napalm-logs — Watchdog Audit

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.

napalm-automation/napalm-logs is sound in substance but carries real gaps (55%). It is not in crisis, but the issues below raise the cost of changing it — friction its consumers ultimately inherit.

It is strongest in Architecture (100%) — the structure is clean and changes stay contained. Code Health (78%) is solid too.

The area that most needs attention is Readiness (46%) — releases are harder to depend on — versioning, release notes and dependency hygiene are thin, so consumers can't easily tell what changed or trust an upgrade. Security (53%) is the next concern — exposure to security and compliance incidents is elevated.

Leadership focus, highest impact first: SAST step to CI running what this repository's stack ships (Security & performance tooling); 1 Leaked secret finding(s) (Secret Scanning); Nothing pauses a release for a human (Deployment & Rollback).

For scale: Small (~5,411 production lines); rebuilding it from scratch would take roughly ~0.1 person-years (~1 engineer). Approximate, ±~30%.

It builds on a genuinely strong Architecture foundation (100%); the priorities above are the highest-leverage way to bring the rest up to that level.

Code Health 78% Strong Architecture 100% ★ Exemplary Maturity 65% Adequate Readiness 46% Weak · gated by P3 Security 53% Adequate · gated by D29, D36

How the score is built — each lens's share of the headline Width 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.

Readiness 46% · 47% weightSecurity 53% · 26% weightMaturity 65% · 14% weightCode Health 78% · 8% weightArchitecture 100% · 4% weight

Raise Readiness 46 → 70 (the Healthy floor) ⇒ headline 55 → ~63.

Code composition — where the lines go

Tests 100%

New since the last scan (2+)

2 finding(s) are new versus the previous scan (2026-07-31) — surfaced by this scheduled scan itself, no pull request required.

D31 · Medium IaC: CKV_DOCKER_3 Dockerfile
D38 · Medium CVE: [GHSA redacted] requirements-dev.txt

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.

Rebuild cost & value ~ Modeled — €1,900–€9,500

Cost to rebuild	€1,900–€9,500 (0.1 person-years (31–100 h), ~1 engineer)
Domain complexity	Standard — harder problems cost more per line
Quality factor	0.8× (at 55% quality) — the last 20% of quality is most of the work
Size & shape	Small · effort split not classified (source measured from disk; the effort-tier breakdown is a C#-only syntax walk)

This codebase represents roughly ~0.1 person-years of build effort (about ~€5,700 to rebuild). Its weakest lens is Readiness at 46% — the part of that asset most exposed by the findings below.

How we model this: boilerplate at a scaffolding rate + logic × domain Standard (×1.0) — standard service × a 0.8× quality factor, at €60–95/h; indicative, ±~30% · size measured directly from source · effort from total production LoC as straight-line logic (the tier split is a C#-only syntax walk), a conservative lower bound. Indicative only — most sensitive to the hourly rate and the domain tier (both tunable in config).

Top priorities

The highest-leverage moves; the full ranked list is in the Roadmap below.

Resolve the 1 Leaked secret finding(s) in Secret Scanning — start with server.key.

+9.9 pts · Low effort · Secret Scanning

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.

+10.3 pts · Medium effort · Security & performance tooling

Nothing pauses a release for a human: publish as a draft release (or gate the release job on a protected tag/manual dispatch) so a bad build can be stopped before users can download it.

+9.9 pts · Medium effort · Deployment & Rollback

Diagnosis — what's actually going on

Value concentrated against a weak lens · Medium · Value at risk

This is a Small asset (~0.1 person-years to rebuild), and its weakest lens is Readiness at 46%. The operational and business risk on an asset this size concentrates there — that's where remediation buys the most protection.

Evidence: valuation: Small, ~0.1 person-years rebuild (5,411 LoC) · weakest lens: Readiness 46%

→ Direct remediation budget at Readiness first — highest risk-reduction per euro on an asset this size.

