Public report — flama, published 5 Aug 2026.
Concrete security findings (CVE IDs, secret matches, dependency versions) are hidden in this version;
ask the repo owner for the full report.
88findings with an exact file:lineof 97 — the remainder are repo-wide signals (a dimension-level measurement, not a single line); open any file:line and verify
45/113dimensions across the health lenses34247 LoC — wide & deep
Executive summary
Read through the Production lens — the standard calibration. *Green* means good enough to run in production. The score is absolute and comparable across repos.
vortico/flama is sound in substance but carries real gaps (54%). It is not in crisis, but the issues below raise the cost of changing it — friction its consumers ultimately inherit.
It is strongest in Domain Modelling (100%) — the domain model is expressive and well-guarded. Architecture (100%) is solid too.
Most urgent: a critical security exposure was detected (see the Security & Compliance lens). Treat it as a priority regardless of the overall grade.
The area that most needs attention is Readiness (47%) — releases are harder to depend on — versioning, release notes and dependency hygiene are thin, so consumers can't easily tell what changed or trust an upgrade. Accessibility (51%) is the next concern — it raises ongoing delivery and operational cost.
Leadership focus, highest impact first: SAST step to CI running what this repository's stack ships (Security & performance tooling); tests that import the unreached modules (directly or through… (Test Coverage); test runner (vitest / jest / playwright) as package.json… (Tooling).
For scale: Medium (~34,247 production lines); rebuilding it from scratch would take roughly ~0.6 person-years (~1–2 engineers). Approximate, ±~30%.
It builds on a genuinely strong Domain Modelling foundation (100%); the priorities above are the highest-leverage way to bring the rest up to that level.
How the score is built — each lens's share of the headlineWidth 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.
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.
0.8× (at 54% quality) — the last 20% of quality is most of the work
Size & shape
Medium · effort split not classified (source measured from disk; the effort-tier breakdown is a C#-only syntax walk)
This codebase represents roughly ~0.6 person-years of build effort (about ~€86,000 to rebuild). Its weakest lens is Readiness at 47% — the part of that asset most exposed by the findings below.
How we model this: boilerplate at a scaffolding rate + logic × domain Standard (×1.2) — domain model × a 0.8× 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
Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing.
Value concentrated against a weak lens · Medium · Value at risk
This is a Medium asset (~0.6 person-years to rebuild), and its weakest lens is Readiness at 47%. The operational and business risk on an asset this size concentrates there — that's where remediation buys the most protection.
→ Direct remediation budget at Readiness first — highest risk-reduction per euro on an asset this size.
Highest-leverage move · Medium · Leverage
Of everything flagged, the best return on effort is: Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing. The rest can wait behind it.
Evidence: priority ranking: top of 5 ranked by impact/effort
→ Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing.
Architecture — module dependency matrix
488 modules, 469 dependencies — every dependency points down the layering, so there are no cycles. 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.)
Findings mapped to OWASP categories; the specific CVEs/secrets are in the Security dimension cards below and findings.md (redacted only on the public version of this report).
OWASP category
Findings
Severity
A06:2021 — Vulnerable & Outdated Components
26
High / Critical
A03:2021 — Injection
8
High / Critical
A02:2021 — Cryptographic Failures
2
High / Critical
A05:2021 — Security Misconfiguration
2
Medium
Roadmap
First, integrate a static application security testing step into the CI pipeline to fail the build on security regressions. Next, increase test coverage by adding tests for currently unreached production modules and configure a test runner in the project scripts to execute them in CI. Then, improve keyboard accessibility by making custom controls operable via keyboard and ensuring anchors have valid hrefs. Finally, enforce accessibility standards by adding an a11y linting plugin and integrating automated accessibility checks into the CI process.
Ranked by impact ÷ effort. "Helps" is the estimated gain on the 0–100 health score.
Do this
Helps
Effort
Dimension
Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing.
Enforce accessibility in the toolchain: add eslint-plugin-jsx-a11y, then assert with your test runner's axe binding (jest-axe, vitest-axe, cypress-axe or @axe-core/playwright) in tests, then gate axe/pa11y/Lighthouse in CI.
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. 43 of 45 evaluated dimensions are computed purely by tools and static analysis (confidence 1.0); 2 documentation/naming judgement(s) are LLM-assisted and labelled advisory. Overall confidence is 0.6 — 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 — 45 dimensions across the health lenses
Each chip is a dimension scored from real signals across architecture, testing, dependencies, security & compliance, documentation, git-history and code quality — in one coherent pass. A surface report typically covers a handful.
How to trust any code-health report — three questions
Can you open the finding? Real findings cite a repo-relative file and line you can open at the cited line — never an absolute scratch path. Here, 88 of 97 do; the remainder are repo-wide signals — a dimension-level measurement, not a single line. (Every path in this report is repo-relative by construction: paths are normalized at the producer and the report is rejected if any rooted path leaks through.)
Is there a tool behind the number? Every score below names the method that produced it — Roslyn, git, a scanner, or (for a handful of documentation/naming dimensions) an LLM labelled sampled · advisory — not a narrative.
Does re-running give the same result? Run it again on the same commit and the score — and this report, byte for byte — is identical. A report whose numbers move between runs is describing the run, not the code.
This report answers yes to all three. That's the bar to hold any assessment to.
Tools & methods
The actual versions used this run (captured at analysis time) — re-run on the same commit for the identical score.
Method
Backs
Version
Evaluator
Roslyn static analysis
Complexity, cohesion, coupling, dead code, API surface, layering
What ran differently this time — a tool absent, degraded, or that fell back to an estimate. Named openly, not folded silently into the scores. A degraded run also records its exact cause in diagnostics.md.
D30 Dependency Vulnerabilities — scanner not present in this environment — The backing tool was not installed where this scan ran, so this dimension was not scored. Install the tool (or run in the hosted environment, where it is always present) for a graded result.
Repo exclusion declarations: 2 pattern(s) declared (.gitattributes linguist-generated/vendored, .editorconfig generated_code) excluded 0 source file(s) from code-quality scoring. Declarations are the repo's own visible statement that a tree is machine-written or vendored — auditable in any diff, honored by GitHub the same way.
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 (jscpd) — 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.
D13 Secret Scanning: Secret detection is signature- and entropy-based on the current tree — a secret that does not match a known pattern, or one already rotated, will not be flagged (a clean scan is "nothing matched", not "no secrets exist").
D15 Churn × Complexity Hotspots: Churn hotspots come from git history — a freshly imported or squashed repository has no churn signal, and recent rewrites can mask a historically risky file.
D16 Bus Factor: Bus-factor is a time-decayed model of commit attribution (who has recently, repeatedly worked a file), not comprehension — pairing, review and reading-without-committing spread knowledge it can't see; bot commits and shared accounts still distort it.
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.
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").
D31 IaC & Container Security: IaC scanning checks Dockerfiles/Terraform/Kubernetes against best-practice rules — it cannot see the live cloud account, runtime configuration, or drift between the committed config and what is actually deployed.
D33 JS/npm Dependency Vulnerabilities: JS/npm CVE matching reads package manifests and lockfiles — risk from how a dependency is used, and advisories not yet published, fall outside this scan.
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.
AC2 Forms & labels: Label association is read from static markup — a label wired up at runtime (JS-set aria-labelledby, framework-injected ids) reads as missing, a present label says nothing about whether its text is correct. A known UI-library field component (e.g. a JSX <TextField>) is now checked conservatively — flagged only when it carries NO label/aria-label/aria-labelledby/id/name — but wrapper/context-labelled libraries (Chakra/Radix FormControl+FormLabel) aren't statically visible (possible false positive) and non-JSX lowercased components are still skipped. A clean result is "no unlabelled native control found", not a labelling proof.
AC3 Page structure: Page structure is read from the static markup tree — landmarks, headings and lang injected at runtime aren't seen, heading ORDER is checked structurally (not against the rendered visual hierarchy), and lang/title/main fire only on full documents, never partials, and the data-table check sees header-cell presence (a <th> exists), not whether each header correctly associates with its cells. Static readiness, not conformance.
AC4 Keyboard semantics: Keyboard semantics are inferred from markup attributes — interactivity wired purely in script, focus managed at runtime, and component-level handlers are invisible. A clean result means "no static keyboard-trap shape", not a keyboard-operability proof.
AC5 ARIA correctness: ARIA correctness is checked against the static role/attribute shape — roles/attributes set dynamically aren't seen, a valid role says nothing about whether it matches the element's real behaviour, and required-state checks are suppressed when a JSX spread could supply them.
AC6 Visual & motion safety: Contrast and motion safety are PARTIAL by construction — literal colours (hex/rgb/hsl/named) in inline styles, in-repo <style> blocks, in-repo .css files, var() tokens, Tailwind neutral utilities and CSS-in-JS top-level declarations are read (same-rule/same-element colour+background pairs only); computed/runtime/theme colour, external-CDN stylesheets, CSS-in-JS dynamic (${…}) and nested-selector colours, cross-element pairs and image contrast stay out of reach, so a clean result is bounded by what the static CSS itself shows.
AC7 A11y enforcement: Enforcement is scored from in-repo config/CI evidence only — an a11y gate enforced in external tooling with no in-repo trace can't be credited, and a configured linter is presence, not proof the rules actually run or block a merge.
