Public report — llama-agents, 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.
194findings with an exact file:lineof 210 — the remainder are repo-wide signals (a dimension-level measurement, not a single line); open any file:line and verify
33/95dimensions across the health lenses48992 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.
run-llama/llama-agents is sound in substance but carries real gaps (66%). 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 (98%) — the structure is clean and changes stay contained. Code Health (79%) is solid too.
Most urgent: a critical security exposure was detected (see the Security & Compliance lens). Treat it as a priority regardless of the overall grade.
The area that most needs attention is Security (64%) — exposure to security and compliance incidents is elevated. Readiness (65%) 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: Document RTO/RPO and a tested restore procedure (a backup… (DR & Backup); Declare <html lang>, a document <title> and a <main> landmark,… (Page structure); accessibility in the toolchain (A11y enforcement).
For scale: Medium (~48,992 production lines); rebuilding it from scratch would take roughly ~0.8 person-years (~1–2 engineers). Approximate, ±~30%.
It builds on a genuinely strong Architecture foundation (98%); the priorities above are the highest-leverage way to bring the rest up to that level.
How the score is built — each lens's share of the headlineWidth is the lens's weight in the worst-heaviest fold (the weakest area pulls hardest); colour is that lens's own band. A lens fixes the score in proportion to its width.
A full-fidelity diff against the previous run's complete recorded findings — line-move tolerant: a finding that only shifted line counts as unchanged, only genuinely new titles/files surface here.
0.9× (at 66% 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 ~0.8 person-years of build effort (about ~€120,000 to rebuild). Its weakest lens is Security at 64% — the part of that asset most exposed by the findings below.
How we model this: boilerplate at a scaffolding rate + logic × domain Low (×0.9) — transaction-script/CRUD × a 0.9× 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
Document RTO/RPO and a tested restore procedure (a backup config alone isn't disaster recovery).
Enforce accessibility in the toolchain: add an accessibility check that can read your UI — no component framework was detected and your pages are rendered by server-side templates, which neither the JSX/Vue ESLint plugins nor the HTML-template linters can parse; run axe/pa11y over the rendered pages, or assert the accessibility invariants over that rendered HTML in the test suite you already have, then assert with your test runner's axe binding (jest-axe, vitest-axe, cypress-axe or @axe-core/playwright) in tests, then gate axe/pa11y/Lighthouse in CI.
Value concentrated against a weak lens · Medium · Value at risk
This is a Medium asset (~0.8 person-years to rebuild), and its weakest lens is Security at 64%. 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: Document RTO/RPO and a tested restore procedure (a backup config alone isn't disaster recovery). The rest can wait behind it.
Evidence: priority ranking: top of 5 ranked by impact/effort
→ Document RTO/RPO and a tested restore procedure (a backup config alone isn't disaster recovery).
Architecture — module dependency matrix
307 modules, 103 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
A05:2021 — Security Misconfiguration
32
High / Critical
A06:2021 — Vulnerable & Outdated Components
25
High / Critical
Roadmap
First, establish a tested disaster recovery plan by documenting RTO/RPO and restore procedures. Next, ensure the page structure is valid by adding required HTML elements and landmarks, while also enforcing accessibility checks in the test suite and CI pipeline. Then, verify that the deployment pipeline enforces required review gates before production promotion. Finally, document significant architectural decisions in a centralized location to preserve context and consequences.
Ranked by impact ÷ effort. "Helps" is the estimated gain on the 0–100 health score.
Do this
Helps
Effort
Dimension
Document RTO/RPO and a tested restore procedure (a backup config alone isn't disaster recovery).
Enforce accessibility in the toolchain: add an accessibility check that can read your UI — no component framework was detected and your pages are rendered by server-side templates, which neither the JSX/Vue ESLint plugins nor the HTML-template linters can parse; run axe/pa11y over the rendered pages, or assert the accessibility invariants over that rendered HTML in the test suite you already have, then assert with your test runner's axe binding (jest-axe, vitest-axe, cypress-axe or @axe-core/playwright) in tests, then gate axe/pa11y/Lighthouse in CI.
The pipeline declares a deployment environment, but whether required reviewers / protection rules are attached to it lives in repository settings we cannot read — confirm the gate is enforced before production promotion.
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. 32 of 33 evaluated dimensions are computed purely by tools and static analysis (confidence 1.0); 1 documentation/naming judgement(s) are LLM-assisted and labelled advisory. Overall confidence is 0.7 — 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 — 33 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, 194 of 210 do; the remainder are repo-wide signals — a dimension-level measurement, not a single line. (Every path in this report is repo-relative by construction: paths are normalized at the producer and the report is rejected if any rooted path leaks through.)
Is there a tool behind the number? Every score below names the method that produced it — Roslyn, git, a scanner, or (for a handful of documentation/naming dimensions) an LLM labelled sampled · advisory — not a narrative.
Does re-running give the same result? Run it again on the same commit and the score — and this report, byte for byte — is identical. A report whose numbers move between runs is describing the run, not the code.
This report answers yes to all three. That's the bar to hold any assessment to.
Tools & methods
The actual versions used this run (captured at analysis time) — re-run on the same commit for the identical score.
Method
Backs
Version
Evaluator
Roslyn static analysis
Complexity, cohesion, coupling, dead code, API surface, layering
What ran differently this time — a tool absent, degraded, or that fell back to an estimate. Named openly, not folded silently into the scores. A degraded run also records its exact cause in diagnostics.md.
D19 Documentation Quality — LLM provider failed — The model provider returned an unusable result, so this LLM-assisted dimension fell back to a measurement gap (confidence 0) rather than a penalty. Re-run with a reachable provider to score it.
D30 Dependency Vulnerabilities — scanner not present in this environment — The backing tool was not installed where this scan ran, so this dimension was not scored. Install the tool (or run in the hosted environment, where it is always present) for a graded result.
Repo exclusion declarations (.gitattributes linguist-generated/vendored, .editorconfig generated_code): none declared — every source file was scored.
Limitations & what we did not check
Watchdog assesses the repository exactly as committed, and only the repository. By design it does not reach outside the source tree: the live cloud account, the running CI/CD pipeline, the host's branch-protection and approval rules, the production configuration, or a restore actually exercised against a backup are all out of scope. That boundary is a feature, not a gap — a repo-relative, deterministic scan re-runs identically on any commit and every finding opens at a real file and line, where a live audit can neither be reproduced nor traced. The visible consequence is that controls which leave no in-repo evidence are reported as "not evidenced" and excluded from the score rather than awarded a number a static scan cannot justify.
Per-dimension blind spots
For each dimension that was measured, what a static, repo-only scan structurally cannot see — the honest edge of the measurement, not a failure of it.
D1 Cyclomatic Complexity: Cyclomatic complexity counts branches statically — it cannot tell an essential decision tree from accidental tangle, nor see complexity that lives in data or configuration (large switch-case token tables, DSL lexers/parsers, data-as-code rule tables) rather than control flow: a tokenizer's many single-character cases read as high complexity though each branch is trivial.
D2 Cognitive Complexity: Cognitive-complexity heuristics approximate how hard code is to follow; genuine domain difficulty and well-named intent that eases reading are not captured.
D3 God Classes: "God class" is sized by members and responsibilities visible in the type — a deliberately broad facade over a coherent subsystem can read the same as an accidental grab-bag. For front-end JS the file-length check is cohesion-aware (a single-responsibility module — one class/IIFE — earns a 3× threshold), but cohesion is approximated from top-level declarations, not true dependency structure.
D4 Code Duplication: Duplication is token-similarity (jscpd) — it finds copy-paste, not semantic duplication expressed differently. Committed machine-written code (scaffolded migrations, designer/codegen output, protobuf/OpenAPI stubs, model snapshots) is EXCLUDED — its repetition is the tool's, not the team's — so the score reflects hand-written duplication only.
D13 Secret Scanning: Secret detection is signature- and entropy-based on the current tree — a secret that does not match a known pattern, or one already rotated, will not be flagged (a clean scan is "nothing matched", not "no secrets exist").
D15 Churn × Complexity Hotspots: Churn hotspots come from git history — a freshly imported or squashed repository has no churn signal, and recent rewrites can mask a historically risky file.
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.
D21 Naming Consistency: Naming quality is an LLM judgement over a bounded sample — it assesses clarity/consistency of the names it sees, not domain-correctness, and is advisory.
D28 Secrets (history): Secret-history scanning sweeps the git log for known patterns — a secret that predates the available history, or never matched a signature, is not found (clean means "nothing matched in the history we can see").
D29 Static Analysis (SAST): SAST findings are pattern-based (semgrep) — it finds classes of bug it has rules for; logic flaws, auth/authorization gaps and issues needing runtime context are out of reach (and clean means "no rule matched").
D31 IaC & Container Security: IaC scanning checks Dockerfiles/Terraform/Kubernetes against best-practice rules — it cannot see the live cloud account, runtime configuration, or drift between the committed config and what is actually deployed.
D33 JS/npm Dependency Vulnerabilities: JS/npm CVE matching reads package manifests and lockfiles — risk from how a dependency is used, and advisories not yet published, fall outside this scan.
D34 Knowledge Freshness: Freshness is decayed commit RECENCY, not comprehension — code read often but rarely committed reads as orphaned, and stable code that genuinely needs no changes is penalised the same as forgotten code; bot/squash commits distort it like the bus factor.
D35 Change Coupling: Change coupling is co-change in COMMITS — files split across separate commits, or coupled only through a shared config/build step, read as uncoupled, and a sweeping commit (rename/format) is excluded so it doesn't couple everything. It shows that files change together, not WHY: a high coupling can be a healthy cohesive pair as readily as a hidden leak.
D40 Network Egress Confinement: Egress confinement is read from committed Kubernetes manifests — a policy applied out-of-band (cluster-default deny, a service mesh, or a cloud firewall/security group off-repo) is invisible, and a present NetworkPolicy is declared config, not proof the cluster admission-controller actually enforces it at runtime.
D41 Kernel & Syscall Confinement: Syscall/MAC confinement is read from committed manifests — a profile applied by a cluster-wide PodSecurity default or a mutating webhook off-repo isn't seen, and a declared seccomp/AppArmor profile is config presence, not proof the node's kernel actually loaded and enforced it.
D42 Runtime Threat Enforcement: Runtime enforcement is read from committed policy files — a Tetragon/Falco/Kyverno stack installed cluster-wide (Helm release, platform add-on) with no in-repo trace can't be credited, and a committed policy is declared intent, not proof the engine is running and blocking in the live cluster.
AC2 Forms & labels: Label association is read from static markup — a label wired up at runtime (JS-set aria-labelledby, framework-injected ids) reads as missing, a present label says nothing about whether its text is correct. A known UI-library field component (e.g. a JSX <TextField>) is now checked conservatively — flagged only when it carries NO label/aria-label/aria-labelledby/id/name — but wrapper/context-labelled libraries (Chakra/Radix FormControl+FormLabel) aren't statically visible (possible false positive) and non-JSX lowercased components are still skipped. A clean result is "no unlabelled native control found", not a labelling proof.
