Public report — activegraph, published 4 Aug 2026.
Concrete security findings (CVE IDs, secret matches, dependency versions) are hidden in this version;
ask the repo owner for the full report.
161findings with an exact file:lineof 170 — the remainder are repo-wide signals (a dimension-level measurement, not a single line); open any file:line and verify
22/94dimensions across the health lenses30056 LoC — wide & deep
Executive summary
Read through the Production lens — the standard calibration. *Green* means good enough to run in production. The score is absolute and comparable across repos.
yoheinakajima/activegraph is sound in substance but carries real gaps (60%). It is not in crisis, but the issues below raise the cost of changing it — friction its consumers ultimately inherit.
It is strongest in Architecture (100%) — the structure is clean and changes stay contained. Code Health (75%) is solid too.
The area that most needs attention is Security (52%) — exposure to security and compliance incidents is elevated. Readiness (60%) is the next concern — 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.
Leadership focus, highest impact first: 17 High finding(s) (Static Analysis (SAST)); SAST step to CI running what this repository's stack ships (Security & performance tooling); Record significant decisions one document per decision (Architecture documentation).
For scale: Medium (~30,056 production lines); rebuilding it from scratch would take roughly ~0.4 person-years (~1 engineer). Approximate, ±~30%.
It builds on a genuinely strong Architecture foundation (100%); the priorities above are the highest-leverage way to bring the rest up to that level.
How the score is built — each lens's share of the headlineWidth is the lens's weight in the worst-heaviest fold (the weakest area pulls hardest); colour is that lens's own band. A lens fixes the score in proportion to its width.
0.8× (at 60% 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 ~0.4 person-years of build effort (about ~€52,000 to rebuild). Its weakest lens is Security at 52% — the part of that asset most exposed by the findings below.
How we model this: boilerplate at a scaffolding rate + logic × domain Standard (×1.0) — standard service × a 0.8× quality factor, at €60–95/h; indicative, ±~30% · size measured directly from source · effort from total production LoC as straight-line logic (the tier split is a C#-only syntax walk), a conservative lower bound. Indicative only — most sensitive to the hourly rate and the domain tier (both tunable in config).
Top priorities
The highest-leverage moves; the full ranked list is in the Roadmap below.
1
Resolve the 17 High finding(s) in Static Analysis (SAST) — start with publish.yml (5), docs.yml (4), deploy-verification.yml (2).
The top-ranked fix costs roughly 3–10 engineer-days once. Not doing it costs about 23.8–143 engineer-days every year, paid as drag on the ~367,478 lines this team changes annually — a bill that arrives whether or not anyone books it. On those figures the fix breaks even in roughly 1–5 months and is free after that. Method, stated so this is not read as a quotation: debt from the ranked task's effort band; interest = annual changed lines (measured, annualised from the 90-day window) ÷ an ASSUMED 150–400 lines per engineer-day × the 3–6% drag implied by the code-quality signals; breaking point = debt ÷ annual interest. A modelled planning range built from measured inputs and one named assumption — not a quotation, a valuation, or a certified figure.
Evidence: D15 churn: 90,611 line(s) changed over a 90-day window ⇒ ~367,478/year · D1/D2/D4 code quality: averaging 7.0/10 ⇒ a 3–6% drag on each change · top-ranked remediation: Medium effort ⇒ about 3–10 engineer-day(s)
→ Do the top-ranked fix now if this code will still be yours in 5 months.
Value concentrated against a weak lens · Medium · Value at risk
This is a Medium asset (~0.4 person-years to rebuild), and its weakest lens is Security at 52%. The operational and business risk on an asset this size concentrates there — that's where remediation buys the most protection.
→ Direct remediation budget at Security 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: Resolve the 17 High finding(s) in Static Analysis (SAST) — start with publish.yml (5), docs.yml (4), deploy-verification.yml (2). The rest can wait behind it.
Evidence: priority ranking: top of 5 ranked by impact/effort
→ Resolve the 17 High finding(s) in Static Analysis (SAST) — start with publish.yml (5), docs.yml (4), deploy-verification.yml (2).
A velocity tax on every change · Medium · Economics
The code-quality signals (complexity, duplication, cohesion) average 7.0/10, which acts as a tax on every change in the weaker areas: modifications there plausibly cost on the order of 3–6% more than in clean code, and the tax compounds as the codebase grows. (A modelled estimate, not a measured fact.)
Evidence: D1/D2/D4 code quality: averaging 7.0/10 across the code-quality signals actually measured
→ Pay it down where churn is highest — the hotspots — not everywhere; that's where the tax is actually paid.
Architecture — module dependency matrix
137 modules, 193 dependencies — every dependency points down the layering, so there are no cycles. Rows and columns are the same modules, ordered so that a module only depends on ones above it. A cell means the row depends on the column, and its number is how many type pairs create that dependency. Read one thing: is anything above the diagonal? A mark there is a dependency cycle. (A cycle is all this shows — an unusual but cycle-free dependency sits below the diagonal like any other.)
Findings mapped to OWASP categories; the specific CVEs/secrets are in the Security dimension cards below and findings.md (redacted only on the public version of this report).
OWASP category
Findings
Severity
A03:2021 — Injection
17
High / Critical
Roadmap
Begin by resolving the 17 high-severity findings in static analysis, prioritizing the issues in publish.yml, docs.yml, and deploy-verification.yml. Next, integrate a SAST step into the CI pipeline to ensure security regressions fail the build, and address the missing Software Bill of Materials (SBOM) for supply-chain provenance. Document significant architectural decisions in a dedicated ADR structure and add a 'Testing' section to the root README to clarify how to run the test suite.
Ranked by impact ÷ effort. "Helps" is the estimated gain on the 0–100 health score.
Do this
Helps
Effort
Dimension
Resolve the 17 High finding(s) in Static Analysis (SAST) — start with publish.yml (5), docs.yml (4), deploy-verification.yml (2).
Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing.
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. 20 of 22 evaluated dimensions are computed purely by tools and static analysis (confidence 1.0); 2 documentation/naming judgement(s) are LLM-assisted and labelled advisory. Overall confidence is 0.5 — 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 — 22 dimensions across the health lenses
Each chip is a dimension scored from real signals across architecture, testing, dependencies, security & compliance, documentation, git-history and code quality — in one coherent pass. A surface report typically covers a handful.
How to trust any code-health report — three questions
Can you open the finding? Real findings cite a repo-relative file and line you can open at the cited line — never an absolute scratch path. Here, 161 of 170 do; the remainder are repo-wide signals — a dimension-level measurement, not a single line. (Every path in this report is repo-relative by construction: paths are normalized at the producer and the report is rejected if any rooted path leaks through.)
Is there a tool behind the number? Every score below names the method that produced it — Roslyn, git, a scanner, or (for a handful of documentation/naming dimensions) an LLM labelled sampled · advisory — not a narrative.
Does re-running give the same result? Run it again on the same commit and the score — and this report, byte for byte — is identical. A report whose numbers move between runs is describing the run, not the code.
This report answers yes to all three. That's the bar to hold any assessment to.
Tools & methods
The actual versions used this run (captured at analysis time) — re-run on the same commit for the identical score.