Highest-leverage move · Medium · Leverage

Of everything flagged, the best return on effort is: Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing. The rest can wait behind it.

Evidence: priority ranking: top of 5 ranked by impact/effort

→ Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing.

Architecture — module dependency matrix

33 modules, 17 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.)

At a glance — Code Health · 78% · Strong

The Good

D1 · Python Cyclomatic Complexity 8.4 D3 · Python God Classes 8.7 D4 · Python Code Duplication 9.4

The Ugly

D2 · Python Cognitive Complexity 4.8

At a glance — Architecture · 100% · Exemplary

The Good

D35 · Python Change Coupling 10.0

At a glance — Maturity · 65% · Adequate

The Good

D15 · Python Churn × Complexity Hotspots 10.0 D16 · Python Bus Factor 9.4 D19 · Python Documentation Quality 8.0 D21 · Python Naming Consistency 10.0 D34 · Python Knowledge Freshness 10.0 M3 · Repo Folder & project structure 8.0 M4 · Repo Documentation accuracy 7.0

The Bad

M1 · Repo Documentation (README) 6.0 M2 · Repo Architecture documentation 5.0

At a glance — Readiness · 46% · Weak · gated by P3

The Good

P1 · Repo CI/CD gates 10.0 P6 · Repo Release Hygiene 10.0

The Bad

D13 · Python Secret Scanning 5.0 P4 · Repo Deployment & Rollback 5.0

The Ugly

P3 · Repo Security & performance tooling 0.0

At a glance — Security · 53% · Adequate · gated by D29, D36

The Good

D31 · Python IaC & Container Security 9.2 D38 · Python OSV Dependency Vulnerabilities 9.4

The Bad

D28 · Python Secrets (history) 6.0

The Ugly

D29 · Python Static Analysis (SAST) 1.9 D36 · Python Supply-chain Provenance & Signing 0.0

Security & Compliance — OWASP Top-10 mapping

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	11	High / Critical
A02:2021 — Cryptographic Failures	5	High / Critical
A05:2021 — Security Misconfiguration	3	High / Critical
A06:2021 — Vulnerable & Outdated Components	3	Medium

Roadmap

First, integrate a static analysis tool into the CI pipeline to automatically fail the build on security regressions. Next, immediately resolve the single leaked secret found in the codebase. Then, implement a draft release process to prevent users from downloading bad builds. Finally, add a quick-start section to the README and document key architectural decisions in a dedicated folder.

Ranked by impact ÷ effort. "Helps" is the estimated gain on the 0–100 health score.

Do this	Helps	Effort	Dimension
Resolve the 1 Leaked secret finding(s) in Secret Scanning — start with server.key.	+9.9 pts	Low	Secret Scanning
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.	+10.3 pts	Medium	Security & performance tooling
Nothing pauses a release for a human: publish as a draft release (or gate the release job on a protected tag/manual dispatch) so a bad build can be stopped before users can download it.	+9.9 pts	Medium	Deployment & Rollback
Resolve the 9 High finding(s) in Static Analysis (SAST) — start with docker_publish.yml (4), pythonpublish.yml (3), code.yml (2).	+3.3 pts	Low	Static Analysis (SAST)
Add a build/run (quick start) section to the root README — the first thing a newcomer needs.	+3.6 pts	Medium	Documentation (README)
Record significant decisions one document per decision — dated, stating the context, the decision and its consequences — and keep them together wherever your design docs already live (a conventional `docs/adr/` tree with `NNNN-title.md` names is the most discoverable form).	+3.6 pts	Medium	Architecture documentation
Reconcile the README with reality: README claims a CLI daemon that listens to syslog and publishes over secured channels but the repository contains no such executable.	+3.2 pts	Medium	Documentation accuracy
Resolve the 3 Secret finding(s) in Secrets (history) — start with alerta.rst (2), server.key.	+1.6 pts	Low	Secrets (history)

File quality

Per-file score 0–10 — a quality signature. Of 17 files carrying findings, judged against the Production bar: 6% slop · 82% mixed · 12% near-clean.

