Public report — minimind, published 26 Sep 2026. Concrete security findings (which rule fired, in which file, on which line; CVE IDs, secret matches, dependency versions) are REDACTED in this version; ask the repo owner for the full report.
Public
Codebase survey Measured under the Code Assurance Index · rubric rubric-2026.09.15 (frozen) · verify this survey Filed cd_f25a4745f9004b9e93aff978d3f62b75 Filed 26 September 2026, 09:41 UTC Public

Jingyaogong/minimind

Measured 26 September 2026, 09:34 UTC

44% At Risk

Small · 4,097 LoC · rebuild ~0.1 person-years · weakest lens: Readiness (22%)

Findings by grade

6 critical 65 serious 35 minor 42 could not be resolved — could be critical — see Limitations

This survey was produced by

Watchdog
Producer
Canine Development
Analyzer
Watchdog engine 1.0.0
Measured
26 September 2026, 09:34 UTC

A measurement, not a certificate. The Code Assurance Index does not certify, approve or guarantee this codebase; it records a reproducible number and the evidence it was computed from. The standard is authored by Canine Development, who also build Watchdog — its only implementation today. That is said here so the number is checked rather than believed.

Grounded in facts. Every number here is computed, not narrated — reproducible, tool-backed, and traceable to a line of code. How to trust this ▸

24/27dimensions tool-verifieddeterministic · confidence 1.0 · 3 LLM-assisted, advisory
69findings with an exact file:lineof 106 — the remainder are repo-wide signals (a dimension-level measurement, not a single line); open any file:line and verify
27/114dimensions across the health lenses4097 LoC — wide & deep
Chapters

Executive summary

⚠ A critical security finding caps this grade — resolve it before relying on the score below; see the Security lens.

The system holds a 44% health score, placing it at risk. While the underlying architecture is robust and the codebase is small, the lack of operational safeguards creates a fragile foundation for future growth. This standing means that while the asset is currently manageable, it lacks the resilience required for safe, rapid expansion without introducing significant operational risk.

The value tied up in this system is modest, representing a small codebase that could be rebuilt for approximately €3,600. However, the team modifies nearly 10,000 lines of related code annually, meaning the cost of maintaining this asset is driven by the effort required to change it, not just its size. The primary risk is not the code itself, but the absence of automated verification, which turns every update into a manual, error-prone process.

The most critical issue is operational fragility. With a readiness score of just 22%, the system lacks the testing and observability needed to ensure stability. This creates a high risk of undetected defects reaching production, leading to costly outages or security exposures. The absence of automated tests means that every change carries a hidden tax, slowing delivery and increasing the likelihood of regression errors that could damage customer trust.

A secondary concern is the velocity tax on development. Although the code structure is clean, the lack of automated checks forces engineers to manually verify behavior, adding 4–10% overhead to every change. This inefficiency compounds over time, draining resources that could otherwise be spent on new features. The cost of inaction is significant, as the annual drag on productivity far exceeds the one-time effort required to fix it.

The system’s strengths lie in its solid architecture and clean code health, which provide a stable base for improvement. The high scores in these areas indicate that the core logic is well-structured and maintainable, reducing the risk of structural debt.

Focus first on adding a CI workflow that runs tests on every change. This single action breaks even within months, paying for itself by preventing future defects and accelerating delivery. It is the highest-leverage move, addressing the most critical risk with minimal effort. The picture is partial, as several key areas like security and performance were not measured, so this assessment should be updated as more data becomes available.

How the score is built — each lens's share of the headline Width is the lens's weight in the worst-heaviest fold (the weakest area pulls hardest); colour is that lens's own band. A lens fixes the score in proportion to its width.
Readiness 22% · 47% weightMaturity 38% · 26% weightCode Health 84% · 14% weightSecurity 91% · 8% weightArchitecture 98% · 4% weight

Raise Readiness 22 → 70 (the Healthy floor) ⇒ headline 44 → ~60.

New since the last scan (69+)

69 finding(s) are new versus the previous scan (2026-08-07) — surfaced by this scheduled scan itself, no pull request required.

  • D1 · train_ppo.ppo_train_epoch (cyclomatic 29) trainer/train_ppo.py
  • D1 · train_agent.rl_train_epoch (cyclomatic 26) trainer/train_agent.py
  • D1 · train_agent.calculate_rewards (cyclomatic 25) trainer/train_agent.py
  • D1 · train_grpo.grpo_train_epoch (cyclomatic 23) trainer/train_grpo.py
  • D1 · train_distillation.train_epoch (cyclomatic 19) trainer/train_distillation.py
  • D1 · MiniMindForCausalLM.generate (cyclomatic 18) model/model_minimind.py
  • D1 · web_demo.main (cyclomatic 18) scripts/web_demo.py
  • D1 · eval_toolcall.chat_api (cyclomatic 16) scripts/eval_toolcall.py
  • D2 · train_ppo.ppo_train_epoch (cognitive 73) trainer/train_ppo.py
  • D2 · train_agent.calculate_rewards (cognitive 67) trainer/train_agent.py
  • D2 · train_agent.rl_train_epoch (cognitive 52) trainer/train_agent.py
  • D2 · train_grpo.grpo_train_epoch (cognitive 44) trainer/train_grpo.py
  • D2 · eval_toolcall.chat_api (cognitive 42) scripts/eval_toolcall.py
  • D2 · trainer_utils.lm_checkpoint (cognitive 40) trainer/trainer_utils.py
  • D2 · train_distillation.train_epoch (cognitive 38) trainer/train_distillation.py
  • D2 · web_demo.main (cognitive 34) scripts/web_demo.py
  • D2 · MiniMindForCausalLM.generate (cognitive 31) model/model_minimind.py
  • D2 · serve_openai_api.generate_stream_response (cognitive 31) scripts/serve_openai_api.py
  • D2 · train_agent.rollout_single (cognitive 26) trainer/train_agent.py
  • D2 · train_full_sft.train_epoch (cognitive 25) trainer/train_full_sft.py
  • D2 · train_pretrain.train_epoch (cognitive 25) trainer/train_pretrain.py
  • D2 · eval_toolcall.run_case (cognitive 24) scripts/eval_toolcall.py
  • D2 · train_dpo.train_epoch (cognitive 22) trainer/train_dpo.py
  • D2 · train_lora.train_epoch (cognitive 22) trainer/train_lora.py
  • D2 · train_tokenizer.get_texts (cognitive 18) trainer/train_tokenizer.py
  • D2 · SFTDataset.generate_labels (cognitive 17) dataset/lm_dataset.py
  • D2 · DPODataset.generate_loss_mask (cognitive 17) dataset/lm_dataset.py
  • D2 · train_grpo.calculate_rewards (cognitive 17) trainer/train_grpo.py
  • D4 · Near-duplicate member family (3 members, 30 shared lines) trainer/train_full_sft.py
  • D4 · Near-duplicate member pair (22 shared lines) trainer/train_distillation.py
  • D4 · Duplicated block (15 lines × 3) trainer/train_full_sft.py
  • D4 · Duplicated block (12–14 lines × 4) trainer/train_distillation.py
  • D4 · Duplicated block (2–11 lines × 5) trainer/train_distillation.py
  • D4 · Duplicated block (11 lines × 2) trainer/train_grpo.py
  • D4 · Duplicated block (10 lines × 2) dataset/lm_dataset.py
  • D4 · Duplicated block (8 lines × 2) trainer/train_ppo.py
  • D4 · Duplicated block (7 lines × 5) trainer/train_distillation.py
  • D4 · Duplicated block (7 lines × 2) trainer/train_agent.py
  • D4 · Duplicated block (5–7 lines × 2) trainer/train_agent.py
  • D4 · Duplicated block (6 lines × 2) dataset/lm_dataset.py
  • D4 · Duplicated block (6 lines × 2) scripts/web_demo.py
  • D4 · Duplicated block (5 lines × 2) eval_llm.py
  • D4 · Duplicated block (5 lines × 2) scripts/convert_model.py
  • D4 · Duplicated block (5 lines × 2) trainer/train_agent.py
  • D4 · Duplicated block (5 lines × 2) trainer/train_agent.py
  • D4 · Duplicated block (5 lines × 2) trainer/train_grpo.py
  • D4 · Duplicated block (7 lines × 2) scripts/web_demo.py
  • D4 · Duplicated block (5 lines × 2) eval_llm.py
  • D4 · Duplicated block (8 lines × 2) trainer/train_distillation.py
  • D4 · Duplicated block (5 lines × 3) trainer/train_full_sft.py
  • D15 · Hotspot: trainer/train_ppo.py trainer/train_ppo.py
  • D15 · Hotspot: trainer/train_agent.py trainer/train_agent.py
  • D15 · Hotspot: trainer/train_grpo.py trainer/train_grpo.py
  • D15 · Hotspot: model/model_minimind.py model/model_minimind.py
  • D15 · Hotspot: scripts/web_demo.py scripts/web_demo.py
  • D15 · Hotspot: trainer/trainer_utils.py trainer/trainer_utils.py
  • D15 · Repeated repair: eval_llm.py eval_llm.py
  • D15 · Repeated repair: scripts/serve_openai_api.py scripts/serve_openai_api.py
  • D30 · REDACTED
  • D30 · REDACTED
  • D30 · REDACTED
  • D30 · REDACTED
  • D30 · REDACTED
  • D30 · REDACTED
  • D30 · REDACTED
  • D30 · REDACTED
  • D30 · REDACTED
  • D30 · REDACTED
  • D35 · Change coupling: train_grpo.py ↔ train_ppo.py trainer/train_grpo.py

A full-fidelity diff against the previous run's complete recorded findings — line-move tolerant: a finding that only shifted line counts as unchanged, only genuinely new titles/files surface here.

Rebuild cost & value ~ Modeled — €1,200–€6,000
Cost to rebuild€1,200–€6,000 (0.1 person-years (20–63 h), ~1 engineer)
Domain complexityStandard — harder problems cost more per line
Quality factor0.7× (at 44% quality) — the last 20% of quality is most of the work
Size & shapeSmall · effort split not classified (source measured from disk; the effort-tier breakdown is a C#-only syntax walk)

This codebase represents roughly ~0.1 person-years of build effort (about ~€3,600 to rebuild). Its weakest lens is Readiness at 22% — the part of that asset most exposed by the findings below.

How we model this: boilerplate at a scaffolding rate + logic × domain Standard (×1.0) — standard service × a 0.7× quality factor, at €60–95/h; indicative, ±~30% · size measured directly from source · effort from total production LoC as straight-line logic (the tier split is a C#-only syntax walk), a conservative lower bound. Indicative only — most sensitive to the hourly rate and the domain tier (both tunable in config).

Top priorities

The highest-leverage moves; the full ranked list is in the Roadmap below.