Method
Backs
Version
Evaluator
Roslyn static analysis
Complexity, cohesion, coupling, dead code, API surface, layering
What ran differently this time — a tool absent, degraded, or that fell back to an estimate. Named openly, not folded silently into the scores. A degraded run also records its exact cause in diagnostics.md.
D30 Dependency Vulnerabilities — scanner not present in this environment — The backing tool was not installed where this scan ran, so this dimension was not scored. Install the tool (or run in the hosted environment, where it is always present) for a graded result.
Repo exclusion declarations (.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.
D16 Bus Factor: Bus-factor is a time-decayed model of commit attribution (who has recently, repeatedly worked a file), not comprehension — pairing, review and reading-without-committing spread knowledge it can't see; bot commits and shared accounts still distort it.
D19 Documentation Quality: Documentation quality is judged by an LLM over a bounded sample of docs — it reads what is written, not whether the docs match the running system, and it is advisory, not a measurement.
D21 Naming Consistency: Naming quality is an LLM judgement over a bounded sample — it assesses clarity/consistency of the names it sees, not domain-correctness, and is advisory.
D28 Secrets (history): Secret-history scanning sweeps the git log for known patterns — a secret that predates the available history, or never matched a signature, is not found (clean means "nothing matched in the history we can see").
D29 Static Analysis (SAST): SAST findings are pattern-based (semgrep) — it finds classes of bug it has rules for; logic flaws, auth/authorization gaps and issues needing runtime context are out of reach (and clean means "no rule matched").
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.
P4 Deployment & Rollback: Approval/branch-protection rules live in repository settings the scan cannot see — only their in-repo evidence (config files, workflows) is checked, so a control enforced purely in the host's settings reads as "not evidenced".
P6 Release Hygiene: Rollback/observability controls are inferred from repo artefacts (pipelines, dashboards-as-code) — controls configured in external tooling, with no in-repo trace, cannot be credited.
The LLM boundary
LLM-set scores this run (3): D19, D21, M4 (model: Local LLM). For these, a model reads a bounded sample and sets the numeric score (documentation, ADR quality, naming, comment value, onboarding) — D25 sets the ADR-conformance fraction over sampled code, D22 judges API accuracy over a sample. These are sampled and advisory by design: they vary at the margins between runs and are never a deterministic measurement. Every other score in this report is tool-computed at confidence 1.0.
What it measures: How tangled the control flow is — methods with many branches are hard to test and change.
Method: Cyclomatic complexity per method (1 + decision points), computed exhaustively across production source; test projects separated by convention. Deterministic.
+ 21 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Resolve the 1 Runtime._invoke_llm_body (cyclomatic 71) finding(s) in Cyclomatic Complexity — start with runtime.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 runtime._verify_replay (cyclomatic 31) finding(s) in Cyclomatic Complexity — start with runtime.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 manifest.load_manifest (cyclomatic 30) finding(s) in Cyclomatic Complexity — start with manifest.py. — One of this dimension's main actionable groups (1 warning-level).
Enforce Cyclomatic Complexity in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d1_recommendation.md · top locations in Appendix A, every location in findings.md.
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.
+ 56 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Resolve the 1 Runtime._invoke_llm_body (cognitive 161) finding(s) in Cognitive Complexity — start with runtime.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 native._node_compatible (cognitive 56) finding(s) in Cognitive Complexity — start with native.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 Runtime._ensure_registry (cognitive 49) finding(s) in Cognitive Complexity — start with runtime.py. — One of this dimension's main actionable groups (1 warning-level).
Enforce Cognitive Complexity in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d2_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D3 · God Classes9.2 / 10Exemplary✓ Tool-verified
What it measures: Over-large classes that try to do too much ("god classes").
Method: God-class detection by line and method-count thresholds per logical type (partial classes unified), filtered for generated code and registration/contract false positives. Deterministic.
What it measures: Copy-pasted code that should be shared instead.
Method: Code duplication via token-stream sliding windows with type-aware normalization (locals masked, type names preserved), density-scored per KLoC of production code. Deterministic.
What it measures: Whether any secrets (keys, tokens, passwords) have leaked into the code.
Method: In-process native secret scanner (entropy plus signature patterns) across all tracked files; no external tool. A clean result is a measured 10, not no-data zero. Deterministic.
What it measures: Files that change often and are also complex — the riskiest hotspots.
Method: Per production file churn times cyclomatic complexity over a rolling window, computed from git and Roslyn/JS/Razor analysis. Exhaustive, deterministic per commit date.
Detailed fixes: d15_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D16 · Bus Factor7.9 / 10Strong✓ Tool-verified
What it measures: Whether knowledge is concentrated in too few people (the "bus factor").
Method: Living knowledge per author via time-decayed commit attribution (6-month half-life, focus weighting) across largest source files. Deterministic, avoids blame's mechanical-refactor false positives.
36 source file(s) have their living knowledge concentrated in one author (≥90% of recent, decayed contribution). The largest is activegraph/packs/__init__.py.
Off-boarding risk: anonymized user #1
Further sole-owners (lower concentration)
What to do
Resolve the 1 Off-boarding risk finding(s) in Bus Factor. — One of this dimension's main actionable groups (1 recommendation-level).
Resolve the 1 Further sole-owners (lower concentration) finding(s) in Bus Factor. — One of this dimension's main actionable groups (1 recommendation-level).
Detailed fixes: d16_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether the project's documentation is clear, complete, and useful.
Method: Judged by language model at low temperature (0.0-0.1) on a deterministic doc sample (READMEs plus first 25 architecture docs), with two-pass stability filtering. Advisory, sampled.
Active Graph's documentation is clear, complete, and well-structured. The README gives a strong value proposition ('Behaviors react to a shared graph instead of talking; every run is resumable, forkable, and diff-able'), an install section covering all provider packages with cross-provider swap guidance, and a 30-second quickstart that links to the full tutorial. A dedicated examples/babyagi README explains what BabyAGI on Active Graph does differently from the original loop (global variable for typed graph) and shows how trace inspection is the source of truth. The docs also include a public robots.txt entry point directing crawlers away from private documentation, which is standard practice.
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.
What it measures: Real static-analysis (SAST) findings — likely security bugs in the code, any language.
Method: Polyglot static analysis via semgrep across the repo using the pinned, image-baked p/security-audit + p/owasp-top-ten rulesets (no scan-time registry fetch); severity rules (ERROR/WARNING/INFO) map to a full-band severity-weighted score. Exhaustive, deterministic; degrades on parse failure.
Coverage: semgrep pattern rules over all files — exhaustive for the rule set, blind to classes of bug without a rule (clean = no rule matched).
High: github-actions-mutable-action-tag · ×17.github/workflows/deploy-verification.yml:41detected by semgrep finding
What to do
Resolve the 17 High finding(s) in Static Analysis (SAST) — start with publish.yml (5), docs.yml (4), deploy-verification.yml (2). — One of this dimension's main actionable groups (17 issue-level).
Detailed fixes: d29_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether anyone still has living knowledge of each file, or it has been orphaned — last understood long ago by someone now gone quiet. The sibling of the bus factor: D16 asks who owns it, D34 asks whether anyone still knows it.
Method: File orphaning as total living-knowledge decay below one focused-commit's worth within a year, computed per-file from the D16 decay model. Exhaustive, deterministic over fixed history.