AC3 Page structure: Page structure is read from the static markup tree — landmarks, headings and lang injected at runtime aren't seen, heading ORDER is checked structurally (not against the rendered visual hierarchy), and lang/title/main fire only on full documents, never partials, and the data-table check sees header-cell presence (a <th> exists), not whether each header correctly associates with its cells. Static readiness, not conformance.
AC6 Visual & motion safety: Contrast and motion safety are PARTIAL by construction — literal colours (hex/rgb/hsl/named) in inline styles, in-repo <style> blocks, in-repo .css files, var() tokens, Tailwind neutral utilities and CSS-in-JS top-level declarations are read (same-rule/same-element colour+background pairs only); computed/runtime/theme colour, external-CDN stylesheets, CSS-in-JS dynamic (${…}) and nested-selector colours, cross-element pairs and image contrast stay out of reach, so a clean result is bounded by what the static CSS itself shows.
AC7 A11y enforcement: Enforcement is scored from in-repo config/CI evidence only — an a11y gate enforced in external tooling with no in-repo trace can't be credited, and a configured linter is presence, not proof the rules actually run or block a merge.
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".
P5 DR & Backup: Backup/restore and disaster-recovery readiness is judged from in-repo evidence — a config that exists is not a tested restore, so the absence of positive evidence is reported as "not evidenced", never scored as present.
P6 Release Hygiene: Rollback/observability controls are inferred from repo artefacts (pipelines, dashboards-as-code) — controls configured in external tooling, with no in-repo trace, cannot be credited.
The LLM boundary
LLM-set scores this run (2): D21, M4 (model: Local LLM). For these, a model reads a bounded sample and sets the numeric score (documentation, ADR quality, naming, comment value, onboarding) — D25 sets the ADR-conformance fraction over sampled code, D22 judges API accuracy over a sample. These are sampled and advisory by design: they vary at the margins between runs and are never a deterministic measurement. Every other score in this report is tool-computed at confidence 1.0.
What it measures: How tangled the control flow is — methods with many branches are hard to test and change.
Method: Cyclomatic complexity per method (1 + decision points), computed exhaustively across production source; test projects separated by convention. Deterministic.
32 method(s) exceeded the cyclomatic complexity threshold of 15; the worst was build.get_workflow_representation at 48. A further 2 method(s) were over the threshold but excluded as flat dispatchers (a long switch/match over independent cases: many branches, almost no nesting), the largest being workflow._get_node_color at 17 — they are counted neither in the figure above nor in this dimension's score.
+ 27 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Resolve the 1 build.get_workflow_representation (cyclomatic 48) finding(s) in Cyclomatic Complexity — start with build.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 pytest_cmd.pytest_cmd (cyclomatic 43) finding(s) in Cyclomatic Complexity — start with pytest_cmd.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 deployment_config.read_deployment_config (cyclomatic 41) finding(s) in Cyclomatic Complexity — start with deployment_config.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.
+ 59 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Resolve the 1 build.get_workflow_representation (cognitive 99) finding(s) in Cognitive Complexity — start with build.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 pytest_cmd.pytest_cmd (cognitive 91) finding(s) in Cognitive Complexity — start with pytest_cmd.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 deployment_config.read_deployment_config (cognitive 86) finding(s) in Cognitive Complexity — start with deployment_config.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 Classes8.9 / 10Strong✓ 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.
Resolve the 11 FileTooLong finding(s) in God Classes — start with deployment.py, k8s_client.py, reduce.py. — One of this dimension's main actionable groups (11 warning-level).
Enforce God Classes in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d3_recommendation.md · top locations in Appendix A, every location in findings.md.
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 Factor5.3 / 10Adequate✓ 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.
69 source file(s) have their living knowledge concentrated in one author (≥90% of recent, decayed contribution). The largest is packages/llama-agents-control-plane/src/llama_agents/control_plane/k8s_client.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 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 IaC: DS-0029 · ×4docker/Dockerfiledetected by trivy finding
Critical IaC: KSV-0041operator/config/rbac/role.yamldetected by trivy finding
Medium IaC: KSV-0048 · ×16operator/config/rbac/role.yamldetected by trivy finding
Low IaC: DS-0026 · ×11docker/Dockerfiledetected by trivy finding
What to do
Resolve the 16 Medium IaC finding(s) in IaC & Container Security — start with kind-gc-cronjob.yaml (6), seaweedfs.yaml (6), role.yaml (2). — One of this dimension's main actionable groups (16 warning-level).
Resolve the 4 High IaC finding(s) in IaC & Container Security — start with Dockerfile, role.yaml, kind-gc-cronjob.yaml. — One of this dimension's main actionable groups (4 issue-level).
Resolve the 1 Critical IaC finding(s) in IaC & Container Security — start with role.yaml. — One of this dimension's main actionable groups (1 issue-level).
Detailed fixes: d31_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 build provenance finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 recommendation-level).
Detailed fixes: d36_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether dependencies have known published vulnerabilities (CVEs) per the OSV database — read natively from whatever lockfile the repository ships (Cargo, npm, Go, Python, Maven, RubyGems, …). D33 and D30 add ecosystem-specific scanners on top for npm and .NET.
Method: Multi-ecosystem dependency-CVE scan via osv-scanner --recursive (queries the osv.dev database + parses lockfiles natively across ecosystems: npm package-lock/yarn/pnpm/bun, Go go.mod, Rust Cargo.lock, Maven/Gradle pom.xml/gradle.lockfile, PyPI requirements.txt/poetry.lock/Pipfile.lock, Composer composer.lock, RubyGems Gemfile.lock, Hex mix.lock, pub pubspec.lock, Swift Package.resolved); severity tally (Critical/High/Medium/Low) to 0-10 tight normalizer (8.0). NotApplicable only when the repo declares no supported non-.NET dependency lockfile (a NuGet-only repo stays NotApplicable — .NET CVEs are D30's domain); coverage needs a resolved lockfile. Additive to D33 (trivy fs); exhaustive + deterministic, DB kept fresh.
High CVE: [GHSA redacted] · ×12uv.lockdetected by osv-scanner finding
High vulnerability: [GHSA redacted] · ×2uv.lockdetected by osv-scanner finding
Medium CVE: PYSEC-2026-2132 · ×9uv.lockdetected by osv-scanner finding
Medium vulnerability: [GHSA redacted] · ×2operator/go.moddetected by osv-scanner finding
What to do
Resolve the 12 High CVE finding(s) in OSV Dependency Vulnerabilities — start with uv.lock (9), pnpm-lock.yaml (2), go.mod. — One of this dimension's main actionable groups (12 issue-level).
Resolve the 9 Medium CVE finding(s) in OSV Dependency Vulnerabilities — start with go.mod (5), uv.lock (4). — One of this dimension's main actionable groups (9 warning-level).
Resolve the 2 High vulnerability finding(s) in OSV Dependency Vulnerabilities — start with uv.lock, go.mod. — One of this dimension's main actionable groups (2 issue-level).
Detailed fixes: d38_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether Kubernetes workloads restrict network EGRESS with a NetworkPolicy (or Cilium policy), limiting where a compromised pod can send data or reach a command-and-control server. Presence of committed egress-restricting policy, not runtime enforcement.
Method: Deterministic YAML-manifest inspection (no external tool, no Roslyn — language-agnostic): Kubernetes workloads gate applicability; credits a NetworkPolicy / Cilium policy that restricts egress (policyTypes: [Egress] / egress rules). Reward-leaning (neutral floor climbing to 10, never a deduction — baseline misconfigs stay with D31). Deterministic.
What it measures: Whether Kubernetes workloads confine the kernel boundary — a seccomp profile (RuntimeDefault/Localhost) plus an AppArmor/SELinux mandatory-access-control layer — shrinking the syscall attack surface a container escape would use. Presence of committed confinement config, not runtime enforcement.
Method: Deterministic YAML-manifest inspection (no external tool, no Roslyn): on Kubernetes workloads, credits a seccomp profile (RuntimeDefault/Localhost) and an AppArmor/SELinux MAC layer. Reward-leaning (neutral floor climbing to 10); NotApplicable without workloads. Deterministic.
Resolve the 1 No seccomp profile finding(s) in Kernel & Syscall Confinement. — One of this dimension's main actionable groups (1 recommendation-level).
Resolve the 1 No AppArmor/SELinux confinement finding(s) in Kernel & Syscall Confinement. — One of this dimension's main actionable groups (1 recommendation-level).
Detailed fixes: d41_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether the Kubernetes deployment wires runtime threat enforcement — a detection engine (Tetragon/Falco) and/or an admission-control policy gate (Kyverno / OPA Gatekeeper / PodSecurity). Presence of committed policy, not a runtime guarantee.
Method: Deterministic YAML-manifest inspection (no external tool, no Roslyn): on Kubernetes workloads, credits a runtime threat-detection engine (Tetragon TracingPolicy / Falco) and an admission-control policy (Kyverno / OPA Gatekeeper / PodSecurity). Reward-leaning; NotApplicable without workloads. Deterministic.
Resolve the 1 No runtime threat detection finding(s) in Runtime Threat Enforcement. — One of this dimension's main actionable groups (1 recommendation-level).
Resolve the 1 No admission-control policy finding(s) in Runtime Threat Enforcement. — One of this dimension's main actionable groups (1 recommendation-level).
Detailed fixes: d42_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
Frontend & cross-cutting dimensions
R = React/JS · M = Maturity · P = Readiness.
AC2 · Forms & labels10.0 / 10Exemplary○ Nothing flagged
Other · Accessibility — Whether form controls have a programmatic label (an associated label, aria-label or aria-labelledby), buttons have text, links have an accessible name, fieldsets have a non-empty legend, known UI-library field components carry a label prop, and a placeholder isn't used as the only label. Static markup readiness, not a WCAG conformance claim.
Method: Static markup-model scan: inputs/selects/textareas checked for an associated label[for]/wrapping label/aria-label/aria-labelledby (per document), buttons for accessible text, fieldsets for a legend; placeholder-only labelling flagged. Deterministic, hard fact per control.
Other · Accessibility — Whether pages declare a language (well-formed BCP-47) and a non-empty title, expose exactly one main landmark and a sane heading order with non-empty headings, keep zoom enabled, title their iframes, give data tables header cells, and avoid meta-refresh. Static markup readiness, not a WCAG conformance claim.
Method: Static markup-model scan: html lang, document <title>, a main landmark and heading order on full documents only, plus zoom-disabling viewports, untitled iframes and meta-refresh anywhere. Deterministic, per structural checkpoint.
No <main> (or role="main") means no "skip to content" target and a weaker landmark map. Wrap the primary content in <main>. — index.html:2
What to do
Declare <html lang>, a document <title> and a <main> landmark, keep headings in order, leave zoom enabled, title iframes and drop meta-refresh.
Other · Accessibility — Whether focus outlines aren't removed without a replacement, motion respects prefers-reduced-motion, and literal CSS colour pairs meet contrast — PARTIAL: inline styles, in-repo <style> blocks, in-repo .css files, var() tokens, Tailwind neutral utilities and CSS-in-JS literals are read (hex/rgb/hsl/named), never computed/runtime/external-CDN colour. Static markup readiness, not a WCAG conformance claim.
