Public report — st2, 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.
124findings with an exact file:lineof 132 — the remainder are repo-wide signals (a dimension-level measurement, not a single line); open any file:line and verify
20/94dimensions across the health lenses54185 LoC — wide & deep
Executive summary
Read through the Preview lens: this repo is pre-1.0 / in development, so the colour bands are relaxed to what a preview needs — *green* means good enough for a preview, not yet production-stable. Code correctness and security stay near-strict even here; the score itself is absolute and comparable across repos.
sh1nu11b1/st2 carries serious gaps (38%). Several issues below can materially affect correctness, security, or the cost of changing it — and propagate to everything that depends on it.
It is strongest in Architecture (96%) — the structure is clean and changes stay contained. Code Health (83%) 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 (18%) — 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. Security (45%) is the next concern — exposure to security and compliance incidents is elevated.
Leadership focus, highest impact first: CI workflow that builds and runs the test suite on every push/PR (CI/CD gates); Run what this repository's stack ships (Security & performance tooling); 1 Leaked secret finding(s) (Secret Scanning).
For scale: Medium (~54,185 production lines); rebuilding it from scratch would take roughly ~0.5 person-years (~1 engineer). Approximate, ±~30%.
It builds on a genuinely strong Architecture foundation (96%); 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.
0.7× (at 38% 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 ~0.5 person-years of build effort (about ~€76,000 to rebuild). Its weakest lens is Readiness at 18% — the part of that asset most exposed by the findings below.
How we model this: boilerplate at a scaffolding rate + logic × domain Low (×0.9) — transaction-script/CRUD × a 0.7× quality factor, at €60–95/h; indicative, ±~30% · size measured directly from source · effort from total production LoC as straight-line logic (the tier split is a C#-only syntax walk), a conservative lower bound. Indicative only — most sensitive to the hourly rate and the domain tier (both tunable in config).
Top priorities
The highest-leverage moves; the full ranked list is in the Roadmap below.
1
Resolve the 1 Leaked secret finding(s) in Secret Scanning — start with dummy_rsa.
Run what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — — locally for now, since there is no CI pipeline here yet, and as a step of the first workflow you add so a security regression fails the build instead of landing.
Value concentrated against a weak lens · High · Value at risk
This is a Medium asset (~0.5 person-years to rebuild), and its weakest lens is Readiness at 18%. 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 CI workflow that builds and runs the test suite on every push/PR. The rest can wait behind it.
Evidence: priority ranking: top of 5 ranked by impact/effort
→ Add a CI workflow that builds and runs the test suite on every push/PR.
Findings mapped to OWASP categories; the specific CVEs/secrets are in the Security dimension cards below and findings.md (redacted only on the public version of this report).
OWASP category
Findings
Severity
A03:2021 — Injection
10
Medium
A02:2021 — Cryptographic Failures
7
High / Critical
A06:2021 — Vulnerable & Outdated Components
6
High / Critical
Roadmap
First, establish a CI/CD pipeline to build the code and run the test suite on every push or pull request. Integrate security and performance tooling, such as SAST and secret scanning, into this pipeline to ensure that security regressions fail the build. Immediately address the single leaked secret found in the repository. Finally, implement versioning in the build manifest or tag releases with semver to ensure traceability, and document significant architectural decisions in a dedicated directory.
Ranked by impact ÷ effort. "Helps" is the estimated gain on the 0–100 health score.
Do this
Helps
Effort
Dimension
Resolve the 1 Leaked secret finding(s) in Secret Scanning — start with dummy_rsa.
Run what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — — locally for now, since there is no CI pipeline here yet, and as a step of the first workflow you add so a security regression fails the build instead of landing.
Stamp a version in your build/package manifest (e.g. csproj <Version>, package.json, pyproject.toml, Cargo.toml, or a VERSION file) or tag releases with semver so builds and releases are traceable.
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).
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. 19 of 20 evaluated dimensions are computed purely by tools and static analysis (confidence 1.0); 1 documentation/naming judgement(s) are LLM-assisted and labelled advisory. Overall confidence is 0.4 — 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 — 20 dimensions across the health lenses
D1D2D3D4D13D15D21D28D29D34D35D38AX5M1M2M3M4P1P3P6
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, 124 of 132 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.
D19 Documentation Quality — LLM provider failed — The model provider returned an unusable result, so this LLM-assisted dimension fell back to a measurement gap (confidence 0) rather than a penalty. Re-run with a reachable provider to score it.
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 (.gitattributes linguist-generated/vendored, .editorconfig generated_code): none declared — every source file was scored.
Limitations & what we did not check
Watchdog assesses the repository exactly as committed, and only the repository. By design it does not reach outside the source tree: the live cloud account, the running CI/CD pipeline, the host's branch-protection and approval rules, the production configuration, or a restore actually exercised against a backup are all out of scope. That boundary is a feature, not a gap — a repo-relative, deterministic scan re-runs identically on any commit and every finding opens at a real file and line, where a live audit can neither be reproduced nor traced. The visible consequence is that controls which leave no in-repo evidence are reported as "not evidenced" and excluded from the score rather than awarded a number a static scan cannot justify.
Per-dimension blind spots
For each dimension that was measured, what a static, repo-only scan structurally cannot see — the honest edge of the measurement, not a failure of it.
D1 Cyclomatic Complexity: Cyclomatic complexity counts branches statically — it cannot tell an essential decision tree from accidental tangle, nor see complexity that lives in data or configuration (large switch-case token tables, DSL lexers/parsers, data-as-code rule tables) rather than control flow: a tokenizer's many single-character cases read as high complexity though each branch is trivial.
D2 Cognitive Complexity: Cognitive-complexity heuristics approximate how hard code is to follow; genuine domain difficulty and well-named intent that eases reading are not captured.
D3 God Classes: "God class" is sized by members and responsibilities visible in the type — a deliberately broad facade over a coherent subsystem can read the same as an accidental grab-bag. For front-end JS the file-length check is cohesion-aware (a single-responsibility module — one class/IIFE — earns a 3× threshold), but cohesion is approximated from top-level declarations, not true dependency structure.
D4 Code Duplication: Duplication is token-similarity (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.
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").
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.
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.
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 (2): 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.
11 method(s) exceeded the cyclomatic complexity threshold of 15; the worst was Router.__call__ at 56. A further 1 method(s) were over the threshold but excluded as flat dispatchers (a long switch/match over independent cases: many branches, almost no nesting), the largest being resolvers.get_resolver_for_resource_type at 17 — they are counted neither in the figure above nor in this dimension's score.
+ 6 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Resolve the 1 Router.__call__ (cyclomatic 56) finding(s) in Cyclomatic Complexity — start with router.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 ActionChainRunner.run (cyclomatic 25) finding(s) in Cyclomatic Complexity — start with action_chain_runner.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 PackRegisterController.post (cyclomatic 25) finding(s) in Cyclomatic Complexity — start with packs.py. — One of this dimension's main actionable groups (1 warning-level).
Stand up a CI pipeline, then gate Cyclomatic Complexity in it to reach Verified (currently Documented). — This repository has no CI pipeline, so there is nothing to add a gate to yet — the pipeline comes first. Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d1_recommendation.md · top locations in Appendix A, every location in findings.md.
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.
