keon/algorithms

Executive summary

keon/algorithms scores 66 out of 100 and is rated Adequate, while three of its five lenses score higher still: Code Health at 96, rated Exemplary, Architecture at 87, rated Adequate, and Security and Compliance at 86, rated Strong.

The score is lower than those three because the weakest lens counts most, and here that is Production Readiness, which scores 55 and is rated Adequate. Its Security & performance tooling dimension scores 0 out of 10, and until that reaches 4, Production Readiness can't be rated higher than Adequate. The first actions are to resolve the 1 Documentation finding(s) in Documentation Quality and to add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing, and if Production Readiness rose to 70, the score would rise from 66 to about 71.

Two findings are critical, all in Static Analysis (SAST).

Watchdog couldn't measure 14 dimensions in this code, among them Code Coverage, Dependency Hygiene and License Compliance, so they are left out of the score. The product code is 21,830 lines, which would take an estimated 0.2 person-years, around €33,000, to rebuild.

Code Health 96 ★ Exemplary Architecture 87 Adequate, capped by Project Cohesion Maturity 62 Adequate, capped by Architecture documentation Production Readiness 55 Adequate, capped by Security & performance tooling Security and Compliance 86 Strong

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.

Production Readiness 55 · counts for 47%Maturity 62 · counts for 26%Security and Compliance 86 · counts for 14%Architecture 87 · counts for 8%Code Health 96 · counts for 4%

If Production Readiness rose from 55 to 70, the lowest score rated Strong, the score would rise from 66 to about 71.

Code composition: what the lines of code are

Product code 79%Tests 21%

New since the last survey: 1 finding

This critical or serious finding wasn't in the previous survey, on 4 October 2026.

Supply-chain Provenance & Signing · REDACTED

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 €33,000 · Estimate

Cost to rebuild	€11,000 to €55,000, for 182 to 579 hours of work, about 0.2 person-years for one engineer
Difficulty of the problem	Standard, judged from the kind of software and how it is built. Code that solves a harder problem, such as payments or medical bookings, costs more per line to write.
Quality factor	×0.9 at a score of 66. Better code takes more work to write, so the factor is ×1.0 at a score of 70, lower below it and higher above it.
Size	Medium: 21,830 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.

Resolve the 1 Documentation finding(s) in Documentation Quality — start with README.md.

Raises the score by about 4 points · Low effort · Documentation Quality

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 7 points · Medium effort · Security & performance tooling

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 6 points · Medium effort · Architecture documentation

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.

Where the investment is most exposed · Value at risk

This code would cost about €33,000 to build again, about 0.2 person-years of work. Its weakest lens is Production Readiness, at 55 (Adequate). Production Readiness is about whether the product is ready to run in production and to change without breaking. Because the weakest lens counts most in the score, work on Production Readiness also moves the score most.

Based on: the rebuild estimate · the Production Readiness grade · how much each lens counts

What to do: Start with the Production Readiness actions in Top priorities.

If you do one thing first · First action

Resolve the 1 Documentation finding(s) in Documentation 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 Documentation 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

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.

87 modules, with 7 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; 47 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 →	7 common.tree_node	32 graph.clone_graph	33 graph.minimum_spanning_tree	34 linked_list.add_two_numbers	35 linked_list.intersection	36 linked_list.kth_to_last	37 tree.deepest_left
1 array
2 array.merge_intervals
3 backtracking
4 bit_manipulation
5 common.graph
6 common.list_node
7 common.tree_node
8 compression
9 compression.huffman_coding
10 data_structures
11 data_structures.avl_tree
12 data_structures.b_tree
13 data_structures.bst
14 data_structures.fenwick_tree
15 data_structures.graph
16 data_structures.hash_table
17 data_structures.heap
18 data_structures.iterative_segment_tree
19 data_structures.kd_tree
20 data_structures.linked_list
21 data_structures.priority_queue
22 data_structures.queue
23 data_structures.red_black_tree
24 data_structures.segment_tree
25 data_structures.separate_chaining_hash_table
26 data_structures.sqrt_decomposition
27 data_structures.stack
28 data_structures.trie
29 data_structures.union_find
30 data_structures.veb_tree
31 dynamic_programming
32 graph.clone_graph
33 graph.minimum_spanning_tree
34 linked_list.add_two_numbers
35 linked_list.intersection
36 linked_list.kth_to_last
37 tree.deepest_left
38 graph		1	1
39 linked_list				1	1	1
40 tree	4						1

Uses a module listed above itPart of a loopThe module itself

At a glance — Code Health · 96% · Exemplary ·

The Good

D1 · Python Cyclomatic Complexity 9.5 D3 · Python God Classes 10.0 D4 · Python Code Duplication 9.7 D17 · Python Explicit Debt 10.0 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 X23 · Repo Unguarded diagnostic materialisation 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 X3 · Repo Exception handling 10.0 X30 · Repo Support guard that admits what it rejects 10.0

The Bad

D2 · Python Cognitive Complexity 6.2

At a glance — Architecture · 87% · Adequate · gated by D26 ·

The Good

D6 · Python Cohesion (LCOM4) 9.7 D35 · Python Change Coupling 10.0 AX10 · Repo Code composition 10.0 AX3 · Repo Project dependency cycles 10.0 AX4 · Repo Dependency direction 10.0

The Bad

D22 · Python Internal API Consistency 6.0

The Ugly

D26 · Python Project Cohesion 0.0

At a glance — Maturity · 62% · Adequate · gated by M2 ·

The Good

D15 · Python Churn × Complexity Hotspots 10.0 D16 · Python Bus Factor 8.2 D19 · Python Documentation Quality 7.0 D21 · Python Naming Consistency 10.0 D34 · Python Knowledge Freshness 9.7 M1 · Repo Documentation (README) 8.0 M3 · Repo Folder & project structure 8.0 M4 · Repo Documentation accuracy 8.0

The Ugly

M2 · Repo Architecture documentation 2.0

At a glance — Readiness · 55% · Adequate · gated by P3 ·

The Good

D9 · Python Test Distribution 10.0 D11 · Python Test Reliability 10.0 D13 · Python Secret Scanning 10.0 P1 · Repo CI/CD gates 10.0 P10 · Repo Library API & versioning 10.0 P4 · Repo Deployment & Rollback 7.0

The Bad

P6 · Repo Release Hygiene 5.0

The Ugly

P3 · Repo Security & performance tooling 0.0

At a glance — Security · 86% · Strong ·

The Good

D28 · Python Secrets (history) 10.0 D29 · Python Static Analysis (SAST) 7.1 D36 · Python Supply-chain Provenance & Signing 7.5 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 X24 · Repo Document value interpolated into markup unescaped 10.0

The Ugly

X17 · Repo Uncapped recursion over a caller-supplied document 3.1

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 category	Findings	Severity
A03:2025 — Software Supply Chain Failures	3	Medium
A05:2025 — Injection	2	High / Critical

Roadmap

First, integrate static security analysis into the CI pipeline to prevent security regressions from merging. Second, establish a structured record of significant design decisions in a dedicated documentation folder to improve architectural clarity. Third, address immediate documentation quality issues, starting with the README, and maintain a changelog to track release changes. Finally, review the project structure to ensure modules are appropriately sized and cohesive.

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

Do this	Helps	Effort	Dimension
Resolve the 1 Documentation finding(s) in Documentation Quality — start with README.md.	+3.9 pts	Low	Documentation Quality
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.	+7.5 pts	Medium	Security & performance tooling
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.	+5.8 pts	Medium	Architecture documentation
Keep a changelog (e.g. Keep-a-Changelog) recording what shipped in each release.	+3.7 pts	Medium	Release Hygiene
Resolve the 1 Projects may be oversized for their cohesion finding(s) in Project Cohesion.	+0.9 pts	Low	Project Cohesion
Resolve the 1 Further sole-owners (lower concentration) finding(s) in Bus Factor.	+0.8 pts	Low	Bus Factor
Resolve the 1 Off-boarding risk finding(s) in Bus Factor.	+0.8 pts	Low	Bus Factor
Resolve the 2 REDACTED finding(s) in Static Analysis (SAST) — start with REDACTED, REDACTED.	+0.6 pts	Low	Static Analysis (SAST)

File quality

Per-file score 0–10 — a quality signature. Of 38 files carrying findings, judged against the Production bar: 0% slop · 26% mixed · 74% near-clean.

