · ×2scripts/create_provider_plugin.py:395
Edited copy of a member (40 corresponding lines)langextract/providers/ollama.py:495
Duplicated block (32–35 lines × 2)langextract/providers/ollama.py:499
Duplicated block (18–31 lines × 2)langextract/_compat/inference.py:31
Duplicated block (21–27 lines × 2)langextract/providers/schemas/gemini.py:126

+ 4 more group(s) — more in Appendix A; the complete list is findings.md.

What to do

  1. Resolve the 2 Duplicated block (15 lines × 2) finding(s) in Code Duplication — start with create_provider_plugin.py, REDACTED. — One of this dimension's main actionable groups (2 warning-level).
  2. Resolve the 1 Edited copy of a member (40 corresponding lines) finding(s) in Code Duplication — start with ollama.py. — One of this dimension's main actionable groups (1 warning-level).
  3. Resolve the 1 Duplicated block (32–35 lines × 2) finding(s) in Code Duplication — start with ollama.py. — One of this dimension's main actionable groups (1 warning-level).
  4. 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.

D6 · Cohesion (LCOM4)9.1 / 10Stronggated by 1 serious finding✓ Tool-verified

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.

Maturity: Documented → Verified → Prevented · effective 9.1 / 10 · rule-coverage 100% · ceiling Verified

1 of 14 classes have LCOM4 above 3.

Low cohesion: BaseLanguageModel (LCOM4 5)langextract/core/base_model.py:32

What to do

  1. Resolve the 1 Low cohesion finding(s) in Cohesion (LCOM4) — start with base_model.py. — One of this dimension's main actionable groups (1 warning-level).
  2. Enforce Cohesion (LCOM4) in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Verified — provenance only; does not change the score.

Detailed fixes: d6_recommendation.md · top locations in Appendix A, every location in findings.md.

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.

Maturity: Documented → Verified → Prevented · effective 10.0 / 10 · rule-coverage 100% · ceiling Documented

701 test methods: 675 unit, 26 integration, 0 BDD, 0 e2e. The Python suite contributes 701 test function(s) across 30 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.

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.

Maturity: Documented → Verified → Prevented · effective 10.0 / 10 · rule-coverage 100% · ceiling Verified

0 flaky across 1 measured tier(s). Python (2 distributions): measured (0 flaky).

✓ On the Gold path — maintain.

Detailed fixes: d11_recommendation.md.

D13 · Secret Scanning10.0 / 10Exemplary○ Nothing flagged

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.

Maturity: Documented → Verified → Prevented · effective 10.0 / 10 · rule-coverage 100% · ceiling Prevented

Secret scan ran and found no leaked secrets.

✓ On the Gold path — maintain.

Detailed fixes: d13_recommendation.md.

D14 · License Compliance10.0 / 10Exemplary○ Nothing flagged

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.

Maturity: Documented → Verified → Prevented · effective 10.0 / 10 · rule-coverage 100% · ceiling Verified

0 of 57 shipped Python distribution(s) use a banned license. Licences were resolved from PyPI over the distributions a consumer installs — this repository's 17 declared runtime requirement(s) closed transitively over each distribution's published `requires_dist` (40 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. 2 of them publish no licence on PyPI this pass can read; that is missing data, not a violation, and none of them is charged. This repository publishes itself under Apache-2.0, which is its own choice and is not judged here.

✓ On the Gold path — maintain.

Detailed fixes: d14_recommendation.md.

D15 · Churn × Complexity Hotspots9.5 / 10Stronggated by 3 serious findings✓ Tool-verified

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.

Maturity: Documented → Verified → Prevented · effective 9.5 / 10 · rule-coverage 100% · ceiling Documented

Top hotspots: langextract/extraction.py (3×49=147); langextract/providers/gemini.py (3×23=69); langextract/providers/gemini_batch.py (3×19=57)

Hotspot: langextract/extraction.py · ×3langextract/extraction.py:45

What to do

  1. Resolve the 3 Hotspot finding(s) in Churn × Complexity Hotspots — start with extraction.py, gemini.py, gemini_batch.py. — One of this dimension's main actionable groups (3 warning-level).

Detailed fixes: d15_recommendation.md · top locations in Appendix A, every location in findings.md.

D17 · Explicit Debt9.9 / 10Stronggated by 6 serious findings✓ Tool-verified

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.

Maturity: Documented → Verified → Prevented · effective 9.9 / 10 · rule-coverage 100% · ceiling Prevented

6 deducted task-comment markers across 15094 LoC (0.0/KLoC) → score 9.9. 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.

TodoComment · ×6REDACTED:415

What to do

  1. Resolve the 6 TodoComment finding(s) in Explicit Debt — start with provider.py (2), REDACTED, extraction.py. — One of this dimension's main actionable groups (6 warning-level).
  2. Enforce Explicit Debt in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.

Detailed fixes: d17_recommendation.md · top locations in Appendix A, every location in findings.md.

D19 · Documentation Quality10.0 / 10Exemplary○ Nothing flagged

What it measures: Whether the project's documentation is clear, complete, and useful.

Method: Every README, docs/ document and root guide parsed by its markup and measured in Unicode letters. Published: a runnable or packaged .NET project whose README (or a document it links, or a root guide) shows no example, and a root README naming none of three or more projects. Overview, licence, boilerplate, wall-of-text, empty-section and XML-doc rows are measured and withheld (counted in gaps) after adjudication. Deterministic, exhaustive, advisory.

Maturity: Documented → Verified → Prevented · effective 10.0 / 10 · rule-coverage 100% · ceiling Documented

1 of 1 documentation facets pass in full. 5 document(s) read across 1 unit(s).

✓ On the Gold path — maintain.

Detailed fixes: d19_recommendation.md.

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.

Maturity: Documented → Verified → Prevented · effective 0.0 / 10 · rule-coverage 100% · ceiling Documented

No architecture decision records were found.

No ADRs found

What to do

  1. Resolve the 1 No ADRs found finding(s) in ADR Quality. — One of this dimension's main actionable groups (1 recommendation-level).

Detailed fixes: d20_recommendation.md · top locations in Appendix A, every location in findings.md.

D26 · Project Cohesion0.0 / 10Critical✓ Tool-verified

What it measures: Whether each project is a focused, coherent unit rather than an oversized grab-bag.

Method: Project size overshoot penalties (LoC / public-type count / namespace count, 2-of-3 flag) weighted by log magnitude. Exhaustive across projects, deterministic, LLM-independent.

Maturity: Documented → Verified → Prevented · effective 0.0 / 10 · rule-coverage 100% · ceiling Documented

1 of 1 build units (Python) flagged as possibly oversized/incoherent.

Projects may be oversized for their cohesion

What to do

  1. 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.

D28 · Secrets (history)10.0 / 10Exemplary○ Nothing flagged

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.