1
Resolve the 1 No automated tests finding(s) in Code Coverage.
+16.4 pts · Low effort · Code Coverage
2
Resolve the 1 No tests found finding(s) in Test Distribution.
+16.4 pts · Low effort · Test Distribution
3
Add a CI workflow that builds and runs the test suite on every push/PR.
+21.1 pts · Medium effort · CI/CD gates

Diagnosis — what's actually going on

Value concentrated against a weak lens · High · Value at risk
This is a Small asset (~0.1 person-years to rebuild), and its weakest lens is Readiness at 22%. The operational and business risk on an asset this size concentrates there — that's where remediation buys the most protection.
Evidence: valuation: Small, ~0.1 person-years rebuild (4,097 LoC) · weakest lens: Readiness 22%
→ Direct remediation budget at Readiness first — highest risk-reduction per euro on an asset this size.
The top fix pays for itself · High · Economics
The top-ranked fix costs roughly 1–3 engineer-days once. Not doing it costs about 1–6.2 engineer-days every year, paid as drag on the ~9,612 lines this team changes annually — a bill that arrives whether or not anyone books it. On those figures the fix breaks even in roughly 2–35 months and is free after that. Method, stated so this is not read as a quotation: debt from the ranked task's effort band; interest = annual changed lines (measured, annualised from the 90-day window) ÷ an ASSUMED 150–400 lines per engineer-day × the 4–10% drag implied by the code-quality signals; breaking point = debt ÷ annual interest. A modelled planning range built from measured inputs and one named assumption — not a quotation, a valuation, or a certified figure.
Evidence: D15 churn: 2,370 line(s) changed over a 90-day window ⇒ ~9,612/year · D1/D2/D4 code quality: averaging 6.3/10 ⇒ a 4–10% drag on each change · top-ranked remediation: Low effort ⇒ about 1–3 engineer-day(s)
→ Do the top-ranked fix now if this code will still be yours in 35 months.
Highest-leverage move · Medium · Leverage
Of everything flagged, the best return on effort is: Add a CI workflow that builds and runs the test suite on every push/PR. The rest can wait behind it.
Evidence: priority ranking: top of 5 ranked by impact/effort
→ Add a CI workflow that builds and runs the test suite on every push/PR.
A velocity tax on every change · Medium · Economics
The code-quality signals (complexity, duplication, cohesion) average 6.3/10, which acts as a tax on every change in the weaker areas: modifications there plausibly cost on the order of 4–10% more than in clean code, and the tax compounds as the codebase grows. (A modelled estimate, not a measured fact.)
Evidence: D1/D2/D4 code quality: averaging 6.3/10 across the code-quality signals actually measured
→ Pay it down where churn is highest — the hotspots — not everywhere; that's where the tax is actually paid.

Architecture — module dependency matrix

Rows and columns are the same modules, ordered so that a module only depends on ones above it. A cell means the row depends on the column, and its number is how many type pairs create that dependency. Read one thing: is anything above the diagonal? A mark there is a dependency cycle. (A cycle is all this shows — an unusual but cycle-free dependency sits below the diagonal like any other.)

12 modules, 3 dependencies. Every dependency points down the layering — no cycles.

Module dependency matrix. The row depends on the column; the number is how many type pairs create the dependency. A cell above the diagonal is part of a dependency cycle.
depends on →1 (global)2 dataset3 dataset.lm_dataset4 model5 model.model_lora6 model.model_minimind7 scripts.serve_openai_api8 trainer.rollout_engine9 trainer.trainer_utils10 scripts11 trainer12 trainer.train_ppo
1 (global)
2 dataset
3 dataset.lm_dataset
4 model
5 model.model_lora
6 model.model_minimind
7 scripts.serve_openai_api
8 trainer.rollout_engine
9 trainer.trainer_utils
10 scripts1
11 trainer1
12 trainer.train_ppo1
Dependency, pointing down the layeringAbove the diagonal — part of a cycleThe module itself
(global)datasetdataset.lm_datasetmodelmodel.model_loramodel.model_minimind…ipts.serve_openai_apitrainer.rollout_enginetrainer.trainer_utilsscriptstrainertrainer.train_ppo(global)1dataset2dataset.lm_dataset3model4model.model_lora5model.model_minimind6…ipts.serve_openai_api7trainer.rollout_engine8trainer.trainer_utils9scripts10trainer11trainer.train_ppo12111

At a glance — Code Health · 84% · Adequate · gated by D2 ·

At a glance — Architecture · 98% · Exemplary ·

At a glance — Maturity · 38% · Weak · gated by M2, M4 ·

At a glance — Readiness · 22% · Critical · gated by D8, D9, P1, P3 ·

At a glance — Security · 91% · Exemplary ·

Security & Compliance — OWASP Top-10 mapping

Findings mapped to OWASP categories; the specific CVEs/secrets are in the Security dimension cards below and findings.md (redacted only on the public version of this report).

OWASP categoryFindingsSeverity
A06:2021 — Vulnerable & Outdated Components10High / Critical

Roadmap

Establish a CI/CD pipeline to automatically build and test every push, then address the single gaps in code coverage and test distribution to ensure comprehensive validation. Simultaneously, document key architectural decisions in a structured format and reconcile the README with the actual codebase to eliminate misleading claims about model capabilities and branches.

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

Do thisHelpsEffortDimension
Resolve the 1 No automated tests finding(s) in Code Coverage.+16.4 ptsLowCode Coverage
Resolve the 1 No tests found finding(s) in Test Distribution.+16.4 ptsLowTest Distribution
Add a CI workflow that builds and runs the test suite on every push/PR.+21.1 ptsMediumCI/CD gates
Record significant decisions one document per decision — dated, stating the context, the decision and its consequences — and keep them together wherever your design docs already live (a conventional `docs/adr/` tree, with each file named `NNNN-title` in whatever markup those docs already use, is the most discoverable form).+13.6 ptsMediumArchitecture documentation
Reconcile the README with reality: README claims MiniMind-V and MiniMind-O vision while only minimind-v1-moe exists; claims dLM/diffusion model but no such branch exists.+13.6 ptsMediumDocumentation accuracy
Group production code under src/ (or split deliberately, e.g. backend/ + frontend/) so production and tooling code aren't mixed at the root.+10.4 ptsMediumFolder & project structure
Add a 'Testing' section to the root README — how to run the test suite.+8.6 ptsMediumDocumentation (README)
Improve Documentation Quality — currently 7.0/10.+3.9 ptsMediumDocumentation Quality

File quality

Per-file score 0–10 — a quality signature. Of 18 files carrying findings, judged against the Production bar: 6% slop · 55% mixed · 39% near-clean.

FileScoreBandWorst signal
REDACTED0.5SlopDependency Vulnerabilities: Critical CVE: REDACTED
trainer/train_grpo.py5.6MixedCyclomatic Complexity: train_grpo.grpo_train_epoch (cyclomatic 23)
trainer/train_agent.py7.0MixedCyclomatic Complexity: train_agent.rl_train_epoch (cyclomatic 26)
trainer/train_distillation.py7.0MixedCyclomatic Complexity: train_distillation.train_epoch (cyclomatic 19)
scripts/web_demo.py7.2MixedCyclomatic Complexity: web_demo.main (cyclomatic 18)
trainer/train_full_sft.py7.2MixedCognitive Complexity: train_full_sft.train_epoch (cognitive 25)
dataset/lm_dataset.py7.2MixedCognitive Complexity: SFTDataset.generate_labels (cognitive 17)
trainer/train_ppo.py7.4MixedCyclomatic Complexity: train_ppo.ppo_train_epoch (cyclomatic 29)
scripts/eval_toolcall.py7.4MixedCyclomatic Complexity: eval_toolcall.chat_api (cyclomatic 16)
model/model_minimind.py7.8MixedCyclomatic Complexity: MiniMindForCausalLM.generate (cyclomatic 18)
eval_llm.py7.8MixedCode Duplication: Duplicated block (5 lines × 2)
trainer/trainer_utils.py8.5Near-cleanCognitive Complexity: trainer_utils.lm_checkpoint (cognitive 40)
scripts/serve_openai_api.py8.5Near-cleanCognitive Complexity: serve_openai_api.generate_stream_response (cognitive 31)
trainer/train_pretrain.py8.5Near-cleanCognitive Complexity: train_pretrain.train_epoch (cognitive 25)
trainer/train_dpo.py8.5Near-cleanCognitive Complexity: train_dpo.train_epoch (cognitive 22)
trainer/train_lora.py8.5Near-cleanCognitive Complexity: train_lora.train_epoch (cognitive 22)
trainer/train_tokenizer.py8.5Near-cleanCognitive Complexity: train_tokenizer.get_texts (cognitive 18)
scripts/convert_model.py8.5Near-cleanCode Duplication: Duplicated block (5 lines × 2)

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 — 6

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 — 65

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 — 35

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

Could not be resolved — 42

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. 24 of 27 evaluated dimensions are computed purely by tools and static analysis (confidence 1.0); 3 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 — 27 dimensions across the health lenses
D1D2D3D4D8D9D12D13D14D15D17D19D21D28D29D30D34D35D43D44AX10M1M2M3M4P1P3

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

How to trust any code-health report — three questions
  1. Can you open the finding? Real findings cite a repo-relative file and line you can open at the cited line — never an absolute scratch path. Here, 69 of 106 do; the remainder are repo-wide signals — a dimension-level measurement, not a single line. (Every path in this report is repo-relative by construction: paths are normalized at the producer and the report is rejected if any rooted path leaks through.)
  2. Is there a tool behind the number? Every score below names the method that produced it — Roslyn, git, a scanner, or (for a handful of documentation/naming dimensions) an LLM labelled sampled · advisory — not a narrative.
  3. Does re-running give the same result? Run it again on the same commit and the score — and this report, byte for byte — is identical. A report whose numbers move between runs is describing the run, not the code.

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

Tools & methods

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

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

Every finding is locatable in findings.md. Run 01a0dd10-ab68-77cd-b5a6-8025712427eb.

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.

  • D16 Bus Factor — not measured this run — This is a true statement about the repository that carries nothing for its owner to act on, so it is reported here rather than as a defect in their code. Single-maintainer repository — bus factor is not applicable (19 contributor(s) across 408 commit(s) sampled, automation and bot accounts excluded). One of them holds 92% of the history; the other 18 hold 0.4% each on average, below the 5% at which there is somebody to hand the work to. That is a single maintainer with drive-by contributors, not a team whose knowledge has concentrated — so the bus factor is not applicable and there is nothing here for the owner to act on.
  • D20 ADR Quality — 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. The ADR expectation is gated on whether a repository ships a deployable product. Our repo-kind classifier reads .NET project files and the host and package markers of the other ecosystems it knows (a Go `package main`, a Cargo binary, a package.json `bin` or `start` script, Python console scripts or a Django/WSGI entry point, a Spring Boot / WAR / Gradle application build, a Rack or Rails app, a Composer project, an OTP release or escript, a Dockerfile, a Procfile, and the corresponding published-package manifests, including an OTP `*.app.src` and a Claude Code `.claude-plugin/plugin.json`), and found none here, so we could not tell what kind of repository this is and did not assess its ADR log. This is a gap in our analyzer, not a finding about this repository.
  • D22 Internal API Consistency — not measured this run — This is a true statement about the repository that carries nothing for its owner to act on, so it is reported here rather than as a defect in their code. This repository's source is written only in languages whose packages are published through a manifest, and no package manifest of any ecosystem exists in the tree (no package.json, pyproject.toml, setup.py, gemspec, Cargo.toml, pom.xml, build.gradle, composer.json, pubspec.yaml or mix.exs). With no publication act there is no intentionally-exposed surface for API consistency to be judged over.
  • AX3 Project dependency cycles — 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 which project references which. That is a language-neutral question, but the project-reference graph is collected from MSBuild .csproj files only, and this repository commits none — so there was no graph to read, and re-running the same commit reads the same nothing. No module manifest of any ecosystem this engine recognises was found either, so the collector owed is one that derives the module graph from the source's own imports. That is a COLLECTOR gap in this analyzer — no change to the scan image closes it — and not a finding that the repository is free of what this check looks for.
  • AX4 Dependency direction — 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 direction each project reference points. That is a language-neutral question, but the project-reference graph is collected from MSBuild .csproj files only, and this repository commits none — so there was no graph to read, and re-running the same commit reads the same nothing. No module manifest of any ecosystem this engine recognises was found either, so the collector owed is one that derives the module graph from the source's own imports. That is a COLLECTOR gap in this analyzer — no change to the scan image closes it — and not a finding that the repository is free of what this check looks for.
  • AX6 Interface segregation — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. This check is computed over the public interfaces this run's compilations declare, and none was loaded, so it had nothing to read. That is a gap in this analyzer's language reach — not a finding that the repository is free of what this check looks for.
  • AX8 Test isolation — 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 which projects are test projects, and what they reference. That is a language-neutral question, but the project-reference graph is collected from MSBuild .csproj files only, and this repository commits none — so there was no graph to read, and re-running the same commit reads the same nothing. No module manifest of any ecosystem this engine recognises was found either, so the collector owed is one that derives the module graph from the source's own imports. That is a COLLECTOR gap in this analyzer — no change to the scan image closes it — and 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.
  • C2 Access Controls — 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 authorization 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 authorization controls.
  • C3 Audit Trail — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. These audit 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 audit controls.
  • C4 Data Retention — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. These retention 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 retention controls.
  • C5 Data-Subject Rights — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. These data-subject rights 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 data-subject rights controls.
  • ED5 Idempotency — 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 finds retry-prone mutations by walking the repository's declared types, and NONE was loaded on this run, so it had nothing to look at. That is a limit of the analyzer's reach — it reads .NET projects — not a finding that this repository has no command handlers or message consumers.
  • 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 is implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • 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 is implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • 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.
  • X1 Async 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 is implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • X12 Unreachable branch — 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 implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • X13 Undrained process stream — 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 implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • X14 Bypassable address classification — 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 implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • X15 Unvalidated length from an untrusted reader — 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 implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • 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 is implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • X17 Uncapped recursion over a caller-supplied document — 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 implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • 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 is implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • X19 Unrestored process-global state — 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 implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • X2 Cancellation propagation — 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 implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • X20 Mistyped argument guard — 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 implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • X21 Side-effecting pattern guard — 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 implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • X22 Contradicted release guard — 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 implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • X23 Unguarded diagnostic materialisation — 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 implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • X24 Document value interpolated into markup unescaped — 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 implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • X25 Inert configuration knob — 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 implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • X26 Unsynchronised callback handoff — 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 implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • X27 Collection changed while being enumerated — 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 implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • X28 Index access outside its own emptiness guard — 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 implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • 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 is implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • X3 Exception handling — 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 implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • X30 Support guard that admits what it rejects — 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 implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • X32 Type resolved by simple name across every loaded assembly — 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 implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • 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 is implemented over the C# syntax tree, and no C# was loaded on this run, 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.
  • 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 is implemented over the C# syntax tree, and no C# was loaded on this run, 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.