What it measures: Whether files that change together actually belong together — pairs that repeatedly co-change in git history despite having no explicit code dependency, surfacing the hidden/logical coupling (and boundaries in the wrong place) a static scan can't see.
Method: Pairwise co-occurrence over the per-commit file sets in git history (production source only — tests and generated dropped): Degree-of-Coupling = shared ÷ min individual revisions, reported above noise floors (each file ≥10 revisions, ≥5 shared commits, ≥50% strength); sweeping commits excluded. Deterministic over fixed history.
Coverage: Population: PRODUCTION source files only — test and generated files are dropped before pairing, so a class co-changing with its own test (trivially ~100%) can't drown the real production↔production coupling. Pairs ranked by Degree-of-Coupling; coupling through a build step, config, or non-source file isn't seen.
What it measures: Whether the build pipeline provides supply-chain integrity — generated provenance/attestation, signed artifacts (cosign/sigstore), an SBOM, and pinned build actions. Presence of the configuration, not a runtime guarantee.
Method: Supply-chain provenance/signing read deterministically from CI/build config (.github/workflows, .gitlab-ci.yml, azure-pipelines, Jenkinsfile, .circleci) + the release surface: four signals — generated provenance/attestation (SLSA/in-toto/actions-attest), artifact signing (cosign/sigstore/gitsign), an SBOM (syft/sbom-action/*.spdx.json/*.cdx.json), and SHA-pinned build actions — scored 10·present/denom. NotApplicable without a build pipeline. Detects configuration presence, not runtime enforcement.
Resolve the 1 Unpinned build actions finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 PR-triggered workflow without a permissions block finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 No SBOM finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 recommendation-level).
Detailed fixes: d36_recommendation.md · top locations in Appendix A, every location in findings.md.
Maturity · Maturity — Whether the repo and its projects have a README, and whether it's substantive and current.
Method: Filesystem scan: README presence, word count, and headings for depth; git history for staleness. Exhaustive across root and project dirs, deterministic.
What to do
Add a 'Testing' section to the root README — how to run the test suite.
Add an 'Architecture' / 'How it works' section to the root README — the high-level shape.
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.
Readiness · Readiness — Whether SAST, secret/dependency scanning and performance benchmarking are wired in (presence, not runtime).
Method: Filesystem scan: SAST configuration, dependency-update automation, secret scanning, and a benchmark harness or benchmark step — in this repository's own ecosystem. Exhaustive, deterministic.
No static application security testing detected. For this repository's stack, add bandit, `semgrep --config=p/python`, or CodeQL's python pack as a CI step.
What to do
Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing.
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 automated and safely reversible (probes, rolling updates, approval gates) — from manifests/pipeline files, not the live environment.
Method: Filesystem scan: deployment manifests/IaC (K8s YAML, Helm, Terraform) for rolling updates, probes, approval gates, migration hooks. Exhaustive, deterministic.
Readiness · Readiness — Whether releases are traceable — a maintained changelog and explicit version stamping.
Method: Filesystem scan: changelog file presence and version tags in csproj or git tags. Exhaustive, deterministic.
Do you agree with this assessment?
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 — 72 check(s) not relevant to this codebase
These checks had nothing to measure here (no tests, no git history, the codebase is small, or the architecture style doesn't apply), so they're omitted above rather than scored low.
AC1 Text alternatives — No web markup found — accessibility is not applicable to this repository.
AC2 Forms & labels — No web markup found — accessibility is not applicable to this repository.
AC3 Page structure — No web markup found — accessibility is not applicable to this repository.
AC4 Keyboard semantics — No web markup found — accessibility is not applicable to this repository.
AC5 ARIA correctness — No web markup found — accessibility is not applicable to this repository.
AC6 Visual & motion safety — No web markup found — accessibility is not applicable to this repository.
AC7 A11y enforcement — No web markup found — accessibility is not applicable to this repository.
AX1 Captive dependencies — no DI registrations detected
AX10 Code composition — not assessed — code composition is computed by ROLE over a document set that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
AX2 Stateful singletons — no singleton implementations detected
AX3 Project dependency cycles — not assessed — project cycles and dependency direction are computed over a project-reference graph that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
AX4 Dependency direction — not assessed — project cycles and dependency direction are computed over a project-reference graph that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
AX5 Architecture & structure — not assessed — architecture style/structure is computed from a project graph (projects, types, module namespaces) that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
AX6 Interface segregation — not assessed — interface segregation is computed over a type surface that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
AX7 Slice cohesion — not applicable — not a vertical-slice architecture
AX8 Test isolation — not assessed — test isolation is computed from a project graph (which projects are test projects, and what they reference) that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
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 — ~18198 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.
D17 Explicit Debt — explicit-debt markers are read through a C# workspace today, so they were not read for this repository's language — this asserts nothing about how many markers the code carries. Not scored — this is a gap in the analyzer, not a finding about this repository
D18 Solution Shape — D18 scores the shape of a .NET solution; this repository has no .NET solution or project files, so the dimension does not apply.
D20 ADR Quality — N/A — ADRs are expected on deployable products with a user-facing host, not consumed libraries; no ADR log is required here.
D22 Internal API Consistency — No exposed public API
D23 Boundary Type-Coupling — Production source is present (.py) 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.
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.
D38 OSV Dependency Vulnerabilities — No supported non-.NET dependency lockfile found outside build output (npm package-lock/yarn/pnpm/bun, Go go.mod, Rust Cargo.lock, Maven pom.xml, Gradle lockfiles, Python requirements.txt/poetry.lock/Pipfile.lock/pdm.lock, PHP composer.lock, Ruby Gemfile.lock, Elixir mix.lock, Dart pubspec.lock, Swift Package.resolved); nothing for OSV to scan. A NuGet-only repo stays NotApplicable — .NET CVEs are D30's domain.
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 — applicable but not scored (2 of 3 signals for this style — below the bar we score at): 164 value object(s); 1 domain event(s)
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 — Reported, not scored — this card publishes what the CI gate does with the test inventory rather than grading it. The findings above are its output.
P2 Observability — Observability was not assessed: this check reads a source model that does not carry this repository's product — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of a logging idiom this check recognises is NOT evidence that this repo lacks structured logging (it may log through its own ecosystem's logger). This is a gap in the analyzer, not a finding about this repository.
P5 DR & Backup — not evidenced — repo shows no backup/RTO/RPO controls; absence of evidence is not evidence of a working control
P7 Outbound HTTP resilience — not measured — the application kind could not be determined for this repo
P8 Schema migrations — not assessed — schema-migration practice is read from a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
P9 Domain vs controller coverage — no coverage report found on disk — produce a coverage report in a standard format (`coverage run -m pytest` then `coverage xml`) into the repo working tree before the scan — a CI step is the usual place, since the artefact is commonly gitignored, or wire coverage collection into CI, to enable this cross-layer check
PF1 Benchmark discipline — Performance was not assessed: this lens reads a source model that was not loaded for this repository, because the repository is written in a language this lens does not yet model or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository — in particular it is NOT a statement that this repo is unpackaged or performance-careless.
PF2 Allocation hygiene — Performance was not assessed: this lens reads a source model that was not loaded for this repository, because the repository is written in a language this lens does not yet model or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository — in particular it is NOT a statement that this repo is unpackaged or performance-careless.