Maturity: Documented → Verified → Prevented · effective 10.0 / 10 · rule-coverage 100% · ceiling Documented

gitleaks scanned the full history AND the current working tree and found no secrets.

✓ On the Gold path — maintain.

Detailed fixes: d28_recommendation.md.

D29 · Static Analysis (SAST)0.7 / 10Critical✓ Tool-verified

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).

Maturity: Documented → Verified → Prevented · effective 0.7 / 10 · rule-coverage 100% · ceiling Documented

38 finding(s): 0 critical, 35 high, 1 medium, 2 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. Separately, one or more rules could not re-parse an embedded snippet in 2 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

+ 1 more group(s) — more in Appendix A; the complete list is findings.md.

What to do

  1. Resolve the 11 REDACTED finding(s) in Static Analysis (SAST) — start with REDACTED (7), REDACTED (2), REDACTED (2). — One of this dimension's main actionable groups (11 issue-level).
  2. Resolve the 2 REDACTED finding(s) in Static Analysis (SAST) — start with REDACTED, REDACTED. — One of this dimension's main actionable groups (2 issue-level).
  3. 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.

D31 · IaC & Container Security9.5 / 10Adequategated by 1 critical finding✓ Tool-verified

What it measures: Whether Dockerfiles / Terraform / Kubernetes config follow security best practices.

Method: IaC/container misconfiguration scan via trivy config (REDACTED/Terraform/K8s/Helm/CloudFormation); severity rules to 0-10 moderate normalizer. NotApplicable without manifests. Exhaustive, deterministic.

Maturity: Documented → Verified → Prevented · effective 9.5 / 10 · rule-coverage 100% · ceiling Documented

3 finding(s): 0 critical, 1 high, 2 medium, 0 low.

REDACTED
REDACTED

What to do

  1. Resolve the 1 High IaC finding(s) in IaC & Container Security — start with REDACTED. — One of this dimension's main actionable groups (1 issue-level).
  2. Resolve the 2 Medium IaC finding(s) in IaC & Container Security — start with REDACTED (2). — One of this dimension's main actionable groups (2 warning-level).

Detailed fixes: d31_recommendation.md · top locations in Appendix A, every location in findings.md.

D34 · Knowledge Freshness9.7 / 10Exemplary✓ Tool-verified

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.

Maturity: Documented → Verified → Prevented · effective 9.7 / 10 · rule-coverage 100% · ceiling Documented

1 of 38 significant source file(s) are orphaned — their living knowledge has decayed to nothing, so no one currently understands them. The largest is langextract/providers/openai_batch.py. Counted over 38 of the 53 production source files in this repository: the rest are under the ~2,400-byte size floor this dimension measures over.

Orphaned files with no living knowledge
Orphaned filelangextract/providers/openai_batch.py:1

✓ On the Gold path — maintain.

Detailed fixes: d34_recommendation.md · top locations in Appendix A, every location in findings.md.

D35 · Change Coupling10.0 / 10Exemplary✓ Tool-verified

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.

Maturity: Documented → Verified → Prevented · effective 10.0 / 10 · rule-coverage 100% · ceiling Documented

No strong hidden change-coupling between production files.

✓ On the Gold path — maintain.

Detailed fixes: d35_recommendation.md.

D36 · Supply-chain Provenance & Signing5.0 / 10Adequate✓ Tool-verified

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.

Maturity: Documented → Verified → Prevented · effective 5.0 / 10 · rule-coverage 100% · ceiling Documented

2/4 supply-chain integrity signals present (provenance, signing, SBOM, pinned actions).

REDACTED
REDACTED
REDACTED
REDACTED
REDACTED

What to do

  1. Resolve the 1 REDACTED finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 warning-level).
  2. Resolve the 1 REDACTED finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 warning-level).
  3. Resolve the 1 REDACTED finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 recommendation-level).

Detailed fixes: d36_recommendation.md · top locations in Appendix A, every location in findings.md.

D44 · Platform End-of-Life10.0 / 10Exemplary✓ Tool-verified

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.

Maturity: Documented → Verified → Prevented · effective 10.0 / 10 · rule-coverage 100% · ceiling Documented

0 end-of-life runtime(s) and 0 end-of-life framework(s), read from 1 platform declaration(s) and 17 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.

✓ On the Gold path — maintain.

Detailed fixes: d44_recommendation.md.

Frontend & cross-cutting dimensions

R = React/JS · M = Maturity · P = Readiness.

AX10 · Code composition9.8 / 10Exemplary✓ Tool-verified

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.
AX3 · Project dependency cycles10.0 / 10Exemplary✓ Tool-verified

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.

AX4 · Dependency direction10.0 / 10Exemplary✓ Tool-verified

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.

M1 · Documentation (README)9.3 / 10Exemplary✓ Tool-verified

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.

What to do

  • Add an 'Architecture' / 'How it works' section to the root README — the high-level shape.
M2 · Architecture documentation2.0 / 10Critical✓ Tool-verified

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.
M3 · Folder & project structure8.0 / 10Strong✓ Tool-verified

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.
M4 · Documentation accuracy10.0 / 10Exemplary○ Nothing flagged

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.

P1 · CI/CD gates10.0 / 10Exemplary○ Nothing flagged

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.

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.

P3 · Security & performance tooling3.0 / 10Weak✓ Tool-verified

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 73147 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.
  • Add gitleaks/trufflehog in CI to block PRs that introduce committed secrets.
P4 · Deployment & Rollback10.0 / 10Exemplary✓ Tool-verified

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.

P6 · Release Hygiene5.0 / 10Adequate✓ Tool-verified

Readiness · Readiness — Whether releases are traceable — a maintained changelog and explicit version stamping.

Method: Filesystem scan: changelog file presence and version tags in csproj or git tags. Exhaustive, deterministic.

  • No CHANGELOG/HISTORY/RELEASES file — what shipped when isn't easy to reconstruct for support or audit. (Versioning/tagging makes releases traceable, but a changelog records the what.)

What to do

  • Keep a changelog (e.g. Keep-a-Changelog) recording what shipped in each release.
X1 · Async correctness10.0 / 10Exemplary○ Nothing flagged

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.

X12 · Unreachable branch10.0 / 10Exemplary○ Nothing flagged

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.

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.

X14 · Bypassable address classification10.0 / 10Exemplary○ Nothing flagged

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.

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.

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.

X19 · Unrestored process-global state10.0 / 10Exemplary○ Nothing flagged

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.

X21 · Side-effecting pattern guard10.0 / 10Exemplary○ Nothing flagged

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.

X22 · Contradicted release guard10.0 / 10Exemplary○ Nothing flagged

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.