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.
  • D8 Code Coverage: Coverage is measured by building and running the test suite inside Watchdog's isolated image — the target repo is never modified, and nothing on your systems runs. So coverage exists only when the suite builds and runs within the inline time budget; one that needs external services, can't build, or exceeds the budget yields no coverage (D8 then degrades to not-measured, not a low score). Line coverage also says nothing about assertion quality.
  • 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.
  • D12 Dependency Hygiene: Dependency health reads manifests and lockfiles — a vulnerability in a vendored/copied dependency, or risk from how a dependency is actually used, is outside this view.
  • D13 Secret Scanning: Secret detection is signature- and entropy-based on the current tree — a secret that does not match a known pattern, or one already rotated, will not be flagged (a clean scan is "nothing matched", not "no secrets exist").
  • D14 License Compliance: License compatibility is checked against declared package metadata and a policy — mislabelled or missing license metadata, and obligations that depend on how you distribute, are not resolved here.
  • D15 Churn × Complexity Hotspots: Churn hotspots come from git history — a freshly imported or squashed repository has no churn signal, and recent rewrites can mask a historically risky file.
  • D17 Explicit Debt: Acknowledged-debt signals (TODO/FIXME, suppressions, dead code) are textual — undocumented debt that nobody marked, and debt that lives in design rather than annotations, is invisible. Committed machine-written code (scaffolded migrations, designer/codegen output, generated stubs) is excluded — it is never the team's dead code to delete.
  • D19 Documentation Quality: Documentation quality is judged by an LLM over a bounded sample of docs — it reads what is written, not whether the docs match the running system, and it is advisory, not a measurement. Its critique rows are drawn from a closed category vocabulary and each row means the same thing in every run, so two scans can be compared row by row; the SET that fires is still a sample, and does not repeat exactly. Measured on one frozen input, six scans at one engine SHA: 2-5 critique rows per scan, 8 distinct rows across the six, 3 of those 8 seen in only one scan. So a D19 row is evidence about the documentation, but a COUNT of D19 rows is not a quantity — never read a change in it as an improvement or a regression.
  • 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.
  • 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").
  • D30 Dependency Vulnerabilities: CVE matching depends on accurate package/version metadata and on the advisory databases — a vulnerability with no published advisory, or in code not declared as a dependency, is not seen. Coverage needs a RESOLVED graph: an unpinned REDACTED, or a pom without a resolved build, yields partial coverage rather than a clean verdict. An ecosystem the analyzer cannot scan is reported as unmeasured, never as clean.
  • 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.
  • D43 Malicious Dependencies: Only packages some vulnerability database has already NAMED as malicious are seen — a compromise published in the last hours, or never reported at all, is invisible here, and this dimension reading 10 is not evidence that a dependency is trustworthy. There is no typosquat or dependency-confusion analysis: a package nobody has reported is simply absent from the feeds. Coverage is the dependency scan's: an ecosystem that could not be scanned is disclosed as unmeasured, never as clean.
  • 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.

The LLM boundary

LLM-set scores this run (3): D19, D21, 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 Complexity7.6 / 10Strong✓ 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 7.6 / 10 · rule-coverage 100% · ceiling Prevented

8 method(s) exceeded the cyclomatic complexity threshold of 15; the worst was train_ppo.ppo_train_epoch at 29.

train_ppo.ppo_train_epoch (cyclomatic 29)trainer/train_ppo.py:78
train_agent.rl_train_epoch (cyclomatic 26)trainer/train_agent.py:242
train_agent.calculate_rewards (cyclomatic 25)trainer/train_agent.py:188
train_grpo.grpo_train_epoch (cyclomatic 23)trainer/train_grpo.py:71
train_distillation.train_epoch (cyclomatic 19)trainer/train_distillation.py:39

+ 3 more group(s) — more in Appendix A; the complete list is findings.md.

What to do

  1. Resolve the 1 train_ppo.ppo_train_epoch (cyclomatic 29) finding(s) in Cyclomatic Complexity — start with train_ppo.py. — One of this dimension's main actionable groups (1 warning-level).
  2. Resolve the 1 train_agent.rl_train_epoch (cyclomatic 26) finding(s) in Cyclomatic Complexity — start with train_agent.py. — One of this dimension's main actionable groups (1 warning-level).
  3. Resolve the 1 train_agent.calculate_rewards (cyclomatic 25) finding(s) in Cyclomatic Complexity — start with train_agent.py. — One of this dimension's main actionable groups (1 warning-level).
  4. Stand up a CI pipeline, then gate Cyclomatic Complexity in it to reach Verified (currently Documented). — This repository has no CI pipeline, so there is nothing to add a gate to yet — the pipeline comes first. Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.

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

D2 · Cognitive Complexity2.9 / 10Weak✓ Tool-verified

What it measures: How hard the code is for a person to follow, beyond raw branching.

Method: Cognitive complexity per method (Sonar-style nesting-penalized score), computed exhaustively over production code, excluding test projects. Deterministic.

Maturity: Documented → Verified → Prevented · effective 2.9 / 10 · rule-coverage 100% · ceiling Prevented

20 method(s) exceeded the cognitive complexity threshold of 15; the worst was train_ppo.ppo_train_epoch at 73.

train_ppo.ppo_train_epoch (cognitive 73)trainer/train_ppo.py:78
train_agent.calculate_rewards (cognitive 67)trainer/train_agent.py:188
train_agent.rl_train_epoch (cognitive 52)trainer/train_agent.py:242
train_grpo.grpo_train_epoch (cognitive 44)trainer/train_grpo.py:71
eval_toolcall.chat_api (cognitive 42)scripts/eval_toolcall.py:133

+ 15 more group(s) — more in Appendix A; the complete list is findings.md.

What to do

  1. Resolve the 1 train_ppo.ppo_train_epoch (cognitive 73) finding(s) in Cognitive Complexity — start with train_ppo.py. — One of this dimension's main actionable groups (1 warning-level).
  2. Resolve the 1 train_agent.calculate_rewards (cognitive 67) finding(s) in Cognitive Complexity — start with train_agent.py. — One of this dimension's main actionable groups (1 warning-level).
  3. Resolve the 1 train_agent.rl_train_epoch (cognitive 52) finding(s) in Cognitive Complexity — start with train_agent.py. — One of this dimension's main actionable groups (1 warning-level).
  4. Stand up a CI pipeline, then gate Cognitive Complexity in it to reach Verified (currently Documented). — This repository has no CI pipeline, so there is nothing to add a gate to yet — the pipeline comes first. Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.

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

D3 · God 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.

D4 · Code Duplication8.4 / 10Strong✓ 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 8.4 / 10 · rule-coverage 100% · ceiling Verified

20 duplicated block group(s) detected. A further 2 rows report members as variants of one another; they aggregate block groups already counted above and are not themselves counted.

Duplicated block (5 lines × 2) · ×6eval_llm.py:24
Duplicated block (8 lines × 2) · ×2trainer/train_ppo.py:237
Duplicated block (7 lines × 2) · ×2trainer/train_agent.py:316
Duplicated block (6 lines × 2) · ×2dataset/lm_dataset.py:75
Near-duplicate member family (3 members, 30 shared lines)trainer/train_full_sft.py:25

+ 9 more group(s) — more in Appendix A; the complete list is findings.md.

What to do

  1. Resolve the 6 Duplicated block (5 lines × 2) finding(s) in Code Duplication — start with eval_llm.py (2), train_agent.py (2), convert_model.py. — One of this dimension's main actionable groups (6 warning-level).
  2. Resolve the 2 Duplicated block (8 lines × 2) finding(s) in Code Duplication — start with train_ppo.py, train_distillation.py. — One of this dimension's main actionable groups (2 warning-level).
  3. Resolve the 2 Duplicated block (7 lines × 2) finding(s) in Code Duplication — start with train_agent.py, web_demo.py. — One of this dimension's main actionable groups (2 warning-level).
  4. Stand up a CI pipeline, then gate Code Duplication in it to reach Verified (currently Documented). — This repository has no CI pipeline, so there is nothing to add a gate to yet — the pipeline comes first. 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.

D8 · Code Coverage0.0 / 10Critical✓ Tool-verified

What it measures: How much of the code is actually exercised by tests.

Method: Coverage from coverlet runs or committed reports (Cobertura/OpenCover/lcov), computed per-file with structured exclusions for generated, trivial, and glue code. When the suite can't be built/run in-image AND no report is committed, coverage is reported NOT-MEASURED (excluded from the score) with the precondition to make it measurable — never a LoC-ratio proxy folded in as if measured. Deterministic.

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

No automated tests — no test code was found in this repository.

No automated tests

What to do

  1. Resolve the 1 No automated tests finding(s) in Code Coverage. — One of this dimension's main actionable groups (1 issue-level).
  2. Stand up a CI pipeline, then gate Code Coverage in it to reach Verified (currently Documented). — This repository has no CI pipeline, so there is nothing to add a gate to yet — the pipeline comes first. Hardens enforcement from Documented toward Verified — provenance only; does not change the score.

Detailed fixes: d8_recommendation.md · top locations in Appendix A, every location in findings.md.

D9 · Test Distribution0.0 / 10Critical✓ 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 0.0 / 10 · rule-coverage 100% · ceiling Documented

No test suite found.

No tests found

What to do

  1. Resolve the 1 No tests found finding(s) in Test Distribution. — One of this dimension's main actionable groups (1 recommendation-level).

Detailed fixes: d9_recommendation.md · top locations in Appendix A, every location in findings.md.

D12 · Dependency Hygiene8.6 / 10Strong✓ Tool-verified

What it measures: Whether dependencies are current, secure, and not bloated.

Method: Manifest scan via dotnet list package across all projects; worst-signal-per-package deduction (saturating for vulnerabilities, capped-linear for deprecation/outdated) per KLoC. Exhaustive, deterministic.