Method: Static markup/CSS scan: inline outline:none/0, literal inline colour/background contrast against the 4.5:1 AA floor, and <style>-block animation without a prefers-reduced-motion guard. Deterministic but PARTIAL — only inline styles and in-repo CSS literals are visible.
Do you agree with this assessment?
AC7 · A11y enforcement4.0 / 10Weak✓ Tool-verified
Other · Accessibility — Whether accessibility is ENFORCED in the toolchain — an accessibility checker configured over the markup (an a11y lint rule set, e.g. eslint-plugin-jsx-a11y or vuejs-accessibility where the project lints JavaScript) and an automated accessibility assertion wired into tests or CI (axe/pa11y/Lighthouse or an equivalent) — on the Documented→Verified→Prevented ladder.
Method: Repo config/CI scan: an accessibility checker configured over the markup (an a11y lint rule set such as eslint-plugin-jsx-a11y / vuejs-accessibility where JavaScript is linted) and an automated accessibility assertion in tests or CI (axe/pa11y/Lighthouse or equivalent), graded on the Documented→Verified→Prevented rungs. Deterministic, presence/rung detection.
No accessibility enforcement found — no a11y linter (an accessibility check that can read your UI — no component framework was detected and your pages are rendered by server-side templates, which neither the JSX/Vue ESLint plugins nor the HTML-template linters can parse; run axe/pa11y over the rendered pages, or assert the accessibility invariants over that rendered HTML in the test suite you already have) and no axe/pa11y/Lighthouse in tests or CI. Start by running that check over your rendered pages in CI.
What to do
Enforce accessibility in the toolchain: add an accessibility check that can read your UI — no component framework was detected and your pages are rendered by server-side templates, which neither the JSX/Vue ESLint plugins nor the HTML-template linters can parse; run axe/pa11y over the rendered pages, or assert the accessibility invariants over that rendered HTML in the test suite you already have, then assert with your test runner's axe binding (jest-axe, vitest-axe, cypress-axe or @axe-core/playwright) in tests, then gate axe/pa11y/Lighthouse in CI.
Other · Architecture — Whether the codebase has a recognisable, scale-appropriate structure (a named architectural style, or modular enough for its size) rather than being an ad-hoc ball of mud.
Method: Roslyn plus csproj analysis: architecture style detection (DDD, clean, vertical-slice, CQRS) and structure fitness for repo size. Deterministic.
Maturity · Maturity — Whether the repo and its projects have a README, and whether it's substantive and current.
Method: Filesystem scan: README presence, word count, and headings for depth; git history for staleness. Exhaustive across root and project dirs, deterministic.
What to do
Add a build/run (quick start) section to the root README — the first thing a newcomer needs.
Add a 'Testing' section to the root README — how to run the test suite.
Add an 'Architecture' / 'How it works' section to the root README — the high-level shape.
Add a README to the 1 of 16 project(s) that lack one — worth up to 0.1 pts.
Maturity · Maturity — Whether key decisions (ADRs) and the high-level shape (C4/diagrams) are written down.
Method: Filesystem scan: ADR folder/naming conventions or content, plus Mermaid/PlantUML/C4/architecture.md discovery. Exhaustive, deterministic.
No Architecture Decision Records found — no conventional ADR directory, no `NNNN-title.md` documents and nothing ADR-shaped by content. Design rationale recorded elsewhere (a design-notes tree, a mailing list, pull-request discussion) is not visible to this check and is not re-findable per decision, so a future maintainer cannot ask why one choice was made and get an answer.
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 README actually describes the code that exists (LLM-judged, advisory).
Method: Judged by language model at low temperature: README accuracy versus actual projects, within a disclosed tolerance. Advisory, not a measured number.
README advertises a microservices architecture, but the repo is a single project with no service manifests
What to do
Reconcile the README with reality: README advertises a microservices architecture, but the repo is a single project with no service manifests.
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.
What to do
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.
What to do
The pipeline declares a deployment environment, but whether required reviewers / protection rules are attached to it lives in repository settings we cannot read — confirm the gate is enforced before production promotion.
Do you agree with this assessment?
P5 · DR & Backup4.0 / 10Weak✓ Tool-verified
Readiness · Readiness — Whether disaster recovery is planned and codified — backups, geo-recovery, RTO/RPO, persistence guarantees — from IaC + container manifests + docs, never the live cloud.
Method: Filesystem scan: disaster recovery, backup, geo-recovery, RTO/RPO, persistence guarantees from IaC, manifests, and docs. Exhaustive, deterministic, never a live environment.
What to do
Document RTO/RPO and a tested restore procedure (a backup config alone isn't disaster recovery).
Enable purge protection / soft-delete (and prevent_destroy on critical resources) so data stores can't be lost to an accidental or malicious delete.
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?
WCAG coverage — what static analysis assessed
Statically assessed 10 of 55 WCAG 2.2 Level A/AA success criteria (18%; ≈20% of the 50 WCAG 2.1 AA criteria for EN 301 549). The other 45 require runtime or manual evaluation. Partial signal only (a clean result is necessary, not sufficient; static analysis fully verifies none). This is accessibility readiness, not a conformance claim — a WCAG conformance claim requires manual evaluation (WCAG-EM 1.0).
The score is the rank-weighted fold of these lenses (worst-heaviest), each including its meta-dimensions; a lens with a Critical contributor is capped at Fair (its band reads "gated by …") and is never the strongest area however high its average.
Not included — 62 check(s) not relevant to this codebase
These checks had nothing to measure here (no tests, no git history, the codebase is small, or the architecture style doesn't apply), so they're omitted above rather than scored low.
AC1 Text alternatives — No image/media element found in the parsed markup — AC1 not applicable here.
AC4 Keyboard semantics — No interactive element found in the parsed markup — AC4 not applicable here.
AC5 ARIA correctness — No ARIA usage found in the parsed markup — AC5 not applicable here.
AX1 Captive dependencies — no DI registrations detected
AX10 Code composition — not assessed — code composition is computed by ROLE over a document set that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
AX2 Stateful singletons — no singleton implementations detected
AX3 Project dependency cycles — not assessed — project cycles and dependency direction are computed over a project-reference graph that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
AX4 Dependency direction — not applicable to a transaction-script/CRUD architecture (the inward-dependency rule is for layered/clean styles)
AX6 Interface segregation — not assessed — interface segregation is computed over a type surface that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
AX7 Slice cohesion — not applicable — not a vertical-slice architecture
AX8 Test isolation — not assessed — test isolation is computed from a project graph (which projects are test projects, and what they reference) that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
AX9 CQS / query purity — no CQRS query handlers detected — query purity is not applicable to this codebase
AXB2 Runtime readiness — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
C1 Data Protection — Not assessed: these personal data controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks personal data controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
C2 Access Controls — Not assessed: these authorization controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks authorization controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
C3 Audit Trail — Not assessed: these audit controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks audit controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
C4 Data Retention — Not assessed: these retention controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks retention controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
C5 Data-Subject Rights — Not assessed: these data-subject rights controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks data-subject rights controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
D10 Test Quality — ~61132 lines of test source are present (.go, .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), package.json and a Go module (go.mod/go.sum)), 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.
D19 Documentation Quality — LLM evaluation failed
D20 ADR Quality — N/A — ADRs are expected on deployable products with a user-facing host, not consumed libraries; no ADR log is required here.
D22 Internal API Consistency — No exposed public API
D23 Boundary Type-Coupling — Production source is present (.go, .py, .ts) 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), package.json and a Go module (go.mod/go.sum) — not scanned yet) — where an OSV-supported manifest exists, dependency vulnerabilities for this repository are reported under D38 instead.
D32 Data Compliance (PII/GDPR) — No PII/GDPR-handling patterns detected (p/gdpr ruleset) — no data-compliance surface to assess.
D37 Vulnerability-disclosure Policy — No vulnerability-disclosure policy file found (SECURITY.md/.markdown/.rst/.txt at root or under .github/.forgejo/.gitea/docs, .well-known/security.txt). A coordinated-disclosure policy may live off-repo, so this is not evidenced rather than failed.
D39 IL Efficiency — D39 measures the IL emitted by a .NET build; this repository has no .NET solution or project files, so the dimension does not apply.
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 .go, .py, .ts, 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 (.go, .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 (1 of 2 signals for this style — below the bar we score at): 229 value object(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.
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.
SC1 Supply-chain hygiene — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
X1 Async correctness — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
X2 Cancellation propagation — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
X3 Exception handling — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
X4 Structured logging — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
X5 Nullable reference types — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
Appendix A — Findings (grouped)
The findings behind the scores, grouped by severity, then by dimension and kind. The high-severity issues are enumerated in full below; items per group are capped at 25 with any overflow stated explicitly per group, never silently truncated. The complete machine-readable list of every finding (all severities) is the companion findings.md in this report's bundle.
Issue — 20 finding(s)
D38 · OSV Dependency Vulnerabilities· High CVE · ×12
High CVE: [GHSA redacted] uv.lock— aiohttp 3.14.0: [GHSA redacted] — aiohttp is not declared by the manifests that govern this lockfile: it is pulled in transitively here (the 3.12.14 this repo declares for it belongs to a separate install root and does not reach this one), so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package aiohttp`)). This is 1 of 11 advisories with a published fix this scan raises against aiohttp 3.14.0, and their fixed versions do not agree — anything below 3.14.3 still leaves at least one of them open. Take this package to 3.14.3 or later: that is the floor for the package, not this row's target alone. This one row stands for the 11 advisories this scan raises against aiohttp 3.14.0: [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], PYSEC-2026-2112, PYSEC-2026-2113, PYSEC-2026-237.
High CVE: [GHSA redacted] uv.lock— bedrock-agentcore 1.3.2: [GHSA redacted] — bedrock-agentcore is not declared by the manifests that govern this lockfile: it is pulled in transitively here (the 1.2.0 this repo declares for it belongs to a separate install root and does not reach this one), so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package bedrock-agentcore`)). This is 1 of 2 advisories with a published fix this scan raises against bedrock-agentcore 1.3.2, and their fixed versions do not agree — anything below 1.18.1 still leaves at least one of them open. Take this package to 1.18.1 or later: that is the floor for the package, not this row's target alone. This one row stands for the 2 advisories this scan raises against bedrock-agentcore 1.3.2: [GHSA redacted], [GHSA redacted].
High CVE: [GHSA redacted] uv.lock— gitpython 3.1.50: [GHSA redacted] — gitpython is not declared by the manifests that govern this lockfile: it is pulled in transitively here (the 3.1.40 this repo declares for it belongs to a separate install root and does not reach this one), so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package gitpython`)). This is 1 of 12 advisories with a published fix this scan raises against gitpython 3.1.50, and their fixed versions do not agree — anything below 3.1.57 still leaves at least one of them open. Take this package to 3.1.57 or later: that is the floor for the package, not this row's target alone. This one row stands for the 12 advisories this scan raises against gitpython 3.1.50: [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted].
High CVE: [GHSA redacted] operator/go.mod— go.opentelemetry.io/otel/sdk 1.40.0: [GHSA redacted] — go.opentelemetry.io/otel/sdk is not a DIRECT requirement of this module: go.mod records it as `// indirect`, pulled in transitively, so raise it in place (run `go -C operator get go.opentelemetry.io/otel/sdk@v1.43.0`, which updates the require line operator/go.mod already holds for it).