PF3 Async & latency hygiene — Performance was not assessed: this lens reads a source model that was not loaded for this repository, because the repository is written in a language this lens does not yet model or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository — in particular it is NOT a statement that this repo is unpackaged or performance-careless.
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.
High: github-actions-mutable-action-tag .github/workflows/deploy-verification.yml:41— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/checkout@<40-character SHA>`. This step references `actions/checkout@v4`; resolve the SHA it points at today with `gh api repos/actions/checkout/commits/v4 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/deploy-verification.yml:42— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/setup-python@<40-character SHA>`. This step references `actions/setup-python@v5`; resolve the SHA it points at today with `gh api repos/actions/setup-python/commits/v5 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/docs.yml:43— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/checkout@<40-character SHA>`. This step references `actions/checkout@v4`; resolve the SHA it points at today with `gh api repos/actions/checkout/commits/v4 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/docs.yml:44— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/setup-python@<40-character SHA>`. This step references `actions/setup-python@v5`; resolve the SHA it points at today with `gh api repos/actions/setup-python/commits/v5 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/docs.yml:72— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/upload-pages-artifact@<40-character SHA>`. This step references `actions/upload-pages-artifact@v3`; resolve the SHA it points at today with `gh api repos/actions/upload-pages-artifact/commits/v3 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/docs.yml:86— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/deploy-pages@<40-character SHA>`. This step references `actions/deploy-pages@v4`; resolve the SHA it points at today with `gh api repos/actions/deploy-pages/commits/v4 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/docstrings.yml:32— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/checkout@<40-character SHA>`. This step references `actions/checkout@v4`; resolve the SHA it points at today with `gh api repos/actions/checkout/commits/v4 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/docstrings.yml:33— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/setup-python@<40-character SHA>`. This step references `actions/setup-python@v5`; resolve the SHA it points at today with `gh api repos/actions/setup-python/commits/v5 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/publish.yml:36— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/checkout@<40-character SHA>`. This step references `actions/checkout@v4`; resolve the SHA it points at today with `gh api repos/actions/checkout/commits/v4 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/publish.yml:37— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/setup-python@<40-character SHA>`. This step references `actions/setup-python@v5`; resolve the SHA it points at today with `gh api repos/actions/setup-python/commits/v5 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/publish.yml:46— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/upload-artifact@<40-character SHA>`. This step references `actions/upload-artifact@v4`; resolve the SHA it points at today with `gh api repos/actions/upload-artifact/commits/v4 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/publish.yml:65— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/download-artifact@<40-character SHA>`. This step references `actions/download-artifact@v4`; resolve the SHA it points at today with `gh api repos/actions/download-artifact/commits/v4 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/publish.yml:70— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: pypa/gh-action-pypi-publish@<40-character SHA>`. This step references `pypa/gh-action-pypi-publish@release/v1`; resolve the SHA it points at today with `gh api repos/pypa/gh-action-pypi-publish/commits/release/v1 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/types.yml:31— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/checkout@<40-character SHA>`. This step references `actions/checkout@v4`; resolve the SHA it points at today with `gh api repos/actions/checkout/commits/v4 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/types.yml:32— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/setup-python@<40-character SHA>`. This step references `actions/setup-python@v5`; resolve the SHA it points at today with `gh api repos/actions/setup-python/commits/v5 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/wheel-completeness.yml:48— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/checkout@<40-character SHA>`. This step references `actions/checkout@v4`; resolve the SHA it points at today with `gh api repos/actions/checkout/commits/v4 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/wheel-completeness.yml:49— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/setup-python@<40-character SHA>`. This step references `actions/setup-python@v5`; resolve the SHA it points at today with `gh api repos/actions/setup-python/commits/v5 --jq .sha`.
Hotspot: activegraph/runtime/runtime.py activegraph/runtime/runtime.py— activegraph/runtime/runtime.py changed 35 times in last 90 days, max complexity 71. 3 of those changes were fix/bug commits, so the churn is repair rather than feature work. Before the next change lands here, cover the area it touches with tests, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time.
Hotspot: activegraph/core/graph.py activegraph/core/graph.py— activegraph/core/graph.py changed 17 times in last 90 days, max complexity 15. 1 of those changes was a fix/bug commit, so the churn is repair rather than feature work. Before the next change lands here, cover the area it touches with tests, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time.
Hotspot: activegraph/packs/__init__.py activegraph/packs/__init__.py— activegraph/packs/__init__.py changed 11 times in last 90 days, max complexity 21. 1 of those changes was a fix/bug commit, so the churn is repair rather than feature work. Before the next change lands here, cover the area it touches with tests, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time.
Hotspot: activegraph/packs/loader.py activegraph/packs/loader.py— activegraph/packs/loader.py changed 8 times in last 90 days, max complexity 28. 1 of those changes was a fix/bug commit, so the churn is repair rather than feature work. Before the next change lands here, cover the area it touches with tests, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time.
Hotspot: activegraph/cli/main.py activegraph/cli/main.py— activegraph/cli/main.py changed 8 times in last 90 days, max complexity 20. 1 of those changes was a fix/bug commit, so the churn is repair rather than feature work. Before the next change lands here, cover the area it touches with tests, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time.
Hotspot: activegraph/packs/manifest.py activegraph/packs/manifest.py— activegraph/packs/manifest.py changed 4 times in last 90 days, max complexity 30. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, behind tests written first.
Hotspot: activegraph/llm/openai.py activegraph/llm/openai.py— activegraph/llm/openai.py changed 5 times in last 90 days, max complexity 18. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, behind tests written first.
Hotspot: activegraph/trace/causal.py activegraph/trace/causal.py— activegraph/trace/causal.py changed 3 times in last 90 days, max complexity 26. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, behind tests written first.
Hotspot: activegraph/llm/prompt.py activegraph/llm/prompt.py— activegraph/llm/prompt.py changed 3 times in last 90 days, max complexity 25. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, behind tests written first.
Hotspot: activegraph/core/ids.py activegraph/core/ids.py— activegraph/core/ids.py changed 3 times in last 90 days, max complexity 18. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, behind tests written first.
Duplicated block (14 lines × 2) activegraph/behaviors/decorators.py:183— activegraph/behaviors/decorators.py:183-196 | activegraph/packs/__init__.py:738-751 — 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 `activegraph/behaviors/decorators.py:183` 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 (14 lines × 2) activegraph/behaviors/decorators.py:374— activegraph/behaviors/decorators.py:374-387 | activegraph/packs/__init__.py:883-896 — 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 `activegraph/behaviors/decorators.py:374` 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 (14 lines × 2) activegraph/llm/anthropic.py:46— activegraph/llm/anthropic.py:46-61 | activegraph/llm/openai.py:69-82 — 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 (14 lines × 2) activegraph/llm/recorded.py:167— activegraph/llm/recorded.py:167-180 | activegraph/llm/recorded.py:319-332 — 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 `activegraph/llm/recorded.py:319` 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 (14 lines × 2) activegraph/packs/diligence/fixtures/__init__.py:216— activegraph/packs/diligence/fixtures/__init__.py:216-229 | activegraph/packs/diligence/fixtures/__init__.py:239-252 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `activegraph/packs/diligence/fixtures/__init__.py:216` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (14 lines × 2) activegraph/runtime/registration_errors.py:47— activegraph/runtime/registration_errors.py:47-60 | activegraph/runtime/registration_errors.py:165-178 — 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 `activegraph/runtime/registration_errors.py:47` 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 (14 lines × 2) activegraph/runtime/runtime.py:678— activegraph/runtime/runtime.py:678-692 | activegraph/runtime/runtime.py:781-794 — 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 (14 lines × 2) activegraph/runtime/runtime.py:3310— activegraph/runtime/runtime.py:3310-3325 | activegraph/runtime/runtime.py:3485-3498 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
Duplicated block (14 lines × 2) activegraph/store/postgres.py:409— activegraph/store/postgres.py:409-422 | activegraph/store/sqlite.py:293-306 — 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 `activegraph/store/postgres.py:409` 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 (14 lines × 2) examples/operate_a_run.py:73— examples/operate_a_run.py:73-86 | examples/resume_and_fork.py:55-68 — 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 `examples/resume_and_fork.py:55` 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.