File	Score	Band	Worst signal
REDACTED	5.1	Mixed	Static Analysis (SAST): High: REDACTED
REDACTED	7.2	Mixed	Static Analysis (SAST): High: REDACTED
algorithms/data_structures/red_black_tree.py	7.4	Mixed	Cyclomatic Complexity: RBTree.delete_fixup (cyclomatic 18)
algorithms/math/diffie_hellman_key_exchange.py	7.4	Mixed	Code Duplication: Duplicated block (20–26 lines × 2)
algorithms/string/strip_url_params.py	7.8	Mixed	Cyclomatic Complexity: strip_url_params.strip_url_params1 (cyclomatic 20)
algorithms/tree/bst_validate_bst.py	7.8	Mixed	Cyclomatic Complexity: bst_validate_bst.validate_bst (cyclomatic 16)
algorithms/graph/bellman_ford.py	7.8	Mixed	Cognitive Complexity: bellman_ford.bellman_ford_paths (cognitive 21)
algorithms/graph/maximum_flow_bfs.py	7.8	Mixed	Cognitive Complexity: maximum_flow_bfs.maximum_flow_bfs (cognitive 20)
algorithms/dynamic_programming/longest_increasing.py	7.8	Mixed	Code Duplication: Duplicated block (13 lines × 2)
algorithms/stack/is_consecutive.py	7.8	Mixed	Code Duplication: Duplicated block (12 lines × 2)
algorithms/string/text_justification.py	8.5	Near-clean	Cognitive Complexity: text_justification.text_justification (cognitive 30)
algorithms/matrix/sudoku_validator.py	8.5	Near-clean	Cognitive Complexity: sudoku_validator.valid_solution_hashtable (cognitive 28)
algorithms/graph/count_islands_bfs.py	8.5	Near-clean	Cognitive Complexity: count_islands_bfs.count_islands (cognitive 23)
algorithms/sorting/cycle_sort.py	8.5	Near-clean	Cognitive Complexity: cycle_sort.cycle_sort (cognitive 22)
algorithms/graph/topological_sort_dfs.py	8.5	Near-clean	Cognitive Complexity: topological_sort_dfs.top_sort (cognitive 21)
algorithms/array/three_sum.py	8.5	Near-clean	Cognitive Complexity: three_sum.three_sum (cognitive 20)
algorithms/graph/maximum_flow_dfs.py	8.5	Near-clean	Cognitive Complexity: maximum_flow_dfs.maximum_flow_dfs (cognitive 20)
algorithms/graph/word_ladder.py	8.5	Near-clean	Cognitive Complexity: word_ladder.ladder_length (cognitive 20)
algorithms/map/longest_palindromic_subsequence.py	8.5	Near-clean	Cognitive Complexity: longest_palindromic_subsequence.longest_palindromic_subsequence (cognitive 20)
algorithms/heap/skyline.py	8.5	Near-clean	Cognitive Complexity: skyline.get_skyline (cognitive 19)

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 — 2

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 — 55

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 — 18

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

Could not be resolved — 15

Something this survey could not settle from the outside, and which could be critical or serious. Either a control was required and no positive evidence of it exists in the repository — a backup job that nothing shows was ever restored from proves nothing about restores — or our own analysis could not run over that part of the tree. This is not a clean result. These are excluded from the score rather than awarded a pass, so the number on the cover neither rewards nor penalises them: if you act on this survey without resolving them, you carry that risk yourself. Each one is named under Limitations.

Methodology & how to trust this report

Watchdog is a deep, periodic assessment — run each sprint, monthly, or quarterly, taking the time to go wider and deeper than a quick check and surfacing in one coherent report what you'd otherwise piece together from a dozen separate tools. It scores deterministically: the same commit yields the same score, every run. 45 of 50 evaluated dimensions are computed purely by tools and static analysis (confidence 1.0); 5 documentation/naming judgement(s) are LLM-assisted and labelled advisory. 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 — 50 dimensions across the health lenses

D1D2D3D4D6D9D11D13D15D16D17D19D21D22D26D28D29D34D35D36D44AX10AX3AX4M1M2M3M4P1P10P3P4P6X1X12X13X14X15X17X19X21X22X23X24X25X26X27X28X3X30

Each chip is a dimension scored from real signals across architecture, testing, dependencies, security & compliance, documentation, git-history and code quality — in one coherent pass. A surface report typically covers a handful.

How to trust any code-health report — three questions

Can you open the finding? Real findings cite a repo-relative file and line you can open at the cited line — never an absolute scratch path. Here, 60 of 75 do; the remainder are repo-wide signals — a dimension-level measurement, not a single line. (Every path in this report is repo-relative by construction: paths are normalized at the producer and the report is rejected if any rooted path leaks through.)
Is there a tool behind the number? Every score below names the method that produced it — Roslyn, git, a scanner, or (for a handful of documentation/naming dimensions) an LLM labelled sampled · advisory — not a narrative.
Does re-running give the same result? Run it again on the same commit and the score — and this report, byte for byte — is identical. A report whose numbers move between runs is describing the run, not the code.

This report answers yes to all three. That's the bar to hold any assessment to.

Tools & methods

The actual versions used this run (captured at analysis time) — re-run on the same commit for the identical score.

Method	Backs	Version	Evaluator
Roslyn static analysis	Complexity, cohesion, coupling, dead code, API surface, layering	5.3.0	✓ deterministic
Native secret scanner	Hardcoded secrets / credentials	1.0.0	✓ deterministic
Watchdog duplication detector (in-process)	Code duplication	1.0.0	✓ deterministic
Coverage (coverlet / dotnet-coverage)	Line & branch coverage	10.0.400	✓ deterministic
NuGet / dotnet	Outdated, vulnerable & deprecated dependencies	10.0.400	✓ deterministic
git / LibGit2Sharp	Churn hotspots, knowledge concentration, history	2.43.0 · 0.31.0	✓ deterministic
gitleaks · semgrep · trivy	Secrets in history, SAST, CVEs, IaC & container, PII / GDPR	1.86.0 · 0.69.3	✓ deterministic
LLM (sampled · advisory)	Documentation quality, ADR conformance, naming — sampled over a bounded sample; advisory, never a deterministic measurement	Local LLM	◐ LLM · sampled · advisory

Every finding is locatable in findings.md. Run 01a15a89-b635-7f3c-95fc-af22c7c34e9b.

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 — 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. Not scored — 0 shipped Python distribution(s) were read, but the outdated signal needs pypi.org, and no declaration here carries an exact pin to ask about — a floor or a range installs the newest release it admits and cannot be behind one, so this dimension's own question is only partly answered. NOT a finding that these dependencies are current or healthy.
D14 License Compliance — 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. Not scored — this repository's package manifest is not parsed for licence data yet. A gap in the analyzer's language coverage, NOT a finding that the repository's licenses are compliant (a Python pyproject.toml/requirements.txt (pip/uv/Poetry)), which this pass does not parse yet — so this dimension asserts nothing about this repository's licensing in either direction.
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.
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.
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.
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.
X4 Structured logging — 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, Python, PHP, Dart, Ruby, Scala and Rust source only, and no C# was loaded, and this repository's JavaScript/TypeScript, Python and PHP make no call through a named logger, 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").
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.
D16 Bus Factor: Bus-factor is a time-decayed model of commit attribution (who has recently, repeatedly worked a file), not comprehension — pairing, review and reading-without-committing spread knowledge it can't see; bot commits and shared accounts still distort it.
D17 Explicit Debt: Acknowledged-debt signals (TODO/FIXME, suppressions, dead code) are textual — undocumented debt that nobody marked, and debt that lives in design rather than annotations, is invisible. Committed machine-written code (scaffolded migrations, designer/codegen output, generated stubs) is excluded — it is never the team's dead code to delete.
D19 Documentation Quality: Documentation quality is judged by an LLM over a bounded sample of docs — it reads what is written, not whether the docs match the running system, and it is advisory, not a measurement. Its critique rows are drawn from a closed category vocabulary and each row means the same thing in every run, so two scans can be compared row by row; the SET that fires is still a sample, and does not repeat exactly. Measured on one frozen input, six scans at one engine SHA: 2-5 critique rows per scan, 8 distinct rows across the six, 3 of those 8 seen in only one scan. So a D19 row is evidence about the documentation, but a COUNT of D19 rows is not a quantity — never read a change in it as an improvement or a regression.
D21 Naming Consistency: Naming quality is an LLM judgement over a bounded sample — it assesses clarity/consistency of the names it sees, not domain-correctness, and is advisory.
D22 Internal API Consistency: API-surface coherence is an LLM judgement over a sample of the public surface — consistency of intent across the whole API is approximated, not exhaustively verified.
D26 Project Cohesion: Project focus is sized from members/namespaces per project — a project that is broad by deliberate design reads the same as one that has sprawled.
D28 Secrets (history): Secret-history scanning sweeps the git log for known patterns — a secret that predates the available history, or never matched a signature, is not found (clean means "nothing matched in the history we can see").
D29 Static Analysis (SAST): SAST findings are pattern-based (semgrep) — it finds classes of bug it has rules for; logic flaws, auth/authorization gaps and issues needing runtime context are out of reach (and clean means "no rule matched").
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 Dockerfile (D31's subject), in a CI workflow (D29's), or in a file this pass does not parse (go.mod, a Gemfile ruby directive) is invisible here, which is why a repository declaring none of them abstains rather than scoring. Only frameworks with a PUBLISHED support policy are tracked: React, Flask and Express publish none, so their age cannot be judged and their absence from a report is not a statement that they are supported.
AX10 Code composition: Role is inferred from namespace/folder convention, not semantics — a domain concept living in a folder named "Services" reads as application, and the split is lines-of-code, not business value. The business-logic-share score is a SOFT, FLOORED signal: it contributes to the Architecture lens but is floored at the Critical gate, so an infrastructure-heavy design (a gateway, an ETL, a driver) is legitimately low without being nuked to zero.
M4 Documentation accuracy: Onboarding quality is an LLM read of the docs/setup present — it cannot run the onboarding or measure how long a real new joiner takes; the verdict is sampled and advisory.
P4 Deployment & Rollback: Approval/branch-protection rules live in repository settings the scan cannot see — only their in-repo evidence (config files, workflows) is checked, so a control enforced purely in the host's settings reads as "not evidenced".
P6 Release Hygiene: Rollback/observability controls are inferred from repo artefacts (pipelines, dashboards-as-code) — controls configured in external tooling, with no in-repo trace, cannot be credited.