AX9 CQS / query purity: Handlers are found by interface/name convention — a query handler using neither is not seen. Mutation is a resolved write/publish invocation (SaveChanges/repository/bus), so a write hidden behind a hand-rolled wrapper, reflection, or a string-keyed service locator resolves to a non-persistence type and isn't flagged; it detects that a query writes state, not whether the write is a legitimate read-side cache update. Clean means "no resolved write/publish in a query body", not a proof of CQS purity.
M4 Documentation accuracy: Onboarding quality is an LLM read of the docs/setup present — it cannot run the onboarding or measure how long a real new joiner takes; the verdict is sampled and advisory.
P4 Deployment & Rollback: Approval/branch-protection rules live in repository settings the scan cannot see — only their in-repo evidence (config files, workflows) is checked, so a control enforced purely in the host's settings reads as "not evidenced".
P6 Release Hygiene: Rollback/observability controls are inferred from repo artefacts (pipelines, dashboards-as-code) — controls configured in external tooling, with no in-repo trace, cannot be credited.
The LLM boundary
LLM-set scores this run (3): D19, D21, M4 (model: Local LLM). For these, a model reads a bounded sample and sets the numeric score (documentation, ADR quality, naming, comment value, onboarding) — D25 sets the ADR-conformance fraction over sampled code, D22 judges API accuracy over a sample. These are sampled and advisory by design: they vary at the margins between runs and are never a deterministic measurement. Every other score in this report is tool-computed at confidence 1.0.
What it measures: How tangled the control flow is — methods with many branches are hard to test and change.
Method: Cyclomatic complexity per method (1 + decision points), computed exhaustively across production source; test projects separated by convention. Deterministic.
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.
+ 10 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Resolve the 1 SchemaGenerator.get_endpoints (cognitive 28) finding(s) in Cognitive Complexity — start with openapi.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 MLXBackend.generate (cognitive 24) finding(s) in Cognitive Complexity — start with mlx.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 Parser._parse_message (cognitive 24) finding(s) in Cognitive Complexity — start with _base.py. — One of this dimension's main actionable groups (1 warning-level).
Enforce Cognitive Complexity in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d2_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D3 · God Classes9.9 / 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.
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.
What it measures: Whether any secrets (keys, tokens, passwords) have leaked into the code.
Method: In-process native secret scanner (entropy plus signature patterns) across all tracked files; no external tool. A clean result is a measured 10, not no-data zero. Deterministic.
What it measures: 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.
Detailed fixes: d15_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D16 · Bus Factor4.6 / 10Weak✓ Tool-verified
What it measures: Whether knowledge is concentrated in too few people (the "bus factor").
Method: Living knowledge per author via time-decayed commit attribution (6-month half-life, focus weighting) across largest source files. Deterministic, avoids blame's mechanical-refactor false positives.
95 source file(s) have their living knowledge concentrated in one author (≥90% of recent, decayed contribution). The largest is flama/models/streams.py.
Off-boarding risk: anonymized user #1
What to do
Resolve the 1 Off-boarding risk finding(s) in Bus Factor. — One of this dimension's main actionable groups (1 recommendation-level).
Detailed fixes: d16_recommendation.md · top locations in Appendix A, every location in findings.md.
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.
Flama's documentation is strong and complete for a framework project — the README gives an excellent branding/mission statement (the production framework for Predictive and Generative AI), links to PyPI, GitHub workflows, and a detailed Flama logo. The benchmarks/README.md file provides concrete performance data across a rich test suite with a clear 'Latest: 2.0.6' table plus a comparison table ('Comparison') that is visually helpful. Both the README and benchmarks files are well written and reflect the full outline; the visible content shows strong quality.
What to do
Improve Documentation Quality — currently 7.0/10. — Flama's documentation is strong and complete for a framework project — the README gives an excellent branding/mission statement (the production framework for Predictive and Generative AI), links to PyPI, GitHub workflows, and a detailed Flama logo. The benchmarks/README.md file provides concrete performance data across a rich test suite with a clear 'Latest: 2.0.6' table plus a comparison table ('Comparison') that is visually helpful. Both the README and benchmarks files are well written and reflect the full outline; the visible content shows strong quality.
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.
What it measures: Whether any secrets were ever committed — scanned across the full git history, not just now.
Method: Git-history secret scan via gitleaks detect over full history in an isolated checkout; each match flagged High. Exhaustive; degrades cleanly when tool absent.
2 finding(s): 0 critical, 2 high, 0 medium, 0 low. Remediation for historically-committed secrets is credential rotation — they remain in history regardless of later deletion.
Secret: square-access-tokenuv.lock:3867detected by gitleaks finding
Rotate the exposed credentials — git history can't be un-committed
What to do
Resolve the 1 Secret finding(s) in Secrets (history) — start with uv.lock. — One of this dimension's main actionable groups (1 issue-level).
Resolve the 1 Rotate the exposed credentials finding(s) in Secrets (history). — One of this dimension's main actionable groups (1 recommendation-level).
Detailed fixes: d28_recommendation.md · top locations in Appendix A, every location in findings.md.
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).
High: dependabot-missing-cooldown · ×2.github/dependabot.yml:4detected by semgrep finding
Medium: missing-autoescape-disabled · ×6flama/_cli/config/app.py:344detected by semgrep finding
What to do
Resolve the 6 Medium finding(s) in Static Analysis (SAST) — start with codec.py (2), sklearn.py (2), app.py. — One of this dimension's main actionable groups (6 warning-level).
Resolve the 2 High finding(s) in Static Analysis (SAST) — start with dependabot.yml, pyproject.toml. — One of this dimension's main actionable groups (2 issue-level).
Detailed fixes: d29_recommendation.md · top locations in Appendix A, every location in findings.md.
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.
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; coupling through a build step, config, or non-source file isn't seen.
What it measures: Whether the build pipeline provides supply-chain integrity — generated provenance/attestation, signed artifacts (cosign/sigstore), an SBOM, and pinned build actions. Presence of the configuration, not a runtime guarantee.
Method: Supply-chain provenance/signing read deterministically from CI/build config (.github/workflows, .gitlab-ci.yml, azure-pipelines, Jenkinsfile, .circleci) + the release surface: four signals — generated provenance/attestation (SLSA/in-toto/actions-attest), artifact signing (cosign/sigstore/gitsign), an SBOM (syft/sbom-action/*.spdx.json/*.cdx.json), and SHA-pinned build actions — scored 10·present/denom. NotApplicable without a build pipeline. Detects configuration presence, not runtime enforcement.
Resolve the 1 No build provenance finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 recommendation-level).
Resolve the 1 No artifact signing finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 recommendation-level).
Resolve the 1 No SBOM finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 recommendation-level).
Detailed fixes: d36_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether the repository publishes a coordinated-vulnerability-disclosure policy (SECURITY.md or security.txt) with a reporting contact, so finders know how to report a vulnerability. Presence of a policy file with a contact, not whether the policy is adequate or honoured.
Method: Vulnerability-disclosure policy read deterministically from the repo: a SECURITY.md (root/.github/docs) or .well-known/security.txt / security.txt, regex-checked for a reporting contact (email / URL / mailto). Present + contact → 10; present without a contact → 4; NotApplicable when no policy file exists (it may live off-repo). Detects the policy file's presence + contact, not its adequacy.
What it measures: Whether dependencies have known published vulnerabilities (CVEs) per the OSV database — read natively from whatever lockfile the repository ships (Cargo, npm, Go, Python, Maven, RubyGems, …). D33 and D30 add ecosystem-specific scanners on top for npm and .NET.
Method: Multi-ecosystem dependency-CVE scan via osv-scanner --recursive (queries the osv.dev database + parses lockfiles natively across ecosystems: npm package-lock/yarn/pnpm/bun, Go go.mod, Rust Cargo.lock, Maven/Gradle pom.xml/gradle.lockfile, PyPI requirements.txt/poetry.lock/Pipfile.lock, Composer composer.lock, RubyGems Gemfile.lock, Hex mix.lock, pub pubspec.lock, Swift Package.resolved); severity tally (Critical/High/Medium/Low) to 0-10 tight normalizer (8.0). NotApplicable only when the repo declares no supported non-.NET dependency lockfile (a NuGet-only repo stays NotApplicable — .NET CVEs are D30's domain); coverage needs a resolved lockfile. Additive to D33 (trivy fs); exhaustive + deterministic, DB kept fresh.
High CVE: [GHSA redacted] · ×12uv.lockdetected by osv-scanner finding
High vulnerability: [GHSA redacted] · ×4uv.lockdetected by osv-scanner finding
Critical CVE: [GHSA redacted]uv.lockdetected by osv-scanner finding
Medium CVE: [GHSA redacted] · ×7uv.lockdetected by osv-scanner finding
Low CVE: [GHSA redacted] · ×2uv.lockdetected by osv-scanner finding
What to do
Resolve the 12 High CVE finding(s) in OSV Dependency Vulnerabilities — start with uv.lock (7), pnpm-lock.yaml (5). — One of this dimension's main actionable groups (12 issue-level).
Resolve the 4 High vulnerability finding(s) in OSV Dependency Vulnerabilities — start with pnpm-lock.yaml (2), uv.lock, Cargo.lock. — One of this dimension's main actionable groups (4 issue-level).