High CVE: [GHSA redacted] uv.lock— jaraco-context 6.0.1: [GHSA redacted] — jaraco-context is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package jaraco-context`)).
High CVE: [GHSA redacted] pnpm-lock.yaml— js-yaml 3.14.2: [GHSA redacted] — js-yaml is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or pin js-yaml to 3.15.0 with an `overrides` entry (the `overrides:` block in `pnpm-workspace.yaml` for pnpm)). This one row stands for the 2 advisories this scan raises against js-yaml 3.14.2: [GHSA redacted], [GHSA redacted].
High CVE: [GHSA redacted] uv.lock— nltk 3.9.4: [GHSA redacted] — nltk is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package nltk`)). This one row stands for the 6 advisories this scan raises against nltk 3.9.4: [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], PYSEC-2026-597.
High CVE: [GHSA redacted] pnpm-lock.yaml— picomatch 2.3.1: [GHSA redacted] — picomatch is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or pin picomatch to 2.3.2 with an `overrides` entry (the `overrides:` block in `pnpm-workspace.yaml` for pnpm)). This one row stands for the 2 advisories this scan raises against picomatch 2.3.1: [GHSA redacted], [GHSA redacted].
High CVE: [GHSA redacted] uv.lock— pillow 12.2.0: [GHSA redacted] — pillow is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package pillow`)). This one row stands for the 13 advisories this scan raises against pillow 12.2.0: [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted].
High CVE: [GHSA redacted] uv.lock— pyjwt 2.12.1: [GHSA redacted] — pyjwt is not declared by the manifests that govern this lockfile: it is pulled in transitively here (the 2.10.1 this repo declares for it belongs to a separate install root and does not reach this one), so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package pyjwt`)). This one row stands for the 5 advisories this scan raises against pyjwt 2.12.1: [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], PYSEC-2026-177.
High CVE: [GHSA redacted] uv.lock— python-multipart 0.0.27: [GHSA redacted] — python-multipart is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package python-multipart`)). This is 1 of 4 advisories with a published fix this scan raises against python-multipart 0.0.27, and their fixed versions do not agree — anything below 0.0.31 still leaves at least one of them open. Take this package to 0.0.31 or later: that is the floor for the package, not this row's target alone. This one row stands for the 4 advisories this scan raises against python-multipart 0.0.27: [GHSA redacted], PYSEC-2026-3037, PYSEC-2026-3040, PYSEC-2026-3041.
High CVE: [GHSA redacted] uv.lock— starlette 0.46.2: [GHSA redacted] — starlette is not declared by the manifests that govern this lockfile: it is pulled in transitively here (the 0.39.0 this repo declares for it belongs to a separate install root and does not reach this one), so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package starlette`)). This is 1 of 7 advisories with a published fix this scan raises against starlette 0.46.2, and their fixed versions do not agree — anything below 0.49.1 still leaves at least one of them open. Take this package to 0.49.1 or later: that is the floor for the package, not this row's target alone. This one row stands for the 7 advisories this scan raises against starlette 0.46.2: [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], PYSEC-2026-248.
High IaC: DS-0029 docker/Dockerfile— 'apt-get' missing '--no-install-recommends'
High IaC: KSV-0056 operator/config/rbac/role.yaml— Manage Kubernetes networking
High IaC: KSV-0014 operator/tilt/k8s-manifests/kind-gc-cronjob.yaml— Root file system is not read-only
High IaC: KSV-0014 operator/tilt/k8s-manifests/seaweedfs.yaml— Root file system is not read-only
D38 · OSV Dependency Vulnerabilities· High vulnerability · ×2
High vulnerability: [GHSA redacted] uv.lock— cryptography 46.0.7: [GHSA redacted] — cryptography is not declared by the manifests that govern this lockfile: it is pulled in transitively here (the 44.0.0 this repo declares for it belongs to a separate install root and does not reach this one), so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package cryptography`)). This is 1 of 4 advisories with a published fix this scan raises against cryptography 46.0.7, and their fixed versions do not agree — anything below 50.0.0 still leaves at least one of them open. Take this package to 50.0.0 or later: that is the floor for the package, not this row's target alone. This one row stands for the 4 advisories this scan raises against cryptography 46.0.7: [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted].
High vulnerability: [GHSA redacted] operator/go.mod— google.golang.org/grpc 1.79.3: [GHSA redacted] — google.golang.org/grpc is not a DIRECT requirement of this module: go.mod records it as `// indirect`, pulled in transitively, so raise it in place (run `go -C operator get google.golang.org/grpc@v1.82.1`, which updates the require line operator/go.mod already holds for it).
Boundary-crossing change coupling: server.py ↔ __init__.py packages/llama-agents-server/src/llama_agents/server/server.py— `packages/llama-agents-server/src/llama_agents/server/server.py` (context llama-agents-server) and `packages/llama-index-workflows/src/workflows/protocol/__init__.py` (context llama-index-workflows) sit in DIFFERENT parts of the tree yet change together 80% of the time (8 of the 10 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well) — the bounded-context boundary may be in the wrong place, or one context is leaking into the other. This is the behavioural boundary violation a static scan can't see.
Medium IaC: KSV-0048 operator/config/rbac/role.yaml— Manage Kubernetes workloads and pods
Medium IaC: KSV-0049 operator/config/rbac/role.yaml— Manage configmaps
Medium IaC: KSV-0001 operator/tilt/k8s-manifests/kind-gc-cronjob.yaml— Can elevate its own privileges
Medium IaC: KSV-0012 operator/tilt/k8s-manifests/kind-gc-cronjob.yaml— Runs as root user One securityContext edit clears this facet's near-duplicate rules together: KSV-0012, KSV-0020, KSV-0021.
Medium IaC: KSV-0023 operator/tilt/k8s-manifests/kind-gc-cronjob.yaml— hostPath volumes mounted
Medium IaC: KSV-0104 operator/tilt/k8s-manifests/kind-gc-cronjob.yaml— Seccomp policies disabled One securityContext edit clears this facet's near-duplicate rules together: KSV-0030, KSV-0104.
Medium IaC: KSV-0125 operator/tilt/k8s-manifests/kind-gc-cronjob.yaml— Restrict container images to trusted registries
Medium IaC: KSV-0001 operator/tilt/k8s-manifests/seaweedfs.yaml— Can elevate its own privileges
Medium IaC: KSV-0012 operator/tilt/k8s-manifests/seaweedfs.yaml— Runs as root user One securityContext edit clears this facet's near-duplicate rules together: KSV-0012, KSV-0020, KSV-0021.
Medium IaC: KSV-0013 operator/tilt/k8s-manifests/seaweedfs.yaml— Image tag ":latest" used
Medium IaC: KSV-0104 operator/tilt/k8s-manifests/seaweedfs.yaml— Seccomp policies disabled One securityContext edit clears this facet's near-duplicate rules together: KSV-0030, KSV-0104.
Medium IaC: KSV-0125 operator/tilt/k8s-manifests/seaweedfs.yaml— Restrict container images to trusted registries
Medium IaC: CKV_K8S_37 operator/tilt/k8s-manifests/seaweedfs.yaml:1— Minimize the admission of containers with capabilities assigned
Medium IaC: CKV_K8S_37 operator/tilt/k8s-manifests/kind-gc-cronjob.yaml:4— Minimize the admission of containers with capabilities assigned
Medium IaC: CKV_DOCKER_2 docker/operator.Dockerfile:1— Ensure that HEALTHCHECK instructions have been added to container images
Medium IaC: CKV_DOCKER_2 docker/Dockerfile:1— Ensure that HEALTHCHECK instructions have been added to container images
FileTooLong: commands/deployment.py packages/llamactl/src/llama_agents/cli/commands/deployment.py:0— FileTooLong — 1322 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: control_plane/k8s_client.py packages/llama-agents-control-plane/src/llama_agents/control_plane/k8s_client.py:0— FileTooLong — 1085 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: control_loop/reduce.py packages/llama-index-workflows/src/workflows/runtime/control_loop/reduce.py:0— FileTooLong — 1024 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: dbos/runtime.py packages/llama-agents-dbos/src/llama_agents/dbos/runtime.py:0— FileTooLong — 807 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: git/_git_service.py packages/llama-agents-control-plane/src/llama_agents/control_plane/git/_git_service.py:0— FileTooLong — 663 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: workflow/__init__.py packages/llama-index-utils-workflow/src/llama_index/utils/workflow/__init__.py:0— FileTooLong — 641 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: server/_api.py packages/llama-agents-server/src/llama_agents/server/_api.py:0— FileTooLong — 572 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: context/state_store.py packages/llama-index-workflows/src/workflows/context/state_store.py:0— FileTooLong — 548 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: commands/auth.py packages/llamactl/src/llama_agents/cli/commands/auth.py:0— FileTooLong — 542 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: dev_cli/changesets.py src/dev_cli/changesets.py:0— FileTooLong — 511 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: representation/validate.py packages/llama-index-workflows/src/workflows/representation/validate.py:0— FileTooLong — 504 significant lines (blank, comment-only and punctuation-only lines excluded). To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
D38 · OSV Dependency Vulnerabilities· Medium CVE · ×9
Medium CVE: PYSEC-2026-2132 uv.lock— click 8.3.2: PYSEC-2026-2132 — upgrade to 8.3.3
Medium CVE: GO-2026-5158 operator/go.mod— go.opentelemetry.io/otel 1.41.0 (go.opentelemetry.io/otel/baggage, go.opentelemetry.io/otel/propagation): GO-2026-5158 — go.opentelemetry.io/otel is not a DIRECT requirement of this module: go.mod records it as `// indirect`, pulled in transitively, so raise it in place (run `go -C operator get go.opentelemetry.io/otel@v1.42.0`, which updates the require line operator/go.mod already holds for it).
Medium CVE: [GHSA redacted] operator/go.mod— golang.org/x/net 0.48.0: [GHSA redacted] — golang.org/x/net is not a DIRECT requirement of this module: go.mod records it as `// indirect`, pulled in transitively, so raise it in place (run `go -C operator get golang.org/x/net@v0.55.0`, which updates the require line operator/go.mod already holds for it). This one row stands for the 8 advisories this scan raises against golang.org/x/net 0.48.0: [GHSA redacted], GO-2026-4918, GO-2026-5025, GO-2026-5026, GO-2026-5027, GO-2026-5029, GO-2026-5030, GO-2026-5942.
Medium CVE: GO-2026-5024 operator/go.mod— golang.org/x/sys 0.40.0 (golang.org/x/sys/windows): GO-2026-5024 — golang.org/x/sys is not a DIRECT requirement of this module: go.mod records it as `// indirect`, pulled in transitively, so raise it in place (run `go -C operator get golang.org/x/sys@v0.44.0`, which updates the require line operator/go.mod already holds for it).
Medium CVE: GO-2026-5970 operator/go.mod— golang.org/x/text 0.32.0 (golang.org/x/text/unicode/norm): GO-2026-5970 — golang.org/x/text is not a DIRECT requirement of this module: go.mod records it as `// indirect`, pulled in transitively, so raise it in place (run `go -C operator get golang.org/x/text@v0.39.0`, which updates the require line operator/go.mod already holds for it).