+ 38 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Resolve the 1 Router.__call__ (cognitive 113) finding(s) in Cognitive Complexity — start with router.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 MultiColumnTable.format (cognitive 52) finding(s) in Cognitive Complexity — start with table.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 PackRegisterController.post (cognitive 43) finding(s) in Cognitive Complexity — start with packs.py. — One of this dimension's main actionable groups (1 warning-level).
Stand up a CI pipeline, then gate Cognitive Complexity in it to reach Verified (currently Documented). — This repository has no CI pipeline, so there is nothing to add a gate to yet — the pipeline comes first. Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d2_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D3 · God Classes9.8 / 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.
Resolve the 1 Leaked secret finding(s) in Secret Scanning — start with dummy_rsa. — One of this dimension's main actionable groups (1 issue-level).
Stand up a CI pipeline, then gate Secret Scanning in it to reach Verified (currently Documented). — This repository has no CI pipeline, so there is nothing to add a gate to yet — the pipeline comes first. Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d13_recommendation.md · top locations in Appendix A, every location in findings.md.
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.
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.
10 finding(s): 0 critical, 10 high, 0 medium, 0 low. Remediation for historically-committed secrets is credential rotation — they remain in history regardless of later deletion.
Secret: generic-api-key · ×5st2common/st2common/openapi.yaml.j2:42detected by gitleaks finding
Rotate the exposed credentials — git history can't be un-committed
What to do
Resolve the 5 Secret finding(s) in Secrets (history) — start with openapi.yaml.j2 (3), .codecov.yml, st2_kvstore_demo.crypto.key.json. — One of this dimension's main actionable groups (5 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).
Medium: insecure-ssl-version · ×6conf/nginx/st2.conf:34detected by semgrep finding
Low: request-host-used · ×4conf/HA/nginx/st2.conf.blueprint.sample:16detected by semgrep finding
What to do
Resolve the 6 Medium finding(s) in Static Analysis (SAST) — start with st2.conf, trace.py, trigger.py. — One of this dimension's main actionable groups (6 warning-level).
Resolve the 4 Low finding(s) in Static Analysis (SAST) — start with st2.conf.blueprint.sample, st2.conf.controller.sample, st2.conf. — One of this dimension's main actionable groups (4 recommendation-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.
280 of 280 significant source file(s) are orphaned — their living knowledge has decayed to nothing, so no one currently understands them. The largest is st2client/st2client/commands/action.py.
Largest orphaned file · ×3st2client/st2client/commands/action.py
Dormant codebase
What to do
Resolve the 3 Largest orphaned file finding(s) in Knowledge Freshness — start with action.py, resolvers.py, action_chain_runner.py. — One of this dimension's main actionable groups (3 recommendation-level).
Resolve the 1 Dormant codebase finding(s) in Knowledge Freshness. — One of this dimension's main actionable groups (1 recommendation-level).
Detailed fixes: d34_recommendation.md · top locations in Appendix A, every location in findings.md.
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 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] · ×3fixed-requirements.txtdetected by osv-scanner finding
Critical CVE: [GHSA redacted]fixed-requirements.txtdetected by osv-scanner finding
Medium CVE: [GHSA redacted] · ×2fixed-requirements.txtdetected by osv-scanner finding
What to do
Resolve the 3 High CVE finding(s) in OSV Dependency Vulnerabilities — start with fixed-requirements.txt (3). — One of this dimension's main actionable groups (3 issue-level).
Resolve the 1 Critical CVE finding(s) in OSV Dependency Vulnerabilities — start with fixed-requirements.txt. — One of this dimension's main actionable groups (1 issue-level).
Resolve the 2 Medium CVE finding(s) in OSV Dependency Vulnerabilities — start with fixed-requirements.txt (2). — One of this dimension's main actionable groups (2 warning-level).
Detailed fixes: d38_recommendation.md · top locations in Appendix A, every location in findings.md.
Other · Architecture — Whether the codebase has a recognisable, scale-appropriate structure (a named architectural style, or modular enough for its size) rather than being an ad-hoc ball of mud.
Method: Roslyn plus csproj analysis: architecture style detection (DDD, clean, vertical-slice, CQRS) and structure fitness for repo size. Deterministic.
Maturity · Maturity — Whether the repo and its projects have a README, and whether it's substantive and current.
Method: Filesystem scan: README presence, word count, and headings for depth; git history for staleness. Exhaustive across root and project dirs, deterministic.
What to do
Add a build/run (quick start) section to the root README — the first thing a newcomer needs.
Add a 'Testing' section to the root README — how to run the test suite.
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.
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).
Maturity · Maturity — Whether the repo is organised deliberately — src/test separation and consistent project naming.
Method: Filesystem scan: src/test folder separation and namespace-prefix consistency (majority RootNamespace agreement). Exhaustive across projects, deterministic.
Production code isn't grouped under a src/ folder — it's spread across several top-level directories, so there's no one place that says 'this is the product'.
What to do
Group production code under src/ (or split deliberately, e.g. backend/ + frontend/) so production and tooling code aren't mixed at the root.
Maturity · Maturity — Whether the README actually describes the code that exists (LLM-judged, advisory).
Method: Judged by language model at low temperature: README accuracy versus actual projects, within a disclosed tolerance. Advisory, not a measured number.
README advertises Docker containerisation, but no Dockerfile/compose file exists
What to do
Reconcile the README with reality: README advertises Docker containerisation, but no Dockerfile/compose file exists.
Do you agree with this assessment?
P1 · CI/CD gates0.0 / 10Critical✓ Tool-verified
Readiness · Readiness — Whether an automated pipeline builds and tests every change.
Method: Filesystem scan: CI workflow files (.github/workflows, .gitlab-ci.yml, etc.) for build and test stages. Exhaustive, deterministic.
No CI workflow found (.github/workflows, azure-pipelines.yml, .gitlab-ci.yml, …) — changes aren't gated by an automated build/test.
What to do
Add a CI workflow that builds and runs the test suite on every push/PR.
Readiness · Readiness — Whether SAST, secret/dependency scanning and performance benchmarking are wired in (presence, not runtime).
Method: Filesystem scan: SAST configuration, dependency-update automation, secret scanning, and a benchmark harness or benchmark step — in this repository's own ecosystem. Exhaustive, deterministic.
No static application security testing detected. For this repository's stack, add bandit, `semgrep --config=p/python`, or CodeQL's python pack — this repository has no CI pipeline yet, so run it locally to clear the existing findings, then make it a step of the first workflow you add so a regression fails the build.
What to do
Run what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — — locally for now, since there is no CI pipeline here yet, and as a step of the first workflow you add so a security regression fails the build instead of landing.
Enable Dependabot/Renovate or a dependency-review gate.
Add gitleaks/trufflehog in CI to block PRs that introduce committed secrets.
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.
What to do
Stamp a version in your build/package manifest (e.g. csproj <Version>, package.json, pyproject.toml, Cargo.toml, or a VERSION file) or tag releases with semver so builds and releases are traceable.
Do you agree with this assessment?
Reference — by lens
The score is the rank-weighted fold of these lenses (worst-heaviest), each including its meta-dimensions; a lens with a Critical contributor is capped at Fair (its band reads "gated by …") and is never the strongest area however high its average.
Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Not included — 74 check(s) not relevant to this codebase
These checks had nothing to measure here (no tests, no git history, the codebase is small, or the architecture style doesn't apply), so they're omitted above rather than scored low.