The LLM boundary

LLM-set scores this run (5): D19, D21, D22, D26, M4 (model: Local LLM). For these, a model reads a bounded sample and sets the numeric score; each names its own sample and method on its card. They are sampled and advisory by design: they vary at the margins between runs and are never a deterministic measurement. Every other score in this report is tool-computed at confidence 1.0.

Dimensions

D1 · Cyclomatic Complexity9.5 / 10Stronggated by 3 serious findings✓ 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 9.5 / 10 · rule-coverage 100% · ceiling Prevented

3 method(s) exceeded the cyclomatic complexity threshold of 15; the worst was strip_url_params.strip_url_params1 at 20.

strip_url_params.strip_url_params1 (cyclomatic 20)algorithms/string/strip_url_params.py:21

RBTree.delete_fixup (cyclomatic 18)algorithms/data_structures/red_black_tree.py:208

bst_validate_bst.validate_bst (cyclomatic 16)algorithms/tree/bst_validate_bst.py:19

What to do

Bring the 3 bodies over 15 down to 15 or less in Cyclomatic Complexity — start with strip_url_params.strip_url_params1 (cyclomatic 20), RBTree.delete_fixup (cyclomatic 18), bst_validate_bst.validate_bst (cyclomatic 16). — This score is capped by its worst body, so a finding fixed alone moves it by almost nothing — the next one down takes its place. Refactoring these 3 together lifts Cyclomatic Complexity from 9.5 to about 10.0/10, projected with the scoring formula itself and assuming each lands exactly at 15.
Enforce Cyclomatic Complexity in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.

Detailed fixes: d1_recommendation.md · top locations in Appendix A, every location in findings.md.

Do you agree with this assessment?

D2 · Cognitive Complexity6.2 / 10Adequate✓ 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 6.2 / 10 · rule-coverage 100% · ceiling Prevented

25 method(s) exceeded the cognitive complexity threshold of 15; the worst was strip_url_params.strip_url_params1 at 72.

strip_url_params.strip_url_params1 (cognitive 72)algorithms/string/strip_url_params.py:21

RBTree.delete_fixup (cognitive 35)algorithms/data_structures/red_black_tree.py:208

text_justification.text_justification (cognitive 30)algorithms/string/text_justification.py:18

sudoku_validator.valid_solution_hashtable (cognitive 28)algorithms/matrix/sudoku_validator.py:20

count_islands_bfs.count_islands (cognitive 23)algorithms/graph/count_islands_bfs.py:20

+ 20 more group(s) — more in Appendix A; the complete list is findings.md.

What to do

Bring the 2 bodies over 30 down to 30 or less in Cognitive Complexity — start with strip_url_params.strip_url_params1 (cognitive 72), RBTree.delete_fixup (cognitive 35). — This score is capped by its worst body, so a finding fixed alone moves it by almost nothing — the next one down takes its place. Refactoring these 2 together lifts Cognitive Complexity from 6.2 to about 7.2/10, projected with the scoring formula itself and assuming each lands exactly at 30; a cleaner split scores higher.
Enforce Cognitive Complexity in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.

Detailed fixes: d2_recommendation.md · top locations in Appendix A, every location in findings.md.

Do you agree with this assessment?

D3 · God Classes10.0 / 10Exemplary✓ 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 10.0 / 10 · rule-coverage 100% · ceiling Prevented

0 god class(es) detected.

✓ On the Gold path — maintain.

Detailed fixes: d3_recommendation.md.

Do you agree with this assessment?

D4 · Code Duplication9.7 / 10Stronggated by 16 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

16 duplicated block group(s) detected.

Duplicated block (17 lines × 3) · ×2algorithms/math/diffie_hellman_key_exchange.py:50

Duplicated block (20–26 lines × 2)algorithms/math/diffie_hellman_key_exchange.py:27

Duplicated block (19–25 lines × 2)algorithms/math/extended_gcd.py:18

Duplicated block (23 lines × 2)algorithms/math/generate_strobogrammtic.py:34

Duplicated block (21 lines × 2)algorithms/graph/maximum_flow_bfs.py:55

+ 10 more group(s) — more in Appendix A; the complete list is findings.md.

What to do

Resolve the 2 Duplicated block (17 lines × 3) finding(s) in Code Duplication — start with diffie_hellman_key_exchange.py (2). — One of this dimension's main actionable groups (2 warning-level).
Resolve the 1 Duplicated block (20–26 lines × 2) finding(s) in Code Duplication — start with diffie_hellman_key_exchange.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 Duplicated block (19–25 lines × 2) finding(s) in Code Duplication — start with extended_gcd.py. — One of this dimension's main actionable groups (1 warning-level).
Enforce Code Duplication in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Verified — provenance only; does not change the score.

Detailed fixes: d4_recommendation.md · top locations in Appendix A, every location in findings.md.

Do you agree with this assessment?

D6 · Cohesion (LCOM4)9.7 / 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 9.7 / 10 · rule-coverage 100% · ceiling Verified

1 of 41 classes have LCOM4 above 3.

Low cohesion: BST (LCOM4 4)algorithms/data_structures/bst.py:25

What to do

Resolve the 1 Low cohesion finding(s) in Cohesion (LCOM4) — start with bst.py. — One of this dimension's main actionable groups (1 warning-level).
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.

Do you agree with this assessment?

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

590 test methods: 590 unit, 0 integration, 0 BDD, 0 e2e. The Python suite contributes 590 test function(s) across 28 file(s) declaring at least one — every `def test…` in a file pytest or unittest would collect, which is those frameworks' own definition of a case; a parametrize table counts once, so this is a floor. Its tier split is read from file names and paths only.

✓ On the Gold path — maintain.

Detailed fixes: d9_recommendation.md.

Do you agree with this assessment?

D11 · Test Reliability10.0 / 10Exemplary✓ Tool-verified

What it measures: Whether the tests pass reliably, with no flakiness.

Method: Suite re-run N times within tiered wall-clock budgets (unit to e2e); tests failing non-deterministically across runs flagged; guarded tests retried when #if guards detected.

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

0 flaky across 1 measured tier(s). Python (repository root, 28 test files): measured (0 flaky).

✓ On the Gold path — maintain.

Detailed fixes: d11_recommendation.md.

Do you agree with this assessment?

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.

Do you agree with this assessment?

D15 · Churn × Complexity Hotspots10.0 / 10Exemplary✓ 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 10.0 / 10 · rule-coverage 100% · ceiling Documented

No churn × complexity hotspots in the window.

✓ On the Gold path — maintain.

Detailed fixes: d15_recommendation.md.

Do you agree with this assessment?

D16 · Bus Factor8.2 / 10Strong✓ Tool-verified

What it measures: Whether knowledge is concentrated in too few people (the "bus factor").

Method: Living knowledge per author via time-decayed commit attribution (6-month half-life, focus weighting) across largest source files. Deterministic, avoids blame's mechanical-refactor false positives.

Maturity: Documented → Verified → Prevented · effective 8.2 / 10 · rule-coverage 100% · ceiling Documented

13 source file(s) have their living knowledge concentrated in one author (≥90% of recent, decayed contribution). The largest is algorithms/data_structures/veb_tree.py. Counted over 71 of the 405 production source files in this repository: the rest are under the ~2,400-byte size floor this dimension measures over.

Off-boarding risk: anonymized user #1

Further sole-owners (lower concentration)

What to do

Resolve the 1 Off-boarding risk finding(s) in Bus Factor. — One of this dimension's main actionable groups (1 recommendation-level).
Resolve the 1 Further sole-owners (lower concentration) finding(s) in Bus Factor. — One of this dimension's main actionable groups (1 recommendation-level).

Detailed fixes: d16_recommendation.md · top locations in Appendix A, every location in findings.md.

Do you agree with this assessment?

D17 · Explicit Debt10.0 / 10Exemplary○ Nothing flagged

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 10.0 / 10 · rule-coverage 100% · ceiling Prevented

0 deducted task-comment markers across 21830 LoC (0.0/KLoC) → score 10.0. Task comments only: this repository's language is read without a compiler, so D17's suppression, dead-code and commented-out-code arms did not run and this score counts fewer marker kinds than a .NET repository's would.

✓ On the Gold path — maintain.

Detailed fixes: d17_recommendation.md.

Do you agree with this assessment?

D19 · Documentation QualityStrong◐ Sampled · advisory

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

Method: Judged by language model at low temperature (0.0-0.1) on a deterministic doc sample (READMEs plus first 25 architecture docs), with two-pass stability filtering. Advisory, sampled.