Medium CVE: [GHSA redacted] uv.lock— pydantic-settings 2.13.1: [GHSA redacted] — pydantic-settings is not declared by the manifests that govern this lockfile: it is pulled in transitively here (the 2.10.1 this repo declares for it belongs to a separate install root and does not reach this one), so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package pydantic-settings`)).
Medium CVE: [GHSA redacted] uv.lock— pymdown-extensions 10.21.3: [GHSA redacted] — pymdown-extensions is not declared by the manifests that govern this lockfile: it is pulled in transitively here (the 10.21.2 this repo declares for it belongs to a separate install root and does not reach this one), so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package pymdown-extensions`)).
Medium CVE: [GHSA redacted] uv.lock— setuptools 80.9.0: [GHSA redacted] — setuptools is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package setuptools`)).
Medium CVE: GO-2025-3503 operator/go.mod— stdlib 1.24.0 (net/http): GO-2025-3503 — fixed in Go 1.24.1; pin a build toolchain at or above it (go.mod `toolchain` directive, or your CI's Go version) — the `go` directive is a minimum language version, not the compiler that builds your binaries. This one row stands for the 46 advisories this scan raises against stdlib 1.24.0: GO-2025-3503, GO-2025-3563, GO-2025-3749, GO-2025-3750, GO-2025-3751, GO-2025-3849, GO-2025-3956, GO-2025-4006, GO-2025-4007, GO-2025-4008, GO-2025-4009, GO-2025-4010, GO-2025-4011, GO-2025-4012, GO-2025-4013, GO-2025-4014, GO-2025-4015, GO-2025-4155, GO-2025-4175, GO-2026-4337, GO-2026-4340, GO-2026-4341, GO-2026-4342, GO-2026-4403, GO-2026-4601, GO-2026-4602, GO-2026-4603, GO-2026-4864, GO-2026-4865, GO-2026-4869, GO-2026-4870, GO-2026-4918, GO-2026-4946, GO-2026-4947, GO-2026-4970, GO-2026-4971, GO-2026-4976, GO-2026-4977, GO-2026-4980, GO-2026-4981, GO-2026-4982, GO-2026-4986, GO-2026-5037, GO-2026-5038, GO-2026-5039, GO-2026-5856.
Hotspot: packages/llama-index-workflows/src/workflows/representation/build.py packages/llama-index-workflows/src/workflows/representation/build.py— packages/llama-index-workflows/src/workflows/representation/build.py changed 2 times in last 90 days, max complexity 48. 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: packages/llama-index-workflows/src/workflows/utils.py packages/llama-index-workflows/src/workflows/utils.py— packages/llama-index-workflows/src/workflows/utils.py changed 3 times in last 90 days, max complexity 22. 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: packages/llamactl/src/llama_agents/cli/commands/deployment.py packages/llamactl/src/llama_agents/cli/commands/deployment.py— packages/llamactl/src/llama_agents/cli/commands/deployment.py changed 2 times in last 90 days, max complexity 26. 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: packages/llama-index-workflows/src/workflows/runtime/control_loop/reduce.py packages/llama-index-workflows/src/workflows/runtime/control_loop/reduce.py— packages/llama-index-workflows/src/workflows/runtime/control_loop/reduce.py changed 2 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: packages/llama-index-workflows/src/workflows/representation/validate.py packages/llama-index-workflows/src/workflows/representation/validate.py— packages/llama-index-workflows/src/workflows/representation/validate.py changed 2 times in last 90 days, max complexity 19. 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: packages/llama-index-utils-workflow/src/llama_index/utils/workflow/__init__.py packages/llama-index-utils-workflow/src/llama_index/utils/workflow/__init__.py— packages/llama-index-utils-workflow/src/llama_index/utils/workflow/__init__.py changed 2 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: packages/llama-agents-integration-tests/src/llama_agents_integration_tests/fake_agent_data.py packages/llama-agents-integration-tests/src/llama_agents_integration_tests/fake_agent_data.py— packages/llama-agents-integration-tests/src/llama_agents_integration_tests/fake_agent_data.py changed 2 times in last 90 days, max complexity 15. 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: packages/llama-agents-server/src/llama_agents/server/_store/memory_workflow_store.py packages/llama-agents-server/src/llama_agents/server/_store/memory_workflow_store.py— packages/llama-agents-server/src/llama_agents/server/_store/memory_workflow_store.py changed 2 times in last 90 days, max complexity 15. 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.
Change coupling: memory_workflow_store.py ↔ sqlite_workflow_store.py packages/llama-agents-server/src/llama_agents/server/_store/memory_workflow_store.py— `packages/llama-agents-server/src/llama_agents/server/_store/memory_workflow_store.py` and `packages/llama-agents-server/src/llama_agents/server/_store/sqlite/sqlite_workflow_store.py` change together 75% of the time (12 of the 16 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well) with no explicit dependency — the edge is real but nothing declares it. Read the pair before acting: if one registers itself into the other through a hook or an initialiser, the missing dependency is DELIBERATE — the registration is the link, and it is meant not to be an import — and the thing to add is a comment on each side naming the other, not a merge; if they simply belong together, co-locate them; if neither holds, the coupling is hidden and worth breaking.
Change coupling: commands.py ↔ results.py packages/llama-index-workflows/src/workflows/runtime/types/commands.py— `packages/llama-index-workflows/src/workflows/runtime/types/commands.py` and `packages/llama-index-workflows/src/workflows/runtime/types/results.py` change together 64% of the time (7 of the 11 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well) with no explicit dependency between them. They sit in the same directory, but in this ecosystem each file is its own module — a sibling reference still needs an import — so the missing import edge is real: the coupling runs through shared behaviour, not a declared dependency. If they duplicate structure, extract the common part into one unit; otherwise the coupling is hidden and worth breaking.
Change coupling: context_types.py ↔ results.py packages/llama-index-workflows/src/workflows/context/context_types.py— `packages/llama-index-workflows/src/workflows/context/context_types.py` and `packages/llama-index-workflows/src/workflows/runtime/types/results.py` change together 53% of the time (8 of the 15 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well) with no explicit dependency — the edge is real but nothing declares it. Read the pair before acting: if one registers itself into the other through a hook or an initialiser, the missing dependency is DELIBERATE — the registration is the link, and it is meant not to be an import — and the thing to add is a comment on each side naming the other, not a merge; if they simply belong together, co-locate them; if neither holds, the coupling is hidden and worth breaking.
Change coupling: step_function.py ↔ ticks.py packages/llama-index-workflows/src/workflows/runtime/types/step_function.py— `packages/llama-index-workflows/src/workflows/runtime/types/step_function.py` and `packages/llama-index-workflows/src/workflows/runtime/types/ticks.py` change together 50% of the time (8 of the 16 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well) with no explicit dependency between them. They sit in the same directory, but in this ecosystem each file is its own module — a sibling reference still needs an import — so the missing import edge is real: the coupling runs through shared behaviour, not a declared dependency. If they duplicate structure, extract the common part into one unit; otherwise the coupling is hidden and worth breaking.
Duplicated block (14 lines × 2) packages/llama-agents-server/src/llama_agents/server/_store/postgres_workflow_store.py:512— packages/llama-agents-server/src/llama_agents/server/_store/postgres_workflow_store.py:512-525 | packages/llama-agents-server/src/llama_agents/server/_store/sqlite/sqlite_workflow_store.py:256-269 — 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 (14 lines × 2) packages/llama-index-workflows/src/workflows/representation/validate.py:327— packages/llama-index-workflows/src/workflows/representation/validate.py:327-340 | packages/llama-index-workflows/src/workflows/representation/validate.py:356-369 — 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 (14 lines × 2) packages/llamactl/src/llama_agents/cli/auth/client.py:279— packages/llamactl/src/llama_agents/cli/auth/client.py:279-294 | packages/llamactl/src/llama_agents/cli/auth/client.py:299-312 — 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) packages/llama-index-workflows/src/workflows/runtime/types/internal_state.py:206— packages/llama-index-workflows/src/workflows/runtime/types/internal_state.py:206-216 | packages/llama-index-workflows/src/workflows/runtime/types/internal_state.py:230-240 — 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 `packages/llama-index-workflows/src/workflows/runtime/types/internal_state.py:206` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (11 lines × 2) packages/llamactl/src/llama_agents/cli/commands/deployment.py:992— packages/llamactl/src/llama_agents/cli/commands/deployment.py:992-1002 | packages/llamactl/src/llama_agents/cli/commands/deployment.py:1469-1479 — 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 `packages/llamactl/src/llama_agents/cli/commands/deployment.py:992` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (11 lines × 2) packages/llamactl/src/llama_agents/cli/commands/pkg.py:57— packages/llamactl/src/llama_agents/cli/commands/pkg.py:57-67 | packages/llamactl/src/llama_agents/cli/commands/serve.py:101-111 — 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.
D38 · OSV Dependency Vulnerabilities· Medium vulnerability · ×2
Medium vulnerability: [GHSA redacted] operator/go.mod— github.com/google/cel-go 0.23.2: [GHSA redacted] — github.com/google/cel-go is not a DIRECT requirement of this module: go.mod records it as `// indirect`, pulled in transitively, so raise it in place (run `go -C operator get github.com/google/cel-go@v0.29.0`, which updates the require line operator/go.mod already holds for it).
Medium vulnerability: [GHSA redacted] uv.lock— uv 0.11.6: [GHSA redacted] — uv is not declared in this repo's manifests: it is pulled in transitively, so upgrade the dependency that requires it (or raise its floor in your own manifest and re-resolve (`uv lock --upgrade-package uv`)).
Duplicated block (13 lines × 2) packages/llama-agents-appserver/src/llama_agents/appserver/routers/deployments.py:47— packages/llama-agents-appserver/src/llama_agents/appserver/routers/deployments.py:47-61 | packages/llama-agents-appserver/src/llama_agents/appserver/routers/deployments.py:73-85 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (13 lines × 2) packages/llama-index-workflows/src/workflows/representation/build.py:319— packages/llama-index-workflows/src/workflows/representation/build.py:319-331 | packages/llama-index-workflows/src/workflows/representation/build.py:339-351 — 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 `packages/llama-index-workflows/src/workflows/representation/build.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. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (8 lines × 2) packages/llama-agents-control-plane/src/llama_agents/control_plane/backup/storage.py:77— packages/llama-agents-control-plane/src/llama_agents/control_plane/backup/storage.py:77-84 | packages/llama-agents-control-plane/src/llama_agents/control_plane/build_api/build_storage.py:146-153 — 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 `packages/llama-agents-control-plane/src/llama_agents/control_plane/backup/storage.py:77` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
Duplicated block (8 lines × 2) packages/llamactl/src/llama_agents/cli/commands/deployment.py:189— packages/llamactl/src/llama_agents/cli/commands/deployment.py:189-196 | packages/llamactl/src/llama_agents/cli/commands/deployment.py:1258-1265 — 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.
build.get_workflow_representation (cyclomatic 48) packages/llama-index-workflows/src/workflows/representation/build.py:167— build.get_workflow_representation has cyclomatic complexity 48 (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.
pytest_cmd.pytest_cmd (cyclomatic 43) src/dev_cli/commands/pytest_cmd.py:459— pytest_cmd.pytest_cmd has cyclomatic complexity 43 (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.
deployment_config.read_deployment_config (cyclomatic 41) packages/llama-agents-core/src/llama_agents/core/deployment_config.py:29— deployment_config.read_deployment_config has cyclomatic complexity 41 (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.
init._create (cyclomatic 33) packages/llamactl/src/llama_agents/cli/commands/init.py:66— init._create has cyclomatic complexity 33 (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.