Maturity: Documented → Verified → Prevented · effective 8.6 / 10 · rule-coverage 100% · ceiling Verified

28 outdated, 0 yanked pinned Python distributions. Only EXACT pins are graded for currency: a floor or a range already installs the newest release it admits, so reporting one would report this repository for being current. Whether a deliberate upper cap has itself gone stale is a different and weaker question, and is not asked. Whether any distribution is UNMAINTAINED is not graded — PyPI publishes no maintenance status, and release age does not stand in for one. Lockfiles are not read (poetry.lock, uv.lock, Pipfile.lock, pdm.lock), so a lock-resolved install is outside this verdict. Known CVEs in this dependency graph are D30's question.

Outdated: datasets · ×28

What to do

  1. Stand up a CI pipeline, then gate Dependency Hygiene in it to reach Verified (currently Documented). — This repository has no CI pipeline, so there is nothing to add a gate to yet — the pipeline comes first. Hardens enforcement from Documented toward Verified — provenance only; does not change the score.

Detailed fixes: d12_recommendation.md · top locations in Appendix A, every location in findings.md.

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

What it measures: Whether any secrets (keys, tokens, passwords) have leaked into the code.

Method: In-process native secret scanner (entropy plus signature patterns) across all tracked files; no external tool. A clean result is a measured 10, not no-data zero. Deterministic.

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

Secret scan ran and found no leaked secrets.

✓ On the Gold path — maintain.

Detailed fixes: d13_recommendation.md.

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

What it measures: Whether the licenses of third-party packages are compatible with your policy.

Method: Third-party package licenses resolved from declared package metadata and checked against the configured policy (allow/deny/copyleft). Deterministic; clean = no incompatible license found at metadata depth.

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

0 of 147 shipped Python distribution(s) use a banned license. Licences were resolved from PyPI over the distributions a consumer installs — this repository's 31 declared runtime requirement(s) closed transitively over each distribution's published `requires_dist` (116 reached that way). Requirements it states ONLY under an extra, a PEP 735 dependency group, a Poetry dev group or a dev-named requirements file are excluded: pip does not install any of them for a consumer. ★ This repository commits no dependency lockfile that this pass reads, so each licence is the one PyPI publishes for the distribution's CURRENT release rather than for a pinned version. 16 of them publish no licence on PyPI this pass can read; that is missing data, not a violation, and none of them is charged.

✓ On the Gold path — maintain.

Detailed fixes: d14_recommendation.md.

D15 · Churn × Complexity Hotspots8.6 / 10Strong✓ 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 8.6 / 10 · rule-coverage 100% · ceiling Documented

Top hotspots: trainer/train_ppo.py (9×29=261); trainer/train_agent.py (8×26=208); trainer/train_grpo.py (7×23=161) Repeated repair below the complexity floor: eval_llm.py (3 of 5 changes were fixes); scripts/serve_openai_api.py (3 of 3 changes were fixes)

Hotspot: trainer/train_ppo.py · ×6trainer/train_ppo.py:78
Repeated repair: eval_llm.py · ×2eval_llm.py:32

What to do

  1. Resolve the 6 Hotspot finding(s) in Churn × Complexity Hotspots — start with train_ppo.py, train_agent.py, train_grpo.py. — One of this dimension's main actionable groups (6 warning-level).
  2. Resolve the 2 Repeated repair finding(s) in Churn × Complexity Hotspots — start with eval_llm.py, serve_openai_api.py. — One of this dimension's main actionable groups (2 warning-level).

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

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 4097 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.

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 overview of the project: it states what MiniMind is (a 64M tiny language model trained for 2 hours at $3 cost), lists its features (MoE, data cleaning, SFT/LoRA/RLHF/Tool Use/Agentic RL/蒸馏), names the models (MiniMind-V, MiniMind-O, dLM, Linear), and explains licensing. It also describes a quick-start flow with installation steps ('克隆仓库、安装依赖') and outlines model-training phases ('Ⅰ 🚀 模型推理; Ⅱ 🛠️ 模型训练'). The README is complete for its role as an overview but the body ends in a clipped inline outline, so the detailed usage, architecture, and contribution docs are not flagged.

What to do

  1. Improve Documentation Quality — currently 7.0/10. — The repository's root README is a well-written overview of the project: it states what MiniMind is (a 64M tiny language model trained for 2 hours at $3 cost), lists its features (MoE, data cleaning, SFT/LoRA/RLHF/Tool Use/Agentic RL/蒸馏), names the models (MiniMind-V, MiniMind-O, dLM, Linear), and explains licensing. It also describes a quick-start flow with installation steps ('克隆仓库、安装依赖') and outlines model-training phases ('Ⅰ 🚀 模型推理; Ⅱ 🛠️ 模型训练'). The README is complete for its role as an overview but the body ends in a clipped inline outline, so the detailed usage, architecture, and contribution docs are not flagged.

Detailed fixes: d19_recommendation.md.

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.

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

What it measures: Whether any secrets were ever committed — scanned across the full git history, not just now.

Method: Secret scan via TWO gitleaks detect passes in an isolated checkout — the full git history, then a second --no-git pass over the working tree as it stands — merged and de-duplicated by (rule, file, line); each match flagged High. Both invocations are recorded in the audit trail. Exhaustive; when the tool is absent, or when its output cannot be parsed into the expected shape, the dimension is WITHHELD as an explicit measurement gap on our side — unscored and excluded from the lens, never a hedged middling score.

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

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

✓ On the Gold path — maintain.

Detailed fixes: d28_recommendation.md.

D29 · Static Analysis (SAST)10.0 / 10Exemplary○ Nothing flagged

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

semgrep found no security issues.

✓ On the Gold path — maintain.

Detailed fixes: d29_recommendation.md.

D30 · Dependency Vulnerabilities5.3 / 10Adequate✓ Tool-verified

What it measures: Whether any dependency has a known published vulnerability (CVE), direct or transitive, in ANY ecosystem the repository declares — Dart pub, Elixir/Hex, Go modules, Java and Kotlin via Maven/Gradle, JavaScript/npm, .NET/NuGet, PHP/Composer, Python/PyPI, RubyGems, Rust/Cargo and Swift.

Method: Dependency-CVE scan across every ecosystem the repository declares, scored ONCE. Three sources are unioned and deduplicated by advisory identity (rule id + alias closure, CVE<->GHSA) scoped to package+version, keeping the worst severity: `osv-scanner --recursive` over osv.dev for Dart pub, Elixir/Hex, Go, Java and Kotlin via Maven/Gradle, npm, PHP/Composer, Python/PyPI, RubyGems, Rust/Cargo and Swift; `trivy fs --scanners vuln` for npm lockfiles; and `dotnet list package --vulnerable --include-transitive` for NuGet (with per-advisory collapse of the project x target-framework fan-out), plus a DECLARED-dependency arm that resolves a published gem's gemspec against rubygems.org where no Gemfile.lock is committed. `SeverityScore(c,h,m,l, normalizer 8.0)`. NotApplicable only when NO ecosystem is readable; if any applicable ecosystem could not be scanned the findings are REPORTED and the score is withheld. Supersedes the npm and OSV arms, retired 2026-09-05.

Maturity: Documented → Verified → Prevented · effective 5.3 / 10 · rule-coverage 100% · ceiling Documented

10 finding(s): 1 critical, 4 high, 5 medium, 0 low.

REDACTED
REDACTED
REDACTED

What to do

  1. Resolve the 5 Medium CVE finding(s) in Dependency Vulnerabilities — start with REDACTED (5). — One of this dimension's main actionable groups (5 warning-level).
  2. Resolve the 4 High CVE finding(s) in Dependency Vulnerabilities — start with REDACTED (4). — One of this dimension's main actionable groups (4 issue-level).
  3. Resolve the 1 Critical CVE finding(s) in Dependency Vulnerabilities — start with REDACTED. — One of this dimension's main actionable groups (1 issue-level).

Detailed fixes: d30_recommendation.md · top locations in Appendix A, every location in findings.md.

D34 · Knowledge Freshness10.0 / 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 10.0 / 10 · rule-coverage 100% · ceiling Documented

Every significant source file has living knowledge — recently and meaningfully worked. Counted over 19 of the 22 production source files in this repository: the rest are under the ~2,400-byte size floor this dimension measures over.

✓ On the Gold path — maintain.

Detailed fixes: d34_recommendation.md.

D35 · Change Coupling9.6 / 10Stronggated by 1 serious finding✓ 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 9.6 / 10 · rule-coverage 100% · ceiling Documented

Strongest change-coupling: train_grpo.py↔train_ppo.py 73%

Change coupling: train_grpo.py ↔ train_ppo.pytrainer/train_grpo.py

What to do

  1. Resolve the 1 Change coupling finding(s) in Change Coupling — start with train_grpo.py. — One of this dimension's main actionable groups (1 warning-level).

Detailed fixes: d35_recommendation.md · top locations in Appendix A, every location in findings.md.

D43 · Malicious Dependencies10.0 / 10Exemplary✓ Tool-verified

What it measures: Whether any dependency the repository declares is published as MALICIOUS rather than merely vulnerable — a package that is an attacker's work, in any ecosystem osv-scanner reads. Scored apart from D30 because the answer is binary: there is no safe version to upgrade to, and the fix is to remove the package and rotate every credential it could have read.

Method: The same dependency scan D30 reads, partitioned on the scanner's own classification rather than rescanned: a row is MALICIOUS when its id is in the `MAL-` space (the ossf/malicious-packages feed) OR its `database_specific.cwe_ids` carries `CWE-506` ("Embedded Malicious Code"). Both channels are structural; the summary text is deliberately NOT read, because a malicious-package record whose summary says only "Critical severity vulnerability" is a real shape ([GHSA redacted]) and a text matcher misses it. Scored BINARY: any surviving row is 0, whatever its severity and however many CVEs sit beside it — a hostile dependency is not a quantity. Applicability and degradation are D30's: NotApplicable only when no ecosystem is readable, and an unscannable ecosystem degrades rather than reading clean. SCORED, not informational.

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

No dependency in any ecosystem this repository declares is published as malicious.

✓ On the Gold path — maintain.

Detailed fixes: d43_recommendation.md.

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

What it measures: Whether anyone still ships security patches for the platform this repository RUNS ON — the runtime it pins and the framework majors its own constraints hold it to. Separate from D12 because the question differs: a current Django on an end-of-life Python is perfectly up to date and completely unsupported, and the fix is a migration rather than a version bump. What the repository says it merely SUPPORTS is never charged.

Method: End-of-life PLATFORM read from the repository's own declarations and graded against a FROZEN, dated table of vendor support dates — no network, no feed, no API, so this dimension answers identically inside a closed scan fence. Two subjects: a RUNTIME the project pins (a single or all-end-of-life TargetFramework, a .nvmrc or .python-version, a requires-python CAP) and a FRAMEWORK major a dependency constraint cannot move off (a caret, tilde or exact version; `vue@^2.7.16` pins Vue 2). A FLOOR is deliberately never charged — `requires-python = ">=3.8"` states what a package SUPPORTS, not what it runs on — and a multi-target project is charged only when EVERY target is out of support. Runtime 4.0/product capped 8.0, framework 1.5 capped 4.5. The table is safe to freeze because a statement about support that ended in the past cannot become false: it loses recall as it ages, never precision, and a test asserts every entry predates the freeze date. Disjoint from D31 (a container image's OS layer) and D29 (the toolchain a CI workflow installs). Abstains when the repository declares no platform this pass reads — never scores it clean.

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

0 end-of-life runtime(s) and 0 end-of-life framework(s), read from 0 platform declaration(s) and 31 dependency declaration(s). This dimension reads what the repository says about ITSELF — a pinned target framework, a version file, a capped requires-python, a framework major a constraint cannot move off. A FLOOR is deliberately never charged: `requires-python = ">=3.8"` states what the package SUPPORTS, not what it runs on, and a well-maintained library declares exactly that while running its own CI on a current release. The end-of-life facts are FROZEN and dated, so this dimension needs no network and answers identically inside a closed scan fence; as the table ages it loses recall and never precision, because a statement about support that ended in the past cannot become false. The OS layer of a container image is D31's question and the toolchain a CI workflow installs is D29's; this row is neither.