AC1 Text alternatives — Frontend below the scale floor (5 DOM element(s) < 25) — too little surface to assess accessibility.
AC2 Forms & labels — Frontend below the scale floor (5 DOM element(s) < 25) — too little surface to assess accessibility.
AC3 Page structure — Frontend below the scale floor (5 DOM element(s) < 25) — too little surface to assess accessibility.
AC4 Keyboard semantics — Frontend below the scale floor (5 DOM element(s) < 25) — too little surface to assess accessibility.
AC5 ARIA correctness — Frontend below the scale floor (5 DOM element(s) < 25) — too little surface to assess accessibility.
AC6 Visual & motion safety — Frontend below the scale floor (5 DOM element(s) < 25) — too little surface to assess accessibility.
AC7 A11y enforcement — Frontend below the scale floor (5 DOM element(s) < 25) — too little surface to assess accessibility.
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 applicable to a transaction-script/CRUD architecture (the inward-dependency rule is for layered/clean styles)
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
AX9 CQS / query purity — no CQRS query handlers detected — query purity is not applicable to this codebase
AXB2 Runtime readiness — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
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 — ~44797 lines of test source are present (.py) 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)), which this pass does not parse yet — so this dimension asserts nothing about this repository's licensing in either direction.
D16 Bus Factor — dormant codebase — no living knowledge left to concentrate
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.
D19 Documentation Quality — LLM evaluation failed
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) 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) — not scanned yet) — where an OSV-supported manifest exists, dependency vulnerabilities for this repository are reported under D38 instead.
D31 IaC & Container Security — No Infrastructure-as-Code or container manifests found (Dockerfile, Terraform, Kubernetes/Helm, CloudFormation); nothing to scan.
D32 Data Compliance (PII/GDPR) — No PII/GDPR-handling patterns detected (p/gdpr ruleset) — no data-compliance surface to assess.
D33 JS/npm Dependency Vulnerabilities — No JS/npm manifest or lockfile found outside build output (package.json, package-lock.json, yarn.lock, pnpm-lock.yaml, bun.lockb); no JS dependencies to scan.
D36 Supply-chain Provenance & Signing — No CI/build pipeline found (.github/.forgejo/.gitea workflows, .gitlab-ci.yml, azure-pipelines*.yml, .pipelines/, .vsts-ci/, Jenkinsfile, .circleci); there is no build to attest provenance for.
D37 Vulnerability-disclosure Policy — No vulnerability-disclosure policy file found (SECURITY.md/.markdown/.rst/.txt at root or under .github/.forgejo/.gitea/docs, .well-known/security.txt). A coordinated-disclosure policy may live off-repo, so this is not evidenced rather than failed.
D39 IL Efficiency — D39 measures the IL emitted by a .NET build; this repository has no .NET solution or project files, so the dimension does not apply.
D40 Network Egress Confinement — No Kubernetes/orchestration workloads found in the repository manifests; network egress policy is a cluster-native control that may live at the platform/firewall layer, so there is nothing to assess here.
D41 Kernel & Syscall Confinement — No Kubernetes/orchestration workloads found in the repository manifests; seccomp/AppArmor/SELinux confinement is a workload-level control, so there is nothing to assess here.
D42 Runtime Threat Enforcement — No Kubernetes/orchestration workloads found in the repository manifests; runtime threat-detection and admission-control policy are cluster-level controls, so there is nothing to assess here.
D5 Coupling — Inter-project coupling could not be assessed — no analyzable project graph was found for this repository. Not scored: a gap in the analyzer's reach, not a verdict about this repository. (Coupling here is Martin afferent/efferent/instability plus reference cycles across a project-reference graph, read today from .NET project files; other ecosystems' module graphs are not read yet.)
D6 Cohesion (LCOM4) — Cohesion (LCOM4) is measured over a C#/VB class graph, and this repository's production source is .py, which this pass does not read — so no class could be assessed. Not scored — this is a gap in the analyzer, not a finding about this repository.
D7 Architectural Integrity — no checkable ADRs, and no project-reference graph for the cycle pass to read — so this dimension makes no claim about dependency cycles in either direction (where this repository's language has an import-cycle lens, cycles are reported there). Architectural integrity not assessed
D8 Code Coverage — Coverage not included — suite not readable by the collector
D9 Test Distribution — Test source is present (.py) 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.
DM1 Domain Modelling — not scored — this repository shows only 1 of the 3 signals this check looks for (1 aggregate root(s) (types guarding their own state behind command methods — this language has no AggregateRoot base to inherit))
ED1 Event-Driven — not scored — this repository shows none of the 3 signals this check looks for
ED5 Idempotency — no mutating command handlers or message consumers detected — idempotency check not applicable
ES1 Event Sourcing — not scored — this repository shows none of the 3 signals this 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 — no CI workflow found
P2 Observability — Observability was not assessed: this check reads a source model that does not carry this repository's product — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of a logging idiom this check recognises is NOT evidence that this repo lacks structured logging (it may log through its own ecosystem's logger). This is a gap in the analyzer, not a finding about this repository.
P4 Deployment & Rollback — not evidenced — no deploy/rollback/approval signal in the repo; absence of evidence is not evidence of a manual release
P5 DR & Backup — not evidenced — repo shows no backup/RTO/RPO controls; absence of evidence is not evidence of a working control
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.
S1 Web-Security Posture — Not assessed: these web-security controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks web-security controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
X1 Async correctness — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
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.
Boundary-crossing change coupling: actionrunner.py ↔ sensormanager.py st2actions/st2actions/cmd/actionrunner.py— `st2actions/st2actions/cmd/actionrunner.py` (context st2actions) and `st2reactor/st2reactor/cmd/sensormanager.py` (context st2reactor) sit in DIFFERENT parts of the tree yet change together 65% of the time (11 of the 17 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well) — the bounded-context boundary may be in the wrong place, or one context is leaking into the other. This is the behavioural boundary violation a static scan can't see.
Boundary-crossing change coupling: wsgi.py ↔ wsgi.py st2api/st2api/wsgi.py— `st2api/st2api/wsgi.py` (context st2api) and `st2stream/st2stream/wsgi.py` (context st2stream) sit in DIFFERENT parts of the tree yet change together 55% of the time (6 of the 11 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well) — the bounded-context boundary may be in the wrong place, or one context is leaking into the other. This is the behavioural boundary violation a static scan can't see.
Boundary-crossing change coupling: templating.py ↔ datatransform.py st2common/st2common/util/templating.py— `st2common/st2common/util/templating.py` (context st2common) and `st2reactor/st2reactor/rules/datatransform.py` (context st2reactor) sit in DIFFERENT parts of the tree yet change together 55% of the time (6 of the 11 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well) — the bounded-context boundary may be in the wrong place, or one context is leaking into the other. This is the behavioural boundary violation a static scan can't see.
Boundary-crossing change coupling: action_chain_runner.py ↔ datatransform.py contrib/runners/action_chain_runner/action_chain_runner.py— `contrib/runners/action_chain_runner/action_chain_runner.py` (context contrib) and `st2reactor/st2reactor/rules/datatransform.py` (context st2reactor) sit in DIFFERENT parts of the tree yet change together 55% of the time (6 of the 11 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well) — the bounded-context boundary may be in the wrong place, or one context is leaking into the other. This is the behavioural boundary violation a static scan can't see.