_ControlLoopRunner.run (cyclomatic 32) packages/llama-index-workflows/src/workflows/runtime/control_loop/runner.py:353— _ControlLoopRunner.run has cyclomatic complexity 32 (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.
GitService._check_github_access_type (cyclomatic 26) packages/llama-agents-control-plane/src/llama_agents/control_plane/git/_git_service.py:251— GitService._check_github_access_type has cyclomatic complexity 26 (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.
deployment._execute_deployment_operation (cyclomatic 26) packages/llamactl/src/llama_agents/cli/commands/deployment.py:1120— deployment._execute_deployment_operation 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.
DeploymentService.update_deployment (cyclomatic 23) packages/llama-agents-control-plane/src/llama_agents/control_plane/manage_api/deployments_service.py:290— DeploymentService.update_deployment has cyclomatic complexity 23 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
iter_utils.merge_generators (cyclomatic 22) packages/llama-agents-core/src/llama_agents/core/iter_utils.py:56— iter_utils.merge_generators 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.
utils.inspect_signature (cyclomatic 22) packages/llama-index-workflows/src/workflows/utils.py:63— utils.inspect_signature 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.
auth._run_device_authentication (cyclomatic 22) packages/llamactl/src/llama_agents/cli/commands/auth.py:436— auth._run_device_authentication 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.
runner.run_workflow (cyclomatic 21) packages/llama-agents-dbos/tests/fixtures/runner.py:70— runner.run_workflow 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.
GitService._validate_github_application (cyclomatic 20) packages/llama-agents-control-plane/src/llama_agents/control_plane/git/_git_service.py:697— GitService._validate_github_application has cyclomatic complexity 20 (threshold 15). To reduce it, separate the branches: extract each independent case into its own named function so the top-level body reads as a short sequence of named decisions.
reduce._apply_step_result (cyclomatic 20) packages/llama-index-workflows/src/workflows/runtime/control_loop/reduce.py:520— reduce._apply_step_result 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.
projects.use_project (cyclomatic 20) packages/llamactl/src/llama_agents/cli/commands/projects.py:95— projects.use_project 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.
interactive._blessed_select (cyclomatic 20) packages/llamactl/src/llama_agents/cli/interactive.py:66— interactive._blessed_select 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.
deployments.apply_deployment_update (cyclomatic 19) packages/llama-agents-core/src/llama_agents/core/schema/deployments.py:431— deployments.apply_deployment_update has cyclomatic complexity 19 (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.
validate.build_step_graph (cyclomatic 19) packages/llama-index-workflows/src/workflows/representation/validate.py:57— validate.build_step_graph has cyclomatic complexity 19 (threshold 15). To reduce it, break up the iteration: give each loop body a named function, and split a multi-phase loop into one function per phase so no single body carries the whole pipeline.
validate._validate_event_connectivity (cyclomatic 19) packages/llama-index-workflows/src/workflows/representation/validate.py:451— validate._validate_event_connectivity 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.
workflow._render_pyvis (cyclomatic 18) packages/llama-index-utils-workflow/src/llama_index/utils/workflow/__init__.py:108— workflow._render_pyvis 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.
build_gc._get_referenced_build_ids_from_replicasets (cyclomatic 17) packages/llama-agents-control-plane/src/llama_agents/control_plane/build_api/build_gc.py:26— build_gc._get_referenced_build_ids_from_replicasets has cyclomatic complexity 17 (threshold 15). To reduce it, separate the branches: extract each independent case into its own named function so the top-level body reads as a short sequence of named decisions.
pytest_cmd.extract_failed_test_names (cyclomatic 17) src/dev_cli/commands/pytest_cmd.py:118— pytest_cmd.extract_failed_test_names has cyclomatic complexity 17 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
EventEnvelope.parse (cyclomatic 16) packages/llama-agents-client/src/llama_agents/client/protocol/serializable_events.py:98— EventEnvelope.parse 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.
GitService.validate_repository (cyclomatic 16) packages/llama-agents-control-plane/src/llama_agents/control_plane/git/_git_service.py:135— GitService.validate_repository 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.
k8s_client._llamadeployment_to_response (cyclomatic 16) packages/llama-agents-control-plane/src/llama_agents/control_plane/k8s_client.py:965— k8s_client._llamadeployment_to_response 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.
DeploymentService.create_deployment (cyclomatic 16) packages/llama-agents-control-plane/src/llama_agents/control_plane/manage_api/deployments_service.py:153— DeploymentService.create_deployment 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.
git_util.clone_repo_sync (cyclomatic 16) packages/llama-agents-core/src/llama_agents/core/git/git_util.py:263— git_util.clone_repo_sync 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.
validate.validate_graph (cyclomatic 16) packages/llama-index-workflows/src/workflows/representation/validate.py:162— validate.validate_graph 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.
validate._collect_catch_error_handlers (cyclomatic 16) packages/llama-index-workflows/src/workflows/representation/validate.py:389— validate._collect_catch_error_handlers 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.
utils._event_list_element_types (cyclomatic 16) packages/llama-index-workflows/src/workflows/utils.py:199— utils._event_list_element_types 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.
serve._maybe_inject_llama_cloud_credentials (cyclomatic 16) packages/llamactl/src/llama_agents/cli/commands/serve.py:208— serve._maybe_inject_llama_cloud_credentials 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.
yaml_template.render (cyclomatic 16) packages/llamactl/src/llama_agents/cli/yaml_template.py:77— yaml_template.render 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.
LLM evaluation failed — JSON parse error: Expected end of string, but instead reached end of data. Path: $.findings[0].suggestion | LineNumber: 0 | BytePositionInLine: 1083.
build.get_workflow_representation (cognitive 99) packages/llama-index-workflows/src/workflows/representation/build.py:167— build.get_workflow_representation has cognitive complexity 99 (threshold 15). Drivers by points: if/else 60, loops 25, boolean chains 13, ternaries 1 (nesting depth added 50). 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.
pytest_cmd.pytest_cmd (cognitive 91) src/dev_cli/commands/pytest_cmd.py:459— pytest_cmd.pytest_cmd has cognitive complexity 91 (threshold 15). Drivers by points: if/else 52, ternaries 22, loops 13, boolean chains 4 (nesting depth added 46). 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.
deployment_config.read_deployment_config (cognitive 86) packages/llama-agents-core/src/llama_agents/core/deployment_config.py:29— deployment_config.read_deployment_config has cognitive complexity 86 (threshold 15). Drivers by points: if/else 61, boolean chains 11, ternaries 8, error handling 6 (nesting depth added 47). 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.
_ControlLoopRunner.run (cognitive 69) packages/llama-index-workflows/src/workflows/runtime/control_loop/runner.py:353— _ControlLoopRunner.run has cognitive complexity 69 (threshold 15). Drivers by points: if/else 39, error handling 16, loops 12, boolean chains 2 (nesting depth added 37). 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.
init._create (cognitive 65) packages/llamactl/src/llama_agents/cli/commands/init.py:66— init._create has cognitive complexity 65 (threshold 15). Drivers by points: if/else 46, error handling 12, boolean chains 6, loops 1 (nesting depth added 29). 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.
runner.run_workflow (cognitive 51) packages/llama-agents-dbos/tests/fixtures/runner.py:70— runner.run_workflow has cognitive complexity 51 (threshold 15). Drivers by points: if/else 40, loops 9, boolean chains 1, error handling 1 (nesting depth added 29). 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.
utils.inspect_signature (cognitive 47) packages/llama-index-workflows/src/workflows/utils.py:63— utils.inspect_signature has cognitive complexity 47 (threshold 15). Drivers by points: if/else 37, ternaries 6, boolean chains 3, loops 1 (nesting depth added 26). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
iter_utils.merge_generators (cognitive 43) packages/llama-agents-core/src/llama_agents/core/iter_utils.py:56— iter_utils.merge_generators has cognitive complexity 43 (threshold 15). Drivers by points: if/else 21, loops 11, error handling 10, boolean chains 1 (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.
workflow._render_pyvis (cognitive 38) packages/llama-index-utils-workflow/src/llama_index/utils/workflow/__init__.py:108— workflow._render_pyvis has cognitive complexity 38 (threshold 15). Drivers by points: if/else 31, ternaries 3, boolean chains 2, loops 2 (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.
deployment._execute_deployment_operation (cognitive 35) packages/llamactl/src/llama_agents/cli/commands/deployment.py:1120— deployment._execute_deployment_operation has cognitive complexity 35 (threshold 15). Drivers by points: if/else 19, boolean chains 10, ternaries 6 (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.
interactive._blessed_select (cognitive 35) packages/llamactl/src/llama_agents/cli/interactive.py:66— interactive._blessed_select has cognitive complexity 35 (threshold 15). Drivers by points: if/else 29, boolean chains 4, loops 1, ternaries 1 (nesting depth added 18). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
pytest_cmd.extract_failed_test_names (cognitive 35) src/dev_cli/commands/pytest_cmd.py:118— pytest_cmd.extract_failed_test_names has cognitive complexity 35 (threshold 15). Drivers by points: if/else 28, boolean chains 4, 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.
validate._validate_event_connectivity (cognitive 33) packages/llama-index-workflows/src/workflows/representation/validate.py:451— validate._validate_event_connectivity has cognitive complexity 33 (threshold 15). Drivers by points: if/else 19, loops 11, ternaries 2, boolean chains 1 (nesting depth added 15). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
auth._run_device_authentication (cognitive 33) packages/llamactl/src/llama_agents/cli/commands/auth.py:436— auth._run_device_authentication has cognitive complexity 33 (threshold 15). Drivers by points: if/else 23, boolean chains 6, error handling 2, loops 1, ternaries 1 (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.
reduce._apply_step_result (cognitive 32) packages/llama-index-workflows/src/workflows/runtime/control_loop/reduce.py:520— reduce._apply_step_result has cognitive complexity 32 (threshold 15). Drivers by points: if/else 29, loops 3 (nesting depth added 16). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
projects.use_project (cognitive 30) packages/llamactl/src/llama_agents/cli/commands/projects.py:95— projects.use_project has cognitive complexity 30 (threshold 15). Drivers by points: if/else 24, error handling 2, ternaries 2, boolean chains 1, 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.
build_gc._get_referenced_build_ids_from_replicasets (cognitive 29) packages/llama-agents-control-plane/src/llama_agents/control_plane/build_api/build_gc.py:26— build_gc._get_referenced_build_ids_from_replicasets has cognitive complexity 29 (threshold 15). Drivers by points: loops 11, if/else 10, boolean chains 7, 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.