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

The repository's root README is a well-written project overview with an explicit 'algorithms' name, a PyPI badge, and a clear Quick Start covering installation, usage, and six runnable examples (Dijkstra's shortest path, coin change, permutations, binary heap, binary search). It also links to docs/index.html. The architecture/design documentation is present as markdown files but not shown in the summary. The visible content is clean and complete for an overview README; the unshown architecture docs are judged present rather than missing.

Documentation: no project overviewREADME.md

What to do

Resolve the 1 Documentation finding(s) in Documentation Quality — start with README.md. — One of this dimension's main actionable groups (1 recommendation-level).

Detailed fixes: d19_recommendation.md · top locations in Appendix A, every location in findings.md.

Do you agree with this assessment?

D21 · Naming ConsistencyExemplary◐ Sampled · advisory

What it measures: Whether names — types, methods, variables — are clear and consistent.

Method: Judged by language model at low temperature (0.0-0.1) on a deterministic random symbol sample (fixed size, not exhaustive), with disclosed confidence band. Advisory, sampled.

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

0 naming inconsistencies across 0 sampled symbols.

✓ On the Gold path — maintain.

Detailed fixes: d21_recommendation.md.

Do you agree with this assessment?

D22 · Internal API ConsistencyAdequate◐ Sampled · advisory

What it measures: Whether the internal API surface is consistent and coherent.

Method: Judged by language model at low temperature over a sample of the public API surface (IsPackable or .Contracts types). Sampled, advisory; confidence discounted by model uncertainty.

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

5 API inconsistencies across a 400-member sample of 429 exposed types.

Duplicate intent with inconsistent signatures. One method is an instance method on a specific data type (Interval) returning a list of that type, while the other is a standalone function accepting raw lists and returning raw lists. This forces the user to choose between using the class or the function for the same logical operation.

Inconsistent key type constraints. `HashTable` restricts keys to `int`, while `SeparateChainingHashTable` allows `object`. This creates confusion about which hash table implementation to use for generic keys, as the base name `HashTable` implies general usage but is restricted.

Inconsistent naming for deletion. While `del_` is used to avoid Python keyword conflicts, it is inconsistent with standard library conventions (e.g., `dict.pop` or `list.remove`). More importantly, the inconsistency lies in the fact that `del_` is used here, but other structures like `Union` use `unite` or `remove_key` in BTree. However, the primary issue is the inconsistency with `remove_duplicates` which uses a full name. A more critical inconsistency is between `del_` in HashTables and `remove_key` in BTree for the same conceptual operation.

Inconsistent handling of None inputs. `rotate_v3` explicitly handles `None` inputs and returns `None`, while `v1` and `v2` do not show this signature (implying they might crash or are not intended for None). This creates an inconsistent API surface where the same operation has different null-safety guarantees depending on the version chosen.

While `peek` is consistent between Queue and Stack, it is inconsistent with `PriorityQueue.pop` which retrieves the highest priority item but removes it, and `Heap.remove_min` which also removes. More critically, `PriorityQueue` does not expose a `peek` method in the signature list, whereas `Queue` and `Stack` do. This is an inconsistency in feature parity across similar LIFO/FIFO structures.

What to do

Resolve the 1 Duplicate intent with inconsistent signatures. One method is an instance… finding(s) in Internal API Consistency. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 Inconsistent key type constraints. `HashTable` restricts keys to `int`,… finding(s) in Internal API Consistency. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 Inconsistent naming for deletion. While `del_` is used to avoid Python… finding(s) in Internal API Consistency. — One of this dimension's main actionable groups (1 warning-level).

Detailed fixes: d22_recommendation.md · top locations in Appendix A, every location in findings.md.

Do you agree with this assessment?

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

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.

Do you agree with this assessment?

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.

Do you agree with this assessment?

D29 · Static Analysis (SAST)7.1 / 10Adequategated by 2 critical findings✓ 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 7.1 / 10 · rule-coverage 100% · ceiling Documented

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

REDACTED

What to do

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).

Detailed fixes: d29_recommendation.md · top locations in Appendix A, every location in findings.md.

Do you agree with this assessment?

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

2 of 71 significant source file(s) are orphaned — their living knowledge has decayed to nothing, so no one currently understands them. The largest is algorithms/data_structures/red_black_tree.py. Counted over 71 of the 405 production source files in this repository: the rest are under the ~2,400-byte size floor this dimension measures over.

Orphaned file · ×2algorithms/data_structures/red_black_tree.py:1

Orphaned files with no living knowledge

✓ On the Gold path — maintain.

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

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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.

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D36 · Supply-chain Provenance & Signing7.5 / 10Strong✓ 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 7.5 / 10 · rule-coverage 100% · ceiling Documented

3/4 supply-chain integrity signals present (provenance, signing, SBOM, pinned actions).

REDACTED

What to do

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

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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 0 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.

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Frontend & cross-cutting dimensions

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

AX10 · Code composition10.0 / 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.

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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.

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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.

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M1 · Documentation (README)8.0 / 10Strong✓ 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.

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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.

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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.

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M4 · Documentation accuracy8.0 / 10Strong◐ Sampled · advisory

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.

README.md links to `algorithms/map/longest_common_subsequence.py`, but `algorithms/map/longest_common_subsequence.py` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — README.md:356

What to do

Reconcile the README with reality: README.md links to `algorithms/map/longest_common_subsequence.py`, but `algorithms/map/longest_common_subsequence.py` does not exist.

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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.

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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.

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P3 · Security & performance tooling0.0 / 10Critical✓ 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 2052 CI workflow file(s) in this repository, and the scanner and linter configuration checked in beside them. A scan that runs outside CI, one configured in your forge's web UI rather than in a committed file, or a tool whose name is none of those this check carries, is not seen — if that is your case the row is wrong, and saying so is more useful than adding a second scanner.

What to do

Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing.
Enable Dependabot/Renovate or a dependency-review gate.
Add gitleaks/trufflehog in CI to block PRs that introduce committed secrets.

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P4 · Deployment & Rollback7.0 / 10Strong✓ 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.

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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.

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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.

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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.

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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.

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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.

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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.

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X17 · Uncapped recursion over a caller-supplied document3.1 / 10Weak✓ Tool-verified

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.

`find_path` is public and takes `start: Any` from its caller, and it calls itself at line 47 (`find_path`) with a piece of `start` — and nowhere in `find_path` is there a depth parameter, a descent counter, or any other cap. The walk therefore spends one stack frame per LEVEL of the value it is handed, so the nesting of the caller's document — not this code — decides how deep the stack goes. Python's parsers accept far more nesting than its stack allows: `json.loads` turns 3,000 bytes of brackets into 1,500 levels and `xml.etree.ElementTree` parses tens of thousands, while a walk like this one raises `RecursionError` once it passes the interpreter's default limit of 1,000 frames. Wherever that exception lands, the work fails: a request errors, and outside any `try` the worker or the program exits with it. Raising `sys.setrecursionlimit` only moves the number the document has to beat. Thread a depth argument through `find_path` and reject the value past a fixed maximum, or rewrite the descent with an explicit stack. — algorithms/graph/find_path.py:47
`find_all_path` is public and takes `start: Any` from its caller, and it calls itself at line 83 (`find_all_path`) with a piece of `start` — and nowhere in `find_all_path` is there a depth parameter, a descent counter, or any other cap. The walk therefore spends one stack frame per LEVEL of the value it is handed, so the nesting of the caller's document — not this code — decides how deep the stack goes. Python's parsers accept far more nesting than its stack allows: `json.loads` turns 3,000 bytes of brackets into 1,500 levels and `xml.etree.ElementTree` parses tens of thousands, while a walk like this one raises `RecursionError` once it passes the interpreter's default limit of 1,000 frames. Wherever that exception lands, the work fails: a request errors, and outside any `try` the worker or the program exits with it. Raising `sys.setrecursionlimit` only moves the number the document has to beat. Thread a depth argument through `find_all_path` and reject the value past a fixed maximum, or rewrite the descent with an explicit stack. — algorithms/graph/find_path.py:83
`find_shortest_path` is public and takes `start: Any` from its caller, and it calls itself at line 120 (`find_shortest_path`) with a piece of `start` — and nowhere in `find_shortest_path` is there a depth parameter, a descent counter, or any other cap. The walk therefore spends one stack frame per LEVEL of the value it is handed, so the nesting of the caller's document — not this code — decides how deep the stack goes. Python's parsers accept far more nesting than its stack allows: `json.loads` turns 3,000 bytes of brackets into 1,500 levels and `xml.etree.ElementTree` parses tens of thousands, while a walk like this one raises `RecursionError` once it passes the interpreter's default limit of 1,000 frames. Wherever that exception lands, the work fails: a request errors, and outside any `try` the worker or the program exits with it. Raising `sys.setrecursionlimit` only moves the number the document has to beat. Thread a depth argument through `find_shortest_path` and reject the value past a fixed maximum, or rewrite the descent with an explicit stack. — algorithms/graph/find_path.py:120
`__eq__` is public and takes `other: object` from its caller, and it calls itself at line 602 (`self.__eq__`) with a piece of `other` — and nowhere in `__eq__` is there a depth parameter, a descent counter, or any other cap. The walk therefore spends one stack frame per LEVEL of the value it is handed, so the nesting of the caller's document — not this code — decides how deep the stack goes. Python's parsers accept far more nesting than its stack allows: `json.loads` turns 3,000 bytes of brackets into 1,500 levels and `xml.etree.ElementTree` parses tens of thousands, while a walk like this one raises `RecursionError` once it passes the interpreter's default limit of 1,000 frames. Wherever that exception lands, the work fails: a request errors, and outside any `try` the worker or the program exits with it. Raising `sys.setrecursionlimit` only moves the number the document has to beat. Thread a depth argument through `__eq__` and reject the value past a fixed maximum, or rewrite the descent with an explicit stack. — algorithms/math/polynomial.py:602

What to do

Each finding names the externally-callable member that accepts the document and the self-call that descends, and states that no depth parameter or counter exists anywhere in the walk. Confirm from the two, then thread a depth argument through the walk and reject documents past a fixed maximum, or rewrite it with an explicit stack.

