Public report — ascetic-ddd-python, 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.
78findings with an exact file:lineof 87 — the remainder are repo-wide signals (a dimension-level measurement, not a single line); open any file:line and verify
31/101dimensions across the health lenses334 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.
krew-solutions/ascetic-ddd-python is sound in substance but carries real gaps (53%). 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 (97%) — the structure is clean and changes stay contained. Code Health (88%) is solid too.
The area that most needs attention is Readiness (44%) — 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. Maturity (51%) is the next concern — onboarding is slow — key decisions and the architecture aren't written down, so contributors have to reverse-engineer the intent.
Leadership focus, highest impact first: SAST step to CI running what this repository's stack ships (Security & performance tooling); `healthcheck:` to the served compose service — probing… (Deployment & Rollback); Keep a changelog (e.g. Keep-a-Changelog) recording what shipped… (Release Hygiene).
For scale: Hobby (~334 production lines); rebuilding it from scratch would take roughly ~0.2 person-years (~1 engineer). Approximate, ±~30%.
It builds on a genuinely strong Architecture foundation (97%); 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.
How we model this: boilerplate at a scaffolding rate + logic × domain High (×1.4) — DDD/clean architecture, domain model × a 0.7× quality factor, at €60–95/h; indicative, ±~30% · size measured directly from source. Indicative only — most sensitive to the hourly rate and the domain tier (both tunable in config).
Top priorities
The highest-leverage moves; the full ranked list is in the Roadmap below.
1
Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing.
Add a `healthcheck:` to the served compose service — probing the endpoint it already answers on where it has one — with `depends_on: condition: service_healthy` on whatever waits for it, and keep the deployed image tag immutable and recorded so rolling back is re-pointing at the previous tag rather than rebuilding.
Of everything flagged, the best return on effort is: Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing. The rest can wait behind it.
Evidence: priority ranking: top of 5 ranked by impact/effort
→ Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing.
Architecture — module dependency matrix
330 modules, 350 dependencies — every dependency points down the layering, so there are no cycles. Rows and columns are the same modules, ordered so that a module only depends on ones above it. A cell means the row depends on the column, and its number is how many type pairs create that dependency. Read one thing: is anything above the diagonal? A mark there is a dependency cycle. (A cycle is all this shows — an unusual but cycle-free dependency sits below the diagonal like any other.)
Findings mapped to OWASP categories; the specific CVEs/secrets are in the Security dimension cards below and findings.md (redacted only on the public version of this report).
OWASP category
Findings
Severity
A03:2021 — Injection
12
High / Critical
Roadmap
First, integrate static security analysis into the CI pipeline to fail builds on detected vulnerabilities. Next, implement health checks and immutable image tagging to ensure reliable deployments and simple rollbacks. Then, maintain a changelog to track release history. Additionally, enforce test suite execution in CI to gate merges. Finally, expand the README with getting-started instructions and an architecture overview.
Ranked by impact ÷ effort. "Helps" is the estimated gain on the 0–100 health score.
Do this
Helps
Effort
Dimension
Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing.
Add a `healthcheck:` to the served compose service — probing the endpoint it already answers on where it has one — with `depends_on: condition: service_healthy` on whatever waits for it, and keep the deployed image tag immutable and recorded so rolling back is re-pointing at the previous tag rather than rebuilding.
Reconcile the README with reality: README claims a Golang version but no Go package exists; README claims the project is production-ready and stable; evidence shows it is under active development with no versioned release.
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. 27 of 31 evaluated dimensions are computed purely by tools and static analysis (confidence 1.0); 4 documentation/naming judgement(s) are LLM-assisted and labelled advisory. Overall confidence is 0.5 — the weighted average across measured dimensions; it falls as more of the score leans on LLM-assisted judgement and rises when it's fully tool-backed.
Every figure here is one of three kinds, and we label which: ✓ Measured — a deterministic fact (LoC, complexity, coverage); ~ Modeled — an estimate from a stated model (cost, effort, value-at-risk), always a range with its assumptions, never a precise fact; ◐ Advisory — an LLM prose judgement. We never present a modelled estimate as if it were measured. Perfect or absent scores carry their provenance too (ADR-0011): ✓ Tool-verified means the property itself was measured across the surface; ○ Nothing flagged means the probes came back clean — a claim bounded by what a repository can show; ⊘ Not evidenced means a working control (a tested restore, an automated rollback) showed no positive evidence — absence of evidence is not evidence of a control, so it's excluded from the score rather than awarded a spurious 10; ◐ Sampled · advisory marks an LLM verdict over a bounded sample — advisory, never a deterministic measurement.
What we checked — 31 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, 78 of 87 do; the remainder are repo-wide signals — a dimension-level measurement, not a single line. (Every path in this report is repo-relative by construction: paths are normalized at the producer and the report is rejected if any rooted path leaks through.)
Is there a tool behind the number? Every score below names the method that produced it — Roslyn, git, a scanner, or (for a handful of documentation/naming dimensions) an LLM labelled sampled · advisory — not a narrative.
Does re-running give the same result? Run it again on the same commit and the score — and this report, byte for byte — is identical. A report whose numbers move between runs is describing the run, not the code.
This report answers yes to all three. That's the bar to hold any assessment to.
Tools & methods
The actual versions used this run (captured at analysis time) — re-run on the same commit for the identical score.
Method
Backs
Version
Evaluator
Roslyn static analysis
Complexity, cohesion, coupling, dead code, API surface, layering
What ran differently this time — a tool absent, degraded, or that fell back to an estimate. Named openly, not folded silently into the scores. A degraded run also records its exact cause in diagnostics.md.
D30 Dependency Vulnerabilities — scanner not present in this environment — The backing tool was not installed where this scan ran, so this dimension was not scored. Install the tool (or run in the hosted environment, where it is always present) for a graded result.
Repo exclusion declarations (.gitattributes linguist-generated/vendored, .editorconfig generated_code): none declared — every source file was scored.
Limitations & what we did not check
Watchdog assesses the repository exactly as committed, and only the repository. By design it does not reach outside the source tree: the live cloud account, the running CI/CD pipeline, the host's branch-protection and approval rules, the production configuration, or a restore actually exercised against a backup are all out of scope. That boundary is a feature, not a gap — a repo-relative, deterministic scan re-runs identically on any commit and every finding opens at a real file and line, where a live audit can neither be reproduced nor traced. The visible consequence is that controls which leave no in-repo evidence are reported as "not evidenced" and excluded from the score rather than awarded a number a static scan cannot justify.
Per-dimension blind spots
For each dimension that was measured, what a static, repo-only scan structurally cannot see — the honest edge of the measurement, not a failure of it.
D1 Cyclomatic Complexity: Cyclomatic complexity counts branches statically — it cannot tell an essential decision tree from accidental tangle, nor see complexity that lives in data or configuration (large switch-case token tables, DSL lexers/parsers, data-as-code rule tables) rather than control flow: a tokenizer's many single-character cases read as high complexity though each branch is trivial.
D2 Cognitive Complexity: Cognitive-complexity heuristics approximate how hard code is to follow; genuine domain difficulty and well-named intent that eases reading are not captured.
D3 God Classes: "God class" is sized by members and responsibilities visible in the type — a deliberately broad facade over a coherent subsystem can read the same as an accidental grab-bag. For front-end JS the file-length check is cohesion-aware (a single-responsibility module — one class/IIFE — earns a 3× threshold), but cohesion is approximated from top-level declarations, not true dependency structure.
D4 Code Duplication: Duplication is token-similarity (jscpd) — it finds copy-paste, not semantic duplication expressed differently. Committed machine-written code (scaffolded migrations, designer/codegen output, protobuf/OpenAPI stubs, model snapshots) is EXCLUDED — its repetition is the tool's, not the team's — so the score reflects hand-written duplication only.