GitService._check_github_access_type (cognitive 28) packages/llama-agents-control-plane/src/llama_agents/control_plane/git/_git_service.py:251— GitService._check_github_access_type has cognitive complexity 28 (threshold 15). Drivers by points: if/else 15, boolean chains 7, error handling 4, ternaries 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.
validate.build_step_graph (cognitive 28) packages/llama-index-workflows/src/workflows/representation/validate.py:57— validate.build_step_graph has cognitive complexity 28 (threshold 15). Drivers by points: loops 13, if/else 11, boolean chains 3, 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.
validate.validate_graph (cognitive 28) packages/llama-index-workflows/src/workflows/representation/validate.py:162— validate.validate_graph has cognitive complexity 28 (threshold 15). Drivers by points: if/else 25, loops 2, boolean chains 1 (nesting depth added 13). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
DeploymentService.stream_deployment_logs (cognitive 27) packages/llama-agents-control-plane/src/llama_agents/control_plane/manage_api/deployments_service.py:480— DeploymentService.stream_deployment_logs has cognitive complexity 27 (threshold 15). Drivers by points: if/else 19, loops 6, boolean chains 2 (nesting depth added 15). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
process_utils._stream_source (cognitive 26) packages/llama-agents-appserver/src/llama_agents/appserver/process_utils.py:180— process_utils._stream_source has cognitive complexity 26 (threshold 15). Drivers by points: error handling 8, if/else 8, ternaries 6, loops 4 (nesting depth added 15). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
reduce._route_to_accepting_steps (cognitive 26) packages/llama-index-workflows/src/workflows/runtime/control_loop/reduce.py:1266— reduce._route_to_accepting_steps has cognitive complexity 26 (threshold 15). Drivers by points: if/else 21, boolean chains 4, 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.
DeploymentService.update_deployment (cognitive 25) packages/llama-agents-control-plane/src/llama_agents/control_plane/manage_api/deployments_service.py:290— DeploymentService.update_deployment has cognitive complexity 25 (threshold 15). Drivers by points: if/else 14, boolean chains 11 (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.
FakeAgentDataBackend._matches (cognitive 25) packages/llama-agents-integration-tests/src/llama_agents_integration_tests/fake_agent_data.py:67— FakeAgentDataBackend._matches has cognitive complexity 25 (threshold 15). Drivers by points: if/else 15, boolean chains 7, loops 3 (nesting depth added 11). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
validate._collect_catch_error_handlers (cognitive 25) packages/llama-index-workflows/src/workflows/representation/validate.py:389— validate._collect_catch_error_handlers has cognitive complexity 25 (threshold 15). Drivers by points: if/else 15, loops 6, ternaries 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.
utils._event_list_element_types (cognitive 25) packages/llama-index-workflows/src/workflows/utils.py:199— utils._event_list_element_types has cognitive complexity 25 (threshold 15). Drivers by points: if/else 19, boolean chains 3, loops 3 (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.
BackupService._restore_entries (cognitive 24) packages/llama-agents-control-plane/src/llama_agents/control_plane/manage_api/backup_service.py:168— BackupService._restore_entries has cognitive complexity 24 (threshold 15). Drivers by points: if/else 17, boolean chains 2, error handling 2, ternaries 2, 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.
deployments.apply_deployment_update (cognitive 24) packages/llama-agents-core/src/llama_agents/core/schema/deployments.py:431— deployments.apply_deployment_update has cognitive complexity 24 (threshold 15). Drivers by points: if/else 19, loops 2, ternaries 2, boolean chains 1 (nesting depth added 5). 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.
skills_cmd.sync_skills (cognitive 24) src/dev_cli/commands/skills_cmd.py:30— skills_cmd.sync_skills has cognitive complexity 24 (threshold 15). Drivers by points: if/else 20, loops 4 (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.
GitService._validate_github_application (cognitive 23) packages/llama-agents-control-plane/src/llama_agents/control_plane/git/_git_service.py:697— GitService._validate_github_application has cognitive complexity 23 (threshold 15). Drivers by points: error handling 9, if/else 8, boolean chains 5, ternaries 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.
memory_workflow_store._matches_query (cognitive 23) packages/llama-agents-server/src/llama_agents/server/_store/memory_workflow_store.py:28— memory_workflow_store._matches_query has cognitive complexity 23 (threshold 15). Drivers by points: if/else 23 (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.
k8s_client.update_deployment (cognitive 22) packages/llama-agents-control-plane/src/llama_agents/control_plane/k8s_client.py:673— k8s_client.update_deployment has cognitive complexity 22 (threshold 15). Drivers by points: if/else 13, loops 5, error handling 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.
workflow._process_tools_and_handoffs (cognitive 22) packages/llama-index-utils-workflow/src/llama_index/utils/workflow/__init__.py:382— workflow._process_tools_and_handoffs has cognitive complexity 22 (threshold 15). Drivers by points: if/else 14, loops 7, 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.
PersistenceDecorator._on_server_start (cognitive 21) packages/llama-agents-server/src/llama_agents/server/_runtime/persistence_runtime.py:321— PersistenceDecorator._on_server_start has cognitive complexity 21 (threshold 15). Drivers by points: if/else 12, error handling 5, ternaries 2, boolean chains 1, 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.
SerializedContext.from_v0 (cognitive 21) packages/llama-index-workflows/src/workflows/context/context_types.py:220— SerializedContext.from_v0 has cognitive complexity 21 (threshold 15). Drivers by points: if/else 11, loops 10 (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.
serve._maybe_inject_llama_cloud_credentials (cognitive 21) packages/llamactl/src/llama_agents/cli/commands/serve.py:208— serve._maybe_inject_llama_cloud_credentials has cognitive complexity 21 (threshold 15). Drivers by points: if/else 16, boolean chains 3, error handling 1, ternaries 1 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
yaml_template.render (cognitive 21) packages/llamactl/src/llama_agents/cli/yaml_template.py:77— yaml_template.render has cognitive complexity 21 (threshold 15). Drivers by points: if/else 13, boolean chains 3, ternaries 3, loops 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.
bootstrap.bootstrap_app_from_repo (cognitive 20) packages/llama-agents-appserver/src/llama_agents/appserver/bootstrap.py:205— bootstrap.bootstrap_app_from_repo has cognitive complexity 20 (threshold 15). Drivers by points: if/else 16, boolean chains 3, 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.
EventEnvelope.parse (cognitive 20) packages/llama-agents-client/src/llama_agents/client/protocol/serializable_events.py:98— EventEnvelope.parse has cognitive complexity 20 (threshold 15). Drivers by points: if/else 12, boolean chains 4, error handling 2, ternaries 2 (nesting depth added 3). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, 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.
build_gc.gc_build_artifacts (cognitive 19) packages/llama-agents-control-plane/src/llama_agents/control_plane/build_api/build_gc.py:53— build_gc.gc_build_artifacts has cognitive complexity 19 (threshold 15). Drivers by points: if/else 14, loops 3, boolean chains 1, ternaries 1 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
GitService.validate_repository (cognitive 19) packages/llama-agents-control-plane/src/llama_agents/control_plane/git/_git_service.py:135— GitService.validate_repository has cognitive complexity 19 (threshold 15). Drivers by points: if/else 11, boolean chains 3, error handling 3, match/switch 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.
test_git_service.mock_github_repo_and_owner (cognitive 19) packages/llama-agents-control-plane/tests/test_git_service.py:109— test_git_service.mock_github_repo_and_owner has cognitive complexity 19 (threshold 15). Drivers by points: if/else 16, boolean chains 2, ternaries 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.
migrate.run_migrations (cognitive 19) packages/llama-agents-server/src/llama_agents/server/_store/sqlite/migrate.py:58— migrate.run_migrations has cognitive complexity 19 (threshold 15). Drivers by points: if/else 8, error handling 5, loops 4, boolean chains 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.
ui_proxy._ws_proxy (cognitive 18) packages/llama-agents-appserver/src/llama_agents/appserver/routers/ui_proxy.py:31— ui_proxy._ws_proxy has cognitive complexity 18 (threshold 15). Drivers by points: if/else 11, loops 5, error handling 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.
workflow_loader._install_and_add_appserver_if_missing (cognitive 18) packages/llama-agents-appserver/src/llama_agents/appserver/workflow_loader.py:342— workflow_loader._install_and_add_appserver_if_missing has cognitive complexity 18 (threshold 15). Drivers by points: if/else 17, boolean chains 1 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
k8s_client._llamadeployment_to_response (cognitive 18) packages/llama-agents-control-plane/src/llama_agents/control_plane/k8s_client.py:965— k8s_client._llamadeployment_to_response has cognitive complexity 18 (threshold 15). Drivers by points: if/else 11, boolean chains 5, ternaries 2 (nesting depth added 3). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, 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.
DeploymentService.rollback_deployment (cognitive 18) packages/llama-agents-control-plane/src/llama_agents/control_plane/manage_api/deployments_service.py:255— DeploymentService.rollback_deployment has cognitive complexity 18 (threshold 15). Drivers by points: if/else 13, loops 4, 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.
workflow._get_workflow_classes_from_step (cognitive 18) packages/llama-index-utils-workflow/src/llama_index/utils/workflow/__init__.py:540— workflow._get_workflow_classes_from_step has cognitive complexity 18 (threshold 15). Drivers by points: if/else 15, boolean chains 1, error handling 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.
state_store.decode_state (cognitive 18) packages/llama-index-workflows/src/workflows/context/state_store.py:248— state_store.decode_state has cognitive complexity 18 (threshold 15). Drivers by points: if/else 15, error handling 2, 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.
ProjectClient.stream_deployment_logs (cognitive 17) packages/llama-agents-core/src/llama_agents/core/client/manage_client.py:301— ProjectClient.stream_deployment_logs has cognitive complexity 17 (threshold 15). Drivers by points: if/else 9, error handling 4, ternaries 2, 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.
git_util.clone_repo_sync (cognitive 17) packages/llama-agents-core/src/llama_agents/core/git/git_util.py:263— git_util.clone_repo_sync has cognitive complexity 17 (threshold 15). Drivers by points: if/else 10, boolean chains 4, error handling 3 (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.
DBOSRuntime._prepare_launch (cognitive 17) packages/llama-agents-dbos/src/llama_agents/dbos/runtime.py:780— DBOSRuntime._prepare_launch has cognitive complexity 17 (threshold 15). Drivers by points: if/else 11, boolean chains 4, error handling 1, loops 1 (nesting depth added 4). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
DBOSRuntime.destroy (cognitive 17) packages/llama-agents-dbos/src/llama_agents/dbos/runtime.py:959— DBOSRuntime.destroy has cognitive complexity 17 (threshold 15). Drivers by points: if/else 10, error handling 4, ternaries 2, 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.