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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.

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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.

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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.

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X23 · Unguarded diagnostic materialisation10.0 / 10Exemplary○ Nothing flagged

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.

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X24 · Document value interpolated into markup unescaped10.0 / 10Exemplary○ Nothing flagged

Other · Security — Whether text read out of the document being converted is escaped before it is written into generated markup — a value the document's author chose, interpolated into an attribute the surrounding literal delimits, can close that attribute and open another.

Method: Roslyn semantic model over the whole compilation: a string-typed `Value`/`InnerText`/`InnerXml`/`Text` member declared inside `DocumentFormat.OpenXml` or `System.Xml` is a taint SOURCE, propagated through assignments, returns, arguments, tuple elements and string composition to its transitive closure, then read at interpolated-string holes that sit in a markup position the surrounding literal itself delimits. Escaper/encoder calls and enclosing validator conditions cut the flow. Flow- and container-insensitive by construction. A second arm needs no provenance at all and reports a type that CONTRADICTS ITSELF — the same expression escaped at one delimited markup hole and interpolated raw at another hole in the same markup position of the same type, which the type's own escaping proves is a defect without knowing where the value came from. On a repository with no .NET source it reads JavaScript/TypeScript off the token stream with the same rule: a DOM read of raw document text (`getAttribute`, `textContent`, `innerText`, `nodeValue`) is the source, propagated through local bindings and string composition, and judged at template-literal and concatenation holes in the same two delimited markup positions; escapers and validating conditions cut it, and documentation-site, test, vendored and minified scripts are not read. Deterministic, provable per finding. Advisory.

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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.

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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.

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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.

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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.

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X3 · Exception handling10.0 / 10Exemplary○ Nothing flagged

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.

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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.

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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.

Lens	Score	Rating	Impact
Code Health	96%	Exemplary	Strongest area.
Architecture	87%	Adequate — gated by D26	Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Maturity	62%	Adequate — gated by M2	Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Readiness	55%	Adequate — gated by P3	Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Security	86%	Strong	Solid.

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 — 4 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 — 68 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 applicable: this repository's Python imports no dependency-injection container and defines no container of its own — nothing that both registers and resolves bindings, and nothing that names two lifetimes — so nothing holds one lifetime's instance while handing out another's.
AX2 Stateful singletons — No container singleton was found, so there is no shared instance for concurrent requests to race on. No function in this Python code is served by a threaded web framework (a Flask, Bottle or FastAPI route, a Django or Pyramid view), so no module is shared between request threads.
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
AXB1 Runtime evidence locked — no reproducible boot — This repository has nothing the runtime tiers could boot or serve — no markup, no UI framework or web-server dependency, no UI component source, no native UI project and no API definition — nothing here is a surface to boot — so runtime a11y/egress/header evidence has no subject here. Not applicable: this is neither a gap in the scan nor a finding about your code.
AXB2 Runtime readiness — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
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 — ~5781 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 — Not scored — 0 shipped Python distribution(s) were read, but the outdated signal needs pypi.org, and no declaration here carries an exact pin to ask about — a floor or a range installs the newest release it admits and cannot be behind one, so this dimension's own question is only partly answered. NOT a finding that these dependencies are current or healthy.
D14 License Compliance — Package manifest not parsed for licence data — analyzer language-coverage gap
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.
D20 ADR Quality — N/A — ADRs are expected on deployable products with a user-facing host, not consumed libraries; no ADR log is required here.
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. Declaring this codebase's bounded contexts (≥2) would let cross-boundary type coupling be assessed. Declare them in `.codehealth/config.yaml` at the repository root (create it if absent), mapping each context name to the module-path or namespace prefixes that belong to it — e.g. `architecture:` → `contexts:` → `Billing: ["src/billing", "Acme.Billing"]`, `Catalog: ["src/catalog", "Acme.Catalog"]`.
D24 Comment Value — No inline comments to assess — comment value is not applicable here.
D25 ADR Conformance — no ADRs to check
D27 Navigability — D27 counts a call into the standard library or a dependency as unresolved for this repository's language (.py), so too few call sites counted as resolved to assess navigability. Not scored: this is a gap in the analyzer, not a verdict about this repository.
D30 Dependency Vulnerabilities — 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).
D31 IaC & Container Security — No Infrastructure-as-Code or container manifests found (Dockerfile, Docker Compose, Terraform, Kubernetes/Helm, CloudFormation, ARM, Bicep, Ansible); nothing to scan.
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 (3 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), 405 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.
X4 Structured logging — 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
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 — 2 finding(s)

D29 · Static Analysis (SAST) · REDACTED

REDACTED
REDACTED

Serious — 55 finding(s)

X17 · Uncapped recursion over a caller-supplied document · Recursive walk over a caller-supplied document has no depth cap · ×4

Recursive walk over a caller-supplied document has no depth cap algorithms/graph/find_path.py:47 — `find_path` is public and takes `start: Any` from its caller, and it calls itself at line 47 (`find_path`) with a piece of `start` — and nowhere in `find_path` is there a depth parameter, a descent counter, or any other cap. The walk therefore spends one stack frame per LEVEL of the value it is handed, so the nesting of the caller's document — not this code — decides how deep the stack goes. Python's parsers accept far more nesting than its stack allows: `json.loads` turns 3,000 bytes of brackets into 1,500 levels and `xml.etree.ElementTree` parses tens of thousands, while a walk like this one raises `RecursionError` once it passes the interpreter's default limit of 1,000 frames. Wherever that exception lands, the work fails: a request errors, and outside any `try` the worker or the program exits with it. Raising `sys.setrecursionlimit` only moves the number the document has to beat. Thread a depth argument through `find_path` and reject the value past a fixed maximum, or rewrite the descent with an explicit stack.
Recursive walk over a caller-supplied document has no depth cap algorithms/graph/find_path.py:83 — `find_all_path` is public and takes `start: Any` from its caller, and it calls itself at line 83 (`find_all_path`) with a piece of `start` — and nowhere in `find_all_path` is there a depth parameter, a descent counter, or any other cap. The walk therefore spends one stack frame per LEVEL of the value it is handed, so the nesting of the caller's document — not this code — decides how deep the stack goes. Python's parsers accept far more nesting than its stack allows: `json.loads` turns 3,000 bytes of brackets into 1,500 levels and `xml.etree.ElementTree` parses tens of thousands, while a walk like this one raises `RecursionError` once it passes the interpreter's default limit of 1,000 frames. Wherever that exception lands, the work fails: a request errors, and outside any `try` the worker or the program exits with it. Raising `sys.setrecursionlimit` only moves the number the document has to beat. Thread a depth argument through `find_all_path` and reject the value past a fixed maximum, or rewrite the descent with an explicit stack.
Recursive walk over a caller-supplied document has no depth cap algorithms/graph/find_path.py:120 — `find_shortest_path` is public and takes `start: Any` from its caller, and it calls itself at line 120 (`find_shortest_path`) with a piece of `start` — and nowhere in `find_shortest_path` is there a depth parameter, a descent counter, or any other cap. The walk therefore spends one stack frame per LEVEL of the value it is handed, so the nesting of the caller's document — not this code — decides how deep the stack goes. Python's parsers accept far more nesting than its stack allows: `json.loads` turns 3,000 bytes of brackets into 1,500 levels and `xml.etree.ElementTree` parses tens of thousands, while a walk like this one raises `RecursionError` once it passes the interpreter's default limit of 1,000 frames. Wherever that exception lands, the work fails: a request errors, and outside any `try` the worker or the program exits with it. Raising `sys.setrecursionlimit` only moves the number the document has to beat. Thread a depth argument through `find_shortest_path` and reject the value past a fixed maximum, or rewrite the descent with an explicit stack.
Recursive walk over a caller-supplied document has no depth cap algorithms/math/polynomial.py:602 — `__eq__` is public and takes `other: object` from its caller, and it calls itself at line 602 (`self.__eq__`) with a piece of `other` — and nowhere in `__eq__` is there a depth parameter, a descent counter, or any other cap. The walk therefore spends one stack frame per LEVEL of the value it is handed, so the nesting of the caller's document — not this code — decides how deep the stack goes. Python's parsers accept far more nesting than its stack allows: `json.loads` turns 3,000 bytes of brackets into 1,500 levels and `xml.etree.ElementTree` parses tens of thousands, while a walk like this one raises `RecursionError` once it passes the interpreter's default limit of 1,000 frames. Wherever that exception lands, the work fails: a request errors, and outside any `try` the worker or the program exits with it. Raising `sys.setrecursionlimit` only moves the number the document has to beat. Thread a depth argument through `__eq__` and reject the value past a fixed maximum, or rewrite the descent with an explicit stack.