Resolve the 1 Critical CVE finding(s) in OSV Dependency Vulnerabilities — start with uv.lock. — One of this dimension's main actionable groups (1 issue-level).
Detailed fixes: d38_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
Frontend & cross-cutting dimensions
R = React/JS · M = Maturity · P = Readiness.
AC2 · Forms & labels10.0 / 10Exemplary○ Nothing flagged
Other · Accessibility — Whether form controls have a programmatic label (an associated label, aria-label or aria-labelledby), buttons have text, links have an accessible name, fieldsets have a non-empty legend, known UI-library field components carry a label prop, and a placeholder isn't used as the only label. Static markup readiness, not a WCAG conformance claim.
Method: Static markup-model scan: inputs/selects/textareas checked for an associated label[for]/wrapping label/aria-label/aria-labelledby (per document), buttons for accessible text, fieldsets for a legend; placeholder-only labelling flagged. Deterministic, hard fact per control.
Other · Accessibility — Whether pages declare a language (well-formed BCP-47) and a non-empty title, expose exactly one main landmark and a sane heading order with non-empty headings, keep zoom enabled, title their iframes, give data tables header cells, and avoid meta-refresh. Static markup readiness, not a WCAG conformance claim.
Method: Static markup-model scan: html lang, document <title>, a main landmark and heading order on full documents only, plus zoom-disabling viewports, untitled iframes and meta-refresh anywhere. Deterministic, per structural checkpoint.
No <main> (or role="main") means no "skip to content" target and a weaker landmark map. Wrap the primary content in <main>. (×4) — chat.html:2, error_404.html:2, error_500.html:2, …
What to do
Declare <html lang>, a document <title> and a <main> landmark, keep headings in order, leave zoom enabled, title iframes and drop meta-refresh.
Other · Accessibility — Whether interactive behaviour is keyboard-reachable — no click handler on a non-interactive element lacking a role, tabindex and key handler, no positive tabindex, no href-less anchor, no placeholder-href (#/javascript) link acting as a button. Static markup readiness, not a WCAG conformance claim.
Method: Static markup-model scan: click handlers on non-interactive elements lacking role+tabindex+key handler, positive tabindex values, and href-less anchors. Components skipped, spreads suppressed. Deterministic, hard fact per element.
A click handler on a plain element isn't keyboard-operable. Use a <button>, or add role + tabIndex={0} + a key handler. (×3) — ErrorTraceback.tsx:81, URLTree.tsx:62, Menu.tsx:38
What to do
Make custom controls keyboard-operable (role + tabindex + key handler), drop positive tabindex, and give anchors a real href.
Other · Accessibility — Whether ARIA is used correctly — valid non-abstract roles, the ARIA state a role requires, valid (non-misspelled) aria-* attribute names, in-enum values for token-typed aria-* attributes, and no aria-hidden on (or wrapping) a focusable element. Static markup readiness, not a WCAG conformance claim.
Method: Static markup-model scan: role values checked against the WAI-ARIA role set (abstract/invalid flagged), required ARIA state for a role, and aria-hidden on a focusable element. Deterministic, role/attribute level.
Other · Accessibility — Whether focus outlines aren't removed without a replacement, motion respects prefers-reduced-motion, and literal CSS colour pairs meet contrast — PARTIAL: inline styles, in-repo <style> blocks, in-repo .css files, var() tokens, Tailwind neutral utilities and CSS-in-JS literals are read (hex/rgb/hsl/named), never computed/runtime/external-CDN colour. Static markup readiness, not a WCAG conformance claim.
Method: Static markup/CSS scan: inline outline:none/0, literal inline colour/background contrast against the 4.5:1 AA floor, and <style>-block animation without a prefers-reduced-motion guard. Deterministic but PARTIAL — only inline styles and in-repo CSS literals are visible.
Do you agree with this assessment?
AC7 · A11y enforcement4.0 / 10Weak✓ Tool-verified
Other · Accessibility — Whether accessibility is ENFORCED in the toolchain — an accessibility checker configured over the markup (an a11y lint rule set, e.g. eslint-plugin-jsx-a11y or vuejs-accessibility where the project lints JavaScript) and an automated accessibility assertion wired into tests or CI (axe/pa11y/Lighthouse or an equivalent) — on the Documented→Verified→Prevented ladder.
Method: Repo config/CI scan: an accessibility checker configured over the markup (an a11y lint rule set such as eslint-plugin-jsx-a11y / vuejs-accessibility where JavaScript is linted) and an automated accessibility assertion in tests or CI (axe/pa11y/Lighthouse or equivalent), graded on the Documented→Verified→Prevented rungs. Deterministic, presence/rung detection.
No accessibility enforcement found — no a11y linter (eslint-plugin-jsx-a11y) and no axe/pa11y/Lighthouse in tests or CI. Start with the linter to catch issues at author time.
What to do
Enforce accessibility in the toolchain: add eslint-plugin-jsx-a11y, then assert with your test runner's axe binding (jest-axe, vitest-axe, cypress-axe or @axe-core/playwright) in tests, then gate axe/pa11y/Lighthouse in CI.
Other · Architecture — Whether read (query) handlers stay side-effect-free — a query that writes persistent state or raises events breaks CQS and makes reads unsafe to retry, cache, or route to a read replica.
Method: Roslyn scan: CQRS handlers classified query-vs-command by interface (IQueryHandler/ICommandHandler/IRequestHandler<TQuery,TResult>) and name convention (*Query/Get*/Find* vs *Command); each query handler's body checked for persistent-state writes (SaveChanges/repository Add-Update) or event publishes by resolved invocation. Deterministic, type-level, exhaustive over the detected handlers.
Coverage: Population: CQRS handlers identified by IQueryHandler/ICommandHandler/IRequestHandler interface + *Query/Get*/Find*/*Command NAME convention; query purity then checked exhaustively within that set — a query handler using neither convention is invisible, and mutation is a resolved persistence/publish CALL, not full dataflow.
Other · Domain Modelling — Whether aggregates reference each other by identity (id) rather than by direct object reference — the core DDD consistency-boundary rule.
Method: Roslyn (DDD-gated): aggregate roots identified by convention; each aggregate field checked for direct references to other aggregates versus id-only. Deterministic, DDD-native.
Coverage: Population: aggregate roots identified by AggregateRoot/IAggregateRoot base/interface NAME convention; reference-by-identity then checked exhaustively within that set — a root not using those names is invisible.
Other · Domain Modelling — Whether entities protect their state (private/init-only setters) instead of exposing public setters that bypass invariants. Softened when a rehydration framework (Marten/EF) is present.
Method: Roslyn (DDD-gated): public setters on entities detected; score softened when Marten/EF rehydration frameworks present. Deterministic, framework-aware.
Coverage: Population: entities by convention; encapsulation (setter shape) checked exhaustively within the set.
Other · Domain Modelling — Whether clusters of primitives that travel together (a missing value object) are extracted — a low-weight suggestion, LLM-confirmed when configured.
Method: Roslyn (DDD-gated): primitive parameter clusters recurring three or more times across signatures extracted, then confirmed by language model when configured. Advisory, low-weight.
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.
Add a README to the 2 of 2 project(s) that lack one — worth up to 2 pts.
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 `NNNN-title.md` documents and nothing ADR-shaped by content. Design rationale recorded elsewhere (a design-notes tree, a mailing list, pull-request discussion) is not visible to this check and is not re-findable per decision, so a future maintainer cannot ask why one choice was made and get an answer.
No C4/PlantUML/Mermaid diagram or architecture.md — the high-level shape isn't documented.
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 `NNNN-title.md` names is the most discoverable form).
Add a C4 context/container diagram (Structurizr, PlantUML or Mermaid) or an architecture.md overview.
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.
Readiness · Readiness — Whether SAST, secret/dependency scanning and performance benchmarking are wired in (presence, not runtime).
Method: Filesystem scan: SAST configuration, dependency-update automation, secret scanning, and a benchmark harness or benchmark step — in this repository's own ecosystem. Exhaustive, deterministic.
No static application security testing detected. For this repository's stack, add bandit, `semgrep --config=p/python`, or CodeQL's python pack as a CI step.
What to do
Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing.
Add gitleaks/trufflehog in CI to block PRs that introduce committed secrets.
Readiness · Readiness — Whether releases are automated and safely reversible (probes, rolling updates, approval gates) — from manifests/pipeline files, not the live environment.
Method: Filesystem scan: deployment manifests/IaC (K8s YAML, Helm, Terraform) for rolling updates, probes, approval gates, migration hooks. Exhaustive, deterministic.
Readiness · Readiness — Whether releases are traceable — a maintained changelog and explicit version stamping.
Method: Filesystem scan: changelog file presence and version tags in csproj or git tags. Exhaustive, deterministic.
Do you agree with this assessment?
R1 · Type Safety10.0 / 10Exemplary✓ Tool-verified
React / JS · Code Health — How much of the frontend is typed TypeScript vs untyped JavaScript.
Method: Frontend file inventory: the share of typed TypeScript vs untyped JavaScript across the source tree. Deterministic, exhaustive over frontend files.
React / JS · Code Health — Copy-pasted token-identical blocks across the frontend (the D4 clone algorithm over JS/TS tokens, D-386).