D7 Architectural Integrity: Layering is checked against detected/declared rules — an architecture whose boundaries live in convention or in code review, not in a rule a scanner can read, is not enforced here.
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.
D19 Documentation Quality: Documentation quality is judged by an LLM over a bounded sample of docs — it reads what is written, not whether the docs match the running system, and it is advisory, not a measurement.
D20 ADR Quality: ADR quality is an LLM read of the decision records present — it cannot know about decisions made and never recorded, and its verdict is sampled and advisory.
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.
D25 ADR Conformance: ADR conformance is the LLM-scored fraction of sampled code that follows recorded decisions — it checks the decisions that were written down and the slices it sampled, not unrecorded rules or the whole tree.
D28 Secrets (history): Secret-history scanning sweeps the git log for known patterns — a secret that predates the available history, or never matched a signature, is not found (clean means "nothing matched in the history we can see").
D29 Static Analysis (SAST): SAST findings are pattern-based (semgrep) — it finds classes of bug it has rules for; logic flaws, auth/authorization gaps and issues needing runtime context are out of reach (and clean means "no rule matched").
D34 Knowledge Freshness: Freshness is decayed commit RECENCY, not comprehension — code read often but rarely committed reads as orphaned, and stable code that genuinely needs no changes is penalised the same as forgotten code; bot/squash commits distort it like the bus factor.
D35 Change Coupling: Change coupling is co-change in COMMITS — files split across separate commits, or coupled only through a shared config/build step, read as uncoupled, and a sweeping commit (rename/format) is excluded so it doesn't couple everything. It shows that files change together, not WHY: a high coupling can be a healthy cohesive pair as readily as a hidden leak.
DM4 Rich vs anemic model: Behaviour is detected as state mutation inside a method body — a method that enforces an invariant by validating-and-throwing without mutating reads as a query, and mutation delegated through an interface the scan can't resolve isn't credited; entities with zero public properties still drop out of the population. It detects that state changes, not whether the rule is correct.
DM6 Domain ↔ infrastructure boundary: Infrastructure reached through a hand-rolled wrapper, a domain-named facade, reflection, or a string-keyed service locator resolves to a non-infra type and isn't seen; the body scan is symbol resolution over syntax, not full dataflow. A clean result means "no resolved infra reference in a domain body", not a proof of purity.
M4 Documentation accuracy: Onboarding quality is an LLM read of the docs/setup present — it cannot run the onboarding or measure how long a real new joiner takes; the verdict is sampled and advisory.
P4 Deployment & Rollback: Approval/branch-protection rules live in repository settings the scan cannot see — only their in-repo evidence (config files, workflows) is checked, so a control enforced purely in the host's settings reads as "not evidenced".
P6 Release Hygiene: Rollback/observability controls are inferred from repo artefacts (pipelines, dashboards-as-code) — controls configured in external tooling, with no in-repo trace, cannot be credited.
The LLM boundary
LLM-set scores this run (5): D19, D20, D21, D25, M4 (model: Local LLM). For these, a model reads a bounded sample and sets the numeric score (documentation, ADR quality, naming, comment value, onboarding) — D25 sets the ADR-conformance fraction over sampled code, D22 judges API accuracy over a sample. These are sampled and advisory by design: they vary at the margins between runs and are never a deterministic measurement. Every other score in this report is tool-computed at confidence 1.0.
What it measures: How tangled the control flow is — methods with many branches are hard to test and change.
Method: Cyclomatic complexity per method (1 + decision points), computed exhaustively across production source; test projects separated by convention. Deterministic.
What it measures: How hard the code is for a person to follow, beyond raw branching.
Method: Cognitive complexity per method (Sonar-style nesting-penalized score), computed exhaustively over production code, excluding test projects. Deterministic.
+ 18 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Resolve the 2 DAGChangeManager._topo_sort (cognitive 26) finding(s) in Cognitive Complexity — start with dag_change_manager.py (2). — One of this dimension's main actionable groups (2 warning-level).
Resolve the 1 EvaluateWalker.evaluate (cognitive 47) finding(s) in Cognitive Complexity — start with evaluate_visitor.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 ParametrizedSpecificationJsonPath2._convert_node_or_value (cognitive 42) finding(s) in Cognitive Complexity — start with jsonpath2_parser.py. — One of this dimension's main actionable groups (1 warning-level).
Detailed fixes: d2_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D3 · God Classes10.0 / 10Exemplary✓ Tool-verified
What it measures: Over-large classes that try to do too much ("god classes").
Method: God-class detection by line and method-count thresholds per logical type (partial classes unified), filtered for generated code and registration/contract false positives. Deterministic.
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 the code respects its intended layering / architecture rules.
Method: Enforcement rung (Prevented/Verified/Documented) per checkable ADR via Roslyn, plus dependency cycles via the engine shared with D5/AX3. Deterministic, exact.
Of 9 mechanizable ADRs, 9 are prevented by analyzers, 0 by tests, 0 exist only in prose. Coverage: 100 %. Dependency cycles not checked (no project-reference graph; where this repository's language has an import-cycle lens, cycles are reported there).
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.
What it measures: Whether the project's documentation is clear, complete, and useful.
Method: Judged by language model at low temperature (0.0-0.1) on a deterministic doc sample (READMEs plus first 25 architecture docs), with two-pass stability filtering. Advisory, sampled.
ascetic-ddd-python is well documented: a single README with a warning plus an extensive glossary and modules index; the getting-started docs cover core concepts, aggregate definition, specifications, and install (pip/PyPI/Poetry) with build instructions. The architecture/index and modules/index documents are present in the visible portion of the repository but not shown here.
The module index lists seedwork/index through saga/index, but the glossary is also present and sorted, so the outline is complete and no section should be flagged as missing.docs/modules/index.rst
What to do
Resolve the 1 The module index lists seedwork/index through saga/index, but the… finding(s) in Documentation Quality — start with index.rst. — One of this dimension's main actionable groups (1 recommendation-level).
Detailed fixes: d19_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D20 · ADR Quality / 10Strong◐ Sampled · advisory
What it measures: Whether architecture decisions are recorded well (context, decision, consequences).
Method: Per-ADR judgment by language model at low temperature with two-pass stability; confidence is share of ADRs evaluated; enforcement-field presence detected deterministically. Advisory.
Evaluated 10 ADR(s) individually; mean quality 8.2/10 (consistently complete and clear). 0 flagged with a specific gap.
What to do
Improve ADR Quality — currently 8.2/10. — Evaluated 10 ADR(s) individually; mean quality 8.2/10 (consistently complete and clear). 0 flagged with a specific gap.
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 the code actually follows the decisions recorded in the project's ADRs.
Method: Judged by language model at low temperature against ADRs plus a deterministic structural code summary; findings linked to repo-rooted ADR paths for traceability. Advisory.
What it measures: Whether any secrets were ever committed — scanned across the full git history, not just now.
Method: Git-history secret scan via gitleaks detect over full history in an isolated checkout; each match flagged High. Exhaustive; degrades cleanly when tool absent.
What it measures: Real static-analysis (SAST) findings — likely security bugs in the code, any language.
Method: Polyglot static analysis via semgrep across the repo using the pinned, image-baked p/security-audit + p/owasp-top-ten rulesets (no scan-time registry fetch); severity rules (ERROR/WARNING/INFO) map to a full-band severity-weighted score. Exhaustive, deterministic; degrades on parse failure.
Coverage: semgrep pattern rules over all files — exhaustive for the rule set, blind to classes of bug without a rule (clean = no rule matched).
High: github-actions-mutable-action-tag · ×4.github/workflows/docs.yml:26detected by semgrep finding
Medium: missing-autoescape-disabled · ×6ascetic_ddd/cli/scaffold/renderer.py:43detected by semgrep finding
Low: subprocess-shell-true · ×2ascetic_ddd/faker/infrastructure/dump/single_pg_dump.py:76detected by semgrep finding
What to do
Resolve the 6 Medium finding(s) in Static Analysis (SAST) — start with serializer.py (4), renderer.py, pg_sequencer.py. — One of this dimension's main actionable groups (6 warning-level).