D4 · Code Duplication · Duplicated block (17 lines × 3) · ×2

Duplicated block (17 lines × 3) algorithms/math/diffie_hellman_key_exchange.py:50 — algorithms/math/diffie_hellman_key_exchange.py:50-66 | algorithms/math/find_order_simple.py:20-43 | algorithms/math/find_primitive_root_simple.py:21-37 — 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 all 3 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.
Duplicated block (17 lines × 3) algorithms/math/diffie_hellman_key_exchange.py:70 — algorithms/math/diffie_hellman_key_exchange.py:70-86 | algorithms/math/euler_totient.py:18-40 | algorithms/math/find_primitive_root_simple.py:41-57 — 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 all 3 call sites, so a change lands once. The `return` at the foot of the matched lines is the enclosing body's own terminal exit, not an early one: it moves with them unchanged, and each site calls the extracted unit from the position that `return` occupied — no decision has to be handed back and re-acted on.

D1 · Cyclomatic Complexity · strip_url_params.strip_url_params1 (cyclomatic 20) · ×1

strip_url_params.strip_url_params1 (cyclomatic 20) algorithms/string/strip_url_params.py:21 — strip_url_params.strip_url_params1 has cyclomatic complexity 20 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.

D1 · Cyclomatic Complexity · RBTree.delete_fixup (cyclomatic 18) · ×1

RBTree.delete_fixup (cyclomatic 18) algorithms/data_structures/red_black_tree.py:208 — RBTree.delete_fixup has cyclomatic complexity 18 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.

D1 · Cyclomatic Complexity · bst_validate_bst.validate_bst (cyclomatic 16) · ×1

bst_validate_bst.validate_bst (cyclomatic 16) algorithms/tree/bst_validate_bst.py:19 — bst_validate_bst.validate_bst 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 · strip_url_params.strip_url_params1 (cognitive 72) · ×1

strip_url_params.strip_url_params1 (cognitive 72) algorithms/string/strip_url_params.py:21 — strip_url_params.strip_url_params1 has cognitive complexity 72 (threshold 15). Drivers by points: if/else 16 (64 pts), loops 2 (6 pts), boolean chains 2 (nesting depth added 52). 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 · RBTree.delete_fixup (cognitive 35) · ×1

RBTree.delete_fixup (cognitive 35) algorithms/data_structures/red_black_tree.py:208 — RBTree.delete_fixup has cognitive complexity 35 (threshold 15). Drivers by points: if/else 10 (25 pts), boolean chains 9, loops 1 (nesting depth added 15). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · text_justification.text_justification (cognitive 30) · ×1

text_justification.text_justification (cognitive 30) algorithms/string/text_justification.py:18 — text_justification.text_justification has cognitive complexity 30 (threshold 15). Drivers by points: if/else 7 (20 pts), loops 4 (9 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 · sudoku_validator.valid_solution_hashtable (cognitive 28) · ×1

sudoku_validator.valid_solution_hashtable (cognitive 28) algorithms/matrix/sudoku_validator.py:20 — sudoku_validator.valid_solution_hashtable has cognitive complexity 28 (threshold 15). Drivers by points: if/else 6 (14 pts), loops 6 (13 pts), boolean chains 1 (nesting depth added 15). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · count_islands_bfs.count_islands (cognitive 23) · ×1

count_islands_bfs.count_islands (cognitive 23) algorithms/graph/count_islands_bfs.py:20 — count_islands_bfs.count_islands has cognitive complexity 23 (threshold 15). Drivers by points: loops 4 (12 pts), if/else 2 (9 pts), boolean chains 2 (nesting depth added 15). To reduce it, break up the iteration: give each loop body a named function, and split a multi-phase loop into one function per phase so no single body carries the whole pipeline.

D2 · Cognitive Complexity · cycle_sort.cycle_sort (cognitive 22) · ×1

cycle_sort.cycle_sort (cognitive 22) algorithms/sorting/cycle_sort.py:18 — cycle_sort.cycle_sort has cognitive complexity 22 (threshold 15). Drivers by points: loops 6 (13 pts), if/else 3 (9 pts) (nesting depth added 13). To reduce it, break up the iteration: give each loop body a named function, and split a multi-phase loop into one function per phase so no single body carries the whole pipeline.

D2 · Cognitive Complexity · bellman_ford.bellman_ford_paths (cognitive 21) · ×1

bellman_ford.bellman_ford_paths (cognitive 21) algorithms/graph/bellman_ford.py:82 — bellman_ford.bellman_ford_paths has cognitive complexity 21 (threshold 15). Drivers by points: if/else 4 (12 pts), loops 5 (9 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 · topological_sort_dfs.top_sort (cognitive 21) · ×1

topological_sort_dfs.top_sort (cognitive 21) algorithms/graph/topological_sort_dfs.py:59 — topological_sort_dfs.top_sort has cognitive complexity 21 (threshold 15). Drivers by points: if/else 3 (11 pts), loops 4 (9 pts), boolean chains 1 (nesting depth added 13). 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 · bst_validate_bst.validate_bst (cognitive 21) · ×1

bst_validate_bst.validate_bst (cognitive 21) algorithms/tree/bst_validate_bst.py:19 — bst_validate_bst.validate_bst has cognitive complexity 21 (threshold 15). Drivers by points: ternaries 8 (14 pts), if/else 4, boolean chains 3 (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 · three_sum.three_sum (cognitive 20) · ×1

three_sum.three_sum (cognitive 20) algorithms/array/three_sum.py:17 — three_sum.three_sum has cognitive complexity 20 (threshold 15). Drivers by points: loops 4 (11 pts), if/else 3 (6 pts), boolean chains 3 (nesting depth added 10). To reduce it, break up the iteration: give each loop body a named function, and split a multi-phase loop into one function per phase so no single body carries the whole pipeline.

D2 · Cognitive Complexity · maximum_flow_bfs.maximum_flow_bfs (cognitive 20) · ×1

maximum_flow_bfs.maximum_flow_bfs (cognitive 20) algorithms/graph/maximum_flow_bfs.py:21 — maximum_flow_bfs.maximum_flow_bfs has cognitive complexity 20 (threshold 15). Drivers by points: loops 5 (10 pts), if/else 3 (9 pts), boolean chains 1 (nesting depth added 11). To reduce it, break up the iteration: give each loop body a named function, and split a multi-phase loop into one function per phase so no single body carries the whole pipeline.

D2 · Cognitive Complexity · maximum_flow_dfs.maximum_flow_dfs (cognitive 20) · ×1

maximum_flow_dfs.maximum_flow_dfs (cognitive 20) algorithms/graph/maximum_flow_dfs.py:20 — maximum_flow_dfs.maximum_flow_dfs has cognitive complexity 20 (threshold 15). Drivers by points: loops 5 (10 pts), if/else 3 (9 pts), boolean chains 1 (nesting depth added 11). To reduce it, break up the iteration: give each loop body a named function, and split a multi-phase loop into one function per phase so no single body carries the whole pipeline.

D2 · Cognitive Complexity · word_ladder.ladder_length (cognitive 20) · ×1

word_ladder.ladder_length (cognitive 20) algorithms/graph/word_ladder.py:20 — word_ladder.ladder_length has cognitive complexity 20 (threshold 15). Drivers by points: if/else 6 (13 pts), loops 3 (6 pts), boolean chains 1 (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · longest_palindromic_subsequence.longest_palindromic_subsequence (cognitive 20) · ×1

longest_palindromic_subsequence.longest_palindromic_subsequence (cognitive 20) algorithms/map/longest_palindromic_subsequence.py:17 — longest_palindromic_subsequence.longest_palindromic_subsequence has cognitive complexity 20 (threshold 15). Drivers by points: if/else 4 (16 pts), loops 2 (3 pts), boolean chains 1 (nesting depth added 13). 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 · skyline.get_skyline (cognitive 19) · ×1

skyline.get_skyline (cognitive 19) algorithms/heap/skyline.py:19 — skyline.get_skyline has cognitive complexity 19 (threshold 15). Drivers by points: boolean chains 7, loops 3 (7 pts), if/else 3 (5 pts) (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 · is_palindrome.is_palindrome_dict (cognitive 19) · ×1

is_palindrome.is_palindrome_dict (cognitive 19) algorithms/linked_list/is_palindrome.py:85 — is_palindrome.is_palindrome_dict has cognitive complexity 19 (threshold 15). Drivers by points: if/else 7 (13 pts), loops 3 (5 pts), boolean chains 1 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · sort_matrix_diagonally.sort_diagonally (cognitive 19) · ×1

sort_matrix_diagonally.sort_diagonally (cognitive 19) algorithms/matrix/sort_matrix_diagonally.py:20 — sort_matrix_diagonally.sort_diagonally has cognitive complexity 19 (threshold 15). Drivers by points: loops 5 (13 pts), if/else 3 (4 pts), boolean chains 2 (nesting depth added 9). To reduce it, break up the iteration: give each loop body a named function, and split a multi-phase loop into one function per phase so no single body carries the whole pipeline.