Boundary-crossing change coupling: actionrunner.py ↔ api.py st2actions/st2actions/cmd/actionrunner.py— `st2actions/st2actions/cmd/actionrunner.py` (context st2actions) and `st2api/st2api/cmd/api.py` (context st2api) sit in DIFFERENT parts of the tree yet change together 53% of the time (9 of the 17 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well) — the bounded-context boundary may be in the wrong place, or one context is leaking into the other. This is the behavioural boundary violation a static scan can't see.
Boundary-crossing change coupling: st2resultstracker.py ↔ rulesengine.py st2actions/st2actions/cmd/st2resultstracker.py— `st2actions/st2actions/cmd/st2resultstracker.py` (context st2actions) and `st2reactor/st2reactor/cmd/rulesengine.py` (context st2reactor) sit in DIFFERENT parts of the tree yet change together 50% of the time (6 of the 12 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well) — the bounded-context boundary may be in the wrong place, or one context is leaking into the other. This is the behavioural boundary violation a static scan can't see.
D38 · OSV Dependency Vulnerabilities· High CVE · ×3
High CVE: [GHSA redacted] fixed-requirements.txt— gunicorn 19.6.0: [GHSA redacted] — upgrade to 22.0.0 (in 2 dependency files: fixed-requirements.txt, requirements.txt) This one row stands for the 2 advisories this scan raises against gunicorn 19.6.0: [GHSA redacted], [GHSA redacted].
High CVE: [GHSA redacted] fixed-requirements.txt— python-gnupg 0.3.9: [GHSA redacted] — upgrade to 0.4.4 (in 2 dependency files: fixed-requirements.txt, requirements.txt)
High CVE: [GHSA redacted] fixed-requirements.txt— virtualenv 15.1.0: [GHSA redacted] — upgrade to 20.26.6. This is 1 of 2 advisories with a published fix this scan raises against virtualenv 15.1.0, and their fixed versions do not agree — anything below 20.36.1 still leaves at least one of them open. Take this package to 20.36.1 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 virtualenv 15.1.0: [GHSA redacted], [GHSA redacted].
Leaked secret: private-key st2tests/st2tests/resources/ssh/dummy_rsa:1— private-key detected. Treat the value as compromised: it is readable by everyone who has ever had the repository, and deleting the line does not un-publish it. In order — (1) REVOKE it at whatever issued it and issue a replacement, which is the only step that actually closes the exposure; (2) load the replacement at run time from your platform's secret store or the process environment instead of from the tree, so no future value is committable; (3) remove the file or line and add its path to the repository's ignore rules, so it cannot come back; (4) if the value was ever live, purge it from the history as well, since a clone taken before the deletion still carries it. If this is instead a FIXTURE — key material generated for tests and valid nowhere — then the exposure is nil and the fix is to make that legible: generate it in test setup, or keep it under a test-data path, so a reader (and this scan) can tell it from the real thing.
Critical CVE: [GHSA redacted] fixed-requirements.txt— gitpython 2.1.3: [GHSA redacted] — upgrade to 3.1.30. This is 1 of 20 advisories with a published fix this scan raises against gitpython 2.1.3, and their fixed versions do not agree — anything below 3.1.57 still leaves at least one of them open. Take this package to 3.1.57 or later: that is the floor for the package, not this row's target alone. (in 2 dependency files: fixed-requirements.txt, requirements.txt) This one row stands for the 20 advisories this scan raises against gitpython 2.1.3: [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], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted].
Medium: insecure-ssl-version conf/nginx/st2.conf:34— Detected use of an insecure SSL version. Secure SSL versions are TLSv1.2 and TLS1.3; older versions are known to be broken and are susceptible to attacks. Prefer use of TLSv1.2 or later. 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.
Medium: insecure-hash-algorithm-md5 st2common/st2common/models/db/trace.py:101— Detected MD5 hash algorithm which is considered insecure. MD5 is not collision resistant and is therefore not suitable as a cryptographic signature. Use SHA256 or SHA3 instead.
Medium: insecure-hash-algorithm-md5 st2common/st2common/models/db/trigger.py:108— Detected MD5 hash algorithm which is considered insecure. MD5 is not collision resistant and is therefore not suitable as a cryptographic signature. Use SHA256 or SHA3 instead.
Medium: missing-autoescape-disabled st2common/st2common/util/jinja.py:99— 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: insecure-hash-algorithm-md5 st2reactor/st2reactor/container/hash_partitioner.py:109— Detected MD5 hash algorithm which is considered insecure. MD5 is not collision resistant and is therefore not suitable as a cryptographic signature. Use SHA256 or SHA3 instead.
Medium: insecure-file-permissions st2tests/integration/mistral/test_wiring.py:34— These permissions `0755` are widely permissive and grant access to more people than may be necessary. A good default is `0o644` which gives read and write access to yourself and read access to everyone else. 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.
Duplicated block (11 lines × 2) st2client/st2client/commands/auth.py:43— st2client/st2client/commands/auth.py:43-53 | st2client/st2client/commands/auth.py:85-95 — 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 `st2client/st2client/commands/auth.py:43` 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) st2client/st2client/commands/rbac.py:56— st2client/st2client/commands/rbac.py:56-66 | st2client/st2client/commands/rbac.py:122-132 — 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 `st2client/st2client/commands/rbac.py:56` 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) st2client/st2client/commands/resource.py:300— st2client/st2client/commands/resource.py:300-311 | st2client/st2client/commands/resource.py:386-396 — 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 `st2client/st2client/commands/resource.py:300` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
Duplicated block (11 lines × 2) st2client/st2client/commands/rule_enforcement.py:80— st2client/st2client/commands/rule_enforcement.py:80-90 | st2client/st2client/commands/triggerinstance.py:86-96 — 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 `st2client/st2client/commands/rule_enforcement.py:80` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
Duplicated block (11 lines × 2) st2common/st2common/persistence/base.py:135— st2common/st2common/persistence/base.py:135-145 | st2common/st2common/persistence/base.py:174-184 — 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 `st2common/st2common/persistence/base.py:135` 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 (11 lines × 2) tools/diff-db-disk.py:177— tools/diff-db-disk.py:177-188 | tools/diff-db-disk.py:189-199 — 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 `tools/diff-db-disk.py:177` 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. 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 (10 lines × 2) st2actions/st2actions/notifier/notifier.py:255— st2actions/st2actions/notifier/notifier.py:255-264 | st2actions/st2actions/scheduler.py:88-97 — 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. Read the line range as the matched WINDOW rather than a finished unit: at `st2actions/st2actions/notifier/notifier.py:255` 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 (10 lines × 2) st2api/st2api/controllers/v1/actionalias.py:208— st2api/st2api/controllers/v1/actionalias.py:208-217 | st2api/st2api/controllers/v1/triggers.py:275-284 — 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 (10 lines × 2) st2client/st2client/commands/resource.py:558— st2client/st2client/commands/resource.py:558-567 | st2common/st2common/util/loader.py:220-229 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (10 lines × 2) st2client/st2client/utils/interactive.py:238— st2client/st2client/utils/interactive.py:238-247 | st2client/st2client/utils/interactive.py:339-348 — 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 (10 lines × 2) st2common/st2common/services/sensor_watcher.py:69— st2common/st2common/services/sensor_watcher.py:69-78 | st2common/st2common/services/triggerwatcher.py:80-89 — 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 (13 lines × 2) st2api/st2api/controllers/resource.py:377— st2api/st2api/controllers/resource.py:377-389 | st2api/st2api/controllers/v1/policies.py:79-91 — 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. Read the line range as the matched WINDOW rather than a finished unit: at `st2api/st2api/controllers/resource.py:377` 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 (13 lines × 2) st2api/st2api/controllers/v1/actionexecutions.py:292— st2api/st2api/controllers/v1/actionexecutions.py:292-304 | st2api/st2api/controllers/v1/actionexecutions.py:319-331 — 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 (13 lines × 2) st2common/st2common/content/utils.py:76— st2common/st2common/content/utils.py:76-88 | st2common/st2common/content/utils.py:102-114 — 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 (7 lines × 2) contrib/runners/mistral_v2/mistral_v2.py:54— contrib/runners/mistral_v2/mistral_v2.py:54-60 | st2common/st2common/validators/workflow/mistral/v2.py:43-49 — 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. Read the line range as the matched WINDOW rather than a finished unit: at `contrib/runners/mistral_v2/mistral_v2.py:54` 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 (7 lines × 2) st2client/st2client/commands/rule_enforcement.py:118— st2client/st2client/commands/rule_enforcement.py:118-124 | st2client/st2client/commands/triggerinstance.py:127-133 — 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 `st2client/st2client/commands/rule_enforcement.py:118` 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. 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) st2client/st2client/commands/trace.py:179— st2client/st2client/commands/trace.py:179-185 | st2client/st2client/commands/triggerinstance.py:127-133 — 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 `st2client/st2client/commands/trace.py:179` 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.