Method: Copy-pasted token-identical blocks across the frontend (the D4 clone algorithm run over JS/TS tokens). Deterministic.
lib/templates/src/ui/components/Menu.tsx:41 · lib/templates/src/ui/components/Menu.tsx:75 — 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. — Menu.tsx:41
lib/templates/src/apps/debug/error_404.tsx:26 · lib/templates/src/apps/debug/error_500.tsx:32 — the 2 copies sit in sibling files in one directory, so check first whether one of them (or an existing module there) already owns this behaviour and the others should call it; otherwise extract it into one module in that directory and have each site call it. — error_404.tsx:26
lib/templates/src/apps/debug/components/EnvironmentTable.tsx:5 · lib/templates/src/apps/debug/components/RequestTable.tsx:5 — the 2 copies sit in sibling files in one directory, so check first whether one of them (or an existing module there) already owns this behaviour and the others should call it; otherwise extract it into one module in that directory and have each site call it. — EnvironmentTable.tsx:5
lib/templates/src/apps/chatbot/components/message/Thought.tsx:15 · lib/templates/src/apps/chatbot/components/message/Tool.tsx:14 — the 2 copies are spread across 2 files, and the CITED SPAN is not a self-contained block — it runs from inside one construct into the next (the tail of a branch plus the head of the following one, a run of switch arms, the end of a declaration plus the list that follows it) rather than covering a whole unit. So do not lift these lines literally: no call can be substituted for a half-open construct. Extract the enclosing repeated UNIT instead — the whole function, component or branch these lines sit in — and where the repetition IS the construct (a run of switch arms, a stack of near-identical declarations) replace it with one table or registry looked up by key rather than a helper each arm calls. The copies still drift apart the first time only one of them is edited, which is why this is reported. — Thought.tsx:15
lib/templates/src/apps/debug/components/URLTree.tsx:87 · lib/templates/src/apps/debug/components/URLTree.tsx:95 — all 2 copies are in the same file, and the CITED SPAN is not a self-contained block — it runs from inside one construct into the next (the tail of a branch plus the head of the following one, a run of switch arms, the end of a declaration plus the list that follows it) rather than covering a whole unit. So do not lift these lines literally: no call can be substituted for a half-open construct. Extract the enclosing repeated UNIT instead — the whole function, component or branch these lines sit in — and where the repetition IS the construct (a run of switch arms, a stack of near-identical declarations) replace it with one table or registry looked up by key rather than a helper each arm calls. The copies still drift apart the first time only one of them is edited, which is why this is reported. — URLTree.tsx:87
lib/templates/src/apps/chatbot/components/message/Thought.tsx:49 · lib/templates/src/apps/chatbot/components/message/Tool.tsx:65 — the 2 copies are spread across 2 files, and the CITED SPAN is not a self-contained block — it runs from inside one construct into the next (the tail of a branch plus the head of the following one, a run of switch arms, the end of a declaration plus the list that follows it) rather than covering a whole unit. So do not lift these lines literally: no call can be substituted for a half-open construct. Extract the enclosing repeated UNIT instead — the whole function, component or branch these lines sit in — and where the repetition IS the construct (a run of switch arms, a stack of near-identical declarations) replace it with one table or registry looked up by key rather than a helper each arm calls. The copies still drift apart the first time only one of them is edited, which is why this is reported. — Thought.tsx:49
What to do
Extract the duplicated blocks into shared functions/components.
React / JS · Code Health — Per-function cyclomatic/cognitive complexity from the token-level function scanner (D-386) — real branching, not a regex heuristic.
Method: Per-function cyclomatic/cognitive complexity from a token-level function scanner (real branching, not a regex heuristic), computed over every frontend function. Deterministic.
Branch-heavy code is where defects cluster — extract decisions into smaller functions. (×2) — reducer.ts:75, reducer.ts:28
What to do
Break down the listed branch-heavy functions; aim P95 cyclomatic ≤ 5.
Do you agree with this assessment?
R3 · Large Files10.0 / 10Exemplary✓ Tool-verified
React / JS · Code Health — How many source files exceed the large-file threshold.
Method: Components/modules exceeding the large-file threshold, counted exhaustively across the frontend source tree. Deterministic.
Do you agree with this assessment?
R4 · Test Coverage0.0 / 10Critical✓ Tool-verified
React / JS · Readiness — Static test reachability (D-386): the share of production files reachable from any test via the import graph — measured without running anything.
Method: Static test reachability: the share of production files reachable from any test via the import graph — measured without running anything. Deterministic.
0% of 57 production file(s) reachable from 0 test file(s) via the import graph
What to do
Add tests that import the unreached modules (directly or through their public entry).
Do you agree with this assessment?
R6 · Tooling6.6 / 10Adequate✓ Tool-verified
React / JS · Readiness — Whether the project wires up test, lint and typecheck — detected from each package.json script's COMMAND (eslint / tsc / vitest / jest / playwright), not just its name, and corroborated against CI-workflow invocations so a tool run only in CI still counts.
Method: package.json scanned for test/lint/typecheck script wiring. Deterministic presence check.
test ✗ · lint ✓ · typecheck ✓
What to do
Add a test runner (vitest / jest / playwright) as package.json scripts and run them in CI.
Do you agree with this assessment?
R7 · Dead Code10.0 / 10Exemplary✓ Tool-verified
React / JS · Code Health — Files unreachable from every application/tooling/test entry point, and exports nothing imports (module-graph reachability, D-386).
Method: Dead code: files unreachable from every application/tooling/test entry point plus exports nothing imports, via module-graph reachability. Deterministic, exhaustive over the import graph.
1 file(s) (~46 LoC) were excluded from dead-code analysis — declare main/module/exports or a conventional entry (src/index.*, an index.html script) so reachability can see this package.
63 file(s) (~2404 LoC) were excluded from dead-code analysis — declare main/module/exports or a conventional entry (src/index.*, an index.html script) so reachability can see this package. — templates
React / JS · Readiness — npm dependency truthfulness (D-386): unused dependencies, imports not declared anywhere, and type-/test-only packages shipped as production deps.
Method: npm dependency truthfulness: unused dependencies, imports declared nowhere, and type-/test-only packages shipped as production deps — from the manifest + import graph. Deterministic.
Declared in lib/templates/package.json but never imported anywhere in that package or its workspace members — dead weight and attack surface. Verify against build tooling before removing. (×3)
What to do
Remove unused dependencies, declare unlisted imports explicitly, and demote type-/test-only packages to devDependencies.
React / JS · Architecture — Import cycles in the module graph (D-386) — files that can only be understood and changed together.
Method: Import cycles in the module graph, detected exhaustively over JS/TS imports (the same cycle detection as the .NET coupling dimension). Deterministic.
Do you agree with this assessment?
WCAG coverage — what static analysis assessed
Statically assessed 12 of 55 WCAG 2.2 Level A/AA success criteria (22%; ≈24% of the 50 WCAG 2.1 AA criteria for EN 301 549). The other 43 require runtime or manual evaluation. Partial signal only (a clean result is necessary, not sufficient; static analysis fully verifies none). This is accessibility readiness, not a conformance claim — a WCAG conformance claim requires manual evaluation (WCAG-EM 1.0).
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.
Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Not included — 68 check(s) not relevant to this codebase
These checks had nothing to measure here (no tests, no git history, the codebase is small, or the architecture style doesn't apply), so they're omitted above rather than scored low.
AC1 Text alternatives — No image/media element found in the parsed markup — AC1 not applicable here.
AX1 Captive dependencies — no DI registrations detected
AX10 Code composition — not assessed — code composition is computed by ROLE over a document set that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
AX2 Stateful singletons — no 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 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
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 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
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 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
AXB2 Runtime readiness — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
C1 Data Protection — Not assessed: these personal data controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks personal data controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
C2 Access Controls — 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.
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.
D10 Test Quality — ~41439 lines of test source are present (.py, .rs) but the test-quality collector reads C# only, so skipped/assertion-free tests couldn't be counted. Not scored — this is a gap in the analyzer, not a finding about this repository.
D11 Test Reliability — Test reliability not included
D12 Dependency Hygiene — Dependency hygiene not measured — dependency manifest found but not parsed for hygiene
D14 License Compliance — Not scored — this repository's package manifest is not parsed for licence data yet. A gap in the analyzer's language coverage, NOT a finding that the repository's licenses are compliant (a Python pyproject.toml/requirements.txt (pip/uv/Poetry) and a Cargo manifest), which this pass does not parse yet — so this dimension asserts nothing about this repository's licensing in either direction.
D17 Explicit Debt — explicit-debt markers are read through a C# workspace today, so they were not read for this repository's language — this asserts nothing about how many markers the code carries. Not scored — this is a gap in the analyzer, not a finding about this repository
D18 Solution Shape — D18 scores the shape of a .NET solution; this repository has no .NET solution or project files, so the dimension does not apply.
D20 ADR Quality — N/A — ADRs are expected on deployable products with a user-facing host, not consumed libraries; no ADR log is required here.
D22 Internal API Consistency — No exposed public API
D23 Boundary Type-Coupling — Production source is present (.py, .rs, .ts, .tsx) 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 — No calls could be sampled, so navigability was not assessed — tracing effort is measured over resolved call sites and this target exposed none. Not scored — this is a gap in the analyzer's reach, not a verdict about this repository.