D2 · Cognitive Complexity · breaking_bad.bracket (cognitive 19) · ×1

breaking_bad.bracket (cognitive 19) algorithms/string/breaking_bad.py:80 — breaking_bad.bracket has cognitive complexity 19 (threshold 15). Drivers by points: if/else 3 (9 pts), loops 5 (9 pts), boolean chains 1 (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · maximum_flow.edmonds_karp (cognitive 18) · ×1

maximum_flow.edmonds_karp (cognitive 18) algorithms/graph/maximum_flow.py:85 — maximum_flow.edmonds_karp has cognitive complexity 18 (threshold 15). Drivers by points: if/else 3 (9 pts), loops 4 (8 pts), boolean chains 1 (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · intersection.intersection (cognitive 18) · ×1

intersection.intersection (cognitive 18) algorithms/linked_list/intersection.py:23 — intersection.intersection has cognitive complexity 18 (threshold 15). Drivers by points: if/else 6 (10 pts), boolean chains 4, loops 3 (4 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 · sparse_mul.sparse_multiply (cognitive 18) · ×1

sparse_mul.sparse_multiply (cognitive 18) algorithms/matrix/sparse_mul.py:18 — sparse_mul.sparse_multiply has cognitive complexity 18 (threshold 15). Drivers by points: if/else 4 (10 pts), loops 3 (7 pts), boolean chains 1 (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · spiral_traversal.spiral_traversal (cognitive 17) · ×1

spiral_traversal.spiral_traversal (cognitive 17) algorithms/matrix/spiral_traversal.py:17 — spiral_traversal.spiral_traversal has cognitive complexity 17 (threshold 15). Drivers by points: loops 5 (11 pts), if/else 3 (5 pts), boolean chains 1 (nesting depth added 8). To reduce it, break up the iteration: give each loop body a named function, and split a multi-phase loop into one function per phase so no single body carries the whole pipeline.

D2 · Cognitive Complexity · bellman_ford.bellman_ford (cognitive 16) · ×1

bellman_ford.bellman_ford (cognitive 16) algorithms/graph/bellman_ford.py:18 — bellman_ford.bellman_ford has cognitive complexity 16 (threshold 15). Drivers by points: loops 5 (9 pts), if/else 2 (7 pts) (nesting depth added 9). To reduce it, break up the iteration: give each loop body a named function, and split a multi-phase loop into one function per phase so no single body carries the whole pipeline.

D2 · Cognitive Complexity · check_bipartite.check_bipartite (cognitive 16) · ×1

check_bipartite.check_bipartite (cognitive 16) algorithms/graph/check_bipartite.py:18 — check_bipartite.check_bipartite has cognitive complexity 16 (threshold 15). Drivers by points: if/else 4 (13 pts), loops 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 · dijkstra_heapq.dijkstra (cognitive 16) · ×1

dijkstra_heapq.dijkstra (cognitive 16) algorithms/graph/dijkstra_heapq.py:22 — dijkstra_heapq.dijkstra has cognitive complexity 16 (threshold 15). Drivers by points: if/else 4 (8 pts), loops 3 (5 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.

D22 · Internal API Consistency · Duplicate intent with inconsistent signatures. One method is an instance method on a specific data type (Interval) returning a list of that type, while the other is a standalone function accepting raw lists and returning raw lists. This forces the user to choose between using the class or the function for the same logical operation. · ×1

Duplicate intent with inconsistent signatures. One method is an instance method on a specific data type (Interval) returning a list of that type, while the other is a standalone function accepting raw lists and returning raw lists. This forces the user to choose between using the class or the function for the same logical operation. — Remove the standalone `merge_intervals` function and expose the logic via the `Interval` class, or remove the class methods and keep only the standalone function. Standardize on one approach (preferably the standalone function for utility libraries, or the class if encapsulation is key). (signatures: algorithms.array.merge_intervals.Interval.merge(intervals: list[Interval]): list[Interval] | algorithms.array.merge_intervals.merge_intervals(intervals: list[list[int]]): list[list[int]] | None)

D22 · Internal API Consistency · Inconsistent key type constraints. `HashTable` restricts keys to `int`, while `SeparateChainingHashTable` allows `object`. This creates confusion about which hash table implementation to use for generic keys, as the base name `HashTable` implies general usage but is restricted. · ×1

Inconsistent key type constraints. `HashTable` restricts keys to `int`, while `SeparateChainingHashTable` allows `object`. This creates confusion about which hash table implementation to use for generic keys, as the base name `HashTable` implies general usage but is restricted. — Align key types. Either restrict both to `int` (if performance optimization for integers is the goal) or allow `object` (or `Hashable`) in both to support general-purpose usage. Rename `HashTable` to `IntHashTable` if the restriction is intentional. (signatures: algorithms.data_structures.hash_table.HashTable.put(self, key: int, value: object): None | algorithms.data_structures.separate_chaining_hash_table.SeparateChainingHashTable.put(self, key: object, value: object): None)

D22 · Internal API Consistency · Inconsistent naming for deletion. While `del_` is used to avoid Python keyword conflicts, it is inconsistent with standard library conventions (e.g., `dict.pop` or `list.remove`). More importantly, the inconsistency lies in the fact that `del_` is used here, but other structures like `Union` use `unite` or `remove_key` in BTree. However, the primary issue is the inconsistency with `remove_duplicates` which uses a full name. A more critical inconsistency is between `del_` in HashTables and `remove_key` in BTree for the same conceptual operation. · ×1

Inconsistent naming for deletion. While `del_` is used to avoid Python keyword conflicts, it is inconsistent with standard library conventions (e.g., `dict.pop` or `list.remove`). More importantly, the inconsistency lies in the fact that `del_` is used here, but other structures like `Union` use `unite` or `remove_key` in BTree. However, the primary issue is the inconsistency with `remove_duplicates` which uses a full name. A more critical inconsistency is between `del_` in HashTables and `remove_key` in BTree for the same conceptual operation. — Standardize removal methods across data structures. Use `remove(key)` or `delete(key)` consistently. If `del_` is kept for HashTables, consider renaming `BTree.remove_key` to `BTree.del_` or vice versa for consistency within the library. (signatures: algorithms.data_structures.hash_table.HashTable.del_(self, key: int): None | algorithms.data_structures.separate_chaining_hash_table.SeparateChainingHashTable.del_(self, key: object): None)

D22 · Internal API Consistency · Inconsistent handling of None inputs. `rotate_v3` explicitly handles `None` inputs and returns `None`, while `v1` and `v2` do not show this signature (implying they might crash or are not intended for None). This creates an inconsistent API surface where the same operation has different null-safety guarantees depending on the version chosen. · ×1

Inconsistent handling of None inputs. `rotate_v3` explicitly handles `None` inputs and returns `None`, while `v1` and `v2` do not show this signature (implying they might crash or are not intended for None). This creates an inconsistent API surface where the same operation has different null-safety guarantees depending on the version chosen. — Ensure all public methods in the `rotate` module handle `None` consistently, either by raising an error or returning `None`. Ideally, provide a single public entry point that handles this, or document the behavior clearly and align signatures. (signatures: algorithms.array.rotate.rotate_v1(array: list[int], k: int): list[int] | algorithms.array.rotate.rotate_v2(array: list[int], k: int): list[int] | algorithms.array.rotate.rotate_v3(array: list[int] | None, k: int): list[int] | None)

D22 · Internal API Consistency · While `peek` is consistent between Queue and Stack, it is inconsistent with `PriorityQueue.pop` which retrieves the highest priority item but removes it, and `Heap.remove_min` which also removes. More critically, `PriorityQueue` does not expose a `peek` method in the signature list, whereas `Queue` and `Stack` do. This is an inconsistency in feature parity across similar LIFO/FIFO structures. · ×1

While `peek` is consistent between Queue and Stack, it is inconsistent with `PriorityQueue.pop` which retrieves the highest priority item but removes it, and `Heap.remove_min` which also removes. More critically, `PriorityQueue` does not expose a `peek` method in the signature list, whereas `Queue` and `Stack` do. This is an inconsistency in feature parity across similar LIFO/FIFO structures. — Add a `peek` method to `PriorityQueue` and `AbstractHeap` to maintain consistency with `Queue` and `Stack` interfaces. (signatures: algorithms.data_structures.queue.AbstractQueue.peek(self): object | algorithms.data_structures.stack.AbstractStack.peek(self): object)

D36 · Supply-chain Provenance & Signing REDACTED · ×1

REDACTED

D4 · Code Duplication · Duplicated block (20–26 lines × 2) · ×1

Duplicated block (20–26 lines × 2) algorithms/math/diffie_hellman_key_exchange.py:27 — algorithms/math/diffie_hellman_key_exchange.py:27-46 | algorithms/math/prime_check.py:18-43 — 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 (19–25 lines × 2) · ×1

Duplicated block (19–25 lines × 2) algorithms/math/extended_gcd.py:18 — algorithms/math/extended_gcd.py:18-42 | algorithms/math/modular_inverse.py:18-36 — 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 (23 lines × 2) · ×1

Duplicated block (23 lines × 2) algorithms/math/generate_strobogrammtic.py:34 — algorithms/math/generate_strobogrammtic.py:34-56 | algorithms/math/generate_strobogrammtic.py:89-111 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.