FileTooLong: rbac/resolvers.py st2common/st2common/rbac/resolvers.py:0— FileTooLong — 675 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.
FileTooLong: api/action.py st2common/st2common/models/api/action.py:0— FileTooLong — 510 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.
D38 · OSV Dependency Vulnerabilities· Medium CVE · ×2
Medium CVE: [GHSA redacted] fixed-requirements.txt— pymongo 3.4.0: [GHSA redacted] — upgrade to 4.6.3 (in 2 dependency files: fixed-requirements.txt, requirements.txt)
Medium CVE: [GHSA redacted] fixed-requirements.txt— webob 1.6.0: [GHSA redacted] — upgrade to 1.8.10. This is 1 of 2 advisories with a published fix this scan raises against webob 1.6.0, and their fixed versions do not agree — anything below 1.8.10 still leaves at least one of them open. Take this package to 1.8.10 or later: that is the floor for the package, not this row's target alone. (in 2 dependency files: fixed-requirements.txt, requirements.txt) This one row stands for the 2 advisories this scan raises against webob 1.6.0: [GHSA redacted], [GHSA redacted].
Duplicated block (21 lines × 2) contrib/runners/windows_command_runner/windows_command_runner.py:82— contrib/runners/windows_command_runner/windows_command_runner.py:82-102 | contrib/runners/windows_script_runner/windows_script_runner.py:116-136 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice.
Duplicated block (21 lines × 2) st2api/st2api/controllers/v1/actionexecutions.py:473— st2api/st2api/controllers/v1/actionexecutions.py:473-494 | st2api/st2api/controllers/v1/actionexecutions.py:525-545 — 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 (12 lines × 2) st2common/st2common/bootstrap/sensorsregistrar.py:114— st2common/st2common/bootstrap/sensorsregistrar.py:114-125 | st2common/st2common/bootstrap/triggersregistrar.py:112-123 — before extracting anything, compare `st2common/st2common/bootstrap/sensorsregistrar.py` and `st2common/st2common/bootstrap/triggersregistrar.py` as WHOLE FILES: this scan already matched 4 separate duplicated blocks between them, totalling at least 60 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place. 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) st2common/st2common/bootstrap/sensorsregistrar.py:128— st2common/st2common/bootstrap/sensorsregistrar.py:128-139 | st2common/st2common/bootstrap/triggersregistrar.py:126-137 — before extracting anything, compare `st2common/st2common/bootstrap/sensorsregistrar.py` and `st2common/st2common/bootstrap/triggersregistrar.py` as WHOLE FILES: this scan already matched 4 separate duplicated blocks between them, totalling at least 60 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place.
Duplicated block (5 lines × 2) contrib/packs/actions/pack_mgmt/unload.py:47— contrib/packs/actions/pack_mgmt/unload.py:47-52 | st2reactor/st2reactor/container/sensor_wrapper.py:183-187 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice.
Duplicated block (5 lines × 2) contrib/runners/windows_command_runner/windows_command_runner.py:59— contrib/runners/windows_command_runner/windows_command_runner.py:59-63 | contrib/runners/windows_script_runner/windows_script_runner.py:83-87 — 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.
Router.__call__ (cyclomatic 56) st2common/st2common/router.py:214— Router.__call__ has cyclomatic complexity 56 (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.
ActionChainRunner.run (cyclomatic 25) contrib/runners/action_chain_runner/action_chain_runner.py:281— ActionChainRunner.run has cyclomatic complexity 25 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
PackRegisterController.post (cyclomatic 25) st2api/st2api/controllers/v1/packs.py:124— PackRegisterController.post has cyclomatic complexity 25 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
MultiColumnTable.format (cyclomatic 25) st2client/st2client/formatters/table.py:56— MultiColumnTable.format has cyclomatic complexity 25 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
ResourceController._get_all (cyclomatic 23) st2api/st2api/controllers/resource.py:121— ResourceController._get_all has cyclomatic complexity 23 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
actionalias_helpstring.generate_helpstring_result (cyclomatic 21) st2common/st2common/util/actionalias_helpstring.py:25— actionalias_helpstring.generate_helpstring_result has cyclomatic complexity 21 (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.
bootstrap.register_content (cyclomatic 20) st2common/st2common/content/bootstrap.py:331— bootstrap.register_content has cyclomatic complexity 20 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
TraceGetCommand._filter_trace_components (cyclomatic 17) st2client/st2client/commands/trace.py:248— TraceGetCommand._filter_trace_components 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.
schema.modify_schema_allow_default_none (cyclomatic 17) st2common/st2common/util/schema/__init__.py:221— schema.modify_schema_allow_default_none 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.
schema.assign_default_values (cyclomatic 16) st2common/st2common/util/schema/__init__.py:166— schema.assign_default_values 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.
submit_debug_info.main (cyclomatic 16) st2debug/st2debug/cmd/submit_debug_info.py:585— submit_debug_info.main 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.
dormant codebase — no living knowledge left to concentrate — Every one of the 280 significant source file(s) was last meaningfully changed so long ago that no living knowledge remains, so there is no concentration to measure — the bus factor is not scored. This is not a clean bill: nobody currently holds working knowledge of this code (see D34 Knowledge Freshness).
LLM evaluation failed — JSON parse error: Expected end of string, but instead reached end of data. Path: $.findings[1].docPath | LineNumber: 0 | BytePositionInLine: 996.