File	Score	Band	Worst signal
Dockerfile	3.7	Slop	IaC & Container Security: High IaC: DS-0002
examples/server.key	4.4	Mixed	Secret Scanning: Leaked secret: private-key
.github/workflows/docker_publish.yml	4.8	Mixed	Static Analysis (SAST): High: github-actions-mutable-action-tag
.github/workflows/pythonpublish.yml	5.1	Mixed	Static Analysis (SAST): High: github-actions-mutable-action-tag
napalm_logs/utils/__init__.py	5.3	Mixed	Static Analysis (SAST): Medium: ssl-wrap-socket-is-deprecated
docs/publisher/alerta.rst	5.8	Mixed	Secrets (history): Secret: generic-api-key
.github/workflows/code.yml	5.8	Mixed	Static Analysis (SAST): High: github-actions-mutable-action-tag
requirements-dev.txt	6.3	Mixed	OSV Dependency Vulnerabilities: Medium CVE: [GHSA redacted]
napalm_logs/scripts/cli.py	7.2	Mixed	Cyclomatic Complexity: NLOptionParser.parse (cyclomatic 52)
napalm_logs/base.py	7.2	Mixed	Cyclomatic Complexity: NapalmLogs._load_config (cyclomatic 28)
napalm_logs/device.py	7.2	Mixed	Cognitive Complexity: NapalmLogsDeviceProc.start (cognitive 25)
napalm_logs/server.py	7.4	Mixed	Cyclomatic Complexity: NapalmLogsServerProc.start (cyclomatic 16)
napalm_logs/listener/tcp.py	7.4	Mixed	Cognitive Complexity: TCPListener._client_connection (cognitive 20)
napalm_logs/transport/prometheus.py	7.4	Mixed	God Classes: TooManyMethods: PrometheusTransport
napalm_logs/auth.py	7.9	Mixed	Static Analysis (SAST): Medium: ssl-wrap-socket-is-deprecated
tests/test_config.py	8.5	Near-clean	Cognitive Complexity: test_config.generate_tests (cognitive 21)
napalm_logs/transport/alerta.py	8.5	Near-clean	Code Duplication: Duplicated block (13 lines × 2)

Methodology & how to trust this report

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. 22 of 24 evaluated dimensions are computed purely by tools and static analysis (confidence 1.0); 2 documentation/naming judgement(s) are LLM-assisted and labelled advisory. Overall confidence is 0.6 — the weighted average across measured dimensions; it falls as more of the score leans on LLM-assisted judgement and rises when it's fully tool-backed.

Every figure here is one of three kinds, and we label which: ✓ Measured — a deterministic fact (LoC, complexity, coverage); ~ Modeled — an estimate from a stated model (cost, effort, value-at-risk), always a range with its assumptions, never a precise fact; ◐ Advisory — an LLM prose judgement. We never present a modelled estimate as if it were measured. Perfect or absent scores carry their provenance too (ADR-0011): ✓ Tool-verified means the property itself was measured across the surface; ○ Nothing flagged means the probes came back clean — a claim bounded by what a repository can show; ⊘ Not evidenced means a working control (a tested restore, an automated rollback) showed no positive evidence — absence of evidence is not evidence of a control, so it's excluded from the score rather than awarded a spurious 10; ◐ Sampled · advisory marks an LLM verdict over a bounded sample — advisory, never a deterministic measurement.