Resolve the 4 High finding(s) in Static Analysis (SAST) — start with docs.yml (4). — One of this dimension's main actionable groups (4 issue-level).
Resolve the 2 Low finding(s) in Static Analysis (SAST) — start with single_pg_dump.py (2). — One of this dimension's main actionable groups (2 recommendation-level).
Detailed fixes: d29_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether anyone still has living knowledge of each file, or it has been orphaned — last understood long ago by someone now gone quiet. The sibling of the bus factor: D16 asks who owns it, D34 asks whether anyone still knows it.
Method: File orphaning as total living-knowledge decay below one focused-commit's worth within a year, computed per-file from the D16 decay model. Exhaustive, deterministic over fixed history.
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.
Resolve the 10 Change coupling finding(s) in Change Coupling — start with dummy_distributor.py (3), nullable_distributor.py (2), composite_value_provider.py (2). — One of this dimension's main actionable groups (10 warning-level).
Detailed fixes: d35_recommendation.md · top locations in Appendix A, every location in findings.md.
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 No build provenance finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 recommendation-level).
Resolve the 1 No artifact signing finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 recommendation-level).
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.
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.
Other · Domain Modelling — Whether aggregates reference each other by identity (id) rather than by direct object reference — the core DDD consistency-boundary rule.
Method: Roslyn (DDD-gated): aggregate roots identified by convention; each aggregate field checked for direct references to other aggregates versus id-only. Deterministic, DDD-native.
Coverage: Population: aggregate roots identified by AggregateRoot/IAggregateRoot base/interface NAME convention; reference-by-identity then checked exhaustively within that set — a root not using those names is invisible.
Do you agree with this assessment?
DM4 · Rich vs anemic model7.7 / 10Strong✓ Tool-verified
Other · Domain Modelling — Whether aggregates/entities carry the behaviour that protects their invariants, rather than being data bags driven by external services.
Method: Roslyn (DDD-gated): entity method BODIES classified mutator-vs-query — only methods that mutate the entity's own declared state count as invariant-protecting behaviour, so a getter/passthrough doesn't rescue an anemic class. Deterministic, exhaustive over domain-layer entities.
Coverage: Population: entities by name/base convention; rich-vs-anemic judged by classifying each method body mutator-vs-query — logic-bearing domain types outside the convention are invisible.
`BaseKey` is an aggregate/entity with 3 data propert(ies) but no state-changing behaviour (only data and queries) — the business logic lives in a service. — models.py:49
What to do
Move business rules onto the aggregates/entities they govern so invariants are enforced at the source, not in anemic services.
Other · Domain Modelling — Whether entities protect their state (private/init-only setters) instead of exposing public setters that bypass invariants. Softened when a rehydration framework (Marten/EF) is present.
Method: Roslyn (DDD-gated): public setters on entities detected; score softened when Marten/EF rehydration frameworks present. Deterministic, framework-aware.
Coverage: Population: entities by convention; encapsulation (setter shape) checked exhaustively within the set.
`DependentProvider` exposes publicly writable state (aggregate_providers). — dependent_provider.py:55
`VersionedAggregate` exposes publicly writable state (version). — versioned_aggregate.py:15
What to do
Make entity setters private/init-only; change state only through methods that enforce the invariants (Marten/EF can bind via constructor or private setters).
Other · Domain Modelling — Whether the domain layer stays free of infrastructure dependencies (EF/Marten/HTTP/ASP.NET) — the clean-architecture dependency rule.
Method: Roslyn (DDD-gated): domain-layer types scanned for infrastructure usage in member SIGNATURES and inside method/accessor BODIES — resolved calls and object-creations into EF/Marten/HTTP/Mongo/Redis/message-bus types (not just a namespace allowlist). Deterministic, symbol-resolved, exhaustive over domain-layer bodies, DDD-native.
Coverage: Domain layer identified by NAMESPACE heuristic; infrastructure then resolved by symbol in member SIGNATURES and method/accessor BODIES — rename the layer and the check evaporates.
Other · Domain Modelling — Whether clusters of primitives that travel together (a missing value object) are extracted — a low-weight suggestion, LLM-confirmed when configured.
Method: Roslyn (DDD-gated): primitive parameter clusters recurring three or more times across signatures extracted, then confirmed by language model when configured. Advisory, low-weight.
Maturity · Maturity — Whether the repo and its projects have a README, and whether it's substantive and current.
Method: Filesystem scan: README presence, word count, and headings for depth; git history for staleness. Exhaustive across root and project dirs, deterministic.
The root README is 49 words — likely missing build/run/architecture context.
What to do
Expand the README with getting-started, architecture overview and a project map.
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.
Maturity · Maturity — Whether the repo is organised deliberately — src/test separation and consistent project naming.
Method: Filesystem scan: src/test folder separation and namespace-prefix consistency (majority RootNamespace agreement). Exhaustive across projects, deterministic.
Production code isn't grouped under a src/ folder — it all sits under ascetic_ddd/ alongside the root build files, so the conventional src/ boundary between the product and its tooling isn't drawn.
What to do
Group production code under src/ (or split deliberately, e.g. backend/ + frontend/) so production and tooling code aren't mixed at the root.
Maturity · Maturity — Whether the README actually describes the code that exists (LLM-judged, advisory).
Method: Judged by language model at low temperature: README accuracy versus actual projects, within a disclosed tolerance. Advisory, not a measured number.
README claims a Golang version but no Go package exists
README claims the project is production-ready and stable; evidence shows it is under active development with no versioned release
What to do
Reconcile the README with reality: README claims a Golang version but no Go package exists; README claims the project is production-ready and stable; evidence shows it is under active development with no versioned release.
Do you agree with this assessment?
P1 · CI/CD gates8.5 / 10Strong✓ Tool-verified
Readiness · Readiness — Whether an automated pipeline builds and tests every change.
Method: Filesystem scan: CI workflow files (.github/workflows, .gitlab-ci.yml, etc.) for build and test stages. Exhaustive, deterministic.
A CI pipeline exists and the word "test" appears, but no explicit test-runner invocation (your stack's test command, or a test job) was matched — so either the gate runs tests through a step this pass could not recognise, or "test" is incidental here (a path, "latest", a reporter). Check the coverage dimensions first: if this repo has no test suite yet, that is the finding and this row follows from it. If a suite does exist, make the runner step explicit so the gate is unambiguous.
What to do
Run the test suite in CI via an explicit runner step (`pytest` for the toolchain this pipeline already uses) and gate merges on it.
Readiness · Readiness — Whether SAST, secret/dependency scanning and performance benchmarking are wired in (presence, not runtime).
Method: Filesystem scan: SAST configuration, dependency-update automation, secret scanning, and a benchmark harness or benchmark step — in this repository's own ecosystem. Exhaustive, deterministic.
No static application security testing detected. For this repository's stack, add bandit, `semgrep --config=p/python`, or CodeQL's python pack as a CI step.
What to do
Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing.
Enable Dependabot/Renovate or a dependency-review gate.
Add gitleaks/trufflehog in CI to block PRs that introduce committed secrets.
Readiness · Readiness — Whether releases are automated and safely reversible (probes, rolling updates, approval gates) — from manifests/pipeline files, not the live environment.
Method: Filesystem scan: deployment manifests/IaC (K8s YAML, Helm, Terraform) for rolling updates, probes, approval gates, migration hooks. Exhaustive, deterministic.
Deployment is orchestrated by compose, but no service declares a `healthcheck:` and nothing pins a previous image to fall back to — the runtime can tell that the container is up, not that it is serving, so a bad release is harder to detect and reverse.