X23 · Unguarded diagnostic materialisation5.4 / 10Adequate✓ Tool-verified

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 81 is called with an argument built by `list(discovered.keys())` (line 82), 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. — langextract/plugins.py:81
  • `debug` at line 421 is called with an argument built by `sum(len(group) for group in aligned_yaml_extractions)` (line 423), 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. — langextract/resolver.py:421
  • `debug` at line 65 is called with an argument built by `[p.pattern for p in patterns]` (line 69), 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. — langextract/providers/router.py:65
  • `debug` at line 75 is called with an argument built by `[p.pattern for p in patterns]` (line 78), 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. — langextract/providers/router.py:75

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.
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.

X25 · Inert configuration knob10.0 / 10Exemplary○ Nothing flagged

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.

X26 · Unsynchronised callback handoff10.0 / 10Exemplary○ Nothing flagged

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.

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.

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.

X3 · Exception handling6.4 / 10Adequate✓ Tool-verified

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.

  • `except Exception` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter. (×3) — scripts/create_provider_plugin.py:1138, scripts/create_provider_plugin.py:1168, langextract/visualization.py:59
  • 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) — langextract/providers/gemini_batch.py:194, langextract/core/debug_utils.py:184

What to do

  • Swallowed exception (caught, then discarded)
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.

X4 · Structured logging10.0 / 10Exemplary○ Nothing flagged

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.

Reference — by lens

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.

LensScoreRatingImpact
Code Health80%Adequate — gated by D2Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Architecture84%Adequate — gated by D26Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Maturity67%Adequate — gated by M2Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Readiness69%Adequate — gated by P3Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Security70%Adequate — gated by D29Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Unscored — 1 check(s) recorded observations but carry no score

These checks ran and found something, but they do not carry a score — either by design (an advisory check reports evidence rather than grading it) or because they could not be scored here. They are excluded from the score for that reason, not because there was nothing to see.

  • X10 Duplicated predicate — 3 observation(s) recorded · Advisory — this card reports evidence and never carries a score.
Not evidenced — 1 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.

  • P5 DR & Backup — not evidenced — repo shows no backup/RTO/RPO controls; absence of evidence is not evidence of a working control
Not included — 67 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 — No web markup found — accessibility is not applicable to this repository.
  • AC2 Forms & labels — No web markup found — accessibility is not applicable to this repository.
  • AC3 Page structure — No web markup found — accessibility is not applicable to this repository.
  • AC4 Keyboard semantics — No web markup found — accessibility is not applicable to this repository.
  • AC5 ARIA correctness — No web markup found — accessibility is not applicable to this repository.
  • AC6 Visual & motion safety — No web markup found — accessibility is not applicable to this repository.
  • AC7 A11y enforcement — No web markup found — accessibility is not applicable to this repository.
  • AX1 Captive dependencies — 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
  • 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.
  • 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 — compose up failed (exit 18 — an image could not be pulled) — ollama Pulling ollama Error Get "https://registry-1.docker.io/v2/": Forbidden Error response from daemon: Get "https://registry-1.docker.io/v2/": Forbidden; the earlier `compose pull` step reported: langextract Skipped - No image to be pulled ollama Pulling ollama Error Get "https://registry-1.docker.io/v2/": Forbidden Error response from daemon: Get "https://registry-1.docker.io/v2/": Forbidden; the runtime sandbox reaches registries only through the in-fence pull-through mirror, so an image the mirror does not carry cannot be fetched — this is a limit of our sandbox, not of your stack; 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.
  • C3 Audit Trail — No personal data detected in a persisted data model — no PII-named field (email, first_name, date_of_birth, phone, …) or stored credential in a Django, SQLAlchemy, SQLModel, Peewee, Tortoise or MongoEngine model, a migration or a SQL schema — and no database or data-store client (Django ORM, SQLAlchemy, psycopg, PyMongo, …) in the source either, so this repository keeps no sensitive data whose changes an audit trail would record. If it does (through a store configured outside this repository, for instance), the trail belongs to wherever that data is stored.
  • C4 Data Retention — No personal data detected in a persisted data model — no PII-named field (email, first_name, date_of_birth, phone, …) or stored credential on a Django, SQLAlchemy, SQLModel, Peewee, Tortoise or mongoengine model, in a migration or in a SQL schema — and no database or data-store client (django.db, sqlalchemy, psycopg2, pymongo, sqlite3, …) imported either, so this repository keeps no personal data whose lifetime a retention limit would bound.
  • C5 Data-Subject Rights — No personal data detected in a persisted data model — no PII-named field (email, first_name, date_of_birth, phone, …) or stored credential on a Django, SQLAlchemy, SQLModel, Peewee, Tortoise or mongoengine model, in a migration or in a SQL schema — and no database or data-store client (django.db, sqlalchemy, psycopg2, pymongo, sqlite3, …) imported either, so this repository keeps no personal data for a data subject to erase, export or consent over.
  • D10 Test Quality — ~21874 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.
  • D21 Naming Consistency — Naming consistency is measured on C# only; Python, html not measured.
  • D22 Internal API Consistency — API consistency is measured on C# only; the exposed Python surface was not measured.
  • 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 — D24 reads C# and Visual Basic comments only — this repository's production source is .py, which was left unread. Not scored: this is a gap in the analyzer, not a verdict about this repository.
  • 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 — Not scored — no dependency manifest in a supported ecosystem was read for this repository. A gap in the analyzer's language coverage, NOT a finding that the repository is free of vulnerable dependencies (a Python pyproject.toml/requirements.txt (pip/uv/Poetry) — not scanned yet).
  • 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 — Not scored — no dependency manifest in a supported ecosystem was read for this repository. A gap in the analyzer's language coverage, NOT a finding that the repository is free of vulnerable dependencies (a Python pyproject.toml/requirements.txt (pip/uv/Poetry) — not scanned yet).
  • 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 (30 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'.
  • P2 Observability — This repository's Python source (1 module(s), 46 file(s) read) declares no entry point and bootstraps no server, and nothing here deploys a service — it is a library, run inside whatever hosts it, so production observability (structured logging, tracing/metrics, health checks) is N/A. If it grows a binary or a service, the dimension reactivates.
  • P7 Outbound HTTP resilience — not applicable — no HTTP server, API framework or worker entry point was found in the 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.
  • PF3 Async & latency hygiene — Not applicable: this repository declares no async functions, so there is no asynchronous code for a blocking call to stall.
  • 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.
  • X16 Unfloored truncation loop — 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
  • X18 Disposal-pattern correctness — 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
  • 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.
  • X29 Per-element action decided by a fixed element — 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
  • 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.