FileTooLong: runtime/runtime.py activegraph/runtime/runtime.py:0— FileTooLong — 3000 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: cli/main.py activegraph/cli/main.py:0— FileTooLong — 860 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: core/graph.py activegraph/core/graph.py:0— FileTooLong — 792 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: packs/__init__.py activegraph/packs/__init__.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: packs/loader.py activegraph/packs/loader.py:0— FileTooLong — 614 significant lines (blank, comment-only and punctuation-only lines excluded). To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
Duplicated block (8 lines × 2) activegraph/cli/main.py:517— activegraph/cli/main.py:517-524 | activegraph/cli/main.py:834-841 — 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 `activegraph/cli/main.py:517` 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 × 2) activegraph/llm/embedding_cache.py:102— activegraph/llm/embedding_cache.py:102-111 | activegraph/runtime/runtime.py:4461-4468 — 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 (8 lines × 2) activegraph/store/graph_conformance.py:304— activegraph/store/graph_conformance.py:304-311 | activegraph/store/graph_conformance.py:334-341 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (8 lines × 2) activegraph/store/graph_conformance.py:421— activegraph/store/graph_conformance.py:421-428 | activegraph/store/graph_conformance.py:443-452 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (8 lines × 2) activegraph/store/postgres.py:351— activegraph/store/postgres.py:351-358 | activegraph/store/sqlite.py:248-255 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once.
Duplicated block (12 lines × 2) activegraph/llm/anthropic.py:190— activegraph/llm/anthropic.py:190-202 | activegraph/llm/openai.py:242-253 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once.
Duplicated block (12 lines × 2) activegraph/packs/__init__.py:263— activegraph/packs/__init__.py:263-274 | activegraph/packs/__init__.py:316-327 — 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 `activegraph/packs/__init__.py:263` 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 (12 lines × 2) activegraph/runtime/runtime.py:1390— activegraph/runtime/runtime.py:1390-1401 | activegraph/runtime/runtime.py:2523-2535 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (11 lines × 2) activegraph/core/graph.py:742— activegraph/core/graph.py:742-752 | activegraph/core/graph.py:763-773 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (11 lines × 2) activegraph/packs/diligence/fixtures/__init__.py:330— activegraph/packs/diligence/fixtures/__init__.py:330-340 | examples/diligence_with_tools.py:398-408 — 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 (11 lines × 2) activegraph/store/postgres.py:239— activegraph/store/postgres.py:239-249 | activegraph/store/sqlite.py:134-144 — 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 `activegraph/store/postgres.py:239` 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.
TooManyMethods: Runtime activegraph/runtime/runtime.py:312— TooManyMethods — 60 methods. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
TooManyMethods: Graph activegraph/core/graph.py:152— TooManyMethods — 36 methods. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
Duplicated block (15 lines × 2) activegraph/behaviors/decorators.py:319— activegraph/behaviors/decorators.py:319-333 | activegraph/packs/__init__.py:831-845 — 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 (15 lines × 2) activegraph/runtime/registration_errors.py:107— activegraph/runtime/registration_errors.py:107-121 | activegraph/runtime/registration_errors.py:222-236 — 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 `activegraph/runtime/registration_errors.py:107` 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 (10 lines × 3) activegraph/runtime/runtime.py:2331— activegraph/runtime/runtime.py:2331-2340 | activegraph/runtime/runtime.py:2353-2362 | activegraph/runtime/runtime.py:2400-2409 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited.
Duplicated block (10 lines × 3) activegraph/store/falkordb.py:292— activegraph/store/falkordb.py:292-301 | activegraph/store/falkordb.py:391-400 | activegraph/store/falkordb.py:414-423 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Read the line range as the matched WINDOW rather than a finished unit: at `activegraph/store/falkordb.py:292` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (9 lines × 2) activegraph/llm/anthropic.py:380— activegraph/llm/anthropic.py:380-388 | activegraph/llm/openai.py:519-527 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (9 lines × 2) activegraph/store/falkordb.py:371— activegraph/store/falkordb.py:371-379 | activegraph/store/falkordb.py:506-514 — 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) activegraph/llm/cache.py:176— activegraph/llm/cache.py:176-182 | activegraph/llm/recorded.py:236-242 — 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 (7 lines × 2) activegraph/runtime/registry.py:75— activegraph/runtime/registry.py:75-81 | activegraph/runtime/runtime.py:874-883 — 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.
Runtime._invoke_llm_body (cyclomatic 71) activegraph/runtime/runtime.py:1562— Runtime._invoke_llm_body has cyclomatic complexity 71 (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.
runtime._verify_replay (cyclomatic 31) activegraph/runtime/runtime.py:4180— runtime._verify_replay has cyclomatic complexity 31 (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.
manifest.load_manifest (cyclomatic 30) activegraph/packs/manifest.py:177— manifest.load_manifest has cyclomatic complexity 30 (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.
native._node_compatible (cyclomatic 29) activegraph/llm/native.py:76— native._node_compatible has cyclomatic complexity 29 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
loader.load_pack_into_runtime (cyclomatic 28) activegraph/packs/loader.py:55— loader.load_pack_into_runtime has cyclomatic complexity 28 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
Runtime._ensure_registry (cyclomatic 26) activegraph/runtime/runtime.py:949— Runtime._ensure_registry has cyclomatic complexity 26 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
causal.causal_chain (cyclomatic 26) activegraph/trace/causal.py:22— causal.causal_chain has cyclomatic complexity 26 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
prompt._example_instance (cyclomatic 25) activegraph/llm/prompt.py:285— prompt._example_instance 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.
Runtime.promote (cyclomatic 24) activegraph/runtime/runtime.py:3586— Runtime.promote has cyclomatic complexity 24 (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.
Runtime.disable_pack (cyclomatic 22) activegraph/runtime/runtime.py:2796— Runtime.disable_pack has cyclomatic complexity 22 (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.
audit_docstrings.format_report (cyclomatic 22) scripts/audit_docstrings.py:158— audit_docstrings.format_report has cyclomatic complexity 22 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
renderers.print_memo_section (cyclomatic 21) activegraph/cli/renderers.py:22— renderers.print_memo_section 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.
Pack.__post_init__ (cyclomatic 21) activegraph/packs/__init__.py:584— Pack.__post_init__ 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.