D4 · Code Duplication · Duplicated block (21 lines × 2) · ×1

Duplicated block (21 lines × 2) algorithms/graph/maximum_flow_bfs.py:55 — algorithms/graph/maximum_flow_bfs.py:55-75 | algorithms/graph/maximum_flow_dfs.py:54-74 — 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 (20 lines × 2) · ×1

Duplicated block (20 lines × 2) algorithms/stack/stutter.py:44 — algorithms/stack/stutter.py:44-63 | algorithms/stack/switch_pairs.py:48-67 — 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 (18 lines × 2) · ×1

Duplicated block (18 lines × 2) algorithms/backtracking/factor_combinations.py:18 — algorithms/backtracking/factor_combinations.py:18-35 | algorithms/graph/all_factors.py:48-65 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice.

D4 · Code Duplication · Duplicated block (11–13 lines × 2) · ×1

Duplicated block (11–13 lines × 2) algorithms/data_structures/red_black_tree.py:218 — algorithms/data_structures/red_black_tree.py:218-230 | algorithms/data_structures/red_black_tree.py:248-258 — 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 (13 lines × 2) · ×1

Duplicated block (13 lines × 2) algorithms/dynamic_programming/longest_increasing.py:72 — algorithms/dynamic_programming/longest_increasing.py:72-84 | algorithms/dynamic_programming/longest_increasing.py:119-131 — 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 (12 lines × 2) · ×1

Duplicated block (12 lines × 2) algorithms/stack/is_consecutive.py:36 — algorithms/stack/is_consecutive.py:36-47 | algorithms/stack/is_consecutive.py:67-78 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.

D4 · Code Duplication · Duplicated block (10 lines × 2) · ×1

Duplicated block (10 lines × 2) algorithms/map/is_isomorphic.py:38 — algorithms/map/is_isomorphic.py:38-47 | algorithms/map/word_pattern.py:38-47 — 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 (9 lines × 2) · ×1

Duplicated block (9 lines × 2) algorithms/dynamic_programming/longest_increasing.py:61 — algorithms/dynamic_programming/longest_increasing.py:61-69 | algorithms/dynamic_programming/longest_increasing.py:108-116 — 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 (6 lines × 3) · ×1

Duplicated block (6 lines × 3) algorithms/stack/is_consecutive.py:77 — algorithms/stack/is_consecutive.py:77-82 | algorithms/stack/stutter.py:57-62 | algorithms/stack/switch_pairs.py:61-66 — 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 all 3 call sites, so a change lands once.

D4 · Code Duplication · Duplicated block (6 lines × 2) · ×1

Duplicated block (6 lines × 2) algorithms/tree/binary_tree_views.py:117 — algorithms/tree/binary_tree_views.py:117-122 | algorithms/tree/binary_tree_views.py:151-156 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. The `return` at the foot of the matched lines is the enclosing body's own terminal exit, not an early one: it moves with them unchanged, and each site calls the extracted unit from the position that `return` occupied — no decision has to be handed back and re-acted on.

D4 · Code Duplication · Duplicated block (5 lines × 2) · ×1

Duplicated block (5 lines × 2) algorithms/dynamic_programming/k_factor.py:53 — algorithms/dynamic_programming/k_factor.py:53-57 | algorithms/dynamic_programming/k_factor.py:69-73 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `algorithms/dynamic_programming/k_factor.py:53` 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.

D6 · Cohesion (LCOM4) · Low cohesion · ×1

Low cohesion: BST (LCOM4 4) algorithms/data_structures/bst.py:25 — BST's methods fall into 4 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 4 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 — 18 finding(s)

X10 · Duplicated predicate · Duplicated predicate · ×4

Duplicated predicate algorithms/graph/maximum_flow_bfs.py:52 — `new_array[src][k] > 0 and visited[k] == 0` appears character-identically in 2 files — algorithms/graph/maximum_flow_bfs.py, algorithms/graph/maximum_flow_dfs.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 algorithms/tree/path_sum.py:63 — `node.left is None and node.right is None and val == sum` appears character-identically in 2 files — algorithms/tree/path_sum.py, algorithms/tree/path_sum2.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 algorithms/tree/binary_tree_paths.py:47 — `root.left is None and root.right is None` appears character-identically in 2 files — algorithms/tree/binary_tree_paths.py, algorithms/tree/bst_depth_sum.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 algorithms/tree/path_sum.py:38 — `root.left is None and root.right is None and root.val == sum` appears character-identically in 2 files — algorithms/tree/path_sum.py, algorithms/tree/path_sum2.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.

D34 · Knowledge Freshness · Orphaned file · ×2

Orphaned file algorithms/data_structures/red_black_tree.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.
Orphaned file algorithms/data_structures/veb_tree.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.

D16 · Bus Factor · Off-boarding risk · ×1

Off-boarding risk: anonymized user #1 — If anonymized user #1 becomes unavailable, 9 significant file(s) lose their only recent owner: algorithms/math/diffie_hellman_key_exchange.py, algorithms/data_structures/heap.py, algorithms/graph/__init__.py, algorithms/data_structures/bst.py, algorithms/data_structures/avl_tree.py, algorithms/tree/path_sum2.py, algorithms/math/rsa.py, algorithms/compression/elias.py (+1 more). Pair on, review, or document these before any departure.

D16 · Bus Factor · Further sole-owners (lower concentration) · ×1

Further sole-owners (lower concentration) — 4 other contributor(s) are each the sole owner of a small amount of code below the off-boarding threshold — folded into the bus-factor score and metrics (13 single-owned of 71 analysed files in total, counted over production source files of roughly 2,400 bytes or more, excluding vendored, generated and example/demo trees and test files identified by path convention, largest first; 71 of the 405 production source files in this repository met that bar). They are anonymized user #2 (1 file(s)), anonymized user #3 (1 file(s)), anonymized user #4 (1 file(s)), anonymized user #5 (1 file(s)) — spread or document their files in the same way, at lower priority than the named off-boarding risks above. The files folded here: algorithms/data_structures/veb_tree.py, algorithms/string/strip_url_params.py, algorithms/string/is_palindrome.py, algorithms/compression/lzw_compression.py.

D19 · Documentation Quality · Documentation · ×1

Documentation: no project overview README.md — The document begins with a one-line badge banner ('Minimal, clean, and well-documented implementations of data structures and algorithms in Python 3.') before any description of what the project is or does. Expand on the overview to explain which specific algorithms/data structures are implemented (e.g. sorting, graphs, trees) and how they differ from a generic 'data structures and algorithms' set.

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)` (21830 LoC, 429 module-visible types across 22 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 — 2 of 71 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 and example/demo trees and test files identified by path convention, largest first; 71 of the 405 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: algorithms/data_structures/red_black_tree.py, algorithms/data_structures/veb_tree.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.

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'.

M4 · Documentation accuracy · README/code drift · ×1

README/code drift README.md:356 — README.md links to `algorithms/map/longest_common_subsequence.py`, but `algorithms/map/longest_common_subsequence.py` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.

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 2052 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.)

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.

Dimension	Tool	Version	Command	Findings	Raw output
D28 · Secrets (history)	gitleaks	—	gitleaks detect --no-banner --config /opt/gitleaks-rules/watchdog-gitleaks.toml --report-format json --report-path /tmp/watchdog-gitleaks-3193839f913f4892b3220aa117e7f0d9/history.json --exit-code 0 --source .	0	artifacts/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-3193839f913f4892b3220aa117e7f0d9/tree.json --exit-code 0 --source .	0	artifacts/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 .	2	artifacts/raw/semgrep.json
D30 · Dependency Vulnerabilities	none (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 Security	trivy	—	trivy: not applicable — No Infrastructure-as-Code or container manifests found (Dockerfile, Docker Compose, Terraform, Kubernetes/Helm, CloudFormation, ARM, Bicep, Ansible); nothing to scan.	0	—
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 Policy	disclosure	—	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 Confinement	runtime-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 Confinement	runtime-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 Enforcement	runtime-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 Dependencies	none (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 01a15a89-b635-7f3c-95fc-af22c7c34e9b · every finding is also locatable in findings.md, and the complete scoring record (with exit codes + durations) in sidecar.json.

Findings by how serious they are

This survey was produced by

Executive summary

Top priorities

Diagnosis: what the measurements show together

Architecture: which parts of the code depend on which

At a glance — Code Health · 96% · Exemplary ·

At a glance — Architecture · 87% · Adequate · gated by D26 ·

At a glance — Maturity · 62% · Adequate · gated by M2 ·

At a glance — Readiness · 55% · Adequate · gated by P3 ·

At a glance — Security · 86% · Strong ·

Security & Compliance — OWASP Top-10 mapping

Roadmap

File quality

How the grades work

Methodology & how to trust this report

Tools & methods

Run transparency — what happened this run

Limitations & what we did not check

Per-dimension blind spots

The LLM boundary

Dimensions

Frontend & cross-cutting dimensions

Reference — by lens

Appendix A — Findings (grouped)

Appendix B — Reproduction & audit trail

Downloadable artifacts