Critical — 36 finding(s)
D29 · Static Analysis (SAST) · REDACTED
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D29 · Static Analysis (SAST) · REDACTED
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D29 · Static Analysis (SAST) · REDACTED
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D31 · IaC & Container Security · High IaC · ×1
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Serious — 86 finding(s)
D17 · Explicit Debt · TodoComment · ×6
  • TodoComment REDACTED:415 — # TODO: Refactor redundancy between document and text. — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `# REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment langextract/extraction.py:345 — # TODO(v2.0.0): Remove gemini_schema parameter — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `# REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment langextract/resolver.py:111 — # TODO: Review value and requirements for abstract class. — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `# REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment langextract/core/tokenizer.py:160 — # TODO: Evaluate removal for large-context use cases. — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `# REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment examples/custom_provider_plugin/langextract_provider_example/provider.py:77 — # TODO: Replace with your own client initialization — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `# REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment examples/custom_provider_plugin/langextract_provider_example/provider.py:174 — # TODO: Replace this with your own API/model calls — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `# REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
D3 · God Classes · FileTooLong · ×4
  • FileTooLong: langextract/resolver.py langextract/resolver.py — FileTooLong — 817 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 317 over it, 1.63× 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: scripts/pr_security_check.py .github/scripts/pr_security_check.py — FileTooLong — 656 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 156 over it, 1.31× 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: providers/gemini_batch.py langextract/providers/gemini_batch.py — FileTooLong — 609 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 109 over it, 1.22× 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: scripts/create_provider_plugin.py scripts/create_provider_plugin.py — FileTooLong — 521 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 21 over it, 1.04× 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.
D15 · Churn × Complexity Hotspots · Hotspot · ×3
  • Hotspot: langextract/extraction.py langextract/extraction.py:45 — langextract/extraction.py changed 3 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 49 in extraction.extract at line 45. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, with the area under test before it moves. Counted over 2026-07-19..2026-10-17, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-19 03:27:51 -04:00' --until='2026-10-17 03:27:51 -04:00' --full-history --no-merges -- langextract/extraction.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: langextract/providers/gemini.py langextract/providers/gemini.py:490 — langextract/providers/gemini.py changed 3 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 23 in GeminiLanguageModel.infer at line 490. 1 of those changes was a fix/bug commit, and the other 2 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 2026-07-19..2026-10-17, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-19 03:27:51 -04:00' --until='2026-10-17 03:27:51 -04:00' --full-history --no-merges -- langextract/providers/gemini.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: langextract/providers/gemini_batch.py langextract/providers/gemini_batch.py:838 — langextract/providers/gemini_batch.py changed 3 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 19 in gemini_batch.infer_batch at line 838. 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 2026-07-19..2026-10-17, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-19 03:27:51 -04:00' --until='2026-10-17 03:27:51 -04:00' --full-history --no-merges -- langextract/providers/gemini_batch.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.
X3 · Exception handling · Swallowed exception (caught, then discarded) · ×3
  • Swallowed exception (caught, then discarded) scripts/create_provider_plugin.py:1138 — `except Exception` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter.
  • Swallowed exception (caught, then discarded) scripts/create_provider_plugin.py:1168 — `except Exception` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter.
  • Swallowed exception (caught, then discarded) langextract/visualization.py:59 — `except Exception` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter.
D31 · IaC & Container Security · Medium IaC · ×2
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D4 · Code Duplication · Duplicated block (15 lines × 2) · ×2
  • Duplicated block (15 lines × 2) scripts/create_provider_plugin.py:395 — scripts/create_provider_plugin.py:395-409 | scripts/create_provider_plugin.py:411-425 — 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 (15 lines × 2) REDACTED:212 — REDACTED:212-226 | REDACTED:298-312 — 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.
X3 · Exception handling · Swallowed exception (empty catch) · ×2
  • Swallowed exception (empty catch) langextract/providers/gemini_batch.py:194 — 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) langextract/core/debug_utils.py:184 — 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.
D1 · Cyclomatic Complexity · extraction.extract (cyclomatic 49) · ×1
  • extraction.extract (cyclomatic 49) langextract/extraction.py:45 — extraction.extract has cyclomatic complexity 49 (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.
D1 · Cyclomatic Complexity · WordAligner.align_extractions (cyclomatic 30) · ×1
  • WordAligner.align_extractions (cyclomatic 30) langextract/resolver.py:828 — WordAligner.align_extractions 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.
D1 · Cyclomatic Complexity · validate_community_providers.validate (cyclomatic 28) · ×1
  • validate_community_providers.validate (cyclomatic 28) scripts/validate_community_providers.py:70 — validate_community_providers.validate has cyclomatic complexity 28 (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.
D1 · Cyclomatic Complexity · FormatHandler.parse_output (cyclomatic 25) · ×1
  • FormatHandler.parse_output (cyclomatic 25) langextract/core/format_handler.py:169 — FormatHandler.parse_output has cyclomatic complexity 25 (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.
D1 · Cyclomatic Complexity · openai_batch._infer_batch_one_job (cyclomatic 25) · ×1
  • openai_batch._infer_batch_one_job (cyclomatic 25) langextract/providers/openai_batch.py:411 — openai_batch._infer_batch_one_job has cyclomatic complexity 25 (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.
D1 · Cyclomatic Complexity · UnicodeTokenizer.tokenize (cyclomatic 23) · ×1
  • UnicodeTokenizer.tokenize (cyclomatic 23) langextract/core/tokenizer.py:336 — UnicodeTokenizer.tokenize has cyclomatic complexity 23 (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.
D1 · Cyclomatic Complexity · GeminiLanguageModel.infer (cyclomatic 23) · ×1
  • GeminiLanguageModel.infer (cyclomatic 23) langextract/providers/gemini.py:490 — GeminiLanguageModel.infer has cyclomatic complexity 23 (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.
D1 · Cyclomatic Complexity · factory._create_model_with_schema (cyclomatic 22) · ×1
  • factory._create_model_with_schema (cyclomatic 22) langextract/factory.py:235 — factory._create_model_with_schema has cyclomatic complexity 22 (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.
D1 · Cyclomatic Complexity · OpenAILanguageModel.infer (cyclomatic 20) · ×1
  • OpenAILanguageModel.infer (cyclomatic 20) langextract/providers/openai.py:329 — OpenAILanguageModel.infer has cyclomatic complexity 20 (threshold 15). To reduce it, separate the branches: extract each independent case into its own named function so the top-level body reads as a short sequence of named decisions.
D1 · Cyclomatic Complexity · pr_security_check.parse_diff (cyclomatic 19) · ×1