Runtime.__init__ (cyclomatic 21) activegraph/runtime/runtime.py:332— Runtime.__init__ has cyclomatic complexity 21 (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.
retention.pins (cyclomatic 21) activegraph/store/retention.py:179— retention.pins 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.
main.cmd_promote (cyclomatic 20) activegraph/cli/main.py:882— main.cmd_promote has cyclomatic complexity 20 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
EmbeddingCache.from_events (cyclomatic 20) activegraph/llm/embedding_cache.py:77— EmbeddingCache.from_events has cyclomatic complexity 20 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
Runtime.embed (cyclomatic 19) activegraph/runtime/runtime.py:1136— Runtime.embed has cyclomatic complexity 19 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
main.cmd_inspect (cyclomatic 18) activegraph/cli/main.py:248— main.cmd_inspect has cyclomatic complexity 18 (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.
IDGen.reseed_from_events (cyclomatic 18) activegraph/core/ids.py:90— IDGen.reseed_from_events has cyclomatic complexity 18 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
OpenAIProvider.complete (cyclomatic 18) activegraph/llm/openai.py:176— OpenAIProvider.complete has cyclomatic complexity 18 (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.
Runtime._loop (cyclomatic 18) activegraph/runtime/runtime.py:1262— Runtime._loop has cyclomatic complexity 18 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
main.cmd_fork (cyclomatic 17) activegraph/cli/main.py:575— main.cmd_fork has cyclomatic complexity 17 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
Runtime.fork (cyclomatic 17) activegraph/runtime/runtime.py:3393— Runtime.fork has cyclomatic complexity 17 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
Runtime._invoke_tool (cyclomatic 16) activegraph/runtime/runtime.py:2097— Runtime._invoke_tool has cyclomatic complexity 16 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
SinkHandle._run (cyclomatic 16) activegraph/sinks/dispatch.py:306— SinkHandle._run 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.
Runtime._invoke_llm_body (cognitive 161) activegraph/runtime/runtime.py:1562— Runtime._invoke_llm_body has cognitive complexity 161 (threshold 15). Drivers by points: if/else 96, ternaries 29, boolean chains 15, error handling 15, loops 6 (nesting depth added 91). 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.
native._node_compatible (cognitive 56) activegraph/llm/native.py:76— native._node_compatible has cognitive complexity 56 (threshold 15). Drivers by points: if/else 41, loops 11, boolean chains 2, ternaries 2 (nesting depth added 28). 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.
Runtime._ensure_registry (cognitive 49) activegraph/runtime/runtime.py:949— Runtime._ensure_registry has cognitive complexity 49 (threshold 15). Drivers by points: if/else 30, loops 12, ternaries 4, boolean chains 3 (nesting depth added 22). 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.
causal.causal_chain (cognitive 43) activegraph/trace/causal.py:22— causal.causal_chain has cognitive complexity 43 (threshold 15). Drivers by points: if/else 21, ternaries 13, boolean chains 6, loops 3 (nesting depth added 19). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
runtime._verify_replay (cognitive 42) activegraph/runtime/runtime.py:4180— runtime._verify_replay has cognitive complexity 42 (threshold 15). Drivers by points: if/else 26, boolean chains 8, ternaries 6, loops 2 (nesting depth added 12). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
Runtime._loop (cognitive 41) activegraph/runtime/runtime.py:1262— Runtime._loop has cognitive complexity 41 (threshold 15). Drivers by points: if/else 27, loops 9, boolean chains 5 (nesting depth added 25). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
loader.load_pack_into_runtime (cognitive 40) activegraph/packs/loader.py:55— loader.load_pack_into_runtime has cognitive complexity 40 (threshold 15). Drivers by points: if/else 23, loops 14, boolean chains 2, ternaries 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.
audit_docstrings.format_report (cognitive 39) scripts/audit_docstrings.py:158— audit_docstrings.format_report has cognitive complexity 39 (threshold 15). Drivers by points: ternaries 16, if/else 14, loops 9 (nesting depth added 17). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
EmbeddingCache.from_events (cognitive 37) activegraph/llm/embedding_cache.py:77— EmbeddingCache.from_events has cognitive complexity 37 (threshold 15). Drivers by points: if/else 24, loops 6, boolean chains 5, error handling 2 (nesting depth added 19). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
audit_docstrings.collect_ring0 (cognitive 34) scripts/audit_docstrings.py:64— audit_docstrings.collect_ring0 has cognitive complexity 34 (threshold 15). Drivers by points: if/else 25, loops 6, error handling 3 (nesting depth added 21). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
renderers.print_memo_section (cognitive 33) activegraph/cli/renderers.py:22— renderers.print_memo_section has cognitive complexity 33 (threshold 15). Drivers by points: ternaries 12, loops 8, boolean chains 7, if/else 6 (nesting depth added 12). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
prompt._example_instance (cognitive 33) activegraph/llm/prompt.py:285— prompt._example_instance has cognitive complexity 33 (threshold 15). Drivers by points: if/else 20, boolean chains 7, ternaries 5, 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.
Pack.__post_init__ (cognitive 32) activegraph/packs/__init__.py:584— Pack.__post_init__ has cognitive complexity 32 (threshold 15). Drivers by points: if/else 23, loops 5, boolean chains 4 (nesting depth added 12). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
manifest.load_manifest (cognitive 32) activegraph/packs/manifest.py:177— manifest.load_manifest has cognitive complexity 32 (threshold 15). Drivers by points: if/else 22, boolean chains 7, error handling 2, loops 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.
audit_types.collect_allowlist_modules (cognitive 30) scripts/audit_types.py:57— audit_types.collect_allowlist_modules has cognitive complexity 30 (threshold 15). Drivers by points: if/else 19, loops 6, error handling 3, boolean chains 2 (nesting depth added 17). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
Runtime.promote (cognitive 29) activegraph/runtime/runtime.py:3586— Runtime.promote has cognitive complexity 29 (threshold 15). Drivers by points: loops 9, ternaries 9, if/else 8, boolean chains 2, error handling 1 (nesting depth added 6). 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.
gate_docstrings.collect_ring1_coverage (cognitive 29) scripts/gate_docstrings.py:142— gate_docstrings.collect_ring1_coverage has cognitive complexity 29 (threshold 15). Drivers by points: if/else 23, loops 3, error handling 2, boolean chains 1 (nesting depth added 17). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
SinkHandle._run (cognitive 28) activegraph/sinks/dispatch.py:306— SinkHandle._run has cognitive complexity 28 (threshold 15). Drivers by points: if/else 21, boolean chains 3, loops 3, error handling 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.
gate_docstrings.collect_ring0_status (cognitive 28) scripts/gate_docstrings.py:93— gate_docstrings.collect_ring0_status has cognitive complexity 28 (threshold 15). Drivers by points: if/else 19, loops 6, error handling 3 (nesting depth added 17). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
IDGen.reseed_from_events (cognitive 27) activegraph/core/ids.py:90— IDGen.reseed_from_events has cognitive complexity 27 (threshold 15). Drivers by points: if/else 21, boolean chains 5, loops 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.
main.cmd_migrate (cognitive 26) activegraph/cli/main.py:1083— main.cmd_migrate has cognitive complexity 26 (threshold 15). Drivers by points: if/else 14, loops 6, ternaries 5, error handling 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.