D30 Dependency Vulnerabilities — Not scored — no dependency manifest in a supported ecosystem was read for this repository. A gap in the analyzer's language coverage, NOT a finding that the repository is free of vulnerable dependencies (a Python pyproject.toml/requirements.txt (pip/uv/Poetry) and a Cargo manifest — not scanned yet) — where an OSV-supported manifest exists, dependency vulnerabilities for this repository are reported under D38 instead.
D32 Data Compliance (PII/GDPR) — No PII/GDPR-handling patterns detected (p/gdpr ruleset) — no data-compliance surface to assess.
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 C#/VB class graph, and this repository's production source is .py, .rs, .ts, .tsx, 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
D8 Code Coverage — Coverage not included — suite not readable by the collector
D9 Test Distribution — Test source is present (.py, .rs) but the test-pyramid classifier reads C# only, so its unit/integration/BDD/E2E split couldn't be counted. Not scored — this is a gap in the analyzer, not a finding about this repository.
DM2 Strongly-typed ids — no id-bearing domain types detected — strongly-typed-id adoption not assessable
DM3 Integration-event coupling — no integration events detected — coupling check not applicable
DM4 Rich vs anemic model — no data-bearing entities detected — rich-vs-anemic model not assessable
DM6 Domain ↔ infrastructure boundary — no domain-layer types detected — domain↔infrastructure boundary check not applicable
DM7 Repository granularity — no repository abstraction detected (e.g. uses a document session)
ED2 Event/command shape — not scored — deciding whether a command has more than one competing handler requires resolving the call graph, and a call made through an inferred or generic receiver has no resolvable owner in the source. Reported as guidance rather than measured
ED5 Idempotency — no mutating command handlers or message consumers detected — idempotency check not applicable
ES1 Event Sourcing — not scored — this repository shows none of the 3 signals this check looks for
GD1 Unfinished & placeholder code — no source files
IC1 Incompleteness & stubs — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
P12 CI test-gate honesty — Reported, not scored — and nothing was matched here. The coverage check applies to any stack, but the checks for excluded tests, skipped tests and sleep-based synchronisation currently recognise only some ecosystems' test-runner idioms, so on a repository built with another stack the zeros below mean 'not checked', not 'clean'.
P2 Observability — 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.
P5 DR & Backup — not evidenced — repo shows no backup/RTO/RPO controls; absence of evidence is not evidence of a working control
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`) into the repo working tree before the scan — a CI step is the usual place, since the artefact is commonly gitignored, 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.
R11 Import Boundaries — No recognizable feature-sliced/layered src layout — boundary rules not applicable.
R5 Dependency Freshness — uses a pnpm lockfile — dependency freshness not measured here; JS/npm CVEs are scored in D33 (JS/npm Dependency Vulnerabilities)
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.
SC1 Supply-chain hygiene — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
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
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
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
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
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.
Issue — 20 finding(s)
D38 · OSV Dependency Vulnerabilities· High CVE · ×12
High CVE: [GHSA redacted] uv.lock— aiohttp 3.13.5: [GHSA redacted] — aiohttp is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package aiohttp`)). This is 1 of 14 advisories with a published fix this scan raises against aiohttp 3.13.5, and their fixed versions do not agree — anything below 3.14.3 still leaves at least one of them open. Take this package to 3.14.3 or later: that is the floor for the package, not this row's target alone. This one row stands for the 14 advisories this scan raises against aiohttp 3.13.5: [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], PYSEC-2026-2106, PYSEC-2026-2112, PYSEC-2026-2113, PYSEC-2026-237.
High CVE: [GHSA redacted] lib/templates/pnpm-lock.yaml— brace-expansion 1.1.15: [GHSA redacted] — brace-expansion is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or pin brace-expansion to 1.1.16 with an `overrides` entry (`pnpm.overrides` for pnpm)). This is 1 of 3 advisories with a published fix this scan raises against brace-expansion 1.1.15, and their fixed versions do not agree — anything below 1.1.18 still leaves at least one of them open. Take this package to 1.1.18 or later: that is the floor for the package, not this row's target alone. This one row stands for the 3 advisories this scan raises against brace-expansion 1.1.15: [GHSA redacted], [GHSA redacted], [GHSA redacted].
High CVE: [GHSA redacted] lib/templates/pnpm-lock.yaml— brace-expansion 5.0.6: [GHSA redacted] — brace-expansion is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or pin brace-expansion to 5.0.7 with an `overrides` entry (`pnpm.overrides` for pnpm)). This is 1 of 3 advisories with a published fix this scan raises against brace-expansion 5.0.6, and their fixed versions do not agree — anything below 5.0.9 still leaves at least one of them open. Take this package to 5.0.9 or later: that is the floor for the package, not this row's target alone. This one row stands for the 3 advisories this scan raises against brace-expansion 5.0.6: [GHSA redacted], [GHSA redacted], [GHSA redacted].
High CVE: [GHSA redacted] lib/templates/pnpm-lock.yaml— fast-uri 3.1.2: [GHSA redacted] — fast-uri is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or pin fast-uri to 3.1.3 with an `overrides` entry (`pnpm.overrides` for pnpm)). This is 1 of 3 advisories with a published fix this scan raises against fast-uri 3.1.2, and their fixed versions do not agree — anything below 3.1.5 still leaves at least one of them open. Take this package to 3.1.5 or later: that is the floor for the package, not this row's target alone. This one row stands for the 3 advisories this scan raises against fast-uri 3.1.2: [GHSA redacted], [GHSA redacted], [GHSA redacted].
High CVE: [GHSA redacted] lib/templates/pnpm-lock.yaml— js-yaml 4.2.0: [GHSA redacted] — js-yaml is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or pin js-yaml to 4.3.0 with an `overrides` entry (`pnpm.overrides` for pnpm)).
High CVE: [GHSA redacted] uv.lock— keras 3.12.2: [GHSA redacted] — keras is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package keras`)). This is 1 of 3 advisories with a published fix this scan raises against keras 3.12.2, and their fixed versions do not agree — anything below 3.14.0 still leaves at least one of them open. Take this package to 3.14.0 or later: that is the floor for the package, not this row's target alone. This one row stands for the 3 advisories this scan raises against keras 3.12.2: [GHSA redacted], PYSEC-2026-2324, PYSEC-2026-73.
High CVE: [GHSA redacted] uv.lock— mcp 1.27.1: [GHSA redacted] — mcp is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package mcp`)). This is 1 of 3 advisories with a published fix this scan raises against mcp 1.27.1, and their fixed versions do not agree — anything below 1.28.1 still leaves at least one of them open. Take this package to 1.28.1 or later: that is the floor for the package, not this row's target alone. This one row stands for the 3 advisories this scan raises against mcp 1.27.1: [GHSA redacted], [GHSA redacted], [GHSA redacted].
High CVE: [GHSA redacted] uv.lock— pillow 12.2.0: [GHSA redacted] — upgrade to 12.3.0. This one row stands for the 13 advisories this scan raises against pillow 12.2.0: [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted].
High CVE: [GHSA redacted] uv.lock— pyjwt 2.12.1: [GHSA redacted] — pyjwt is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package pyjwt`)). This one row stands for the 5 advisories this scan raises against pyjwt 2.12.1: [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], PYSEC-2026-177.
High CVE: [GHSA redacted] uv.lock— python-multipart 0.0.29: [GHSA redacted] — python-multipart is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package python-multipart`)). This is 1 of 4 advisories with a published fix this scan raises against python-multipart 0.0.29, and their fixed versions do not agree — anything below 0.0.31 still leaves at least one of them open. Take this package to 0.0.31 or later: that is the floor for the package, not this row's target alone. This one row stands for the 4 advisories this scan raises against python-multipart 0.0.29: [GHSA redacted], PYSEC-2026-3037, PYSEC-2026-3040, PYSEC-2026-3041.
High CVE: [GHSA redacted] lib/templates/pnpm-lock.yaml— shell-quote 1.8.4: [GHSA redacted] — shell-quote is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or pin shell-quote to 1.9.0 with an `overrides` entry (`pnpm.overrides` for pnpm)).
High CVE: [GHSA redacted] uv.lock— starlette 0.52.1: [GHSA redacted] — starlette is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package starlette`)). This is 1 of 5 advisories with a published fix this scan raises against starlette 0.52.1, and their fixed versions do not agree — anything below 1.3.1 still leaves at least one of them open. Take this package to 1.3.1 or later: that is the floor for the package, not this row's target alone. This one row stands for the 5 advisories this scan raises against starlette 0.52.1: [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], PYSEC-2026-248.
D38 · OSV Dependency Vulnerabilities· High vulnerability · ×4
High vulnerability: [GHSA redacted] uv.lock— cryptography 48.0.0: [GHSA redacted] — cryptography is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package cryptography`)). This one row stands for the 4 advisories this scan raises against cryptography 48.0.0: [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted].