  • pr_security_check.parse_diff (cyclomatic 19) .github/scripts/pr_security_check.py:299 — pr_security_check.parse_diff 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.
D1 · Cyclomatic Complexity · GeminiLanguageModel.__init__ (cyclomatic 19) · ×1
  • GeminiLanguageModel.__init__ (cyclomatic 19) langextract/providers/gemini.py:237 — GeminiLanguageModel.__init__ has cyclomatic complexity 19 (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.
D1 · Cyclomatic Complexity · gemini_batch.infer_batch (cyclomatic 19) · ×1
  • gemini_batch.infer_batch (cyclomatic 19) langextract/providers/gemini_batch.py:838 — gemini_batch.infer_batch 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.
D1 · Cyclomatic Complexity · gemini._no_text_diagnostic (cyclomatic 17) · ×1
  • gemini._no_text_diagnostic (cyclomatic 17) langextract/providers/gemini.py:107 — gemini._no_text_diagnostic has cyclomatic complexity 17 (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.
D1 · Cyclomatic Complexity · resolver._best_lcs_spans (cyclomatic 17) · ×1
  • resolver._best_lcs_spans (cyclomatic 17) langextract/resolver.py:1326 — resolver._best_lcs_spans 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.
D1 · Cyclomatic Complexity · pr_security_check._check_credential_egress (cyclomatic 16) · ×1
  • pr_security_check._check_credential_egress (cyclomatic 16) .github/scripts/pr_security_check.py:400 — pr_security_check._check_credential_egress has cyclomatic complexity 16 (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.
D1 · Cyclomatic Complexity · Resolver.extract_ordered_extractions (cyclomatic 16) · ×1
  • Resolver.extract_ordered_extractions (cyclomatic 16) langextract/resolver.py:467 — Resolver.extract_ordered_extractions 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.
D2 · Cognitive Complexity · extraction.extract (cognitive 75) · ×1
  • extraction.extract (cognitive 75) langextract/extraction.py:45 — extraction.extract has cognitive complexity 75 (threshold 15). Drivers by points: if/else 30 (49 pts), boolean chains 14, ternaries 3 (7 pts), loops 2 (4 pts), error handling 1 (nesting depth added 25). 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.
D2 · Cognitive Complexity · UnicodeTokenizer.tokenize (cognitive 52) · ×1
  • UnicodeTokenizer.tokenize (cognitive 52) langextract/core/tokenizer.py:336 — UnicodeTokenizer.tokenize has cognitive complexity 52 (threshold 15). Drivers by points: if/else 17 (47 pts), boolean chains 4, loops 1 (nesting depth added 30). 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.
D2 · Cognitive Complexity · WordAligner.align_extractions (cognitive 49) · ×1
  • WordAligner.align_extractions (cognitive 49) langextract/resolver.py:828 — WordAligner.align_extractions has cognitive complexity 49 (threshold 15). Drivers by points: if/else 20 (35 pts), loops 6 (8 pts), ternaries 2 (3 pts), error handling 1 (2 pts), boolean chains 1 (nesting depth added 19). 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.
D2 · Cognitive Complexity · GeminiLanguageModel.infer (cognitive 48) · ×1
  • GeminiLanguageModel.infer (cognitive 48) langextract/providers/gemini.py:490 — GeminiLanguageModel.infer has cognitive complexity 48 (threshold 15). Drivers by points: if/else 10 (19 pts), error handling 4 (12 pts), loops 6 (11 pts), boolean chains 3, ternaries 1 (3 pts) (nesting depth added 24). 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.
D2 · Cognitive Complexity · Resolver.extract_ordered_extractions (cognitive 40) · ×1
  • Resolver.extract_ordered_extractions (cognitive 40) langextract/resolver.py:467 — Resolver.extract_ordered_extractions has cognitive complexity 40 (threshold 15). Drivers by points: if/else 12 (35 pts), loops 2 (3 pts), boolean chains 2 (nesting depth added 24). 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.
D2 · Cognitive Complexity · OpenAILanguageModel.infer (cognitive 39) · ×1
  • OpenAILanguageModel.infer (cognitive 39) langextract/providers/openai.py:329 — OpenAILanguageModel.infer has cognitive complexity 39 (threshold 15). Drivers by points: error handling 5 (15 pts), if/else 9 (13 pts), loops 5 (10 pts), boolean chains 1 (nesting depth added 19). 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.
D2 · Cognitive Complexity · validate_community_providers.validate (cognitive 39) · ×1
  • validate_community_providers.validate (cognitive 39) scripts/validate_community_providers.py:70 — validate_community_providers.validate has cognitive complexity 39 (threshold 15). Drivers by points: if/else 16 (28 pts), boolean chains 5, ternaries 2 (4 pts), loops 2 (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.
D2 · Cognitive Complexity · resolver._best_lcs_spans (cognitive 38) · ×1
  • resolver._best_lcs_spans (cognitive 38) langextract/resolver.py:1326 — resolver._best_lcs_spans has cognitive complexity 38 (threshold 15). Drivers by points: if/else 7 (20 pts), loops 6 (11 pts), ternaries 1 (4 pts), boolean chains 3 (nesting depth added 21). 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.
D2 · Cognitive Complexity · FormatHandler.parse_output (cognitive 35) · ×1
  • FormatHandler.parse_output (cognitive 35) langextract/core/format_handler.py:169 — FormatHandler.parse_output has cognitive complexity 35 (threshold 15). Drivers by points: if/else 15 (25 pts), boolean chains 6, loops 2 (3 pts), error handling 1 (nesting depth added 11). 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.
D2 · Cognitive Complexity · openai_batch._infer_batch_one_job (cognitive 34) · ×1
  • openai_batch._infer_batch_one_job (cognitive 34) langextract/providers/openai_batch.py:411 — openai_batch._infer_batch_one_job has cognitive complexity 34 (threshold 15). Drivers by points: if/else 14 (20 pts), error handling 4 (8 pts), boolean chains 3, loops 3 (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.
D2 · Cognitive Complexity · factory._create_model_with_schema (cognitive 32) · ×1
  • factory._create_model_with_schema (cognitive 32) langextract/factory.py:235 — factory._create_model_with_schema has cognitive complexity 32 (threshold 15). Drivers by points: if/else 15 (24 pts), boolean chains 5, error handling 2 (3 pts) (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.
D2 · Cognitive Complexity · Annotator._annotate_documents_single_pass (cognitive 31) · ×1
  • Annotator._annotate_documents_single_pass (cognitive 31) langextract/annotation.py:285 — Annotator._annotate_documents_single_pass has cognitive complexity 31 (threshold 15). Drivers by points: if/else 6 (15 pts), loops 3 (6 pts), ternaries 2 (6 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.
D2 · Cognitive Complexity · gemini_batch.infer_batch (cognitive 31) · ×1
  • gemini_batch.infer_batch (cognitive 31) langextract/providers/gemini_batch.py:838 — gemini_batch.infer_batch has cognitive complexity 31 (threshold 15). Drivers by points: if/else 14 (20 pts), loops 5 (9 pts), boolean chains 1, ternaries 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.
D2 · Cognitive Complexity · GeminiSchema.from_examples (cognitive 30) · ×1
  • GeminiSchema.from_examples (cognitive 30) langextract/providers/schemas/gemini.py:121 — GeminiSchema.from_examples has cognitive complexity 30 (threshold 15). Drivers by points: if/else 7 (19 pts), loops 5 (11 pts) (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.
D2 · Cognitive Complexity · WordAligner._fuzzy_align_extraction (cognitive 29) · ×1
  • WordAligner._fuzzy_align_extraction (cognitive 29) langextract/resolver.py:630 — WordAligner._fuzzy_align_extraction has cognitive complexity 29 (threshold 15). Drivers by points: if/else 9 (23 pts), loops 2 (3 pts), error handling 1 (2 pts), boolean chains 1 (nesting depth added 16). 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.
D2 · Cognitive Complexity · factory._kwargs_with_environment_defaults (cognitive 27) · ×1
  • factory._kwargs_with_environment_defaults (cognitive 27) langextract/factory.py:58 — factory._kwargs_with_environment_defaults has cognitive complexity 27 (threshold 15). Drivers by points: if/else 6 (19 pts), loops 2 (6 pts), boolean chains 2 (nesting depth added 17). 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.
D2 · Cognitive Complexity · annotation._merge_non_overlapping_extractions (cognitive 26) · ×1