Runtime.disable_pack (cognitive 26) activegraph/runtime/runtime.py:2796— Runtime.disable_pack has cognitive complexity 26 (threshold 15). Drivers by points: if/else 15, loops 6, ternaries 3, boolean chains 2 (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.
audit_types.format_report (cognitive 26) scripts/audit_types.py:218— audit_types.format_report has cognitive complexity 26 (threshold 15). Drivers by points: loops 15, if/else 8, error handling 2, ternaries 1 (nesting depth added 10). To reduce it, break up the iteration: give each loop body a named function, and split a multi-phase loop into one function per phase so no single body carries the whole pipeline.
Runtime.embed (cognitive 25) activegraph/runtime/runtime.py:1136— Runtime.embed has cognitive complexity 25 (threshold 15). Drivers by points: if/else 14, boolean chains 5, ternaries 4, 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.
Runtime.__init__ (cognitive 24) activegraph/runtime/runtime.py:332— Runtime.__init__ has cognitive complexity 24 (threshold 15). Drivers by points: ternaries 12, if/else 7, boolean chains 3, error handling 1, loops 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.
Runtime._invoke_tool (cognitive 24) activegraph/runtime/runtime.py:2097— Runtime._invoke_tool has cognitive complexity 24 (threshold 15). Drivers by points: if/else 13, error handling 8, boolean chains 3 (nesting depth added 7). 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.
main.cmd_promote (cognitive 23) activegraph/cli/main.py:882— main.cmd_promote has cognitive complexity 23 (threshold 15). Drivers by points: if/else 9, error handling 5, loops 5, boolean chains 2, ternaries 2 (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.
runtime._requeue_unfired (cognitive 23) activegraph/runtime/runtime.py:4101— runtime._requeue_unfired has cognitive complexity 23 (threshold 15). Drivers by points: if/else 15, loops 3, ternaries 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.
retention.pins (cognitive 23) activegraph/store/retention.py:179— retention.pins has cognitive complexity 23 (threshold 15). Drivers by points: if/else 13, boolean chains 5, loops 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.
audit_docstrings.collect_ring1 (cognitive 23) scripts/audit_docstrings.py:122— audit_docstrings.collect_ring1 has cognitive complexity 23 (threshold 15). Drivers by points: if/else 17, loops 3, error handling 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.
diff.compute_diff (cognitive 22) activegraph/runtime/diff.py:108— diff.compute_diff has cognitive complexity 22 (threshold 15). Drivers by points: if/else 9, ternaries 8, loops 3, boolean chains 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.
patterns._eval_where (cognitive 22) activegraph/runtime/patterns.py:804— patterns._eval_where has cognitive complexity 22 (threshold 15). Drivers by points: if/else 16, loops 5, 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.
Registry.match (cognitive 22) activegraph/runtime/registry.py:40— Registry.match has cognitive complexity 22 (threshold 15). Drivers by points: if/else 18, boolean chains 3, loops 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.
Graph.remove_sink (cognitive 21) activegraph/core/graph.py:417— Graph.remove_sink has cognitive complexity 21 (threshold 15). Drivers by points: if/else 15, boolean chains 5, 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.
GraphStore._extend_chain_match (cognitive 21) activegraph/core/graph_store.py:234— GraphStore._extend_chain_match has cognitive complexity 21 (threshold 15). Drivers by points: if/else 15, loops 4, boolean chains 2 (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.
main.cmd_fork (cognitive 20) activegraph/cli/main.py:575— main.cmd_fork has cognitive complexity 20 (threshold 15). Drivers by points: if/else 14, error handling 2, ternaries 2, boolean chains 1, loops 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.
GraphStore.neighborhood (cognitive 20) activegraph/core/graph_store.py:170— GraphStore.neighborhood has cognitive complexity 20 (threshold 15). Drivers by points: if/else 16, 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.
OpenAIProvider.complete (cognitive 20) activegraph/llm/openai.py:176— OpenAIProvider.complete has cognitive complexity 20 (threshold 15). Drivers by points: if/else 11, boolean chains 7, error handling 1, loops 1 (nesting depth added 2). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, 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.
behaviors.risk_identifier (cognitive 20) activegraph/packs/diligence/behaviors.py:323— behaviors.risk_identifier has cognitive complexity 20 (threshold 15). Drivers by points: if/else 14, loops 5, boolean chains 1 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
Budget.remaining (cognitive 20) activegraph/runtime/budget.py:88— Budget.remaining has cognitive complexity 20 (threshold 15). Drivers by points: if/else 18, boolean chains 1, 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.
promote.promote_warnings (cognitive 20) activegraph/runtime/promote.py:356— promote.promote_warnings has cognitive complexity 20 (threshold 15). Drivers by points: if/else 13, boolean chains 4, ternaries 2, loops 1 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
_signature.validate_handler_signature (cognitive 19) activegraph/_signature.py:36— _signature.validate_handler_signature has cognitive complexity 19 (threshold 15). Drivers by points: if/else 13, loops 2, ternaries 2, boolean chains 1, error handling 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.
main.cmd_inspect (cognitive 19) activegraph/cli/main.py:248— main.cmd_inspect has cognitive complexity 19 (threshold 15). Drivers by points: if/else 10, loops 4, boolean chains 2, ternaries 2, error handling 1 (nesting depth added 2). 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.
main._print_pack_versions (cognitive 19) activegraph/cli/main.py:370— main._print_pack_versions has cognitive complexity 19 (threshold 15). Drivers by points: if/else 7, boolean chains 5, ternaries 4, loops 3 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
gate_docstrings.main (cognitive 19) scripts/gate_docstrings.py:184— gate_docstrings.main has cognitive complexity 19 (threshold 15). Drivers by points: if/else 11, loops 6, boolean chains 1, ternaries 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.
native._inject (cognitive 18) activegraph/llm/native.py:154— native._inject has cognitive complexity 18 (threshold 15). Drivers by points: loops 10, if/else 7, boolean chains 1 (nesting depth added 7). 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.
loader._locate_pack_manifest (cognitive 18) activegraph/packs/loader.py:336— loader._locate_pack_manifest has cognitive complexity 18 (threshold 15). Drivers by points: if/else 14, loops 3, boolean chains 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.
loader._wrap_with_injection (cognitive 18) activegraph/packs/loader.py:757— loader._wrap_with_injection has cognitive complexity 18 (threshold 15). Drivers by points: if/else 12, boolean chains 2, error handling 2, loops 1, ternaries 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.