Router.__call__ (cognitive 113) st2common/st2common/router.py:214— Router.__call__ has cognitive complexity 113 (threshold 15). Drivers by points: if/else 80, error handling 26, boolean chains 4, loops 3 (nesting depth added 64). 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.
MultiColumnTable.format (cognitive 52) st2client/st2client/formatters/table.py:56— MultiColumnTable.format has cognitive complexity 52 (threshold 15). Drivers by points: if/else 33, loops 10, boolean chains 4, ternaries 4, error handling 1 (nesting depth added 24). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
PackRegisterController.post (cognitive 43) st2api/st2api/controllers/v1/packs.py:124— PackRegisterController.post has cognitive complexity 43 (threshold 15). Drivers by points: if/else 21, boolean chains 12, error handling 5, loops 5 (nesting depth added 16). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
ActionChainRunner.run (cognitive 36) contrib/runners/action_chain_runner/action_chain_runner.py:281— ActionChainRunner.run has cognitive complexity 36 (threshold 15). Drivers by points: if/else 24, error handling 10, boolean chains 1, loops 1 (nesting depth added 14). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
schema.assign_default_values (cognitive 30) st2common/st2common/util/schema/__init__.py:166— schema.assign_default_values has cognitive complexity 30 (threshold 15). Drivers by points: if/else 21, loops 5, boolean chains 4 (nesting depth added 16). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
actionalias_helpstring.generate_helpstring_result (cognitive 29) st2common/st2common/util/actionalias_helpstring.py:25— actionalias_helpstring.generate_helpstring_result has cognitive complexity 29 (threshold 15). Drivers by points: if/else 16, boolean chains 10, 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.
ResourceController._get_all (cognitive 27) st2api/st2api/controllers/resource.py:121— ResourceController._get_all has cognitive complexity 27 (threshold 15). Drivers by points: if/else 14, boolean chains 6, ternaries 4, loops 3 (nesting depth added 4). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
RulesRegistrar._register_rules_from_pack (cognitive 26) st2common/st2common/bootstrap/rulesregistrar.py:105— RulesRegistrar._register_rules_from_pack has cognitive complexity 26 (threshold 15). Drivers by points: if/else 16, error handling 9, loops 1 (nesting depth added 14). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
action._validate_parameters (cognitive 26) st2common/st2common/validators/api/action.py:63— action._validate_parameters has cognitive complexity 26 (threshold 15). Drivers by points: if/else 21, loops 4, boolean chains 1 (nesting depth added 15). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
AuthHandlerBase._get_username_for_request (cognitive 23) st2auth/st2auth/handlers.py:49— AuthHandlerBase._get_username_for_request has cognitive complexity 23 (threshold 15). Drivers by points: error handling 14, if/else 9 (nesting depth added 13). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
mistral.transform_definition (cognitive 23) st2common/st2common/util/workflow/mistral.py:236— mistral.transform_definition has cognitive complexity 23 (threshold 15). Drivers by points: loops 13, if/else 8, ternaries 2 (nesting depth added 12). To reduce it, break up the iteration: give each loop body a named function, and split a multi-phase loop into one function per phase so no single body carries the whole pipeline.
TraceGetCommand._filter_trace_components (cognitive 22) st2client/st2client/commands/trace.py:248— TraceGetCommand._filter_trace_components has cognitive complexity 22 (threshold 15). Drivers by points: if/else 15, boolean chains 4, loops 3 (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
ExecutionResult.format (cognitive 22) st2client/st2client/formatters/execution.py:34— ExecutionResult.format has cognitive complexity 22 (threshold 15). Drivers by points: if/else 12, ternaries 7, loops 2, boolean chains 1 (nesting depth added 13). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
packs.search_pack_index (cognitive 22) st2common/st2common/services/packs.py:159— packs.search_pack_index has cognitive complexity 22 (threshold 15). Drivers by points: if/else 18, loops 3, boolean chains 1 (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
submit_debug_info.main (cognitive 22) st2debug/st2debug/cmd/submit_debug_info.py:585— submit_debug_info.main has cognitive complexity 22 (threshold 15). Drivers by points: if/else 17, boolean chains 2, error handling 2, loops 1 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
PackAsyncCommand.run_and_print (cognitive 21) st2client/st2client/commands/pack.py:105— PackAsyncCommand.run_and_print has cognitive complexity 21 (threshold 15). Drivers by points: if/else 17, boolean chains 3, loops 1 (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
PropertyValueTable.format (cognitive 21) st2client/st2client/formatters/table.py:207— PropertyValueTable.format has cognitive complexity 21 (threshold 15). Drivers by points: if/else 14, loops 5, 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.
runnersregistrar.register_runner (cognitive 21) st2common/st2common/bootstrap/runnersregistrar.py:59— runnersregistrar.register_runner has cognitive complexity 21 (threshold 15). Drivers by points: if/else 12, error handling 4, loops 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.
DownloadGitRepoAction._clone_repo (cognitive 20) contrib/packs/actions/pack_mgmt/download.py:90— DownloadGitRepoAction._clone_repo has cognitive complexity 20 (threshold 15). Drivers by points: if/else 12, boolean chains 4, ternaries 4 (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.
TraceGetCommand._apply_display_filters (cognitive 20) st2client/st2client/commands/trace.py:324— TraceGetCommand._apply_display_filters has cognitive complexity 20 (threshold 15). Drivers by points: if/else 12, loops 5, boolean chains 3 (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.
bootstrap.register_content (cognitive 20) st2common/st2common/content/bootstrap.py:331— bootstrap.register_content has cognitive complexity 20 (threshold 15). Drivers by points: if/else 12, boolean chains 8 (nesting depth added 1). To reduce it, split the body: this score is breadth rather than depth — many checks laid out side by side rather than nested inside one another, so inverting conditions into early returns has nothing left to flatten. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
diff-db-disk._diff (cognitive 20) tools/diff-db-disk.py:159— diff-db-disk._diff has cognitive complexity 20 (threshold 15). Drivers by points: if/else 19, loops 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.
WindowsScriptRunner._get_script_arguments (cognitive 19) contrib/runners/windows_script_runner/windows_script_runner.py:171— WindowsScriptRunner._get_script_arguments has cognitive complexity 19 (threshold 15). Drivers by points: if/else 14, boolean chains 3, loops 2 (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.
Notifier._post_notify_subsection_triggers (cognitive 18) st2actions/st2actions/notifier/notifier.py:127— Notifier._post_notify_subsection_triggers has cognitive complexity 18 (threshold 15). Drivers by points: error handling 9, boolean chains 4, if/else 3, loops 2 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
StandaloneAuthHandler.handle_auth (cognitive 18) st2auth/st2auth/handlers.py:132— StandaloneAuthHandler.handle_auth has cognitive complexity 18 (threshold 15). Drivers by points: error handling 9, if/else 9 (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.
ErrorHandlingMiddleware.__call__ (cognitive 18) st2common/st2common/middleware/error_handling.py:36— ErrorHandlingMiddleware.__call__ has cognitive complexity 18 (threshold 15). Drivers by points: if/else 14, boolean chains 2, error handling 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.
RoleDefinitionFileFormatAPI.validate (cognitive 18) st2common/st2common/models/api/rbac.py:199— RoleDefinitionFileFormatAPI.validate has cognitive complexity 18 (threshold 15). Drivers by points: if/else 11, loops 7 (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.