✓ On the Gold path — maintain.

Detailed fixes: d44_recommendation.md.

Frontend & cross-cutting dimensions

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

AX10 · Code 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.
M1 · Documentation (README)6.7 / 10Adequate✓ Tool-verified

Maturity · Maturity — Whether the repo and its projects have a README, and whether it's substantive and current.

Method: Filesystem scan: README presence, word count, and headings for depth; git history for staleness. Exhaustive across root and project dirs, deterministic.

What to do

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

Maturity · Maturity — Whether key decisions (ADRs) and the high-level shape (C4/diagrams) are written down.

Method: Filesystem scan: ADR folder/naming conventions or content, plus Mermaid/PlantUML/C4/architecture.md discovery. Exhaustive, deterministic.

  • No Architecture Decision Records found — no conventional ADR directory, no numbered `NNNN-title` documents in any markup this check reads, and nothing ADR-shaped by content. Design rationale recorded elsewhere (a design-notes tree, a mailing list, pull-request discussion) is not visible to this check and is not re-findable per decision, so a future maintainer cannot ask why one choice was made and get an answer.

What to do

  • Record significant decisions one document per decision — dated, stating the context, the decision and its consequences — and keep them together wherever your design docs already live (a conventional `docs/adr/` tree, with each file named `NNNN-title` in whatever markup those docs already use, is the most discoverable form).
M3 · Folder & project structure6.0 / 10Adequate✓ 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'.
  • No test surface was found — this check walked the tree for authored source in the languages it models (`.cs`, `.vb`, `.fs`, `.java`, `.kt`, `.scala`, `.py`, `.php`, `.rb`, `.ex`, `.exs`, `.go`, `.erl`, `.hrl`, `.swift`, `.dart`, `.rs`, `.ts`, `.tsx`, `.mts`, `.cts`) and found none of it test-shaped. ★ `.js`, `.jsx`, `.mjs` and `.cjs` are NOT in that walk, so a Jest or Mocha suite written in plain JavaScript is invisible to it and this row is then wrong. If that is your case, say so rather than moving anything. Otherwise there are no tests here to separate from production code, so the folder question hasn't been reached yet.

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.
  • Start a test surface where your build system looks for one (tests/, test/, spec/, or your ecosystem's test source set) — the separation follows from putting the first tests in the right place.
M4 · Documentation accuracy1.0 / 10Critical◐ 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 claims MiniMind-V and MiniMind-O vision while only minimind-v1-moe exists — searched for: `MiniMind-V`, `MiniMind-O`. Each was matched case- and separator-insensitively against file and directory NAMES anywhere in the tree, and against the CONTENTS of manifest files (package.json, *.csproj, *.props, *.slnx, *.yml, Dockerfile); the README's own prose never counts, so a claim is never refuted by merely being made. Nothing outside that search was read — a footprint living only in a submodule, in a file type not listed here, or under a name none of those terms matches is not seen, and this row is then wrong.
  • claims dLM/diffusion model but no such branch exists — searched for: `dLM`, `diffusion`. Each was matched case- and separator-insensitively against file and directory NAMES anywhere in the tree, and against the CONTENTS of manifest files (package.json, *.csproj, *.props, *.slnx, *.yml, Dockerfile); the README's own prose never counts, so a claim is never refuted by merely being made. Nothing outside that search was read — a footprint living only in a submodule, in a file type not listed here, or under a name none of those terms matches is not seen, and this row is then wrong.

What to do

  • Reconcile the README with reality: README claims MiniMind-V and MiniMind-O vision while only minimind-v1-moe exists; claims dLM/diffusion model but no such branch exists.
P1 · CI/CD gates0.0 / 10Critical✓ Tool-verified

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.

  • No CI workflow found (.github/workflows, azure-pipelines.yml, .gitlab-ci.yml, …) — changes aren't gated by an automated build/test.

What to do

  • Add a CI workflow that builds and runs the test suite on every push/PR.
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 — this repository has no CI pipeline yet, so run it locally to clear the existing findings, then make it a step of the first workflow you add so a regression fails the build. What was searched, so you can tell an absence from a miss: the 0 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

  • Run what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — locally for now, since there is no CI pipeline here yet, and as a step of the first workflow you add 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.

Reference — by lens

The score is the rank-weighted fold of these lenses (worst-heaviest), each including its meta-dimensions; a lens with a Critical contributor is capped at Fair (its band reads "gated by …") and is never the strongest area however high its average.

LensScoreRatingImpact
Code Health84%Adequate — gated by D2Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Architecture98%ExemplaryStrongest area.
Maturity38%Weak — gated by M2, M4Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Readiness22%Critical — gated by D8, D9, P1, P3Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Security91%ExemplarySolid.
Not evidenced — 6 control(s) we could not find positive evidence for

These checks grade a working control, and the repository shows no evidence of one. That is deliberately not scored as a zero: a repository cannot show an ops runbook, a database TTL or an infrastructure-side audit log, so absence of evidence here is not evidence the control is missing. It is also not a statement that the check is irrelevant to this codebase — the thing it grades applies; we just could not see it. Excluded from the score either way.