  • annotation._merge_non_overlapping_extractions (cognitive 26) langextract/annotation.py:46 — annotation._merge_non_overlapping_extractions has cognitive complexity 26 (threshold 15). Drivers by points: if/else 6 (19 pts), loops 3 (7 pts) (nesting depth added 17). 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.
D2 · Cognitive Complexity · tokenizer.find_sentence_range (cognitive 22) · ×1
  • tokenizer.find_sentence_range (cognitive 22) langextract/core/tokenizer.py:580 — tokenizer.find_sentence_range has cognitive complexity 22 (threshold 15). Drivers by points: if/else 7 (15 pts), loops 2 (5 pts), boolean chains 2 (nesting depth added 11). 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.
D2 · Cognitive Complexity · GeminiLanguageModel._process_single_prompt (cognitive 22) · ×1
  • GeminiLanguageModel._process_single_prompt (cognitive 22) langextract/providers/gemini.py:424 — GeminiLanguageModel._process_single_prompt has cognitive complexity 22 (threshold 15). Drivers by points: if/else 4 (10 pts), loops 3 (6 pts), error handling 2 (4 pts), boolean chains 2 (nesting depth added 11). 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.
D2 · Cognitive Complexity · io.download_text_from_url (cognitive 20) · ×1
  • io.download_text_from_url (cognitive 20) langextract/io.py:275 — io.download_text_from_url has cognitive complexity 20 (threshold 15). Drivers by points: if/else 7 (11 pts), loops 3 (5 pts), error handling 2 (3 pts), boolean chains 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.
D2 · Cognitive Complexity · ChunkIterator.__next__ (cognitive 19) · ×1
  • ChunkIterator.__next__ (cognitive 19) REDACTED:441 — ChunkIterator.__next__ has cognitive complexity 19 (threshold 15). Drivers by points: if/else 9 (15 pts), loops 2 (3 pts), boolean chains 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.
D2 · Cognitive Complexity · GeminiLanguageModel.__init__ (cognitive 19) · ×1
  • GeminiLanguageModel.__init__ (cognitive 19) langextract/providers/gemini.py:237 — GeminiLanguageModel.__init__ has cognitive complexity 19 (threshold 15). Drivers by points: boolean chains 9, if/else 7 (8 pts), error handling 1, loops 1 (nesting depth added 1). 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.
D2 · Cognitive Complexity · pr_security_check.parse_diff (cognitive 18) · ×1
  • pr_security_check.parse_diff (cognitive 18) .github/scripts/pr_security_check.py:299 — pr_security_check.parse_diff has cognitive complexity 18 (threshold 15). Drivers by points: if/else 4 (9 pts), boolean chains 5, ternaries 1 (3 pts), loops 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.
D2 · Cognitive Complexity · gemini._no_text_diagnostic (cognitive 18) · ×1
  • gemini._no_text_diagnostic (cognitive 18) langextract/providers/gemini.py:107 — gemini._no_text_diagnostic has cognitive complexity 18 (threshold 15). Drivers by points: ternaries 6 (8 pts), if/else 7, boolean chains 3 (nesting depth added 2). 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.
D2 · Cognitive Complexity · router.resolve_provider (cognitive 18) · ×1
  • router.resolve_provider (cognitive 18) langextract/providers/router.py:170 — router.resolve_provider has cognitive complexity 18 (threshold 15). Drivers by points: if/else 3 (8 pts), loops 5 (7 pts), error handling 2 (3 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.
D2 · Cognitive Complexity · openai._collect_extraction_categories (cognitive 18) · ×1
  • openai._collect_extraction_categories (cognitive 18) langextract/providers/schemas/openai.py:85 — openai._collect_extraction_categories has cognitive complexity 18 (threshold 15). Drivers by points: if/else 3 (11 pts), loops 3 (7 pts) (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.
D2 · Cognitive Complexity · FormatHandler.from_resolver_params (cognitive 17) · ×1
  • FormatHandler.from_resolver_params (cognitive 17) langextract/core/format_handler.py:366 — FormatHandler.from_resolver_params has cognitive complexity 17 (threshold 15). Drivers by points: if/else 5 (10 pts), boolean chains 3, loops 2 (3 pts), ternaries 1 (nesting depth added 6). 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.
D2 · Cognitive Complexity · prompt_validation.validate_prompt_alignment (cognitive 17) · ×1
  • prompt_validation.validate_prompt_alignment (cognitive 17) langextract/prompt_validation.py:130 — prompt_validation.validate_prompt_alignment has cognitive complexity 17 (threshold 15). Drivers by points: if/else 4 (12 pts), loops 2 (3 pts), boolean chains 2 (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.
D2 · Cognitive Complexity · gemini_batch._build_request (cognitive 17) · ×1
  • gemini_batch._build_request (cognitive 17) langextract/providers/gemini_batch.py:317 — gemini_batch._build_request has cognitive complexity 17 (threshold 15). Drivers by points: if/else 7 (11 pts), loops 1 (3 pts), ternaries 2, boolean chains 1 (nesting depth added 6). 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.
D2 · Cognitive Complexity · OllamaLanguageModel.__init__ (cognitive 17) · ×1
  • OllamaLanguageModel.__init__ (cognitive 17) langextract/providers/ollama.py:191 — OllamaLanguageModel.__init__ has cognitive complexity 17 (threshold 15). Drivers by points: if/else 7 (9 pts), ternaries 2 (5 pts), boolean chains 3 (nesting depth added 5). 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.
D2 · Cognitive Complexity · OllamaLanguageModel._build_request_options (cognitive 17) · ×1
  • OllamaLanguageModel._build_request_options (cognitive 17) langextract/providers/ollama.py:379 — OllamaLanguageModel._build_request_options has cognitive complexity 17 (threshold 15). Drivers by points: if/else 12 (15 pts), loops 1, ternaries 1 (nesting depth added 3). 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.
D2 · Cognitive Complexity · openai_batch._collect_outputs_from_jsonl (cognitive 17) · ×1
  • openai_batch._collect_outputs_from_jsonl (cognitive 17) langextract/providers/openai_batch.py:302 — openai_batch._collect_outputs_from_jsonl has cognitive complexity 17 (threshold 15). Drivers by points: if/else 3 (6 pts), boolean chains 4, error handling 2 (4 pts), ternaries 1 (2 pts), loops 1 (nesting depth added 6). 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.
D2 · Cognitive Complexity · pr_security_check._check_credential_egress (cognitive 16) · ×1
  • pr_security_check._check_credential_egress (cognitive 16) .github/scripts/pr_security_check.py:400 — pr_security_check._check_credential_egress has cognitive complexity 16 (threshold 15). Drivers by points: if/else 9, boolean chains 7. To reduce it, split the body: this score is breadth rather than depth — many checks laid out side by side rather than nested inside one another, so inverting conditions into early returns has nothing left to flatten. 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.
D2 · Cognitive Complexity · Annotator._annotate_documents_sequential_passes (cognitive 16) · ×1
  • Annotator._annotate_documents_sequential_passes (cognitive 16) langextract/annotation.py:449 — Annotator._annotate_documents_sequential_passes has cognitive complexity 16 (threshold 15). Drivers by points: if/else 2 (5 pts), loops 4 (5 pts), boolean chains 4, ternaries 1 (2 pts) (nesting depth added 5). 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.
D2 · Cognitive Complexity · resolver._select_monotonic_matches (cognitive 16) · ×1
  • resolver._select_monotonic_matches (cognitive 16) langextract/resolver.py:1152 — resolver._select_monotonic_matches has cognitive complexity 16 (threshold 15). Drivers by points: loops 4 (6 pts), ternaries 2 (6 pts), if/else 2 (3 pts), boolean chains 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.
D2 · Cognitive Complexity · visualization.visualize (cognitive 16) · ×1
  • visualization.visualize (cognitive 16) langextract/visualization.py:571 — visualization.visualize has cognitive complexity 16 (threshold 15). Drivers by points: if/else 10 (13 pts), boolean chains 3 (nesting depth added 3). 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.
D29 · Static Analysis (SAST) · REDACTED · ×1
  • REDACTED
D36 · Supply-chain Provenance & Signing · REDACTED · ×1
  • REDACTED
D36 · Supply-chain Provenance & Signing · REDACTED · ×1
  • REDACTED
D4 · Code Duplication · Edited copy of a member (40 corresponding lines) · ×1