Access.add_or_update (cognitive 18) st2common/st2common/persistence/base.py:165— Access.add_or_update has cognitive complexity 18 (threshold 15). Drivers by points: if/else 11, error handling 5, ternaries 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.
Router.add_spec (cognitive 18) st2common/st2common/router.py:156— Router.add_spec has cognitive complexity 18 (threshold 15). Drivers by points: loops 11, if/else 7 (nesting depth added 11). To reduce it, break up the iteration: give each loop body a named function, and split a multi-phase loop into one function per phase so no single body carries the whole pipeline.
schema.modify_schema_allow_default_none (cognitive 18) st2common/st2common/util/schema/__init__.py:221— schema.modify_schema_allow_default_none has cognitive complexity 18 (threshold 15). Drivers by points: if/else 9, boolean chains 8, 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.
mistral._transform_action (cognitive 18) st2common/st2common/util/workflow/mistral.py:165— mistral._transform_action has cognitive complexity 18 (threshold 15). Drivers by points: if/else 13, loops 3, boolean chains 1, ternaries 1 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
ActionAliasExecutionController.post (cognitive 17) st2api/st2api/controllers/v1/aliasexecution.py:53— ActionAliasExecutionController.post has cognitive complexity 17 (threshold 15). Drivers by points: if/else 9, error handling 5, boolean chains 2, ternaries 1 (nesting depth added 4). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition.
BaseFormatter.format (cognitive 17) st2client/st2client/formatters/doc.py:34— BaseFormatter.format has cognitive complexity 17 (threshold 15). Drivers by points: ternaries 10, boolean chains 3, if/else 2, loops 2 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
ShellScriptAction._get_script_arguments (cognitive 17) st2common/st2common/models/system/action.py:214— ShellScriptAction._get_script_arguments has cognitive complexity 17 (threshold 15). Drivers by points: if/else 13, boolean chains 2, loops 2 (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.
action_param_utils.cast_params (cognitive 17) st2common/st2common/models/utils/action_param_utils.py:82— action_param_utils.cast_params has cognitive complexity 17 (threshold 15). Drivers by points: if/else 11, error handling 2, ternaries 2, boolean chains 1, loops 1 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
PythonRunner._get_output_values (cognitive 16) contrib/runners/python_runner/python_runner.py:154— PythonRunner._get_output_values has cognitive complexity 16 (threshold 15). Drivers by points: if/else 14, boolean chains 1, error handling 1 (nesting depth added 3). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
ConcurrencyByAttributePolicyTest.test_over_threshold_delay_executions (cognitive 16) st2actions/tests/unit/policies/test_concurrency_by_attr.py:96— ConcurrencyByAttributePolicyTest.test_over_threshold_delay_executions has cognitive complexity 16 (threshold 15). Drivers by points: if/else 11, loops 5 (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.
ConcurrencyByAttributePolicyTest.test_on_cancellation (cognitive 16) st2actions/tests/unit/policies/test_concurrency_by_attr.py:267— ConcurrencyByAttributePolicyTest.test_on_cancellation has cognitive complexity 16 (threshold 15). Drivers by points: if/else 11, loops 5 (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.
ActionAliasFormatParser.get_extracted_param_value (cognitive 16) st2common/st2common/models/utils/action_alias_utils.py:34— ActionAliasFormatParser.get_extracted_param_value has cognitive complexity 16 (threshold 15). Drivers by points: if/else 7, boolean chains 4, loops 3, ternaries 2 (nesting depth added 3). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition.
RBACRemoteGroupToRoleSyncer.sync (cognitive 16) st2common/st2common/rbac/syncer.py:340— RBACRemoteGroupToRoleSyncer.sync has cognitive complexity 16 (threshold 15). Drivers by points: loops 9, if/else 7 (nesting depth added 5). To reduce it, break up the iteration: give each loop body a named function, and split a multi-phase loop into one function per phase so no single body carries the whole pipeline.
BaseParallelSSHRunner._get_result_status (cognitive 16) st2common/st2common/runners/paramiko_ssh_runner.py:173— BaseParallelSSHRunner._get_result_status has cognitive complexity 16 (threshold 15). Drivers by points: if/else 10, ternaries 4, boolean chains 1, loops 1 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
action_db.serialize_positional_argument (cognitive 16) st2common/st2common/util/action_db.py:227— action_db.serialize_positional_argument has cognitive complexity 16 (threshold 15). Drivers by points: ternaries 11, if/else 5 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
visualize_action_chain.main (cognitive 16) tools/visualize_action_chain.py:36— visualize_action_chain.main has cognitive complexity 16 (threshold 15). Drivers by points: if/else 13, loops 2, boolean chains 1 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
Change coupling clique: app.py, app.py, app.py st2api/st2api/app.py— 3 files — `st2api/st2api/app.py`, `st2auth/st2auth/app.py`, `st2stream/st2stream/app.py` — all change together with no explicit dependency: a fully-connected co-change clique, not 3 separate couplings. They share one concern (thin parallel siblings over a common abstraction), so extract the shared part into ONE unit and the whole clique's coupling clears at once — you do not need to break each pair individually.
Change coupling: action.py ↔ rule.py st2common/st2common/models/db/action.py— `st2common/st2common/models/db/action.py` and `st2common/st2common/models/db/rule.py` change together 73% of the time (8 of the 11 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well) 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.
Duplicated block (31 lines × 2) st2common/st2common/validators/api/reactor.py:61— st2common/st2common/validators/api/reactor.py:61-91 | st2common/st2common/validators/api/reactor.py:112-142 — 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 (19 lines × 2) st2tests/st2tests/fixtures/packs/runners/runner/runner.py:44— st2tests/st2tests/fixtures/packs/runners/runner/runner.py:44-62 | st2tests/st2tests/mocks/runner.py:44-62 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice.