What to do
Add a `healthcheck:` to the served compose service — probing the endpoint it already answers on where it has one — with `depends_on: condition: service_healthy` on whatever waits for it, and keep the deployed image tag immutable and recorded so rolling back is re-pointing at the previous tag rather than rebuilding.
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.
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.
No CHANGELOG/HISTORY/RELEASES file — what shipped when isn't easy to reconstruct for support or audit. (Versioning/tagging makes releases traceable, but a changelog records the what.)
What to do
Keep a changelog (e.g. Keep-a-Changelog) recording what shipped in each release.
Do you agree with this assessment?
Reference — by lens
The score is the rank-weighted fold of these lenses (worst-heaviest), each including its meta-dimensions; a lens with a Critical contributor is capped at Fair (its band reads "gated by …") and is never the strongest area however high its average.
Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Not included — 70 check(s) not relevant to this codebase
These checks had nothing to measure here (no tests, no git history, the codebase is small, or the architecture style doesn't apply), so they're omitted above rather than scored low.
AC1 Text alternatives — No web markup found — accessibility is not applicable to this repository.
AC2 Forms & labels — No web markup found — accessibility is not applicable to this repository.
AC3 Page structure — No web markup found — accessibility is not applicable to this repository.
AC4 Keyboard semantics — No web markup found — accessibility is not applicable to this repository.
AC5 ARIA correctness — No web markup found — accessibility is not applicable to this repository.
AC6 Visual & motion safety — No web markup found — accessibility is not applicable to this repository.
AC7 A11y enforcement — No web markup found — accessibility is not applicable to this repository.
AX1 Captive dependencies — no DI registrations detected
AX10 Code composition — not assessed — code composition is computed by ROLE over a document set that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
AX2 Stateful singletons — no singleton implementations detected
AX3 Project dependency cycles — not assessed — project cycles and dependency direction are computed over a project-reference graph that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
AX4 Dependency direction — not assessed — project cycles and dependency direction are computed over a project-reference graph that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
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 — ~24464 lines of test source are present (.py) but the test-quality collector reads C# only, so skipped/assertion-free tests couldn't be counted. Not scored — this is a gap in the analyzer, not a finding about this repository.
D11 Test Reliability — Test reliability not included
D12 Dependency Hygiene — Dependency hygiene not measured — dependency manifest found but not parsed for hygiene
D14 License Compliance — Not scored — this repository's package manifest is not parsed for licence data yet. A gap in the analyzer's language coverage, NOT a finding that the repository's licenses are compliant (a Python pyproject.toml/requirements.txt (pip/uv/Poetry)), which this pass does not parse yet — so this dimension asserts nothing about this repository's licensing in either direction.
D16 Bus Factor — single-maintainer — knowledge-concentration (bus factor) risk
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.
D22 Internal API Consistency — No exposed public API
D23 Boundary Type-Coupling — Production source is present (.cs, .go, .py) but bounded contexts are resolved over the C#/VB project set, which exposed none, so context scope could not be assessed. Not scored — this is a gap in the analyzer, not a verdict about this repository. Declaring the codebase's bounded contexts (≥2) would let cross-boundary type coupling be assessed — see the recommendation on this dimension for where. Declare them in `.codehealth/config.yaml` at the repository root (create it if absent), mapping each context name to the module-path or namespace prefixes that belong to it — e.g. `architecture:` → `contexts:` → `Billing: ["src/billing", "Acme.Billing"]`, `Catalog: ["src/catalog", "Acme.Catalog"]`.
D24 Comment Value — No inline comments to assess — comment value is not applicable here.
D26 Project Cohesion — Project cohesion is assessed over the .NET project set; this target exposed no projects, so project size and spread could not be assessed. Not scored — this is a gap in the analyzer's reach, not a verdict about this repository.
D27 Navigability — No calls could be sampled, so navigability was not assessed — tracing effort is measured over resolved call sites and this target exposed none. Not scored — this is a gap in the analyzer's reach, not a verdict about this repository.
D30 Dependency Vulnerabilities — Not scored — no dependency manifest in a supported ecosystem was read for this repository. A gap in the analyzer's language coverage, NOT a finding that the repository is free of vulnerable dependencies (a Python pyproject.toml/requirements.txt (pip/uv/Poetry) — not scanned yet) — where an OSV-supported manifest exists, dependency vulnerabilities for this repository are reported under D38 instead.
D31 IaC & Container Security — No Infrastructure-as-Code or container manifests found (Dockerfile, Terraform, Kubernetes/Helm, CloudFormation); nothing to scan.
D32 Data Compliance (PII/GDPR) — No PII/GDPR-handling patterns detected (p/gdpr ruleset) — no data-compliance surface to assess.
D33 JS/npm Dependency Vulnerabilities — No JS/npm manifest or lockfile found outside build output (package.json, package-lock.json, yarn.lock, pnpm-lock.yaml, bun.lockb); no JS dependencies to scan.
D37 Vulnerability-disclosure Policy — No vulnerability-disclosure policy file found (SECURITY.md/.markdown/.rst/.txt at root or under .github/.forgejo/.gitea/docs, .well-known/security.txt). A coordinated-disclosure policy may live off-repo, so this is not evidenced rather than failed.
D39 IL Efficiency — D39 measures the IL emitted by a .NET build; this repository has no .NET solution or project files, so the dimension does not apply.
D40 Network Egress Confinement — No Kubernetes/orchestration workloads found in the repository manifests; network egress policy is a cluster-native control that may live at the platform/firewall layer, so there is nothing to assess here.
D41 Kernel & Syscall Confinement — No Kubernetes/orchestration workloads found in the repository manifests; seccomp/AppArmor/SELinux confinement is a workload-level control, so there is nothing to assess here.
D42 Runtime Threat Enforcement — No Kubernetes/orchestration workloads found in the repository manifests; runtime threat-detection and admission-control policy are cluster-level controls, so there is nothing to assess here.
D5 Coupling — Inter-project coupling could not be assessed — no analyzable project graph was found for this repository. Not scored: a gap in the analyzer's reach, not a verdict about this repository. (Coupling here is Martin afferent/efferent/instability plus reference cycles across a project-reference graph, read today from .NET project files; other ecosystems' module graphs are not read yet.)
D6 Cohesion (LCOM4) — Cohesion (LCOM4) is measured over a C#/VB class graph, and this repository's production source is .go, .py, which this pass does not read — so no class could be assessed. Not scored — this is a gap in the analyzer, not a finding about this repository.
D8 Code Coverage — Coverage not included — suite not readable by the collector
D9 Test Distribution — Test source is present (.py) but the test-pyramid classifier reads C# only, so its unit/integration/BDD/E2E split couldn't be counted. Not scored — this is a gap in the analyzer, not a finding about this repository.
DM2 Strongly-typed ids — no id-bearing domain types detected — strongly-typed-id adoption not assessable
DM3 Integration-event coupling — no integration events detected — coupling check not applicable
DM7 Repository granularity — no repository abstraction detected (e.g. uses a document session)
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 — and nothing was matched here. The coverage check applies to any stack, but the checks for excluded tests, skipped tests and sleep-based synchronisation currently recognise only some ecosystems' test-runner idioms, so on a repository built with another stack the zeros below mean 'not checked', not 'clean'.
P2 Observability — Observability was not assessed: this check reads a source model that does not carry this repository's product — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of a logging idiom this check recognises is NOT evidence that this repo lacks structured logging (it may log through its own ecosystem's logger). This is a gap in the analyzer, not a finding about this repository.
P5 DR & Backup — not evidenced — repo shows no backup/RTO/RPO controls; absence of evidence is not evidence of a working control
P7 Outbound HTTP resilience — not measured — the application kind could not be determined for this repo
P8 Schema migrations — not assessed — schema-migration practice is read from a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
P9 Domain vs controller coverage — no coverage report found on disk — produce a coverage report in a standard format (`coverage run -m pytest` then `coverage xml`) into the repo working tree before the scan — a CI step is the usual place, since the artefact is commonly gitignored, or wire coverage collection into CI, to enable this cross-layer check
PF1 Benchmark discipline — Performance was not assessed: this lens reads a source model that was not loaded for this repository, because the repository is written in a language this lens does not yet model or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository — in particular it is NOT a statement that this repo is unpackaged or performance-careless.