  • Edited copy of a member (40 corresponding lines) langextract/providers/ollama.py:495 — langextract/providers/ollama.py:495-538 | langextract/providers/ollama.py:561-655 — These two members are one piece of code written twice and then edited apart: 40 consecutive lines correspond almost exactly, broken only by small local edits. Most of that correspondence is NOT reported as duplicated blocks below — the edits cut it into fragments and only the largest of them clear the block floor, so the rows below understate it. The repair is at the members' grain — factor the shared implementation into one the two call with their differences as parameters or as an injected step, or, where the difference is systematic (an extra return value, one transport against another), generate one from the other. Left alone, the next edit has to be made twice and the two will drift further apart.
D4 · Code Duplication · Duplicated block (32–35 lines × 2) · ×1
  • Duplicated block (32–35 lines × 2) langextract/providers/ollama.py:499 — langextract/providers/ollama.py:499-533 | langextract/providers/ollama.py:616-647 — 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.
D4 · Code Duplication · Duplicated block (18–31 lines × 2) · ×1
  • Duplicated block (18–31 lines × 2) langextract/_compat/inference.py:31 — langextract/_compat/inference.py:31-61 | langextract/_compat/schema.py:24-41 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once. 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.
D4 · Code Duplication · Duplicated block (21–27 lines × 2) · ×1
  • Duplicated block (21–27 lines × 2) langextract/providers/schemas/gemini.py:126 — langextract/providers/schemas/gemini.py:126-152 | langextract/providers/schemas/openai.py:88-108 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
D4 · Code Duplication · Duplicated block (21–22 lines × 2) · ×1
  • Duplicated block (21–22 lines × 2) langextract/providers/gemini.py:574 — langextract/providers/gemini.py:574-595 | langextract/providers/openai.py:397-417 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `langextract/providers/gemini.py:574` 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.
D4 · Code Duplication · Duplicated block (21 lines × 2) · ×1
  • Duplicated block (21 lines × 2) langextract/providers/schemas/gemini.py:98 — langextract/providers/schemas/gemini.py:98-118 | langextract/providers/schemas/openai.py:216-236 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
D4 · Code Duplication · Duplicated block (17–18 lines × 2) · ×1
  • Duplicated block (17–18 lines × 2) langextract/providers/gemini.py:596 — langextract/providers/gemini.py:596-613 | langextract/providers/openai.py:420-436 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
D4 · Code Duplication · Duplicated block (10–11 lines × 2) · ×1
  • Duplicated block (10–11 lines × 2) langextract/resolver.py:734 — langextract/resolver.py:734-744 | langextract/resolver.py:1270-1279 — 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.
D6 · Cohesion (LCOM4) · Low cohesion · ×1
  • Low cohesion: BaseLanguageModel (LCOM4 5) langextract/core/base_model.py:32 — BaseLanguageModel's methods fall into 5 groups that share no field and call none of each other, against a bar of more than 3 for this run (LCOM4, configurable — your repository's bar is the one quoted here). Each group is a set of methods reachable from one another through shared fields or direct calls, so 5 groups means the type has that many internally-connected clusters with nothing tying them together. Types whose shape makes a high count expected — and which would otherwise dominate this list — are excluded before this row is raised, so this is a genuine split candidate rather than a metric reading. It is still a shape, not a defect: confirm the groups match responsibilities you can name before splitting.
Minor — 20 finding(s)
X23 · Unguarded diagnostic materialisation · Diagnostic log message is built whether or not it is wanted · ×4
  • Diagnostic log message is built whether or not it is wanted langextract/plugins.py:81 — `debug` at line 81 is called with an argument built by `list(discovered.keys())` (line 82), 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 langextract/resolver.py:421 — `debug` at line 421 is called with an argument built by `sum(len(group) for group in aligned_yaml_extractions)` (line 423), 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 langextract/providers/router.py:65 — `debug` at line 65 is called with an argument built by `[p.pattern for p in patterns]` (line 69), 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 langextract/providers/router.py:75 — `debug` at line 75 is called with an argument built by `[p.pattern for p in patterns]` (line 78), 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.
X10 · Duplicated predicate · Duplicated predicate · ×3
  • Duplicated predicate langextract/data_lib.py:50 — `isinstance(value, numbers.Integral) and not isinstance(value, bool)` appears character-identically in 2 files — langextract/data_lib.py, langextract/providers/gemini.py. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
  • Duplicated predicate langextract/providers/gemini.py:578 — `len(batch_prompts) > 1 and self.max_workers > 1` appears character-identically in 2 files — langextract/providers/gemini.py, langextract/providers/openai.py. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
  • Duplicated predicate langextract/providers/schemas/gemini.py:108 — `not format_handler.use_wrapper or format_handler.wrapper_key != data.EXTRACTIONS_KEY` appears character-identically in 2 files — langextract/providers/schemas/gemini.py, langextract/providers/schemas/openai.py. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
D29 · Static Analysis (SAST) · REDACTED · ×2
  • REDACTED
  • REDACTED
D20 · ADR Quality · No ADRs found · ×1
  • 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 1 project(s) overshoot their size bounds, lowering Project Cohesion to 0.0/10. The most over is `(repository root)` (14791 LoC, 129 module-visible types across 10 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.
D34 · Knowledge Freshness · Orphaned files with no living knowledge · ×1
  • Orphaned files with no living knowledge — 1 of 38 analysed file(s) have no living knowledge left — their last meaningful change has decayed away, so if one breaks, no one currently understands it (counted over production source files of roughly 2,400 bytes or more, excluding vendored, generated, example/demo and scaffolding-template trees and test files identified by path convention, largest first; 38 of the 53 production source files in this repository met that bar). None is large enough to earn a read-through of its own, so this row stands in for the per-file rows rather than raising one each — most significant first: langextract/providers/openai_batch.py. Attach the read to the next change that touches one of them: have a second person review that change, and leave behind a short comment or test recording what the file is for, so the knowledge comes back at the cost of a change you were making anyway.
D34 · Knowledge Freshness · Orphaned file · ×1
  • Orphaned file langextract/providers/openai_batch.py:1 — No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
D36 · Supply-chain Provenance & Signing · REDACTED · ×1
  • REDACTED
D36 · Supply-chain Provenance & Signing · REDACTED · ×1
  • REDACTED
D36 · Supply-chain Provenance & Signing · REDACTED · ×1
  • REDACTED
M2 · Architecture documentation · No ADRs · ×1
  • 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.
M3 · Folder & project structure · No src/ separation · ×1
  • 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'.
P3 · Security & performance tooling · No SAST · ×1
  • 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 73147 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.
P6 · Release Hygiene · No changelog · ×1
  • No changelog — No CHANGELOG/HISTORY/RELEASES file — what shipped when isn't easy to reconstruct for support or audit. (Versioning/tagging makes releases traceable, but a changelog records the what.)
Minor — 1 finding(s)
D12 · Dependency Hygiene · Dependency hygiene PARTLY measured · ×1
  • Dependency hygiene PARTLY measured — Python dependencies read, no exact pin to grade for currency — This repository's Python dependencies were read: 17 shipped distribution(s) across 1 manifest(s) (13 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.