Runtime._fire_due_delayed (cognitive 18) activegraph/runtime/runtime.py:1330— Runtime._fire_due_delayed has cognitive complexity 18 (threshold 15). Drivers by points: if/else 16, boolean chains 1, loops 1 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
runtime._validate_embedding_vectors (cognitive 18) activegraph/runtime/runtime.py:4444— runtime._validate_embedding_vectors has cognitive complexity 18 (threshold 15). Drivers by points: if/else 11, loops 3, boolean chains 2, ternaries 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.
sandbox._run_forked_trial_local (cognitive 18) activegraph/sandbox/__init__.py:378— sandbox._run_forked_trial_local has cognitive complexity 18 (threshold 15). Drivers by points: if/else 11, boolean chains 2, loops 2, ternaries 2, error handling 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.
serde._find_non_serializable (cognitive 18) activegraph/store/serde.py:90— serde._find_non_serializable has cognitive complexity 18 (threshold 15). Drivers by points: error handling 6, loops 4, boolean chains 3, ternaries 3, if/else 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.
url.parse_store_url (cognitive 18) activegraph/store/url.py:87— url.parse_store_url has cognitive complexity 18 (threshold 15). Drivers by points: if/else 14, boolean chains 2, 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.
test_doc_links.test_docs_base_url_is_centralized (cognitive 18) tests/test_doc_links.py:283— test_doc_links.test_docs_base_url_is_centralized has cognitive complexity 18 (threshold 15). Drivers by points: if/else 11, loops 6, 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.
openai._extract_tool_calls (cognitive 17) activegraph/llm/openai.py:469— openai._extract_tool_calls has cognitive complexity 17 (threshold 15). Drivers by points: boolean chains 7, if/else 6, error handling 3, loops 1 (nesting depth added 3). 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.
loader._wrap_behavior_for_pack (cognitive 17) activegraph/packs/loader.py:608— loader._wrap_behavior_for_pack has cognitive complexity 17 (threshold 15). Drivers by points: ternaries 15, if/else 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.
Runtime.status (cognitive 17) activegraph/runtime/runtime.py:2592— Runtime.status has cognitive complexity 17 (threshold 15). Drivers by points: if/else 10, boolean chains 3, loops 3, ternaries 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.
Runtime.fork (cognitive 17) activegraph/runtime/runtime.py:3393— Runtime.fork has cognitive complexity 17 (threshold 15). Drivers by points: ternaries 10, boolean chains 3, if/else 3, loops 1 (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.
main.cmd_export_trace (cognitive 16) activegraph/cli/main.py:1007— main.cmd_export_trace has cognitive complexity 16 (threshold 15). Drivers by points: if/else 6, loops 6, error handling 2, 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.
quickstart._prepare_interactive_subdir (cognitive 16) activegraph/cli/quickstart.py:247— quickstart._prepare_interactive_subdir has cognitive complexity 16 (threshold 15). Drivers by points: if/else 11, loops 4, boolean chains 1 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
runtime._recorded_wall_stop (cognitive 16) activegraph/runtime/runtime.py:4371— runtime._recorded_wall_stop has cognitive complexity 16 (threshold 15). Drivers by points: ternaries 8, if/else 6, 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.
Unpinned build actions — CI references GitHub Actions by a floating ref (@main / @tag) rather than a pinned commit SHA, weakening build integrity. 19 floating ref(s) across 7 workflow file(s), 1 of them mutable BRANCH refs — pin those first. Each floating ref is itemized at file:line by the SAST (D29) lens.
D36 · Supply-chain Provenance & Signing· PR-triggered workflow without a permissions block · ×1
PR-triggered workflow without a permissions block — 5 workflow(s) triggered by pull_request declare no `permissions:` block (wheel-completeness.yml, types.yml, tests.yml …) and so run with the repository's default GITHUB_TOKEN scope, while 2 sibling workflows in the same repository are already scoped. Pull-request runs build the least-trusted code in the repository; give each of these workflows its own least-privilege block — `permissions: {contents: read}` at the top of the workflow, widened per job only where a job genuinely writes.
Duplicated block (23 lines × 2) activegraph/core/graph.py:649— activegraph/core/graph.py:649-671 | activegraph/core/graph.py:705-730 — 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 `activegraph/core/graph.py:649` 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 (21 lines × 2) activegraph/llm/anthropic.py:169— activegraph/llm/anthropic.py:169-189 | activegraph/llm/openai.py:221-241 — 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 `activegraph/llm/anthropic.py:169` 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 (18 lines × 2) activegraph/packs/__init__.py:940— activegraph/packs/__init__.py:940-957 | activegraph/tools/decorators.py:72-92 — 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 `activegraph/packs/__init__.py:940` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (17 lines × 2) activegraph/runtime/runtime.py:2908— activegraph/runtime/runtime.py:2908-2925 | activegraph/runtime/runtime.py:3071-3087 — 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 `activegraph/runtime/runtime.py:2908` 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 (16 lines × 2) activegraph/packs/__init__.py:757— activegraph/packs/__init__.py:757-775 | activegraph/packs/__init__.py:903-918 — 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 `activegraph/packs/__init__.py:757` 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 × 3) scripts/audit_docstrings.py:91— scripts/audit_docstrings.py:91-103 | scripts/audit_types.py:77-89 | scripts/gate_docstrings.py:113-125 — 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 (13 lines × 2) activegraph/runtime/runtime.py:3070— activegraph/runtime/runtime.py:3070-3084 | activegraph/runtime/runtime.py:3102-3114 — 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 `activegraph/runtime/runtime.py:3070` 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) activegraph/packs/loader.py:658— activegraph/packs/loader.py:658-667 | activegraph/packs/loader.py:677-686 — 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 `activegraph/packs/loader.py:658` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (7 lines × 3) activegraph/packs/loader.py:624— activegraph/packs/loader.py:624-630 | activegraph/packs/loader.py:656-662 | activegraph/packs/loader.py:675-681 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited.
Duplicated block (6 lines × 2) activegraph/trace/printer.py:163— activegraph/trace/printer.py:163-168 | activegraph/trace/printer.py:191-196 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (5 lines × 2) activegraph/store/graph_conformance.py:324— activegraph/store/graph_conformance.py:324-329 | activegraph/store/graph_conformance.py:349-353 — 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.
Recommendation — 5 finding(s)
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.
Off-boarding risk: anonymized user #1 — If anonymized user #1 becomes unavailable, 35 significant file(s) lose their only recent owner: activegraph/packs/__init__.py, activegraph/store/sqlite.py, activegraph/store/postgres.py, activegraph/llm/prompt.py, activegraph/runtime/exec_errors.py, activegraph/behaviors/decorators.py, activegraph/observability/migration.py, activegraph/llm/anthropic.py (+27 more). Pair on, review, or document these before any departure.
D16 · Bus Factor· Further sole-owners (lower concentration) · ×1
Further sole-owners (lower concentration) — 1 other contributor(s) are each the sole owner of a small amount of code below the off-boarding threshold — folded into the bus-factor score and metrics (36 single-owned of 85 analysed files in total, counted over production source files of roughly 100 lines or more, excluding tests, vendored, generated and example/demo trees, largest first). They are anonymized user #2 (1 file(s)) — spread or document their files in the same way, at lower priority than the named off-boarding risks above.
D36 · Supply-chain Provenance & Signing· No SBOM · ×1
No SBOM — No SBOM generation or committed SBOM found — produce one with what your ecosystem ships (`cyclonedx-py` over the resolved Python environment/lockfile, `syft` (or `anchore/sbom-action` in CI) over the source tree or released image). Publish it as a release asset (`*.spdx.json` / `*.cdx.json`) so consumers can see what they are installing.
Coverage not included — suite not readable by the collector — Coverage NOT READ here — but this repository measures it: a coverage step in CI (`coverage report`) 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.
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 019fcf2d-8d14-7127-b481-ee062c868a61 · every finding is also locatable in findings.md, and the complete scoring record (with exit codes + durations) in sidecar.json.
Issues: 17 · Warnings: 146 · Recommendations: 5 · Info: 2 — Appendix A · all findings · full markdown report.
Generated by Watchdog — deterministic code-health analysis. 04-08-2026 @ 23:48 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.