PF2 Allocation hygiene — Performance was not assessed: this lens reads a source model that was not loaded for this repository, because the repository is written in a language this lens does not yet model or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository — in particular it is NOT a statement that this repo is unpackaged or performance-careless.
PF3 Async & latency hygiene — Performance was not assessed: this lens reads a source model that was not loaded for this repository, because the repository is written in a language this lens does not yet model or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository — in particular it is NOT a statement that this repo is unpackaged or performance-careless.
S1 Web-Security Posture — Not assessed: these web-security controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks web-security controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
X1 Async correctness — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
X2 Cancellation propagation — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
X3 Exception handling — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
X4 Structured logging — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
X5 Nullable reference types — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
Appendix A — Findings (grouped)
The findings behind the scores, grouped by severity, then by dimension and kind. The high-severity issues are enumerated in full below; items per group are capped at 25 with any overflow stated explicitly per group, never silently truncated. The complete machine-readable list of every finding (all severities) is the companion findings.md in this report's bundle.
High: github-actions-mutable-action-tag .github/workflows/docs.yml:26— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/checkout@<40-character SHA>`. This step references `actions/checkout@v4`; resolve the SHA it points at today with `gh api repos/actions/checkout/commits/v4 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/docs.yml:28— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/setup-python@<40-character SHA>`. This step references `actions/setup-python@v5`; resolve the SHA it points at today with `gh api repos/actions/setup-python/commits/v5 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/docs.yml:38— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/upload-pages-artifact@<40-character SHA>`. This step references `actions/upload-pages-artifact@v3`; resolve the SHA it points at today with `gh api repos/actions/upload-pages-artifact/commits/v3 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/docs.yml:50— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/deploy-pages@<40-character SHA>`. This step references `actions/deploy-pages@v4`; resolve the SHA it points at today with `gh api repos/actions/deploy-pages/commits/v4 --jq .sha`.
Change coupling: nullable_distributor.py ↔ weighted_distributor.py ascetic_ddd/faker/domain/distributors/m2o/nullable_distributor.py— `ascetic_ddd/faker/domain/distributors/m2o/nullable_distributor.py` and `ascetic_ddd/faker/domain/distributors/m2o/weighted_distributor.py` change together 71% of the time (15 of the 21 commits that touched the less-changed of the two, renames followed) 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: composite_value_provider.py ↔ entity_provider.py ascetic_ddd/faker/domain/providers/composite_value_provider.py— `ascetic_ddd/faker/domain/providers/composite_value_provider.py` and `ascetic_ddd/faker/domain/providers/entity_provider.py` change together 71% of the time (17 of the 24 commits that touched the less-changed of the two, renames followed) 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: aggregate_provider.py ↔ entity_provider.py ascetic_ddd/faker/domain/providers/aggregate_provider.py— `ascetic_ddd/faker/domain/providers/aggregate_provider.py` and `ascetic_ddd/faker/domain/providers/entity_provider.py` change together 71% of the time (17 of the 24 commits that touched the less-changed of the two, renames followed) 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: dummy_distributor.py ↔ weighted_distributor.py ascetic_ddd/faker/domain/distributors/m2o/dummy_distributor.py— `ascetic_ddd/faker/domain/distributors/m2o/dummy_distributor.py` and `ascetic_ddd/faker/domain/distributors/m2o/weighted_distributor.py` change together 71% of the time (12 of the 17 commits that touched the less-changed of the two, renames followed) 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: factory.py ↔ factory.py ascetic_ddd/faker/domain/distributors/m2o/factory.py— `ascetic_ddd/faker/domain/distributors/m2o/factory.py` and `ascetic_ddd/faker/infrastructure/distributors/m2o/factory.py` change together 69% of the time (9 of the 13 commits that touched the less-changed of the two, renames followed) 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 (that is what the inversion buys) and the thing to add is a comment saying so, not a merge; if they simply belong together, co-locate them; if neither holds, the coupling is hidden and worth breaking.
Change coupling: dummy_distributor.py ↔ pg_weighted_distributor.py ascetic_ddd/faker/domain/distributors/m2o/dummy_distributor.py— `ascetic_ddd/faker/domain/distributors/m2o/dummy_distributor.py` and `ascetic_ddd/faker/infrastructure/distributors/m2o/pg_weighted_distributor.py` change together 65% of the time (11 of the 17 commits that touched the less-changed of the two, renames followed) 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 (that is what the inversion buys) and the thing to add is a comment saying so, not a merge; if they simply belong together, co-locate them; if neither holds, the coupling is hidden and worth breaking.
Change coupling: dummy_distributor.py ↔ nullable_distributor.py ascetic_ddd/faker/domain/distributors/m2o/dummy_distributor.py— `ascetic_ddd/faker/domain/distributors/m2o/dummy_distributor.py` and `ascetic_ddd/faker/domain/distributors/m2o/nullable_distributor.py` change together 65% of the time (11 of the 17 commits that touched the less-changed of the two, renames followed) 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: interfaces.py ↔ skew_distributor.py ascetic_ddd/faker/domain/distributors/m2o/interfaces.py— `ascetic_ddd/faker/domain/distributors/m2o/interfaces.py` and `ascetic_ddd/faker/domain/distributors/m2o/skew_distributor.py` change together 62% of the time (10 of the 16 commits that touched the less-changed of the two, renames followed) 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: nullable_distributor.py ↔ pg_weighted_distributor.py ascetic_ddd/faker/domain/distributors/m2o/nullable_distributor.py— `ascetic_ddd/faker/domain/distributors/m2o/nullable_distributor.py` and `ascetic_ddd/faker/infrastructure/distributors/m2o/pg_weighted_distributor.py` change together 62% of the time (13 of the 21 commits that touched the less-changed of the two, renames followed) 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 (that is what the inversion buys) and the thing to add is a comment saying so, not a merge; if they simply belong together, co-locate them; if neither holds, the coupling is hidden and worth breaking.
Change coupling: composite_value_provider.py ↔ value_provider.py ascetic_ddd/faker/domain/providers/composite_value_provider.py— `ascetic_ddd/faker/domain/providers/composite_value_provider.py` and `ascetic_ddd/faker/domain/providers/value_provider.py` change together 60% of the time (29 of the 48 commits that touched the less-changed of the two, renames followed) 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.
Medium: missing-autoescape-disabled ascetic_ddd/cli/scaffold/renderer.py:43— Detected a Jinja2 environment without autoescaping. Jinja2 does not autoescape by default. This is dangerous if you are rendering to a browser because this allows for cross-site scripting (XSS) attacks. If you are in a web context, enable autoescaping by setting 'autoescape=True.' You may also consider using 'jinja2.select_autoescape()' to only enable automatic escaping for certain file extensions. This is a semgrep security-AUDIT rule: it reports that a sensitive construct is present, not that it is exploitable here. Confirm whether this site handles untrusted input or is reachable across a trust boundary — and apply the change where it is; where the construct is required by the platform or protocol it calls into, and carries no untrusted data (a syscall/FFI shim, a build- or debug-gated tool, a fixed local surface), record the review and leave the code as it is.
Medium: insecure-hash-algorithm-md5 ascetic_ddd/faker/infrastructure/sequencers/pg_sequencer.py:85— Detected MD5 hash algorithm which is considered insecure. MD5 is not collision resistant and is therefore not suitable as a cryptographic signature. Use SHA256 or SHA3 instead.