High vulnerability: [GHSA redacted] lib/templates/pnpm-lock.yaml— postcss 8.5.15: [GHSA redacted] — this repo declares postcss ^8.4.49, a range that ALREADY admits the fixed 8.5.18, so there is no manifest edit to make here. Re-resolve the lock so postcss moves onto 8.5.18 or later; if the flagged 8.5.15 comes back, a dependency is pinning it — upgrade that dependent, or pin postcss with an `overrides` entry (`pnpm.overrides` for pnpm) so only one copy resolves. This is 1 of 2 advisories with a published fix this scan raises against postcss 8.5.15, and their fixed versions do not agree — anything below 8.5.23 still leaves at least one of them open. Take this package to 8.5.23 or later: that is the floor for the package, not this row's target alone. This one row stands for the 2 advisories this scan raises against postcss 8.5.15: [GHSA redacted], [GHSA redacted].
High vulnerability: [GHSA redacted] Cargo.lock— pyo3 0.28.3: [GHSA redacted] — upgrade to 0.29.0. This one row stands for the 2 advisories this scan raises against pyo3 0.28.3: [GHSA redacted], [GHSA redacted].
High vulnerability: [GHSA redacted] lib/templates/pnpm-lock.yaml— svgo 4.0.1: [GHSA redacted] — svgo is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or pin svgo to 4.0.2 with an `overrides` entry (`pnpm.overrides` for pnpm)).
High: dependabot-missing-cooldown .github/dependabot.yml:4— This Dependabot configuration does not set a cooldown period. Newly published packages can be malicious or unstable. Add a `cooldown` block with `default-days: 7` to each `package-ecosystem` entry under `updates` to wait 7 days before proposing updates to newly published package versions. Reference: https://docs.github.com/en/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file#cooldown. This is a semgrep security-AUDIT rule reporting a POLICY that is absent or weaker than its recommendation, not an exploitable defect. Confirm whether the current setting is a deliberate decision for this repository — and apply the change where it is not; where it is (a policy your release process already enforces elsewhere, or one this repository has consciously opted out of), record the decision and leave the configuration as it is. This configuration file has 4 such entries; one cooldown decision clears them all — reported once.
High: uv-missing-dependency-cooldown pyproject.toml:135— This pyproject.toml configures uv but does not set a dependency cooldown. Newly published packages can be malicious or unstable. Add `exclude-newer = "7 days"` under `[tool.uv]` to wait 7 days before resolving newly published package versions. Added in: 0.9.17 Reference: https://docs.astral.sh/uv/concepts/resolution/#dependency-cooldowns. This is a semgrep security-AUDIT rule reporting a POLICY that is absent or weaker than its recommendation, not an exploitable defect. Confirm whether the current setting is a deliberate decision for this repository — and apply the change where it is not; where it is (a policy your release process already enforces elsewhere, or one this repository has consciously opted out of), record the decision and leave the configuration as it is.
Critical CVE: [GHSA redacted] uv.lock— vllm 0.21.0: [GHSA redacted] — upgrade to 0.22.0. This is 1 of 12 advisories with a published fix this scan raises against vllm 0.21.0, and their fixed versions do not agree — anything below 0.24.0 still leaves at least one of them open. Take this package to 0.24.0 or later: that is the floor for the package, not this row's target alone. This one row stands for the 12 advisories this scan raises against vllm 0.21.0: [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], PYSEC-2026-227.
Warning — 65 finding(s)
D38 · OSV Dependency Vulnerabilities· Medium CVE · ×7
Medium CVE: [GHSA redacted] uv.lock— diskcache 5.6.3: [GHSA redacted] — no fixed version has been published yet. Track the advisory; diskcache is not declared in this repo's manifests: it is pulled in transitively, so the action is on the dependency that requires it — upgrade or replace that dependent.
Medium CVE: [GHSA redacted] lib/templates/pnpm-lock.yaml— dompurify 3.4.8: [GHSA redacted] — dompurify is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or pin dompurify to 3.4.11 with an `overrides` entry (`pnpm.overrides` for pnpm)). This is 1 of 3 advisories with a published fix this scan raises against dompurify 3.4.8, and their fixed versions do not agree — anything below 3.4.12 still leaves at least one of them open. Take this package to 3.4.12 or later: that is the floor for the package, not this row's target alone. This one row stands for the 3 advisories this scan raises against dompurify 3.4.8: [GHSA redacted], [GHSA redacted], [GHSA redacted].
Medium CVE: [GHSA redacted] uv.lock— pydantic-settings 2.14.1: [GHSA redacted] — pydantic-settings is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package pydantic-settings`)).
Medium CVE: [GHSA redacted] uv.lock— setuptools 80.10.2: [GHSA redacted] — setuptools is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package setuptools`)).
Medium CVE: [GHSA redacted] uv.lock— setuptools 81.0.0: [GHSA redacted] — setuptools is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package setuptools`)).
Medium CVE: [GHSA redacted] lib/templates/pnpm-lock.yaml— ts-deepmerge 7.0.3: [GHSA redacted] — ts-deepmerge is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (— 8.0.0 is a MAJOR ahead of the resolved 7.0.3, so an `overrides` pin would force a breaking version under a dependent written against 7.0.3; upgrading the declaring package is the remedy, and where no patched release exists in your major line, record the exposure instead).
Medium CVE: [GHSA redacted] lib/templates/pnpm-lock.yaml— unhead 1.11.20: [GHSA redacted] — unhead is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (— 2.1.13 is a MAJOR ahead of the resolved 1.11.20, so an `overrides` pin would force a breaking version under a dependent written against 1.11.20; upgrading the declaring package is the remedy, and where no patched release exists in your major line, record the exposure instead). This is 1 of 3 advisories with a published fix this scan raises against unhead 1.11.20, and their fixed versions do not agree — anything below 2.1.13 still leaves at least one of them open. Take this package to 2.1.13 or later: that is the floor for the package, not this row's target alone. This one row stands for the 3 advisories this scan raises against unhead 1.11.20: [GHSA redacted], [GHSA redacted], [GHSA redacted].
Medium: missing-autoescape-disabled flama/_cli/config/app.py:344— Detected a Jinja2 environment without autoescaping. Jinja2 does not autoescape by default. This is dangerous if you are rendering to a browser because this allows for cross-site scripting (XSS) attacks. If you are in a web context, enable autoescaping by setting 'autoescape=True.' You may also consider using 'jinja2.select_autoescape()' to only enable automatic escaping for certain file extensions. This is a semgrep security-AUDIT rule: it reports that a sensitive construct is present, not that it is exploitable here. Confirm whether this site handles untrusted input or is reachable across a trust boundary — and apply the change where it is; where the construct is required by the platform or protocol it calls into, and carries no untrusted data (a syscall/FFI shim, a build- or debug-gated tool, a fixed local surface), record the review and leave the code as it is.
Medium: python-logger-credential-disclosure flama/authentication/jwt/jwt.py:191— Detected a python logger call with a potential hardcoded secret "Decoded token: %s" being logged. This may lead to secret credentials being exposed. Make sure that the logger is not logging sensitive information. This is a semgrep security-AUDIT rule: it reports that a sensitive construct is present, not that it is exploitable here. Confirm whether this site handles untrusted input or is reachable across a trust boundary — and apply the change where it is; where the construct is required by the platform or protocol it calls into, and carries no untrusted data (a syscall/FFI shim, a build- or debug-gated tool, a fixed local surface), record the review and leave the code as it is.
Medium: python-logger-credential-disclosure flama/models/engine/llm/codec.py:340— Detected a python logger call with a potential hardcoded secret "LLM run start: id=%s input_tokens=%s channel_scanner=%s tool_scanner=%s tool_parser=%s" being logged. This may lead to secret credentials being exposed. Make sure that the logger is not logging sensitive information. This is a semgrep security-AUDIT rule: it reports that a sensitive construct is present, not that it is exploitable here. Confirm whether this site handles untrusted input or is reachable across a trust boundary — and apply the change where it is; where the construct is required by the platform or protocol it calls into, and carries no untrusted data (a syscall/FFI shim, a build- or debug-gated tool, a fixed local surface), record the review and leave the code as it is.
Medium: python-logger-credential-disclosure flama/models/engine/llm/codec.py:376— Detected a python logger call with a potential hardcoded secret "LLM run done: id=%s input_tokens=%s output_tokens=%d stop_reason=%s tool_calls=%d elapsed_ms=%.1f" being logged. This may lead to secret credentials being exposed. Make sure that the logger is not logging sensitive information. This is a semgrep security-AUDIT rule: it reports that a sensitive construct is present, not that it is exploitable here. Confirm whether this site handles untrusted input or is reachable across a trust boundary — and apply the change where it is; where the construct is required by the platform or protocol it calls into, and carries no untrusted data (a syscall/FFI shim, a build- or debug-gated tool, a fixed local surface), record the review and leave the code as it is.
Medium: avoid-pickle flama/serialize/model_serializers/sklearn.py:27— Avoid using `pickle`, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. This is a semgrep security-AUDIT rule: it reports that a sensitive construct is present, not that it is exploitable here. Confirm whether this site handles untrusted input or is reachable across a trust boundary — and apply the change where it is; where the construct is required by the platform or protocol it calls into, and carries no untrusted data (a syscall/FFI shim, a build- or debug-gated tool, a fixed local surface), record the review and leave the code as it is.
Medium: avoid-pickle flama/serialize/model_serializers/sklearn.py:34— Avoid using `pickle`, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. This is a semgrep security-AUDIT rule: it reports that a sensitive construct is present, not that it is exploitable here. Confirm whether this site handles untrusted input or is reachable across a trust boundary — and apply the change where it is; where the construct is required by the platform or protocol it calls into, and carries no untrusted data (a syscall/FFI shim, a build- or debug-gated tool, a fixed local surface), record the review and leave the code as it is.