  • C3 Audit Trail — Not assessed: these audit 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 audit 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.
  • C4 Data Retention — Not assessed: these retention 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 retention 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.
  • C5 Data-Subject Rights — Not assessed: these data-subject rights 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 data-subject rights 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.
  • P4 Deployment & Rollback — not evidenced — no deploy/rollback/approval signal in the repo; absence of evidence is not evidence of a manual release
  • P5 DR & Backup — not evidenced — repo shows no backup/RTO/RPO controls; absence of evidence is not evidence of a working control
  • P6 Release Hygiene — not evidenced — no changelog, version stamp or semver release tag in the repo
Not included — 81 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 — no DI registrations detected
  • AX2 Stateful singletons — no singleton implementations detected
  • AX3 Project dependency cycles — not assessed — project cycles and dependency direction are computed over a project-reference graph that was not loaded for this repository, because the repository commits no project file of a kind this check models. This is a gap in the analyzer, not a finding about this repository
  • AX4 Dependency direction — not assessed — project cycles and dependency direction are computed over a project-reference graph that was not loaded for this repository, because the repository commits no project file of a kind this check models. This is a gap in the analyzer, not a finding about this repository
  • 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 — not assessed — test isolation is computed from a project graph (which projects are test projects, and what they reference) that was not loaded for this repository, because the repository commits no project file of a kind this check models. This is a gap in the analyzer, not a finding about this repository
  • AX9 CQS / query purity — no CQRS query handlers detected — query purity is not applicable to this codebase
  • 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 — Not assessed: these authorization 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 authorization 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.
  • D10 Test Quality — No tests were found in the analyzed repository to assess for quality.
  • D11 Test Reliability — No test suite was found to re-run, so reliability couldn't be assessed. Two searches produced that zero and both came back empty: the classifier that reads the loaded workspace recognised no suite it could run, and a walk of the source on disk — which covers the JS/TS `*.test.*` and `*.spec.*` conventions and probes for a Pester suite — found no test source in any other ecosystem either. Neither search reaches a suite that is missing from the loaded workspace and carries no name either walk recognises, so this is 'no suite found by those two searches', not a verdict that the repository has none.
  • D16 Bus Factor — single-maintainer repository — bus factor is not applicable
  • D18 Solution Shape — D18 scores the shape of a .NET solution; this repository has no .NET solution or project files, so the dimension does not apply.
  • D20 ADR Quality — Repository kind not determined
  • D22 Internal API Consistency — No intentionally-exposed public API to evaluate for consistency.
  • D23 Boundary Type-Coupling — Production source is present (.py) but bounded contexts are resolved over the C#/VB project set, which exposed none, so context scope could not be assessed. Not scored — this is a gap in the analyzer, not a verdict about this repository. Declaring the codebase's bounded contexts (≥2) would let cross-boundary type coupling be assessed — see the recommendation on this dimension for where. 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
  • D26 Project Cohesion — Project cohesion is assessed over the .NET project set; this target exposed no projects, so project size and spread could not be assessed. Not scored — this is a gap in the analyzer's reach, not a verdict about this repository.
  • D27 Navigability — symbol resolution incomplete — navigability not assessed
  • 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.
  • D36 Supply-chain Provenance & Signing — No CI/build pipeline found (.github/.forgejo/.gitea workflows, .circleci, .buildkite, .woodpecker, .teamcity, .gitlab-ci.yml, .travis.yml, bitbucket-pipelines.yml, .drone.yml, .cirrus.yml, .woodpecker.yml, appveyor.yml, azure-pipelines*.yml, .pipelines/, .vsts-ci/, .azuredevops/, Jenkinsfile); there is no build to attest provenance for.
  • 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.
  • D5 Coupling — Inter-project coupling could not be assessed — no analyzable project graph was found for this repository. Not scored: a gap in the analyzer's reach, not a verdict about this repository. (Coupling here is Martin afferent/efferent/instability plus reference cycles across a project-reference graph, read today from .NET project files; other ecosystems' module graphs are not read yet.)
  • D6 Cohesion (LCOM4) — Cohesion (LCOM4) is measured over a CS/VB/GO/SCALA/SWIFT/DART class graph, and this repository's production source is .py, which this pass does not read — so no class could be assessed. Not scored — this is a gap in the analyzer, not a finding about this repository.
  • 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
  • DM1 Domain Modelling — not scored — this repository shows only 1 of the 3 signals this lens looks for (2 value object(s))
  • ED1 Event-Driven — not scored — this repository shows none of the 3 signals this lens looks for
  • ED5 Idempotency — This check finds retry-prone mutations (command handlers and message/event consumers) by walking the repository's declared types, and none was loaded here, so it has nothing to report. That is a limit of the analyzer, not a finding about your code.
  • 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# syntax, and none was loaded for this repository. 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 — no CI workflow found
  • P2 Observability — Observability was not assessed: this check reads a source model that does not carry this repository's product — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of a logging idiom this check recognises is NOT evidence that this repo lacks structured logging (it may log through its own ecosystem's logger). This is a gap in the analyzer, not a finding about this repository.
  • P7 Outbound HTTP resilience — not measured — the application kind could not be determined for this repo
  • P8 Schema migrations — not assessed — schema-migration practice is read from 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
  • 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 — Performance was not assessed: this lens reads a source model that was not loaded for this repository, because the repository is written in a language this lens does not yet model or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository — in particular it is NOT a statement that this repo is unpackaged or performance-careless.
  • PF2 Allocation hygiene — Performance was not assessed: this lens reads a source model that was not loaded for this repository, because the repository is written in a language this lens does not yet model or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository — in particular it is NOT a statement that this repo is unpackaged or performance-careless.
  • PF3 Async & latency hygiene — Performance was not assessed: this lens reads a source model that was not loaded for this repository, because the repository is written in a language this lens does not yet model or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository — in particular it is NOT a statement that this repo is unpackaged or performance-careless.
  • 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.
  • X1 Async 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
  • X12 Unreachable branch — This check reads C# syntax; no C# was loaded for this repository, so it has nothing to report. That is a limit of the analyzer, not a finding about your code.
  • X13 Undrained process stream — This check reads C# syntax; no C# was loaded for this repository, so it has nothing to report. That is a limit of the analyzer, not a finding about your code.
  • X14 Bypassable address classification — This check reads C# syntax; no C# was loaded for this repository, so it has nothing to report. That is a limit of the analyzer, not a finding about your code.
  • X15 Unvalidated length from an untrusted reader — This check reads C# syntax; no C# was loaded for this repository, so it has nothing to report. That is a limit of the analyzer, not a finding about your code.
  • X16 Unfloored truncation loop — This check reads C# syntax; no C# was loaded for this repository, so it has nothing to report. That is a limit of the analyzer, not a finding about your code.
  • X17 Uncapped recursion over a caller-supplied document — This check reads C# syntax; no C# was loaded for this repository, so it has nothing to report. That is a limit of the analyzer, not a finding about your code.
  • X18 Disposal-pattern correctness — This check reads C# syntax; no C# was loaded for this repository, so it has nothing to report. That is a limit of the analyzer, not a finding about your code.
  • X19 Unrestored process-global state — This check reads C# syntax; no C# was loaded for this repository, so it has nothing to report. That is a limit of the analyzer, not a finding about your code.
  • X2 Cancellation propagation — 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
  • X20 Mistyped argument guard — This check reads C# syntax; no C# was loaded for this repository, so it has nothing to report. That is a limit of the analyzer, not a finding about your code.
  • X21 Side-effecting pattern guard — This check reads C# syntax; no C# was loaded for this repository, so it has nothing to report. That is a limit of the analyzer, not a finding about your code.
  • X22 Contradicted release guard — This check reads C# syntax; no C# was loaded for this repository, so it has nothing to report. That is a limit of the analyzer, not a finding about your code.
  • X23 Unguarded diagnostic materialisation — This check reads C# syntax; no C# was loaded for this repository, so it has nothing to report. That is a limit of the analyzer, not a finding about your code.
  • X24 Document value interpolated into markup unescaped — This check reads C# syntax; no C# was loaded for this repository, so it has nothing to report. That is a limit of the analyzer, not a finding about your code.
  • X25 Inert configuration knob — This check reads C# syntax; no C# was loaded for this repository, so it has nothing to report. That is a limit of the analyzer, not a finding about your code.
  • X26 Unsynchronised callback handoff — This check reads C# syntax; no C# was loaded for this repository, so it has nothing to report. That is a limit of the analyzer, not a finding about your code.
  • X27 Collection changed while being enumerated — This check reads C# syntax; no C# was loaded for this repository, so it has nothing to report. That is a limit of the analyzer, not a finding about your code.
  • X28 Index access outside its own emptiness guard — This check reads C# syntax; no C# was loaded for this repository, so it has nothing to report. That is a limit of the analyzer, not a finding about your code.
  • X29 Per-element action decided by a fixed element — This check reads C# syntax; no C# was loaded for this repository, so it has nothing to report. That is a limit of the analyzer, not a finding about your code.
  • X3 Exception handling — 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
  • X30 Support guard that admits what it rejects — This check reads C# syntax; no C# was loaded for this repository, so it has nothing to report. That is a limit of the analyzer, not a finding about your code.
  • X32 Type resolved by simple name across every loaded assembly — This check reads C# syntax; no C# was loaded for this repository, so it has nothing to report. That is a limit of the analyzer, 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
  • 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 — 6 finding(s)
D30 · Dependency Vulnerabilities · High CVE · ×4
  • REDACTED
  • REDACTED
  • REDACTED
  • REDACTED
D30 · Dependency Vulnerabilities · Critical CVE · ×1
  • REDACTED
D8 · Code Coverage · No automated tests · ×1
  • No automated tests — No automated tests — no test code was found in this repository. Untested code is the largest single risk to changing it safely. Start with the code you change most often: add a suite in a framework a runner can collect (pytest or unittest), and run it in CI so the gap cannot reopen.
Serious — 65 finding(s)
D15 · Churn × Complexity Hotspots · Hotspot · ×6
  • Hotspot: trainer/train_ppo.py trainer/train_ppo.py:78 — trainer/train_ppo.py changed 9 times in last 90 days, max cyclomatic complexity 29 in train_ppo.ppo_train_epoch at line 78. 4 of those changes were fix/bug commits, and the other 5 changed it for other reasons — this file is under both repair and feature pressure. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-06-25..2026-09-23, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-06-25 00:07:30 +08:00' --until='2026-09-23 00:07:30 +08:00' --full-history --no-merges -- trainer/train_ppo.py`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
  • Hotspot: trainer/train_agent.py trainer/train_agent.py:242 — trainer/train_agent.py changed 8 times in last 90 days, max cyclomatic complexity 26 in train_agent.rl_train_epoch at line 242. 4 of those changes were fix/bug commits, and the other 4 changed it for other reasons — this file is under both repair and feature pressure. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-06-25..2026-09-23, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-06-25 00:07:30 +08:00' --until='2026-09-23 00:07:30 +08:00' --full-history --no-merges -- trainer/train_agent.py`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
  • Hotspot: trainer/train_grpo.py trainer/train_grpo.py:71 — trainer/train_grpo.py changed 7 times in last 90 days, max cyclomatic complexity 23 in train_grpo.grpo_train_epoch at line 71. 3 of those changes were fix/bug commits, and the other 4 changed it for other reasons — this file is under both repair and feature pressure. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-06-25..2026-09-23, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-06-25 00:07:30 +08:00' --until='2026-09-23 00:07:30 +08:00' --full-history --no-merges -- trainer/train_grpo.py`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
  • Hotspot: model/model_minimind.py model/model_minimind.py:262 — model/model_minimind.py changed 5 times in last 90 days, max cyclomatic complexity 18 in MiniMindForCausalLM.generate at line 262. 4 of those changes were fix/bug commits, so the churn is repair rather than feature work. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-06-25..2026-09-23, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-06-25 00:07:30 +08:00' --until='2026-09-23 00:07:30 +08:00' --full-history --no-merges -- model/model_minimind.py`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
  • Hotspot: scripts/web_demo.py scripts/web_demo.py:316 — scripts/web_demo.py changed 2 times in last 90 days, max cyclomatic complexity 18 in web_demo.main at line 316. 2 of those changes were fix/bug commits, so the churn is repair rather than feature work. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-06-25..2026-09-23, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-06-25 00:07:30 +08:00' --until='2026-09-23 00:07:30 +08:00' --full-history --no-merges -- scripts/web_demo.py`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
  • Hotspot: trainer/trainer_utils.py trainer/trainer_utils.py:65 — trainer/trainer_utils.py changed 2 times in last 90 days, max cyclomatic complexity 15 in trainer_utils.lm_checkpoint at line 65. 1 of those changes was a fix/bug commit, and the other 1 changed it for other reasons — this file is under both repair and feature pressure. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-06-25..2026-09-23, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-06-25 00:07:30 +08:00' --until='2026-09-23 00:07:30 +08:00' --full-history --no-merges -- trainer/trainer_utils.py`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
D4 · Code Duplication · Duplicated block (5 lines × 2) · ×6
  • Duplicated block (5 lines × 2) eval_llm.py:24 — eval_llm.py:24-28 | scripts/serve_openai_api.py:41-45 — 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.
  • Duplicated block (5 lines × 2) scripts/convert_model.py:27 — scripts/convert_model.py:27-31 | scripts/convert_model.py:86-91 — 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. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `scripts/convert_model.py:26` calls `save_pretrained` and `scripts/convert_model.py:85` does not — after which the two agree again for 2 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
  • Duplicated block (5 lines × 2) trainer/train_agent.py:309 — trainer/train_agent.py:309-313 | trainer/train_grpo.py:119-123 — 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.
  • Duplicated block (5 lines × 2) trainer/train_agent.py:329 — trainer/train_agent.py:329-333 | trainer/train_grpo.py:144-150 — 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. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
  • Duplicated block (5 lines × 2) trainer/train_grpo.py:107 — trainer/train_grpo.py:107-111 | trainer/train_ppo.py:103-107 — 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.
  • Duplicated block (5 lines × 2) eval_llm.py:36 — eval_llm.py:36-40 | scripts/eval_toolcall.py:206-210 — 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.
D30 · Dependency Vulnerabilities · Medium CVE · ×5
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D15 · Churn × Complexity Hotspots · Repeated repair · ×2
  • Repeated repair: eval_llm.py eval_llm.py:32 — eval_llm.py changed 5 times in last 90 days and 3 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 8 (its worst body is eval_llm.main at line 32), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: align LoRA inference paths with training output”; “[fix] unify Ctrl+C handling”; “fix: eval_llm.py Ctrl+C 退出异常修复”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-06-25..2026-09-23, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-06-25 00:07:30 +08:00' --until='2026-09-23 00:07:30 +08:00' --full-history --no-merges -- eval_llm.py`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
  • Repeated repair: scripts/serve_openai_api.py scripts/serve_openai_api.py:105 — scripts/serve_openai_api.py changed 3 times in last 90 days and 3 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 14 (its worst body is serve_openai_api.generate_stream_response at line 105), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: align LoRA inference paths with training output”; “[fix] serve_openai_api 非流式端点的 max_tokens 不生效,与流式端点行为不一致”; “[fix] robustness”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-06-25..2026-09-23, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-06-25 00:07:30 +08:00' --until='2026-09-23 00:07:30 +08:00' --full-history --no-merges -- scripts/serve_openai_api.py`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
D4 · Code Duplication · Duplicated block (8 lines × 2) · ×2
  • Duplicated block (8 lines × 2) trainer/train_ppo.py:237 — trainer/train_ppo.py:237-244 | trainer/train_ppo.py:247-254 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
  • Duplicated block (8 lines × 2) trainer/train_distillation.py:122 — trainer/train_distillation.py:122-129 | trainer/train_dpo.py:106-113 — before extracting anything, compare `trainer/train_distillation.py` and `trainer/train_dpo.py` as WHOLE FILES: this scan already matched 4 separate duplicated blocks between them, totalling at least 41 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place.
D4 · Code Duplication · Duplicated block (7 lines × 2) · ×2
  • Duplicated block (7 lines × 2) trainer/train_agent.py:316 — trainer/train_agent.py:316-322 | trainer/train_grpo.py:132-138 — 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.
  • Duplicated block (7 lines × 2) scripts/web_demo.py:307 — scripts/web_demo.py:307-313 | trainer/trainer_utils.py:57-63 — 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 (6 lines × 2) · ×2
  • Duplicated block (6 lines × 2) dataset/lm_dataset.py:75 — dataset/lm_dataset.py:75-80 | dataset/lm_dataset.py:244-249 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
  • Duplicated block (6 lines × 2) scripts/web_demo.py:178 — scripts/web_demo.py:178-183 | scripts/web_demo.py:192-197 — 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. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
D1 · Cyclomatic Complexity · train_ppo.ppo_train_epoch (cyclomatic 29) · ×1
  • train_ppo.ppo_train_epoch (cyclomatic 29) trainer/train_ppo.py:78 — train_ppo.ppo_train_epoch has cyclomatic complexity 29 (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 · train_agent.rl_train_epoch (cyclomatic 26) · ×1
  • train_agent.rl_train_epoch (cyclomatic 26) trainer/train_agent.py:242 — train_agent.rl_train_epoch has cyclomatic complexity 26 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · train_agent.calculate_rewards (cyclomatic 25) · ×1
  • train_agent.calculate_rewards (cyclomatic 25) trainer/train_agent.py:188 — train_agent.calculate_rewards has cyclomatic complexity 25 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · train_grpo.grpo_train_epoch (cyclomatic 23) · ×1
  • train_grpo.grpo_train_epoch (cyclomatic 23) trainer/train_grpo.py:71 — train_grpo.grpo_train_epoch has cyclomatic complexity 23 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · train_distillation.train_epoch (cyclomatic 19) · ×1
  • train_distillation.train_epoch (cyclomatic 19) trainer/train_distillation.py:39 — train_distillation.train_epoch has cyclomatic complexity 19 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · MiniMindForCausalLM.generate (cyclomatic 18) · ×1