Duplicated block (16 lines × 6) st2common/st2common/bootstrap/actionsregistrar.py:44— st2common/st2common/bootstrap/actionsregistrar.py:44-63 | st2common/st2common/bootstrap/aliasesregistrar.py:40-59 | st2common/st2common/bootstrap/policiesregistrar.py:46-65 | st2common/st2common/bootstrap/rulesregistrar.py:43-58 | st2common/st2common/bootstrap/sensorsregistrar.py:41-60 | st2common/st2common/bootstrap/triggersregistrar.py:38-57 — before extracting anything, compare `st2common/st2common/bootstrap/sensorsregistrar.py` and `st2common/st2common/bootstrap/triggersregistrar.py` as WHOLE FILES: this scan already matched 4 separate duplicated blocks between them, totalling at least 60 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `st2common/st2common/bootstrap/rulesregistrar.py:43` 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. 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 (15 lines × 6) st2common/st2common/bootstrap/actionsregistrar.py:76— st2common/st2common/bootstrap/actionsregistrar.py:76-90 | st2common/st2common/bootstrap/aliasesregistrar.py:72-86 | st2common/st2common/bootstrap/policiesregistrar.py:78-93 | st2common/st2common/bootstrap/rulesregistrar.py:71-85 | st2common/st2common/bootstrap/sensorsregistrar.py:74-88 | st2common/st2common/bootstrap/triggersregistrar.py:71-85 — before extracting anything, compare `st2common/st2common/bootstrap/sensorsregistrar.py` and `st2common/st2common/bootstrap/triggersregistrar.py` as WHOLE FILES: this scan already matched 4 separate duplicated blocks between them, totalling at least 60 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place. 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 (14 lines × 2) st2actions/st2actions/policies/concurrency.py:55— st2actions/st2actions/policies/concurrency.py:55-68 | st2actions/st2actions/policies/concurrency_by_attr.py:79-92 — 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 `st2actions/st2actions/policies/concurrency.py:55` 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 (9 lines × 4) st2client/st2client/commands/resource.py:385— st2client/st2client/commands/resource.py:385-393 | st2client/st2client/commands/resource.py:436-444 | st2client/st2client/commands/resource.py:478-486 | st2client/st2client/commands/resource.py:520-528 — all 4 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 `st2client/st2client/commands/resource.py:385` 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 (9 lines × 2) st2client/st2client/commands/rule_enforcement.py:63— st2client/st2client/commands/rule_enforcement.py:63-71 | st2client/st2client/commands/triggerinstance.py:75-83 — 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 `st2client/st2client/commands/rule_enforcement.py:63` 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. 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 (8 lines × 4) st2api/st2api/controllers/v1/actionalias.py:179— st2api/st2api/controllers/v1/actionalias.py:179-186 | st2api/st2api/controllers/v1/runnertypes.py:79-86 | st2api/st2api/controllers/v1/triggers.py:110-118 | st2api/st2api/controllers/v1/triggers.py:253-260 — there are 4 copies across 3 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 4 sites; resolving a subset leaves the remainder to drift apart. 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 (8 lines × 2) st2client/st2client/commands/pack.py:204— st2client/st2client/commands/pack.py:204-211 | st2client/st2client/commands/pack.py:252-259 — 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 (7 lines × 3) st2client/st2client/commands/rule.py:44— st2client/st2client/commands/rule.py:44-50 | st2client/st2client/commands/rule_enforcement.py:60-66 | st2client/st2client/commands/triggerinstance.py:71-78 — 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 `st2client/st2client/commands/rule.py:44` 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 × 4) contrib/runners/mistral_v2/callback/mistral_v2.py:75— contrib/runners/mistral_v2/callback/mistral_v2.py:75-80 | contrib/runners/mistral_v2/mistral_v2.py:56-61 | contrib/runners/mistral_v2/query/mistral_v2.py:51-56 | st2common/st2common/validators/workflow/mistral/v2.py:45-50 — 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. Read the line range as the matched WINDOW rather than a finished unit: at `contrib/runners/mistral_v2/callback/mistral_v2.py:75` 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) st2common/st2common/models/system/action.py:288— st2common/st2common/models/system/action.py:288-293 | st2common/st2common/models/system/action.py:317-322 — 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 `st2common/st2common/models/system/action.py:288` 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. 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.
Low: request-host-used conf/HA/nginx/st2.conf.blueprint.sample:16— '$http_host' and '$host' variables may contain a malicious value from attacker controlled 'Host' request header. Use an explicitly configured host value or a allow list for validation. This is a semgrep security-AUDIT rule: it reports that a sensitive construct is present, not that it is exploitable here. Confirm whether this site handles untrusted input or is reachable across a trust boundary — and apply the change where it is; where the construct is required by the platform or protocol it calls into, and carries no untrusted data (a syscall/FFI shim, a build- or debug-gated tool, a fixed local surface), record the review and leave the code as it is.
Low: request-host-used conf/HA/nginx/st2.conf.controller.sample:21— '$http_host' and '$host' variables may contain a malicious value from attacker controlled 'Host' request header. Use an explicitly configured host value or a allow list for validation. This is a semgrep security-AUDIT rule: it reports that a sensitive construct is present, not that it is exploitable here. Confirm whether this site handles untrusted input or is reachable across a trust boundary — and apply the change where it is; where the construct is required by the platform or protocol it calls into, and carries no untrusted data (a syscall/FFI shim, a build- or debug-gated tool, a fixed local surface), record the review and leave the code as it is.
Low: request-host-used conf/nginx/st2.conf:16— '$http_host' and '$host' variables may contain a malicious value from attacker controlled 'Host' request header. Use an explicitly configured host value or a allow list for validation. 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.
Low: plaintext-http-link st2api/st2api/templates/index.html:2— This link points to a plaintext HTTP URL. Prefer an encrypted HTTPS URL if possible.
D34 · Knowledge Freshness· Largest orphaned file · ×3
Largest orphaned file st2client/st2client/commands/action.py— One of the largest files with no living knowledge remaining — a reasonable place to start a read-through before the aggregate risk above bites.
Largest orphaned file st2common/st2common/rbac/resolvers.py— One of the largest files with no living knowledge remaining — a reasonable place to start a read-through before the aggregate risk above bites.
Largest orphaned file contrib/runners/action_chain_runner/action_chain_runner.py— One of the largest files with no living knowledge remaining — a reasonable place to start a read-through before the aggregate risk above bites.
D11 · Test Reliability· Test reliability not included · ×1
Test reliability not included — Test source is present (.py) 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.
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.)
Dormant codebase — 280 of 280 significant files have no living knowledge — the codebase as a whole is dormant, not 280 separate risks. Re-engage owners or document before change.
Coverage not included — suite not readable by the collector — Coverage NOT READ here — but this repository measures it: a Codecov configuration (.codecov.yml) shows that coverage is collected and tracked in your own CI. The built-in collector has no runner for this ecosystem (.py), so the analyzer could not read the number — a gap in the analyzer's language coverage, not an unmeasured repo. Not scored. To have the real number read, 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.
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 manifest (a Python pyproject.toml/requirements.txt (pip/uv/Poetry)) was found, but this pass cannot parse it 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.
trivy: not applicable — No Infrastructure-as-Code or container manifests found (Dockerfile, Terraform, Kubernetes/Helm, CloudFormation); nothing to scan.
trivy: not applicable — No JS/npm manifest or lockfile found outside build output (package.json, package-lock.json, yarn.lock, pnpm-lock.yaml, bun.lockb); no JS dependencies to scan.
provenance: not applicable — No CI/build pipeline found (.github/.forgejo/.gitea workflows, .gitlab-ci.yml, azure-pipelines*.yml, .pipelines/, .vsts-ci/, Jenkinsfile, .circleci); there is no build to attest provenance for.
disclosure: not applicable — No vulnerability-disclosure policy file found (SECURITY.md/.markdown/.rst/.txt at root or under .github/.forgejo/.gitea/docs, .well-known/security.txt). A coordinated-disclosure policy may live off-repo, so this is not evidenced rather than failed.
runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; network egress policy is a cluster-native control that may live at the platform/firewall layer, so there is nothing to assess here.
runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; seccomp/AppArmor/SELinux confinement is a workload-level control, so there is nothing to assess here.
runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; runtime threat-detection and admission-control policy are cluster-level controls, so there is nothing to assess here.
0
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Run 019fcf60-eca9-7c19-a22b-a21b65b88a52 · every finding is also locatable in findings.md, and the complete scoring record (with exit codes + durations) in sidecar.json.
Issues: 16 · Warnings: 103 · Recommendations: 11 · Info: 2 — Appendix A · all findings · full markdown report.
Generated by Watchdog — deterministic code-health analysis. 05-08-2026 @ 00:44 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.