Hotspot: flama/models/wire/dialect/llm/anthropic/assembler.py flama/models/wire/dialect/llm/anthropic/assembler.py— flama/models/wire/dialect/llm/anthropic/assembler.py changed 3 times in last 90 days, max complexity 16. 1 of those changes was a fix/bug commit, so the churn is repair rather than feature work. Before the next change lands here, cover the area it touches with tests, 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.
Hotspot: flama/models/engine/backend/llm/mlx.py flama/models/engine/backend/llm/mlx.py— flama/models/engine/backend/llm/mlx.py changed 3 times in last 90 days, max complexity 15. 1 of those changes was a fix/bug commit, so the churn is repair rather than feature work. Before the next change lands here, cover the area it touches with tests, 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.
Hotspot: flama/models/wire/dialect/llm/anthropic/parser.py flama/models/wire/dialect/llm/anthropic/parser.py— flama/models/wire/dialect/llm/anthropic/parser.py changed 2 times in last 90 days, max complexity 17. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, behind tests written first.
Hotspot: flama/models/wire/dialect/llm/native/parser.py flama/models/wire/dialect/llm/native/parser.py— flama/models/wire/dialect/llm/native/parser.py changed 2 times in last 90 days, max complexity 16. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, behind tests written first.
Duplicated block (9 lines × 2) flama/models/resources/llm.py:113— flama/models/resources/llm.py:113-121 | flama/models/resources/ml.py:126-134 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (9 lines × 2) flama/models/resources/serving/llm/ollama.py:52— flama/models/resources/serving/llm/ollama.py:52-60 | flama/models/resources/serving/llm/ollama.py:181-189 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (9 lines × 2) flama/models/resources/serving/llm/ollama.py:94— flama/models/resources/serving/llm/ollama.py:94-102 | flama/models/resources/serving/llm/ollama.py:205-213 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (9 lines × 2) flama/models/resources/serving/llm/ollama.py:179— flama/models/resources/serving/llm/ollama.py:179-187 | flama/models/resources/serving/llm/openai.py:366-374 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once.
Duplicated block (12 lines × 2) flama/models/resources/llm.py:48— flama/models/resources/llm.py:48-59 | flama/models/resources/ml.py:104-115 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (12 lines × 2) flama/models/resources/serving/llm/openai.py:132— flama/models/resources/serving/llm/openai.py:132-143 | flama/models/resources/serving/llm/openai.py:309-320 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `flama/models/resources/serving/llm/openai.py:132` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (12 lines × 2) flama/routing/routes/http.py:59— flama/routing/routes/http.py:59-70 | flama/routing/routes/websocket.py:19-30 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `flama/routing/routes/http.py:59` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (6 lines × 2) flama/ddd/repositories/http.py:77— flama/ddd/repositories/http.py:77-82 | flama/ddd/repositories/http.py:97-102 — 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) flama/models/streams.py:312— flama/models/streams.py:312-317 | flama/models/streams.py:329-334 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (6 lines × 2) flama/serialize/protocols/v1.py:252— flama/serialize/protocols/v1.py:252-257 | flama/serialize/protocols/v2.py:413-418 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Change coupling: limit_offset.py ↔ page_number.py flama/pagination/paginators/limit_offset.py— `flama/pagination/paginators/limit_offset.py` and `flama/pagination/paginators/page_number.py` change together 75% of the time (24 of the 32 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well) 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.
Change coupling: applications.py ↔ jsondata.py flama/applications.py— `flama/applications.py` and `flama/codecs/http/jsondata.py` change together 60% of the time (6 of the 10 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well) with no explicit dependency — the edge is real but nothing declares it. Read the pair before acting: if one registers itself into the other through a hook or an initialiser, the missing dependency is DELIBERATE — the registration is the link, and it is meant not to be an import — and the thing to add is a comment on each side naming the other, not a merge; if they simply belong together, co-locate them; if neither holds, the coupling is hidden and worth breaking.
Duplicated block (10 lines × 2) flama/models/resources/serving/llm/openai.py:84— flama/models/resources/serving/llm/openai.py:84-93 | flama/models/resources/serving/llm/openai.py:258-267 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `flama/models/resources/serving/llm/openai.py:84` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (10 lines × 2) flama/serialize/serializer.py:179— flama/serialize/serializer.py:179-188 | flama/serialize/serializer.py:245-254 — 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. 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 (7 lines × 2) flama/ddd/repositories/http.py:184— flama/ddd/repositories/http.py:184-190 | flama/ddd/repositories/http.py:200-206 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (7 lines × 2) flama/resources/crud.py:295— flama/resources/crud.py:295-301 | flama/resources/crud.py:338-344 — 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 (5 lines × 2) flama/models/wire/dialect/llm/anthropic/parser.py:140— flama/models/wire/dialect/llm/anthropic/parser.py:140-144 | flama/models/wire/dialect/llm/anthropic/parser.py:248-252 — 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 (5 lines × 2) flama/schemas/_libs/marshmallow/adapter.py:120— flama/schemas/_libs/marshmallow/adapter.py:120-124 | flama/schemas/_libs/typesystem/adapter.py:99-103 — 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.
Parser._parse_message (cyclomatic 22) flama/models/wire/dialect/_base.py:134— Parser._parse_message has cyclomatic complexity 22 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
AnthropicParser._expand_assistant (cyclomatic 17) flama/models/wire/dialect/llm/anthropic/parser.py:186— AnthropicParser._expand_assistant has cyclomatic complexity 17 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
AnthropicAssembler.envelope (cyclomatic 16) flama/models/wire/dialect/llm/anthropic/assembler.py:45— AnthropicAssembler.envelope 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.
NativeParser._parse_part (cyclomatic 16) flama/models/wire/dialect/llm/native/parser.py:31— NativeParser._parse_part 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.
SchemaGenerator.get_endpoints (cognitive 28) flama/schemas/openapi.py:613— SchemaGenerator.get_endpoints has cognitive complexity 28 (threshold 15). Drivers by points: if/else 16, loops 9, boolean chains 3 (nesting depth added 17). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
MLXBackend.generate (cognitive 24) flama/models/engine/backend/llm/mlx.py:240— MLXBackend.generate has cognitive complexity 24 (threshold 15). Drivers by points: if/else 16, boolean chains 4, ternaries 3, 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.
Parser._parse_message (cognitive 24) flama/models/wire/dialect/_base.py:134— Parser._parse_message has cognitive complexity 24 (threshold 15). Drivers by points: if/else 19, boolean chains 4, match/switch 1 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
AnthropicParser._parse_part (cognitive 22) flama/models/wire/dialect/llm/anthropic/parser.py:93— AnthropicParser._parse_part has cognitive complexity 22 (threshold 15). Drivers by points: if/else 18, boolean chains 3, match/switch 1 (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
AnthropicAssembler.envelope (cognitive 21) flama/models/wire/dialect/llm/anthropic/assembler.py:45— AnthropicAssembler.envelope has cognitive complexity 21 (threshold 15). Drivers by points: if/else 14, boolean chains 3, match/switch 2, loops 1, ternaries 1 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
WorkerType.__new__ (cognitive 19) flama/ddd/workers/_base.py:103— WorkerType.__new__ has cognitive complexity 19 (threshold 15). Drivers by points: if/else 16, error handling 3 (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.
MarkerScanner._scan_outside (cognitive 19) flama/models/engine/llm/decoder/markers.py:192— MarkerScanner._scan_outside has cognitive complexity 19 (threshold 15). Drivers by points: if/else 12, boolean chains 4, ternaries 3 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
Decoder._iter_marker_bodies (cognitive 18) flama/models/engine/llm/decoder/decoder.py:265— Decoder._iter_marker_bodies has cognitive complexity 18 (threshold 15). Drivers by points: if/else 15, loops 3 (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.