Medium: avoid-cPickle ascetic_ddd/utils/serializer.py:13— Avoid using `cPickle`, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. This is a semgrep security-AUDIT rule: it reports that a sensitive construct is present, not that it is exploitable here. Confirm whether this site handles untrusted input or is reachable across a trust boundary — and apply the change where it is; where the construct is required by the platform or protocol it calls into, and carries no untrusted data (a syscall/FFI shim, a build- or debug-gated tool, a fixed local surface), record the review and leave the code as it is.
Medium: avoid-pickle ascetic_ddd/utils/serializer.py:13— Avoid using `pickle`, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. This is a semgrep security-AUDIT rule: it reports that a sensitive construct is present, not that it is exploitable here. Confirm whether this site handles untrusted input or is reachable across a trust boundary — and apply the change where it is; where the construct is required by the platform or protocol it calls into, and carries no untrusted data (a syscall/FFI shim, a build- or debug-gated tool, a fixed local surface), record the review and leave the code as it is.
Medium: avoid-cPickle ascetic_ddd/utils/serializer.py:18— Avoid using `cPickle`, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. This is a semgrep security-AUDIT rule: it reports that a sensitive construct is present, not that it is exploitable here. Confirm whether this site handles untrusted input or is reachable across a trust boundary — and apply the change where it is; where the construct is required by the platform or protocol it calls into, and carries no untrusted data (a syscall/FFI shim, a build- or debug-gated tool, a fixed local surface), record the review and leave the code as it is.
Medium: avoid-pickle ascetic_ddd/utils/serializer.py:18— Avoid using `pickle`, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format. This is a semgrep security-AUDIT rule: it reports that a sensitive construct is present, not that it is exploitable here. Confirm whether this site handles untrusted input or is reachable across a trust boundary — and apply the change where it is; where the construct is required by the platform or protocol it calls into, and carries no untrusted data (a syscall/FFI shim, a build- or debug-gated tool, a fixed local surface), record the review and leave the code as it is.
Duplicated block (15 lines × 2) ascetic_ddd/faker/domain/query/evaluate_visitor.py:77— ascetic_ddd/faker/domain/query/evaluate_visitor.py:77-91 | ascetic_ddd/faker/domain/query/evaluate_visitor.py:157-171 — 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 (15 lines × 2) ascetic_ddd/inbox/inbox.py:161— ascetic_ddd/inbox/inbox.py:161-175 | ascetic_ddd/outbox/outbox.py:192-206 — 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 `ascetic_ddd/inbox/inbox.py:161` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (15 lines × 2) ascetic_ddd/specification/infrastructure/composite_expression_node.py:47— ascetic_ddd/specification/infrastructure/composite_expression_node.py:47-61 | ascetic_ddd/specification/infrastructure/composite_expression_node.py:74-88 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (7 lines × 2) ascetic_ddd/faker/infrastructure/dump/pg_dump.py:30— ascetic_ddd/faker/infrastructure/dump/pg_dump.py:30-36 | ascetic_ddd/faker/infrastructure/dump/single_pg_dump.py:41-47 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (7 lines × 2) ascetic_ddd/rop/rop.py:267— ascetic_ddd/rop/rop.py:267-273 | ascetic_ddd/rop/rop.py:287-293 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (7 lines × 2) ascetic_ddd/specification/domain/jsonpath/examples/jsonpath2_parser_example.py:154— ascetic_ddd/specification/domain/jsonpath/examples/jsonpath2_parser_example.py:154-160 | ascetic_ddd/specification/domain/jsonpath/examples/jsonpath_rfc9535_parser_example.py:106-112 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once.
Duplicated block (6 lines × 2) ascetic_ddd/dag_change/dag_change_manager.py:88— ascetic_ddd/dag_change/dag_change_manager.py:88-93 | ascetic_ddd/dag_change_typed/dag_change_manager.py:143-148 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice.
Duplicated block (6 lines × 2) ascetic_ddd/faker/domain/providers/_mixins.py:429— ascetic_ddd/faker/domain/providers/_mixins.py:429-435 | ascetic_ddd/faker/domain/providers/_mixins.py:445-450 — 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) ascetic_ddd/faker/infrastructure/repositories/pg_repository.py:203— ascetic_ddd/faker/infrastructure/repositories/pg_repository.py:203-208 | ascetic_ddd/faker/infrastructure/repositories/rest_repository.py:106-111 — 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.
DAGChangeManager._topo_sort (cognitive 26) ascetic_ddd/dag_change/dag_change_manager.py:127— DAGChangeManager._topo_sort has cognitive complexity 26 (threshold 15). Drivers by points: if/else 16, loops 9, boolean chains 1 (nesting depth added 14). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
DAGChangeManager._topo_sort (cognitive 26) ascetic_ddd/dag_change_typed/dag_change_manager.py:193— DAGChangeManager._topo_sort has cognitive complexity 26 (threshold 15). Drivers by points: if/else 16, loops 9, boolean chains 1 (nesting depth added 14). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
Duplicated block (19 lines × 2) ascetic_ddd/faker/domain/providers/sequence_provider.py:41— ascetic_ddd/faker/domain/providers/sequence_provider.py:41-59 | ascetic_ddd/faker/domain/providers/value_provider.py:65-83 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once. 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 (19 lines × 2) ascetic_ddd/specification/domain/lambda_filter/lambda_parser.py:332— ascetic_ddd/specification/domain/lambda_filter/lambda_parser.py:332-352 | ascetic_ddd/specification/domain/lambda_filter/lambda_parser.py:355-373 — 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) ascetic_ddd/faker/infrastructure/query/pg_query_compiler.py:225— ascetic_ddd/faker/infrastructure/query/pg_query_compiler.py:225-238 | ascetic_ddd/faker/infrastructure/query/pg_query_compiler.py:244-257 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (14 lines × 2) ascetic_ddd/specification/domain/jsonpath/examples/jsonpath2_parser_example.py:77— ascetic_ddd/specification/domain/jsonpath/examples/jsonpath2_parser_example.py:77-90 | ascetic_ddd/specification/domain/jsonpath/examples/jsonpath_example.py:89-102 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once.
Duplicated block (12 lines × 2) ascetic_ddd/faker/domain/distributors/o2m/skew_distributor.py:75— ascetic_ddd/faker/domain/distributors/o2m/skew_distributor.py:75-86 | ascetic_ddd/faker/domain/distributors/o2m/weighted_distributor.py:103-114 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (12 lines × 2) ascetic_ddd/specification/domain/jsonpath/examples/jsonpath_example.py:22— ascetic_ddd/specification/domain/jsonpath/examples/jsonpath_example.py:22-33 | ascetic_ddd/specification/domain/jsonpath/examples/jsonpath_rfc9535_parser_example.py:26-37 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once.
Duplicated block (10 lines × 2) ascetic_ddd/specification/domain/jsonpath/examples/jsonpath2_parser_example.py:113— ascetic_ddd/specification/domain/jsonpath/examples/jsonpath2_parser_example.py:113-122 | ascetic_ddd/specification/domain/jsonpath/examples/jsonpath_example.py:125-134 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once.
Duplicated block (10 lines × 2) ascetic_ddd/specification/domain/jsonpath/examples/jsonpath2_parser_example.py:140— ascetic_ddd/specification/domain/jsonpath/examples/jsonpath2_parser_example.py:140-149 | ascetic_ddd/specification/domain/jsonpath/examples/jsonpath_example.py:60-69 — 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. 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 (9 lines × 2) ascetic_ddd/specification/domain/jsonpath/examples/jsonpath2_parser_example.py:165— ascetic_ddd/specification/domain/jsonpath/examples/jsonpath2_parser_example.py:165-173 | ascetic_ddd/specification/domain/jsonpath/examples/jsonpath_rfc9535_parser_example.py:138-146 — 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. 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 (9 lines × 2) ascetic_ddd/specification/domain/jsonpath/jsonpath_parser.py:715— ascetic_ddd/specification/domain/jsonpath/jsonpath_parser.py:715-723 | ascetic_ddd/specification/domain/jsonpath/jsonpath_parser.py:748-757 — 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.