  • MiniMindForCausalLM.generate (cyclomatic 18) model/model_minimind.py:262 — MiniMindForCausalLM.generate 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 · web_demo.main (cyclomatic 18) · ×1
  • web_demo.main (cyclomatic 18) scripts/web_demo.py:316 — web_demo.main 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 · eval_toolcall.chat_api (cyclomatic 16) · ×1
  • eval_toolcall.chat_api (cyclomatic 16) scripts/eval_toolcall.py:133 — eval_toolcall.chat_api 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 · train_ppo.ppo_train_epoch (cognitive 73) · ×1
  • train_ppo.ppo_train_epoch (cognitive 73) trainer/train_ppo.py:78 — train_ppo.ppo_train_epoch has cognitive complexity 73 (threshold 15). Drivers by points: if/else 14 (42 pts), ternaries 5 (15 pts), loops 5 (11 pts), boolean chains 5 (nesting depth added 44). 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 · train_agent.calculate_rewards (cognitive 67) · ×1
  • train_agent.calculate_rewards (cognitive 67) trainer/train_agent.py:188 — train_agent.calculate_rewards has cognitive complexity 67 (threshold 15). Drivers by points: ternaries 13 (36 pts), if/else 7 (19 pts), loops 3 (6 pts), error handling 1 (5 pts), boolean chains 1 (nesting depth added 42). 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 · train_agent.rl_train_epoch (cognitive 52) · ×1
  • train_agent.rl_train_epoch (cognitive 52) trainer/train_agent.py:242 — train_agent.rl_train_epoch has cognitive complexity 52 (threshold 15). Drivers by points: if/else 11 (25 pts), loops 4 (10 pts), ternaries 4 (10 pts), boolean chains 7 (nesting depth added 26). 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 · train_grpo.grpo_train_epoch (cognitive 44) · ×1
  • train_grpo.grpo_train_epoch (cognitive 44) trainer/train_grpo.py:71 — train_grpo.grpo_train_epoch has cognitive complexity 44 (threshold 15). Drivers by points: if/else 10 (21 pts), loops 3 (8 pts), ternaries 3 (8 pts), boolean chains 7 (nesting depth added 21). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · eval_toolcall.chat_api (cognitive 42) · ×1
  • eval_toolcall.chat_api (cognitive 42) scripts/eval_toolcall.py:133 — eval_toolcall.chat_api has cognitive complexity 42 (threshold 15). Drivers by points: if/else 10 (29 pts), loops 3 (8 pts), ternaries 1 (4 pts), boolean chains 1 (nesting depth added 27). 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 · trainer_utils.lm_checkpoint (cognitive 40) · ×1
  • trainer_utils.lm_checkpoint (cognitive 40) trainer/trainer_utils.py:65 — trainer_utils.lm_checkpoint has cognitive complexity 40 (threshold 15). Drivers by points: if/else 10 (21 pts), ternaries 6 (17 pts), loops 1 (2 pts) (nesting depth added 23). 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 · train_distillation.train_epoch (cognitive 38) · ×1
  • train_distillation.train_epoch (cognitive 38) trainer/train_distillation.py:39 — train_distillation.train_epoch has cognitive complexity 38 (threshold 15). Drivers by points: if/else 10 (18 pts), ternaries 4 (13 pts), boolean chains 4, loops 2 (3 pts) (nesting depth added 18). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · web_demo.main (cognitive 34) · ×1
  • web_demo.main (cognitive 34) scripts/web_demo.py:316 — web_demo.main has cognitive complexity 34 (threshold 15). Drivers by points: if/else 9 (15 pts), loops 5 (11 pts), error handling 1 (4 pts), boolean chains 2, ternaries 1 (2 pts) (nesting depth added 16). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · MiniMindForCausalLM.generate (cognitive 31) · ×1
  • MiniMindForCausalLM.generate (cognitive 31) model/model_minimind.py:262 — MiniMindForCausalLM.generate has cognitive complexity 31 (threshold 15). Drivers by points: if/else 10 (18 pts), ternaries 5 (9 pts), loops 2 (4 pts) (nesting depth added 14). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · serve_openai_api.generate_stream_response (cognitive 31) · ×1
  • serve_openai_api.generate_stream_response (cognitive 31) scripts/serve_openai_api.py:105 — serve_openai_api.generate_stream_response has cognitive complexity 31 (threshold 15). Drivers by points: if/else 11 (27 pts), boolean chains 1, error handling 1, loops 1, ternaries 1 (nesting depth added 16). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · train_agent.rollout_single (cognitive 26) · ×1
  • train_agent.rollout_single (cognitive 26) trainer/train_agent.py:92 — train_agent.rollout_single has cognitive complexity 26 (threshold 15). Drivers by points: if/else 6 (13 pts), error handling 1 (4 pts), ternaries 2 (4 pts), loops 2 (3 pts), boolean chains 2 (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 · train_full_sft.train_epoch (cognitive 25) · ×1
  • train_full_sft.train_epoch (cognitive 25) trainer/train_full_sft.py:24 — train_full_sft.train_epoch has cognitive complexity 25 (threshold 15). Drivers by points: if/else 4 (9 pts), ternaries 3 (9 pts), boolean chains 4, loops 2 (3 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 · train_pretrain.train_epoch (cognitive 25) · ×1
  • train_pretrain.train_epoch (cognitive 25) trainer/train_pretrain.py:24 — train_pretrain.train_epoch has cognitive complexity 25 (threshold 15). Drivers by points: if/else 4 (9 pts), ternaries 3 (9 pts), boolean chains 4, loops 2 (3 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 · eval_toolcall.run_case (cognitive 24) · ×1
  • eval_toolcall.run_case (cognitive 24) scripts/eval_toolcall.py:177 — eval_toolcall.run_case has cognitive complexity 24 (threshold 15). Drivers by points: ternaries 7 (16 pts), if/else 3 (5 pts), loops 2 (3 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 · train_dpo.train_epoch (cognitive 22) · ×1
  • train_dpo.train_epoch (cognitive 22) trainer/train_dpo.py:53 — train_dpo.train_epoch has cognitive complexity 22 (threshold 15). Drivers by points: if/else 4 (9 pts), ternaries 2 (6 pts), boolean chains 4, 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 · train_lora.train_epoch (cognitive 22) · ×1
  • train_lora.train_epoch (cognitive 22) trainer/train_lora.py:25 — train_lora.train_epoch has cognitive complexity 22 (threshold 15). Drivers by points: if/else 4 (9 pts), ternaries 2 (6 pts), boolean chains 4, 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 · train_tokenizer.get_texts (cognitive 18) · ×1
  • train_tokenizer.get_texts (cognitive 18) trainer/train_tokenizer.py:13 — train_tokenizer.get_texts has cognitive complexity 18 (threshold 15). Drivers by points: if/else 6 (11 pts), ternaries 1 (3 pts), error handling 1 (2 pts), boolean chains 1, loops 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 · SFTDataset.generate_labels (cognitive 17) · ×1
  • SFTDataset.generate_labels (cognitive 17) dataset/lm_dataset.py:91 — SFTDataset.generate_labels has cognitive complexity 17 (threshold 15). Drivers by points: if/else 3 (7 pts), loops 3 (7 pts), ternaries 1 (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. This shape REPEATS in the file: one other method here (DPODataset.generate_loss_mask) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared.
D2 · Cognitive Complexity · DPODataset.generate_loss_mask (cognitive 17) · ×1
  • DPODataset.generate_loss_mask (cognitive 17) dataset/lm_dataset.py:180 — DPODataset.generate_loss_mask has cognitive complexity 17 (threshold 15). Drivers by points: if/else 3 (7 pts), loops 3 (7 pts), ternaries 1 (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. This shape REPEATS in the file: one other method here (SFTDataset.generate_labels) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared.
D2 · Cognitive Complexity · train_grpo.calculate_rewards (cognitive 17) · ×1
  • train_grpo.calculate_rewards (cognitive 17) trainer/train_grpo.py:37 — train_grpo.calculate_rewards has cognitive complexity 17 (threshold 15). Drivers by points: ternaries 3 (11 pts), if/else 1 (3 pts), loops 2 (3 pts) (nesting depth added 11). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D35 · Change Coupling · Change coupling · ×1
  • Change coupling: train_grpo.py ↔ train_ppo.py trainer/train_grpo.py — `trainer/train_grpo.py` and `trainer/train_ppo.py` change together 73% of the time (8 of the 11 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) with no explicit dependency between them. They sit in the same directory, but in this ecosystem each file is its own module — a sibling reference still needs an import — so the missing import edge is real: the coupling runs through shared behaviour, not a declared dependency. If they duplicate structure, extract the common part into one unit; otherwise the coupling is hidden and worth breaking. You can check this without leaving the row: of the 8 shared commits counted here, the most recent 3 are `1e6e909f` [update] trim redundant comments; `7a9137d2` [update] trim verbose ddp comments; `37ac206e` [perf] skip the per-forward RoPE buffer broadcast in the RL trainers — run `git show` on any of them.
D4 · Code Duplication · Near-duplicate member family (3 members, 30 shared lines) · ×1
  • Near-duplicate member family (3 members, 30 shared lines) trainer/train_full_sft.py:25 — trainer/train_full_sft.py:25-72 | trainer/train_lora.py:26-67 | trainer/train_pretrain.py:25-71 — These 3 members are variants of one another: a block of 30 lines reported below appears in every one of them, and the pairwise near-duplicate rows they would otherwise produce are collapsed into this row. Read them as one construct written 3 times. The repair is at the members' grain — factor the shared implementation out once and have all of them call it with their differences as parameters or as an injected step, or, where the difference is systematic, generate them from one template. Extracting the individual blocks below is not the same fix: it leaves every body in place and the next edit still has to be made 3 times.
D4 · Code Duplication · Near-duplicate member pair (22 shared lines) · ×1
  • Near-duplicate member pair (22 shared lines) trainer/train_distillation.py:40 — trainer/train_distillation.py:40-134 | trainer/train_dpo.py:54-119 — These two members are variants of one another: 22 of their lines are already reported as duplicated blocks below, spread through both bodies rather than gathered into one. Read them as a single construct written twice. The repair is at the members' grain — factor the shared pipeline into one implementation the two call with their differences as parameters or as an injected step, or, where the difference is systematic (sync against async, one transport against another), generate one from the other. Extracting the individual blocks below is not the same fix: it leaves the two bodies in place and the next edit still has to be made twice.
D4 · Code Duplication · Duplicated block (15 lines × 3) · ×1
  • Duplicated block (15 lines × 3) trainer/train_full_sft.py:27 — trainer/train_full_sft.py:27-41 | trainer/train_lora.py:28-42 | trainer/train_pretrain.py:27-41 — before extracting anything, compare `trainer/train_full_sft.py` and `trainer/train_lora.py` as WHOLE FILES: this scan already matched 4 separate duplicated blocks between them, totalling at least 39 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place.
D4 · Code Duplication · Duplicated block (12–14 lines × 4) · ×1
  • Duplicated block (12–14 lines × 4) trainer/train_distillation.py:93 — trainer/train_distillation.py:93-104 | trainer/train_dpo.py:85-96 | trainer/train_full_sft.py:38-51 | trainer/train_pretrain.py:38-51 — before extracting anything, compare `trainer/train_distillation.py` and `trainer/train_dpo.py` as WHOLE FILES: this scan already matched 4 separate duplicated blocks between them, totalling at least 41 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place.
D4 · Code Duplication · Duplicated block (2–11 lines × 5) · ×1
  • Duplicated block (2–11 lines × 5) trainer/train_distillation.py:107 — trainer/train_distillation.py:107-108 | trainer/train_dpo.py:99-100 | trainer/train_full_sft.py:44-54 | trainer/train_lora.py:44-53 | trainer/train_pretrain.py:44-54 — before extracting anything, compare `trainer/train_distillation.py` and `trainer/train_dpo.py` as WHOLE FILES: this scan already matched 4 separate duplicated blocks between them, totalling at least 41 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
D4 · Code Duplication · Duplicated block (11 lines × 2) · ×1
  • Duplicated block (11 lines × 2) trainer/train_grpo.py:58 — trainer/train_grpo.py:58-68 | trainer/train_ppo.py:65-75 — 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 `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 (10 lines × 2) · ×1
  • Duplicated block (10 lines × 2) dataset/lm_dataset.py:93 — dataset/lm_dataset.py:93-102 | dataset/lm_dataset.py:182-191 — 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 (7 lines × 5) · ×1
  • Duplicated block (7 lines × 5) trainer/train_distillation.py:98 — trainer/train_distillation.py:98-104 | trainer/train_dpo.py:90-96 | trainer/train_full_sft.py:44-51 | trainer/train_lora.py:44-50 | trainer/train_pretrain.py:44-51 — before extracting anything, compare `trainer/train_distillation.py` and `trainer/train_dpo.py` as WHOLE FILES: this scan already matched 4 separate duplicated blocks between them, totalling at least 41 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place.
D4 · Code Duplication · Duplicated block (5–7 lines × 2) · ×1
  • Duplicated block (5–7 lines × 2) trainer/train_agent.py:357 — trainer/train_agent.py:357-363 | trainer/train_grpo.py:189-193 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `trainer/train_agent.py:357` 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. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
D4 · Code Duplication · Duplicated block (5 lines × 3) · ×1
  • Duplicated block (5 lines × 3) trainer/train_full_sft.py:57 — trainer/train_full_sft.py:57-61 | trainer/train_lora.py:56-60 | trainer/train_pretrain.py:57-61 — before extracting anything, compare `trainer/train_full_sft.py` and `trainer/train_lora.py` as WHOLE FILES: this scan already matched 4 separate duplicated blocks between them, totalling at least 39 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place.
P1 · CI/CD gates · No CI pipeline · ×1
  • No CI pipeline — No CI workflow found (.github/workflows, azure-pipelines.yml, .gitlab-ci.yml, …) — changes aren't gated by an automated build/test.
Minor — 7 finding(s)
M4 · Documentation accuracy · README/code drift · ×2
  • README/code drift — README claims MiniMind-V and MiniMind-O vision while only minimind-v1-moe exists — searched for: `MiniMind-V`, `MiniMind-O`. Each was matched case- and separator-insensitively against file and directory NAMES anywhere in the tree, and against the CONTENTS of manifest files (package.json, *.csproj, *.props, *.slnx, *.yml, Dockerfile); the README's own prose never counts, so a claim is never refuted by merely being made. Nothing outside that search was read — a footprint living only in a submodule, in a file type not listed here, or under a name none of those terms matches is not seen, and this row is then wrong.
  • README/code drift — claims dLM/diffusion model but no such branch exists — searched for: `dLM`, `diffusion`. Each was matched case- and separator-insensitively against file and directory NAMES anywhere in the tree, and against the CONTENTS of manifest files (package.json, *.csproj, *.props, *.slnx, *.yml, Dockerfile); the README's own prose never counts, so a claim is never refuted by merely being made. Nothing outside that search was read — a footprint living only in a submodule, in a file type not listed here, or under a name none of those terms matches is not seen, and this row is then wrong.
D9 · Test Distribution · No tests found · ×1
  • No tests found — No test suite could be collected — no discoverable tests to count. If this repository does test, wiring the suite to a framework a runner can collect (pytest or unittest) is what makes it countable here; a pipeline step that invokes a runner is not evidence on its own, because a runner over an empty suite passes. Tests written as plain executables or shell/PowerShell harnesses are not collectible this way and are not scored here.
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'.
M3 · Folder & project structure · No tests/ separation · ×1
  • No tests/ separation — No test surface was found — this check walked the tree for authored source in the languages it models (`.cs`, `.vb`, `.fs`, `.java`, `.kt`, `.scala`, `.py`, `.php`, `.rb`, `.ex`, `.exs`, `.go`, `.erl`, `.hrl`, `.swift`, `.dart`, `.rs`, `.ts`, `.tsx`, `.mts`, `.cts`) and found none of it test-shaped. ★ `.js`, `.jsx`, `.mjs` and `.cjs` are NOT in that walk, so a Jest or Mocha suite written in plain JavaScript is invisible to it and this row is then wrong. If that is your case, say so rather than moving anything. Otherwise there are no tests here to separate from production code, so the folder question hasn't been reached yet.
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 — this repository has no CI pipeline yet, so run it locally to clear the existing findings, then make it a step of the first workflow you add so a regression fails the build. What was searched, so you can tell an absence from a miss: the 0 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.
Minor — 28 finding(s)
D12 · Dependency Hygiene · Outdated · ×28
  • Outdated: datasets — `datasets` is pinned to 3.6.0, but 5.0.1 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 3.6.0 until the declaration is edited. This crosses a major version, so it is a migration rather than a bump.
  • Outdated: datasketch — `datasketch` is pinned to 1.6.4, but 2.0.0 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 1.6.4 until the declaration is edited. This crosses a major version, so it is a migration rather than a bump.
  • Outdated: einops — `einops` is pinned to 0.8.1, but 0.8.2 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 0.8.1 until the declaration is edited.
  • Outdated: fastapi — `fastapi` is pinned to 0.120.1, but 0.141.1 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 0.120.1 until the declaration is edited.
  • Outdated: flask — `flask` is pinned to 3.0.3, but 3.1.3 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 3.0.3 until the declaration is edited.
  • Outdated: flask-cors — `flask-cors` is pinned to 4.0.0, but 6.0.5 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 4.0.0 until the declaration is edited. This crosses a major version, so it is a migration rather than a bump.
  • Outdated: jinja2 — `jinja2` is pinned to 3.1.2, but 3.1.6 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 3.1.2 until the declaration is edited.
  • Outdated: marshmallow — `marshmallow` is pinned to 3.22.0, but 4.3.1 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 3.22.0 until the declaration is edited. This crosses a major version, so it is a migration rather than a bump.
  • Outdated: modelscope — `modelscope` is pinned to 1.37.0, but 1.40.1 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 1.37.0 until the declaration is edited.
  • Outdated: ngrok — `ngrok` is pinned to 1.4.0, but 1.7.0 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 1.4.0 until the declaration is edited.
  • Outdated: nltk — `nltk` is pinned to 3.8, but 3.10.3 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 3.8 until the declaration is edited.
  • Outdated: numpy — `numpy` is pinned to 1.26.4, but 2.5.3 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 1.26.4 until the declaration is edited. This crosses a major version, so it is a migration rather than a bump.
  • Outdated: openai — `openai` is pinned to 1.59.6, but 3.19.2 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 1.59.6 until the declaration is edited. This crosses a major version, so it is a migration rather than a bump.
  • Outdated: protobuf — `protobuf` is pinned to 6.33.5, but 7.36.2 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 6.33.5 until the declaration is edited. This crosses a major version, so it is a migration rather than a bump.
  • Outdated: psutil — `psutil` is pinned to 5.9.8, but 7.2.2 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 5.9.8 until the declaration is edited. This crosses a major version, so it is a migration rather than a bump.
  • Outdated: pydantic — `pydantic` is pinned to 2.11.5, but 2.13.5 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 2.11.5 until the declaration is edited.
  • Outdated: rich — `rich` is pinned to 13.7.1, but 15.0.0 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 13.7.1 until the declaration is edited. This crosses a major version, so it is a migration rather than a bump.
  • Outdated: scikit-learn — `scikit-learn` is pinned to 1.5.1, but 1.9.1 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 1.5.1 until the declaration is edited.
  • Outdated: sentence-transformers — `sentence-transformers` is pinned to 2.3.1, but 6.1.0 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 2.3.1 until the declaration is edited. This crosses a major version, so it is a migration rather than a bump.
  • Outdated: sentencepiece — `sentencepiece` is pinned to 0.2.0, but 0.2.2 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 0.2.0 until the declaration is edited.
  • Outdated: streamlit — `streamlit` is pinned to 1.50.0, but 1.64.0 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 1.50.0 until the declaration is edited.
  • Outdated: swanlab — `swanlab` is pinned to 0.9.8, but 0.10.1 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 0.9.8 until the declaration is edited.
  • Outdated: tiktoken — `tiktoken` is pinned to 0.10.0, but 0.14.0 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 0.10.0 until the declaration is edited.
  • Outdated: transformers — `transformers` is pinned to 4.57.6, but 5.17.0 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 4.57.6 until the declaration is edited. This crosses a major version, so it is a migration rather than a bump.
  • Outdated: trl — `trl` is pinned to 0.13.0, but 1.14.0 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 0.13.0 until the declaration is edited. This crosses a major version, so it is a migration rather than a bump.
  • + 3 more in this group — see findings.md.