TransformerLLMBackend.prepare_input (cognitive 17) flama/models/engine/backend/llm/_base.py:444— TransformerLLMBackend.prepare_input has cognitive complexity 17 (threshold 15). Drivers by points: if/else 9, loops 3, match/switch 3, boolean chains 2 (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
EventBuffer.__anext__ (cognitive 17) flama/models/transport/output/llm/buffer.py:84— EventBuffer.__anext__ has cognitive complexity 17 (threshold 15). Drivers by points: if/else 7, error handling 6, loops 3, boolean chains 1 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
ResourceType._get_attribute (cognitive 17) flama/resources/resource.py:51— ResourceType._get_attribute has cognitive complexity 17 (threshold 15). Drivers by points: if/else 14, loops 2, error handling 1 (nesting depth added 11). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
OpenAPISchemaRegistry._get_schema_references_from_schema (cognitive 17) flama/schemas/openapi.py:427— OpenAPISchemaRegistry._get_schema_references_from_schema has cognitive complexity 17 (threshold 15). Drivers by points: if/else 13, boolean chains 3, loops 1 (nesting depth added 5). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
AnthropicParser._expand_assistant (cognitive 16) flama/models/wire/dialect/llm/anthropic/parser.py:186— AnthropicParser._expand_assistant has cognitive complexity 16 (threshold 15). Drivers by points: if/else 11, boolean chains 2, match/switch 2, loops 1 (nesting depth added 4). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
OpenAIParser._parse_part (cognitive 16) flama/models/wire/dialect/llm/openai/parser.py:30— OpenAIParser._parse_part has cognitive complexity 16 (threshold 15). Drivers by points: if/else 13, boolean chains 2, match/switch 1 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
Path.__init__ (cognitive 16) flama/url.py:186— Path.__init__ has cognitive complexity 16 (threshold 15). Drivers by points: if/else 8, ternaries 6, boolean chains 2 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
FileTooLong: schemas/openapi.py flama/schemas/openapi.py:0— FileTooLong — 613 significant lines (blank, comment-only and punctuation-only lines excluded). To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
Duplicated block (37 lines × 2) flama/models/_base.py:379— flama/models/_base.py:379-420 | flama/models/_base.py:448-484 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (28 lines × 3) flama/models/resources/serving/llm/anthropic.py:180— flama/models/resources/serving/llm/anthropic.py:180-209 | flama/models/resources/serving/llm/openai.py:154-182 | flama/models/resources/serving/llm/openai.py:331-358 — there are 3 copies across 2 file(s) — more copies than files, so at least one file holds the block twice. Extract it once into a single shared function every call site can reach and call it from all 3 sites; resolving a subset leaves the remainder to drift apart. Read the line range as the matched WINDOW rather than a finished unit: at `flama/models/resources/serving/llm/anthropic.py:180` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (26 lines × 2) flama/pagination/paginators/limit_offset.py:110— flama/pagination/paginators/limit_offset.py:110-135 | flama/pagination/paginators/page_number.py:114-139 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `flama/pagination/paginators/limit_offset.py:110` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (17 lines × 2) flama/models/resources/serving/llm/anthropic.py:89— flama/models/resources/serving/llm/anthropic.py:89-105 | flama/models/resources/serving/llm/openai.py:69-87 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once.
Duplicated block (15 lines × 3) flama/serialize/serializer.py:238— flama/serialize/serializer.py:238-252 | flama/serialize/serializer.py:304-322 | flama/serialize/serializer.py:351-369 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Read the line range as the matched WINDOW rather than a finished unit: at `flama/serialize/serializer.py:238` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (15 lines × 2) flama/models/wire/dialect/llm/openai/assembler.py:82— flama/models/wire/dialect/llm/openai/assembler.py:82-96 | flama/models/wire/dialect/llm/openai/assembler.py:160-176 — 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 (11 lines × 3) flama/models/resources/serving/llm/anthropic.py:58— flama/models/resources/serving/llm/anthropic.py:58-68 | flama/models/resources/serving/llm/openai.py:38-48 | flama/models/resources/serving/llm/openai.py:220-230 — there are 3 copies across 2 file(s) — more copies than files, so at least one file holds the block twice. Extract it once into a single shared function every call site can reach and call it from all 3 sites; resolving a subset leaves the remainder to drift apart. Read the line range as the matched WINDOW rather than a finished unit: at `flama/models/resources/serving/llm/anthropic.py:58` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (11 lines × 2) flama/models/resources/serving/llm/ollama.py:196— flama/models/resources/serving/llm/ollama.py:196-206 | flama/models/resources/serving/llm/openai.py:383-394 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once.
Duplicated block (10 lines × 4) flama/models/wire/dialect/llm/anthropic/parser.py:95— flama/models/wire/dialect/llm/anthropic/parser.py:95-104 | flama/models/wire/dialect/llm/native/parser.py:33-42 | flama/models/wire/dialect/llm/ollama/parser.py:67-76 | flama/models/wire/dialect/llm/openai/parser.py:32-41 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere all 4 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 4 times. 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. 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.
D11 · Test Reliability· Test reliability not included · ×1
Test reliability not included — Test source is present (.py, .rs) but the built-in reliability runner does not support this repository's ecosystem, so flakiness couldn't be assessed. Not scored — this is a gap in the analyzer's language coverage, not a finding about this repository.
Off-boarding risk: anonymized user #1 — If anonymized user #1 becomes unavailable, 95 significant file(s) lose their only recent owner: flama/models/streams.py, flama/schemas/openapi.py, lib/core/src/compression.rs, flama/_cli/commands/model.py, flama/models/engine/backend/llm/_base.py, flama/models/resources/serving/llm/openai.py, flama/models/resources/serving/llm/ollama.py, flama/_cli/config/uvicorn.py (+87 more). Pair on, review, or document these before any departure.
D28 · Secrets (history)· Rotate the exposed credentials · ×1
Rotate the exposed credentials — git history can't be un-committed — Some of these secrets are in git HISTORY: deleting the file does not remove them (the commit persists on every clone, fork and backup). The remediation is to ROTATE each historically-exposed credential and treat it as compromised — not to delete the file. Rewriting history is disruptive and unreliable across existing forks. (Working-tree-only secrets — no commit — can instead be removed from the file and moved to a secret store.)
Low IaC: DS-0026 Dockerfiles/slim/Dockerfile— No HEALTHCHECK defined Without one the runtime only knows whether the process is alive, not whether it is serving, so a wedged container is restarted by nobody. The step: add a `HEALTHCHECK` to the image that probes the service the way a client would — this image already declares `EXPOSE 8000`, so a request to `localhost:8000` on the service's own health or root route, exiting non-zero when it does not answer, is the probe — and give it an `--interval`, a `--timeout` and a `--start-period` long enough to cover startup. If the image ships no HTTP client, probe with whatever the runtime already has, or declare the check in the orchestrator instead and say so here.
No build provenance — No SLSA provenance generation or build attestation found in CI — nothing binds a released artifact to the build that produced it, so a consumer cannot tell your artifact from a substituted one. On GitHub Actions, `actions/attest-build-provenance` (or slsa-github-generator) emits one from the job's own OIDC identity; elsewhere, run `cosign attest` over the released artifact from the release pipeline and publish the attestation beside it.
No artifact signing — No artifact signing found in CI — sign your released artifacts with whatever your ecosystem ships (a GPG/minisign detached signature — or `cosign sign-blob` — over the release archives, or over a checksum file published alongside them, cosign/sigstore for container images, PEP 740 attestations via `pypa/gh-action-pypi-publish` under PyPI Trusted Publishing (OIDC) for wheels/sdists, `cargo publish` from CI under crates.io trusted publishing (OIDC) rather than a long-lived token for crates) so consumers can verify what you built.
D36 · Supply-chain Provenance & Signing· No SBOM · ×1
No SBOM — No SBOM generation or committed SBOM found — produce one with what your ecosystem ships (`cargo sbom` or `cargo cyclonedx` for the Cargo dependency graph — or `cargo auditable build`, which embeds it in the shipped binary, `cyclonedx-py` over the resolved Python environment/lockfile, `syft` (or `anchore/sbom-action` in CI) over the source tree or released image). Publish it as a release asset (`*.spdx.json` / `*.cdx.json`) so consumers can see what they are installing.
Coverage not included — suite not readable by the collector — Coverage NOT MEASURED: test source is present (.py, .rs) but the built-in coverage collector has no runner for this repository's ecosystem — so this suite was never executed by it. Not scored — this is a gap in the analyzer's language coverage, not a defect in the repo. To have real coverage read, produce a coverage report in a standard format (`coverage run -m pytest` then `coverage xml`, or lcov — `cargo llvm-cov --lcov --output-path lcov.info`) into the repo working tree before the scan — a CI step is the usual place, since the artefact is commonly gitignored.
Info — 2 finding(s)
D12 · Dependency Hygiene· Dependency hygiene not measured · ×1
Dependency hygiene not measured — dependency manifest found but not parsed for hygiene — This repository's dependency manifests (a Python pyproject.toml/requirements.txt (pip/uv/Poetry) and a Cargo manifest) were found, but this pass cannot parse them for hygiene, so no package was assessed. Zero packages read is NOT a clean dependency tree, so this is NOT SCORED — a gap in the analyzer, not a verdict about this repository. This row is about dependency HYGIENE — outdated, deprecated or unmaintained direct dependencies; known CVEs in the same dependency graph are a separate question, reported under D38 wherever the manifest is OSV-readable.
D22 · Internal API Consistency· No exposed public API · ×1
No exposed public API — No intentionally-exposed types (IsPackable or .Contracts) to evaluate.
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.
runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; network egress policy is a cluster-native control that may live at the platform/firewall layer, so there is nothing to assess here.
runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; seccomp/AppArmor/SELinux confinement is a workload-level control, so there is nothing to assess here.
runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; runtime threat-detection and admission-control policy are cluster-level controls, so there is nothing to assess here.
0
—
Run 019fcf40-40eb-72bb-8bb6-53c348492b6d · every finding is also locatable in findings.md, and the complete scoring record (with exit codes + durations) in sidecar.json.
Issues: 20 · Warnings: 65 · Recommendations: 10 · Info: 2 — Appendix A · all findings · full markdown report.
Generated by Watchdog — deterministic code-health analysis. 05-08-2026 @ 00:08 UTC.
Downloadable artifacts
Machine-readable and reproducible from this commit + frozen rubric — drop them straight into a contract appendix, a CRA dossier, or a downstream SCA / VEX tool.