EvaluateWalker.evaluate (cyclomatic 27) ascetic_ddd/faker/domain/query/evaluate_visitor.py:70— EvaluateWalker.evaluate has cyclomatic complexity 27 (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.
EvaluateWalker.evaluate_sync (cyclomatic 24) ascetic_ddd/faker/domain/query/evaluate_visitor.py:151— EvaluateWalker.evaluate_sync has cyclomatic complexity 24 (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.
NativeParametrizedSpecification._parse_primary (cyclomatic 23) ascetic_ddd/specification/domain/jsonpath/jsonpath_parser.py:248— NativeParametrizedSpecification._parse_primary 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.
ParametrizedSpecificationJsonPath2._convert_node_or_value (cyclomatic 19) ascetic_ddd/specification/domain/jsonpath/jsonpath2_parser.py:602— ParametrizedSpecificationJsonPath2._convert_node_or_value 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.
EvaluateWalker.evaluate (cognitive 47) ascetic_ddd/faker/domain/query/evaluate_visitor.py:70— EvaluateWalker.evaluate has cognitive complexity 47 (threshold 15). Drivers by points: if/else 39, loops 8 (nesting depth added 20). 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.
ParametrizedSpecificationJsonPath2._convert_node_or_value (cognitive 42) ascetic_ddd/specification/domain/jsonpath/jsonpath2_parser.py:602— ParametrizedSpecificationJsonPath2._convert_node_or_value has cognitive complexity 42 (threshold 15). Drivers by points: if/else 30, loops 5, boolean chains 4, ternaries 3 (nesting depth added 21). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
EvaluateWalker.evaluate_sync (cognitive 38) ascetic_ddd/faker/domain/query/evaluate_visitor.py:151— EvaluateWalker.evaluate_sync has cognitive complexity 38 (threshold 15). Drivers by points: if/else 30, loops 8 (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.
NativeParametrizedSpecification._parse_primary (cognitive 30) ascetic_ddd/specification/domain/jsonpath/jsonpath_parser.py:248— NativeParametrizedSpecification._parse_primary has cognitive complexity 30 (threshold 15). Drivers by points: if/else 24, boolean chains 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.
ast_merge._add_missing_imports (cognitive 28) ascetic_ddd/cli/scaffold/ast_merge.py:55— ast_merge._add_missing_imports has cognitive complexity 28 (threshold 15). Drivers by points: if/else 16, loops 12 (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.
DependentProvider.populate (cognitive 25) ascetic_ddd/faker/domain/providers/dependent_provider.py:156— DependentProvider.populate has cognitive complexity 25 (threshold 15). Drivers by points: if/else 14, loops 10, boolean chains 1 (nesting depth added 11). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
PersistedFactory.create (cognitive 23) ascetic_ddd/faker/domain/fp/factories/persisted_factory.py:51— PersistedFactory.create has cognitive complexity 23 (threshold 15). Drivers by points: if/else 18, ternaries 4, 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.
CompositeValueProvider.populate (cognitive 23) ascetic_ddd/faker/domain/providers/composite_value_provider.py:58— CompositeValueProvider.populate has cognitive complexity 23 (threshold 15). Drivers by points: if/else 12, loops 8, error handling 3 (nesting depth added 14). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
ModelParser._topo_sort_composites (cognitive 21) ascetic_ddd/cli/scaffold/parser.py:369— ModelParser._topo_sort_composites has cognitive complexity 21 (threshold 15). Drivers by points: if/else 15, loops 6 (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.
ast_merge._merge_class (cognitive 20) ascetic_ddd/cli/scaffold/ast_merge.py:125— ast_merge._merge_class has cognitive complexity 20 (threshold 15). Drivers by points: if/else 16, boolean chains 2, loops 2 (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
ParametrizedSpecificationRFC9535._convert_expression_to_spec (cognitive 20) ascetic_ddd/specification/domain/jsonpath/jsonpath_rfc9535_parser.py:330— ParametrizedSpecificationRFC9535._convert_expression_to_spec has cognitive complexity 20 (threshold 15). Drivers by points: if/else 18, boolean chains 2 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
ParametrizedSpecificationJsonPath2._add_parentheses_to_filter (cognitive 19) ascetic_ddd/specification/domain/jsonpath/jsonpath2_parser.py:276— ParametrizedSpecificationJsonPath2._add_parentheses_to_filter has cognitive complexity 19 (threshold 15). Drivers by points: if/else 11, boolean chains 4, loops 4 (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.
BaseCompositeProvider._provider_attrs (cognitive 18) ascetic_ddd/faker/domain/providers/_mixins.py:427— BaseCompositeProvider._provider_attrs has cognitive complexity 18 (threshold 15). Drivers by points: if/else 11, loops 4, boolean chains 3 (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
BaseCompositeProvider._dependent_provider_attrs (cognitive 18) ascetic_ddd/faker/domain/providers/_mixins.py:442— BaseCompositeProvider._dependent_provider_attrs has cognitive complexity 18 (threshold 15). Drivers by points: if/else 11, loops 4, boolean chains 3 (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
AggregateProvider.populate (cognitive 18) ascetic_ddd/faker/domain/providers/aggregate_provider.py:51— AggregateProvider.populate has cognitive complexity 18 (threshold 15). Drivers by points: loops 9, if/else 8, boolean chains 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.
EvaluateVisitor.visit_composite (cognitive 18) ascetic_ddd/faker/domain/query/evaluate_visitor.py:440— EvaluateVisitor.visit_composite has cognitive complexity 18 (threshold 15). Drivers by points: if/else 16, boolean chains 1, 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.
ParametrizedSpecificationJsonPath2._convert_expression_to_spec (cognitive 18) ascetic_ddd/specification/domain/jsonpath/jsonpath2_parser.py:461— ParametrizedSpecificationJsonPath2._convert_expression_to_spec has cognitive complexity 18 (threshold 15). Drivers by points: if/else 10, loops 8 (nesting depth added 7). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
ParametrizedSpecificationJsonPath2._extract_filter_expression (cognitive 17) ascetic_ddd/specification/domain/jsonpath/jsonpath2_parser.py:391— ParametrizedSpecificationJsonPath2._extract_filter_expression has cognitive complexity 17 (threshold 15). Drivers by points: if/else 12, 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.
NativeParametrizedSpecification._parse_path (cognitive 17) ascetic_ddd/specification/domain/jsonpath/jsonpath_parser.py:678— NativeParametrizedSpecification._parse_path has cognitive complexity 17 (threshold 15). Drivers by points: if/else 10, boolean chains 4, ternaries 3 (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.
ast_merge._add_missing_definitions (cognitive 16) ascetic_ddd/cli/scaffold/ast_merge.py:99— ast_merge._add_missing_definitions has cognitive complexity 16 (threshold 15). Drivers by points: if/else 15, 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.
SimpleChangeManager.unregister (cognitive 16) ascetic_ddd/dag_change/simple_change_manager.py:30— SimpleChangeManager.unregister has cognitive complexity 16 (threshold 15). Drivers by points: if/else 12, loops 4 (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.
NativeParametrizedSpecification._bind_placeholder (cognitive 16) ascetic_ddd/specification/domain/jsonpath/jsonpath_parser.py:759— NativeParametrizedSpecification._bind_placeholder has cognitive complexity 16 (threshold 15). Drivers by points: if/else 15, 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.
Unpinned build actions — CI references GitHub Actions by a floating ref (@main / @tag) rather than a pinned commit SHA, weakening build integrity. 4 floating ref(s) across 1 workflow file(s). Each floating ref is itemized at file:line by the SAST (D29) lens.