Appendix B — Reproduction & audit trail

Every external tool invocation behind a deep-scan dimension — the tool, its captured version, the exact command, how many findings it yielded, and a link to the retained raw output. To reproduce any finding: check out the same commit and run the command shown (repo-relative — never an absolute scratch path). The complete raw scanner output is retained verbatim under artifacts/raw/ (indexed in artifacts/raw/index.json); per-invocation exit codes and wall-clock durations are in sidecar.json — kept out of this table so the rendered report stays byte-identical across runs of the same commit.

DimensionToolVersionCommandFindingsRaw output
D28 · Secrets (history)gitleaks—gitleaks detect --no-banner --config /opt/gitleaks-rules/watchdog-gitleaks.toml --report-format json --report-path /tmp/watchdog-gitleaks-1b03c078434d42ccaa6100c5b051e603/history.json --exit-code 0 --source .0artifacts/raw/gitleaks-history.json
D28 · Secrets (history)gitleaks—gitleaks detect --no-git --no-banner --config /opt/gitleaks-rules/watchdog-gitleaks.toml --report-format json --report-path /tmp/watchdog-gitleaks-1b03c078434d42ccaa6100c5b051e603/tree.json --exit-code 0 --source .0artifacts/raw/gitleaks-tree.json
D29 · Static Analysis (SAST)semgrep—semgrep --config /opt/semgrep-rules/security-audit.yml --config /opt/semgrep-rules/owasp-top-ten.yml --config /opt/semgrep-rules/watchdog-sast.yml --json --quiet --timeout 10 --timeout-threshold 3 --metrics off .0artifacts/raw/semgrep.json
D30 · Dependency Vulnerabilitiesosv-scanner—osv-scanner --format json --recursive .10artifacts/raw/osv-scanner.json
D31 · IaC & Container Securitytrivy—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—
D36 · Supply-chain Provenance & Signingprovenance—provenance: not applicable — No CI/build pipeline found (.github/.forgejo/.gitea workflows, .circleci, .buildkite, .woodpecker, .teamcity, .gitlab-ci.yml, .travis.yml, bitbucket-pipelines.yml, .drone.yml, .cirrus.yml, .woodpecker.yml, appveyor.yml, azure-pipelines*.yml, .pipelines/, .vsts-ci/, .azuredevops/, Jenkinsfile); there is no build to attest provenance for.0—
D37 · Vulnerability-disclosure Policydisclosure—disclosure: not applicable — No vulnerability-disclosure policy file found (SECURITY.md/.markdown/.rst/.txt at root or under .github/.forgejo/.gitea/docs, .well-known/security.txt). A coordinated-disclosure policy may live off-repo, so this is not evidenced rather than failed.0—
D40 · Network Egress Confinementruntime-hardening—runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; network egress policy is a cluster-native control that may live at the platform/firewall layer, so there is nothing to assess here.0—
D41 · Kernel & Syscall Confinementruntime-hardening—runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; seccomp/AppArmor/SELinux confinement is a workload-level control, so there is nothing to assess here.0—
D42 · Runtime Threat Enforcementruntime-hardening—runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; runtime threat-detection and admission-control policy are cluster-level controls, so there is nothing to assess here.0—
D43 · Malicious Dependenciesosv-scanner—osv-scanner --format json --recursive .0artifacts/raw/osv-scanner.json

Run 01a0dd10-ab68-77cd-b5a6-8025712427eb · every finding is also locatable in findings.md, and the complete scoring record (with exit codes + durations) in sidecar.json.

Downloadable artifacts

Machine-readable and reproducible from this commit + frozen rubric — drop them straight into a contract appendix, a CRA dossier, or a downstream SCA / VEX tool.

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