DimensionToolVersionCommandFindingsRaw output
D28 · Secrets (history)gitleaks—gitleaks detect --no-banner --config /opt/gitleaks-rules/watchdog-gitleaks.toml --report-format json --report-path /tmp/watchdog-gitleaks-7c72a9b3f98e4d69bfea2e50ba2fb8ce/history.json --exit-code 0 --source .0artifacts/raw/gitleaks-history.json
D28 · Secrets (history)gitleaks—gitleaks detect --no-git --no-banner --config /opt/gitleaks-rules/watchdog-gitleaks.toml --report-format json --report-path /tmp/watchdog-gitleaks-7c72a9b3f98e4d69bfea2e50ba2fb8ce/tree.json --exit-code 0 --source .0artifacts/raw/gitleaks-tree.json
D29 · Static Analysis (SAST)semgrep—semgrep --config /opt/semgrep-rules/security-audit.yml --config /opt/semgrep-rules/owasp-top-ten.yml --config /opt/semgrep-rules/watchdog-sast.yml --json --quiet --timeout 10 --timeout-threshold 3 --metrics off --exclude *.arrow --exclude *.avro --exclude *.csv --exclude *.dat --exclude *.db --exclude *.feather --exclude *.geojson --exclude *.h5 --exclude *.hdf5 --exclude *.jsonl --exclude *.log --exclude *.ndjson --exclude *.npy --exclude *.npz --exclude *.orc --exclude *.parquet --exclude *.pickle --exclude *.pkl --exclude *.psv --exclude *.sqlite --exclude *.sqlite3 --exclude *.tfrecord --exclude *.tsv --exclude *.txt .38artifacts/raw/semgrep.json
D30 · Dependency Vulnerabilitiesnone (dependency manifest found, not scanned for vulnerabilities here)—none (dependency manifest found, not scanned for vulnerabilities here): not applicable — Not scored — no dependency manifest in a supported ecosystem was read for this repository. A gap in the analyzer's language coverage, NOT a finding that the repository is free of vulnerable dependencies (a Python pyproject.toml/requirements.txt (pip/uv/Poetry) — not scanned yet).0—
D31 · IaC & Container Securitytrivy—trivy config --format json --quiet .3artifacts/raw/trivy-config.json
D32 · Data Compliance (PII/GDPR)semgrep—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.0—
D37 · Vulnerability-disclosure Policydisclosure—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.0—
D40 · Network Egress Confinementruntime-hardening—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.0—
D41 · Kernel & Syscall Confinementruntime-hardening—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.0—
D42 · Runtime Threat Enforcementruntime-hardening—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.0—
D43 · Malicious Dependenciesnone (dependency manifest found, not scanned for vulnerabilities here)—none (dependency manifest found, not scanned for vulnerabilities here): not applicable — Not scored — no dependency manifest in a supported ecosystem was read for this repository. A gap in the analyzer's language coverage, NOT a finding that the repository is free of vulnerable dependencies (a Python pyproject.toml/requirements.txt (pip/uv/Poetry) — not scanned yet).0—

Run 01a1720a-04f8-704c-b5a9-611d16be4938 · every finding is also locatable in findings.md, and the complete scoring record (with exit codes + durations) in sidecar.json.

Issues: 36 · Warnings: 86 · Recommendations: 20 · Info: 1 — Appendix A · all findings · full markdown report.
Generated by Watchdog — deterministic code-health analysis. 25-10-2026 @ 07:50 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.

⬇ Findings, MITRE CWE-tagged .sarif⬇ Health changelog .md