Duplicated block (34 lines × 2) ascetic_ddd/faker/infrastructure/distributors/m2o/pg_skew_distributor.py:59— ascetic_ddd/faker/infrastructure/distributors/m2o/pg_skew_distributor.py:59-92 | ascetic_ddd/faker/infrastructure/distributors/m2o/pg_weighted_distributor.py:165-208 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `ascetic_ddd/faker/infrastructure/distributors/m2o/pg_skew_distributor.py:59` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. 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 (23 lines × 2) ascetic_ddd/specification/domain/jsonpath/jsonpath_parser.py:224— ascetic_ddd/specification/domain/jsonpath/jsonpath_parser.py:224-246 | ascetic_ddd/specification/domain/jsonpath/jsonpath_rfc9535_parser.py:118-140 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once.
Duplicated block (21 lines × 2) ascetic_ddd/specification/domain/lambda_filter/lambda_parser.py:376— ascetic_ddd/specification/domain/lambda_filter/lambda_parser.py:376-397 | ascetic_ddd/specification/domain/lambda_filter/lambda_parser.py:416-436 — 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 `ascetic_ddd/specification/domain/lambda_filter/lambda_parser.py:376` 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 (18 lines × 2) ascetic_ddd/faker/domain/providers/_mixins.py:151— ascetic_ddd/faker/domain/providers/_mixins.py:151-169 | ascetic_ddd/faker/domain/providers/_mixins.py:306-323 — 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 `ascetic_ddd/faker/domain/providers/_mixins.py:306` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (13 lines × 2) ascetic_ddd/faker/domain/aop/providers/structure_provider.py:46— ascetic_ddd/faker/domain/aop/providers/structure_provider.py:46-58 | ascetic_ddd/faker/domain/aop/providers/value_provider.py:65-77 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once.
Duplicated block (11 lines × 2) ascetic_ddd/dag_change/dag_change_manager.py:138— ascetic_ddd/dag_change/dag_change_manager.py:138-148 | ascetic_ddd/dag_change_typed/dag_change_manager.py:204-214 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice.
Duplicated block (8 lines × 2) ascetic_ddd/faker/infrastructure/query/pg_query_compiler.py:148— ascetic_ddd/faker/infrastructure/query/pg_query_compiler.py:148-155 | ascetic_ddd/faker/infrastructure/query/pg_query_compiler.py:164-171 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
Duplicated block (5 lines × 2) ascetic_ddd/faker/domain/providers/reference_provider.py:228— ascetic_ddd/faker/domain/providers/reference_provider.py:228-232 | ascetic_ddd/faker/domain/providers/reference_provider.py:272-276 — 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: subprocess-shell-true ascetic_ddd/faker/infrastructure/dump/single_pg_dump.py:76— Found 'subprocess' function 'Popen' with 'shell=True'. This is dangerous because this call will spawn the command using a shell process. Doing so propagates current shell settings and variables, which makes it much easier for a malicious actor to execute commands. Use 'shell=False' instead. This is a semgrep security-AUDIT rule: it reports that a sensitive construct is present, not that it is exploitable here. Confirm whether this site handles untrusted input or is reachable across a trust boundary — and apply the change where it is; where the construct is required by the platform or protocol it calls into, and carries no untrusted data (a syscall/FFI shim, a build- or debug-gated tool, a fixed local surface), record the review and leave the code as it is.
Low: subprocess-shell-true ascetic_ddd/faker/infrastructure/dump/single_pg_dump.py:112— Found 'subprocess' function 'Popen' with 'shell=True'. This is dangerous because this call will spawn the command using a shell process. Doing so propagates current shell settings and variables, which makes it much easier for a malicious actor to execute commands. Use 'shell=False' instead. This is a semgrep security-AUDIT rule: it reports that a sensitive construct is present, not that it is exploitable here. Confirm whether this site handles untrusted input or is reachable across a trust boundary — and apply the change where it is; where the construct is required by the platform or protocol it calls into, and carries no untrusted data (a syscall/FFI shim, a build- or debug-gated tool, a fixed local surface), record the review and leave the code as it is.
D11 · Test Reliability· Test reliability not included · ×1
Test reliability not included — Test source is present (.py) but the built-in reliability runner does not support this repository's ecosystem, so flakiness couldn't be assessed. Not scored — this is a gap in the analyzer's language coverage, not a finding about this repository.
D19 · Documentation Quality· The module index lists seedwork/index through saga/index, but the glossary is also present and sorted, so the outline is complete and no section should be flagged as missing. · ×1
The module index lists seedwork/index through saga/index, but the glossary is also present and sorted, so the outline is complete and no section should be flagged as missing. docs/modules/index.rst
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, 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 (`cyclonedx-py` over the resolved Python environment/lockfile, `syft` (or `anchore/sbom-action` in CI) over the source tree or released image). Publish it as a release asset (`*.spdx.json` / `*.cdx.json`) so consumers can see what they are installing.
Coverage not included — suite not readable by the collector — Coverage NOT MEASURED: test source is present (.py) but the built-in coverage collector has no runner for this repository's ecosystem — so this suite was never executed by it. Not scored — this is a gap in the analyzer's language coverage, not a defect in the repo. To have real coverage read, produce a coverage report in a standard format (`coverage run -m pytest` then `coverage xml`) into the repo working tree before the scan — a CI step is the usual place, since the artefact is commonly gitignored.
Info — 2 finding(s)
D12 · Dependency Hygiene· Dependency hygiene not measured · ×1
Dependency hygiene not measured — dependency manifest found but not parsed for hygiene — This repository's dependency manifest (a Python pyproject.toml/requirements.txt (pip/uv/Poetry)) was found, but this pass cannot parse it for hygiene, so no package was assessed. Zero packages read is NOT a clean dependency tree, so this is NOT SCORED — a gap in the analyzer, not a verdict about this repository. This row is about dependency HYGIENE — outdated, deprecated or unmaintained direct dependencies; known CVEs in the same dependency graph are a separate question, reported under D38 wherever the manifest is OSV-readable.
D22 · Internal API Consistency· No exposed public API · ×1
No exposed public API — No intentionally-exposed types (IsPackable or .Contracts) to evaluate.
Appendix B — Reproduction & audit trail
Every external tool invocation behind a deep-scan dimension — the tool, its captured version, the exact command, how many findings it yielded, and a link to the retained raw output. To reproduce any finding: check out the same commit and run the command shown (repo-relative — never an absolute scratch path). The complete raw scanner output is retained verbatim under artifacts/raw/ (indexed in artifacts/raw/index.json); per-invocation exit codes and wall-clock durations are in sidecar.json — kept out of this table so the rendered report stays byte-identical across runs of the same commit.
trivy: not applicable — No Infrastructure-as-Code or container manifests found (Dockerfile, Terraform, Kubernetes/Helm, CloudFormation); nothing to scan.
trivy: not applicable — No JS/npm manifest or lockfile found outside build output (package.json, package-lock.json, yarn.lock, pnpm-lock.yaml, bun.lockb); no JS dependencies to scan.
disclosure: not applicable — No vulnerability-disclosure policy file found (SECURITY.md/.markdown/.rst/.txt at root or under .github/.forgejo/.gitea/docs, .well-known/security.txt). A coordinated-disclosure policy may live off-repo, so this is not evidenced rather than failed.
runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; network egress policy is a cluster-native control that may live at the platform/firewall layer, so there is nothing to assess here.
runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; seccomp/AppArmor/SELinux confinement is a workload-level control, so there is nothing to assess here.
runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; runtime threat-detection and admission-control policy are cluster-level controls, so there is nothing to assess here.
0
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Run 019fca32-4f27-79fe-9fd3-d1520ec2a643 · every finding is also locatable in findings.md, and the complete scoring record (with exit codes + durations) in sidecar.json.
Issues: 4 · Warnings: 73 · Recommendations: 8 · Info: 2 — Appendix A · all findings · full markdown report.
Generated by Watchdog — deterministic code-health analysis. 04-08-2026 @ 00:35 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.