SqliteWorkflowStore._build_filters (cognitive 17) packages/llama-agents-server/src/llama_agents/server/_store/sqlite/sqlite_workflow_store.py:355— SqliteWorkflowStore._build_filters has cognitive complexity 17 (threshold 15). Drivers by points: if/else 17 (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.
validate.validate_catch_error_handlers (cognitive 17) packages/llama-index-workflows/src/workflows/representation/validate.py:265— validate.validate_catch_error_handlers has cognitive complexity 17 (threshold 15). Drivers by points: if/else 13, loops 3, boolean chains 1 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
projects.get_projects (cognitive 17) packages/llamactl/src/llama_agents/cli/commands/projects.py:42— projects.get_projects has cognitive complexity 17 (threshold 15). Drivers by points: if/else 12, boolean chains 2, error handling 2, 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.
pytest_cmd._run_tests_verbose (cognitive 17) src/dev_cli/commands/pytest_cmd.py:387— pytest_cmd._run_tests_verbose has cognitive complexity 17 (threshold 15). Drivers by points: if/else 13, ternaries 3, loops 1 (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
k8s_client._create_k8s_secret (cognitive 16) packages/llama-agents-control-plane/src/llama_agents/control_plane/k8s_client.py:490— k8s_client._create_k8s_secret has cognitive complexity 16 (threshold 15). Drivers by points: error handling 4, if/else 4, ternaries 4, boolean chains 3, loops 1 (nesting depth added 5). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
k8s_client._create_ingress (cognitive 16) packages/llama-agents-control-plane/src/llama_agents/control_plane/k8s_client.py:552— k8s_client._create_ingress has cognitive complexity 16 (threshold 15). Drivers by points: error handling 5, if/else 4, ternaries 4, boolean chains 3 (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.
k8s_client._list_replicasets_for_deployment_sync (cognitive 16) packages/llama-agents-control-plane/src/llama_agents/control_plane/k8s_client.py:1068— k8s_client._list_replicasets_for_deployment_sync has cognitive complexity 16 (threshold 15). Drivers by points: if/else 8, boolean chains 4, loops 3, 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.
k8s_client.get_replicaset_pods_for_deployment (cognitive 16) packages/llama-agents-control-plane/src/llama_agents/control_plane/k8s_client.py:1163— k8s_client.get_replicaset_pods_for_deployment has cognitive complexity 16 (threshold 15). Drivers by points: if/else 8, boolean chains 4, loops 4 (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.
workflow._render_mermaid (cognitive 16) packages/llama-index-utils-workflow/src/llama_index/utils/workflow/__init__.py:248— workflow._render_mermaid has cognitive complexity 16 (threshold 15). Drivers by points: if/else 13, loops 2, boolean chains 1 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
workflow._get_nested_workflow_representation (cognitive 16) packages/llama-index-utils-workflow/src/llama_index/utils/workflow/__init__.py:594— workflow._get_nested_workflow_representation has cognitive complexity 16 (threshold 15). Drivers by points: if/else 7, error handling 4, loops 4, ternaries 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.
Unpinned build actions — CI references GitHub Actions by a floating ref (@main / @tag) rather than a pinned commit SHA, weakening build integrity. 38 floating ref(s) across 9 workflow file(s). 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 (test.yml, lockfile.yml, lint.yml …) and so run with the repository's default GITHUB_TOKEN scope, while 3 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 (53 lines × 2) packages/llama-agents-server/src/llama_agents/server/_api.py:356— packages/llama-agents-server/src/llama_agents/server/_api.py:356-411 | packages/llama-agents-server/src/llama_agents/server/_api.py:541-593 — 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 `packages/llama-agents-server/src/llama_agents/server/_api.py:356` 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 (18 lines × 2) packages/llama-index-utils-workflow/src/llama_index/utils/workflow/__init__.py:634— packages/llama-index-utils-workflow/src/llama_index/utils/workflow/__init__.py:634-651 | packages/llama-index-utils-workflow/src/llama_index/utils/workflow/__init__.py:661-678 — 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 `packages/llama-index-utils-workflow/src/llama_index/utils/workflow/__init__.py:634` 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 (16 lines × 2) packages/llama-agents-client/src/llama_agents/client/client.py:245— packages/llama-agents-client/src/llama_agents/client/client.py:245-260 | packages/llama-agents-client/src/llama_agents/client/client.py:297-312 — 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 (15 lines × 2) packages/llama-index-utils-workflow/src/llama_index/utils/workflow/__init__.py:68— packages/llama-index-utils-workflow/src/llama_index/utils/workflow/__init__.py:68-82 | packages/llama-index-utils-workflow/src/llama_index/utils/workflow/__init__.py:211-225 — 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 (9 lines × 2) packages/llamactl/src/llama_agents/cli/commands/auth.py:623— packages/llamactl/src/llama_agents/cli/commands/auth.py:623-631 | packages/llamactl/src/llama_agents/cli/commands/auth.py:641-649 — 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. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
Duplicated block (7 lines × 2) src/dev_cli/commands/pytest_cmd.py:417— src/dev_cli/commands/pytest_cmd.py:417-423 | src/dev_cli/commands/pytest_cmd.py:580-586 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (6 lines × 2) packages/llama-agents-core/src/llama_agents/core/schema/deployments.py:224— packages/llama-agents-core/src/llama_agents/core/schema/deployments.py:224-229 | packages/llama-agents-core/src/llama_agents/core/schema/deployments.py:384-389 — 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.
Low IaC: DS-0026 docker/Dockerfile— No HEALTHCHECK defined Without one the runtime only knows whether the process is alive, not whether it is serving, so a wedged container is restarted by nobody. The step: add a `HEALTHCHECK` to the image that probes the service the way a client would — this image already declares `EXPOSE 8000`, so a request to `localhost:8000` on the service's own health or root route, exiting non-zero when it does not answer, is the probe — and give it an `--interval`, a `--timeout` and a `--start-period` long enough to cover startup. If the image ships no HTTP client, probe with whatever the runtime already has, or declare the check in the orchestrator instead and say so here.
Low IaC: KSV-0003 operator/tilt/k8s-manifests/kind-gc-cronjob.yaml— Default capabilities: some containers do not drop all One securityContext edit clears this facet's near-duplicate rules together: KSV-0003, KSV-0004, KSV-0106.
Low IaC: KSV-0011 operator/tilt/k8s-manifests/kind-gc-cronjob.yaml— CPU not limited
Low IaC: KSV-0015 operator/tilt/k8s-manifests/kind-gc-cronjob.yaml— CPU requests not specified
Low IaC: KSV-0016 operator/tilt/k8s-manifests/kind-gc-cronjob.yaml— Memory requests not specified
Low IaC: KSV-0018 operator/tilt/k8s-manifests/kind-gc-cronjob.yaml— Memory not limited
Low IaC: KSV-0003 operator/tilt/k8s-manifests/seaweedfs.yaml— Default capabilities: some containers do not drop all One securityContext edit clears this facet's near-duplicate rules together: KSV-0003, KSV-0004, KSV-0106.
Low IaC: KSV-0011 operator/tilt/k8s-manifests/seaweedfs.yaml— CPU not limited
Low IaC: KSV-0015 operator/tilt/k8s-manifests/seaweedfs.yaml— CPU requests not specified
Low IaC: KSV-0016 operator/tilt/k8s-manifests/seaweedfs.yaml— Memory requests not specified
Low IaC: KSV-0018 operator/tilt/k8s-manifests/seaweedfs.yaml— Memory not limited
D11 · Test Reliability· Test reliability not included · ×1
Test reliability not included — Test source is present (.go, .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, 67 significant file(s) lose their only recent owner: packages/llama-agents-control-plane/src/llama_agents/control_plane/k8s_client.py, packages/llama-agents-server/src/llama_agents/server/_api.py, packages/llama-index-workflows/src/workflows/context/state_store.py, packages/llamactl/src/llama_agents/cli/commands/auth.py, packages/llama-index-workflows/src/workflows/context/context.py, src/dev_cli/changesets.py, packages/llama-agents-server/src/llama_agents/server/_store/postgres_workflow_store.py, packages/llamactl/src/llama_agents/cli/display.py (+59 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 (69 single-owned of 147 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 (2 file(s)) — spread or document their files in the same way, at lower priority than the named off-boarding risks above.
No build provenance — No SLSA provenance generation or build attestation found in CI — nothing binds a released artifact to the build that produced it, so a consumer cannot tell your artifact from a substituted one. On GitHub Actions, `actions/attest-build-provenance` (or slsa-github-generator) emits one from the job's own OIDC identity; elsewhere, run `cosign attest` over the released artifact from the release pipeline and publish the attestation beside it.
No artifact signing — No artifact signing found in CI — sign your released artifacts with whatever your ecosystem ships (a GPG/minisign detached signature — or `cosign sign-blob` — over the release archives, or over a checksum file published alongside them, cosign/sigstore for container images, PEP 740 attestations via `pypa/gh-action-pypi-publish` under PyPI Trusted Publishing (OIDC) for wheels/sdists) so consumers can verify what you built.
D36 · Supply-chain Provenance & Signing· No SBOM · ×1
No SBOM — No SBOM generation or committed SBOM found — produce one with what your ecosystem ships (`cdxgen -t pnpm` (or `syft` below) over the pnpm workspace — `npm sbom` cannot read `pnpm-lock.yaml`, `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.
No seccomp profile — Workloads do not set a seccomp profile (RuntimeDefault or a Localhost profile). Seccomp blocks the syscalls a container never needs, shrinking the kernel attack surface a container escape would use.
No AppArmor/SELinux confinement — Workloads declare no AppArmor or SELinux profile. A mandatory-access-control profile confines what a compromised container can touch on the host, complementing seccomp's syscall filter.
No runtime threat detection — No runtime threat-detection engine (Tetragon TracingPolicy / Falco) is committed. These observe process, file and network activity in-kernel and can alert or kill on malicious behaviour a static scan cannot catch.
D42 · Runtime Threat Enforcement· No admission-control policy · ×1
No admission-control policy — No policy-enforcement gate (Kyverno / OPA Gatekeeper / PodSecurity admission) is committed. Admission control keeps workloads that violate your security baseline from ever reaching the cluster.
Coverage not included — suite not readable by the collector — Coverage NOT READ here — but this repository measures it: a coverage step in CI (`pytest --cov=src --cov`) shows that coverage is collected and tracked in your own CI. The built-in collector has no runner for this ecosystem (.go, .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 (`go test -coverprofile=coverage.out ./...`, or `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 manifests (a Python pyproject.toml/requirements.txt (pip/uv/Poetry), package.json and a Go module (go.mod/go.sum)) were found, but this pass cannot parse them for hygiene, so no package was assessed. Zero packages read is NOT a clean dependency tree, so this is NOT SCORED — a gap in the analyzer, not a verdict about this repository. This row is about dependency HYGIENE — outdated, deprecated or unmaintained direct dependencies; known CVEs in the same dependency graph are a separate question, reported under D38 wherever the manifest is OSV-readable. Your package.json IS read in this same run: the frontend dependency lens (R8) parses it for unused declarations, undeclared imports and misplaced production dependencies — what is missing here is the outdated/deprecated/unmaintained signal for those npm packages, not the manifest.
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.
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.
Run 019fcf32-343e-7ed7-8c2c-62f66cf57848 · every finding is also locatable in findings.md, and the complete scoring record (with exit codes + durations) in sidecar.json.
Issues: 20 · Warnings: 166 · Recommendations: 22 · Info: 2 — Appendix A · all findings · full markdown report.
Generated by Watchdog — deterministic code-health analysis. 04-08-2026 @ 23:53 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.