What we checked — 24 dimensions across the health lenses

D1D2D3D4D13D15D16D19D21D28D29D31D34D35D36D38M1M2M3M4P1P3P4P6

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, 47 of 58 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	5.3.0	✓ deterministic
Native secret scanner	Hardcoded secrets / credentials	1.0.0	✓ deterministic
jscpd	Code duplication	—	✓ deterministic
Coverage (coverlet / dotnet-coverage)	Line & branch coverage	10.0.302	✓ deterministic
NuGet / dotnet	Outdated, vulnerable & deprecated dependencies	10.0.302	✓ deterministic
git / LibGit2Sharp	Churn hotspots, knowledge concentration, history	2.43.0 · 0.31.0	✓ deterministic
gitleaks · semgrep · trivy · checkov	Secrets in history, SAST, CVEs, IaC & container, PII / GDPR	1.86.0 · 0.69.3 · 3.2.533	✓ deterministic
LLM (sampled · advisory)	Documentation quality, ADR conformance, naming — sampled over a bounded sample; advisory, never a deterministic measurement	Local LLM	◐ LLM · sampled · advisory

Every finding is locatable in findings.md. Run 019fcf31-fd5e-7943-b39e-e4573d779555.

The exact command behind every deep-scan dimension — tool, version, invocation and retained raw output — is in Appendix B — Reproduction & audit trail.

Run transparency — what happened this run

What ran differently this time — a tool absent, degraded, or that fell back to an estimate. Named openly, not folded silently into the scores. A degraded run also records its exact cause in diagnostics.md.

D30 Dependency Vulnerabilities — scanner not present in this environment — The backing tool was not installed where this scan ran, so this dimension was not scored. Install the tool (or run in the hosted environment, where it is always present) for a graded result.

Repo exclusion declarations (.gitattributes linguist-generated/vendored, .editorconfig generated_code): none declared — every source file was scored.

Limitations & what we did not check

Watchdog assesses the repository exactly as committed, and only the repository. By design it does not reach outside the source tree: the live cloud account, the running CI/CD pipeline, the host's branch-protection and approval rules, the production configuration, or a restore actually exercised against a backup are all out of scope. That boundary is a feature, not a gap — a repo-relative, deterministic scan re-runs identically on any commit and every finding opens at a real file and line, where a live audit can neither be reproduced nor traced. The visible consequence is that controls which leave no in-repo evidence are reported as "not evidenced" and excluded from the score rather than awarded a number a static scan cannot justify.

Per-dimension blind spots

For each dimension that was measured, what a static, repo-only scan structurally cannot see — the honest edge of the measurement, not a failure of it.

D1 Cyclomatic Complexity: Cyclomatic complexity counts branches statically — it cannot tell an essential decision tree from accidental tangle, nor see complexity that lives in data or configuration (large switch-case token tables, DSL lexers/parsers, data-as-code rule tables) rather than control flow: a tokenizer's many single-character cases read as high complexity though each branch is trivial.
D2 Cognitive Complexity: Cognitive-complexity heuristics approximate how hard code is to follow; genuine domain difficulty and well-named intent that eases reading are not captured.
D3 God Classes: "God class" is sized by members and responsibilities visible in the type — a deliberately broad facade over a coherent subsystem can read the same as an accidental grab-bag. For front-end JS the file-length check is cohesion-aware (a single-responsibility module — one class/IIFE — earns a 3× threshold), but cohesion is approximated from top-level declarations, not true dependency structure.
D4 Code Duplication: Duplication is token-similarity (jscpd) — it finds copy-paste, not semantic duplication expressed differently. Committed machine-written code (scaffolded migrations, designer/codegen output, protobuf/OpenAPI stubs, model snapshots) is EXCLUDED — its repetition is the tool's, not the team's — so the score reflects hand-written duplication only.
D13 Secret Scanning: Secret detection is signature- and entropy-based on the current tree — a secret that does not match a known pattern, or one already rotated, will not be flagged (a clean scan is "nothing matched", not "no secrets exist").
D15 Churn × Complexity Hotspots: Churn hotspots come from git history — a freshly imported or squashed repository has no churn signal, and recent rewrites can mask a historically risky file.
D16 Bus Factor: Bus-factor is a time-decayed model of commit attribution (who has recently, repeatedly worked a file), not comprehension — pairing, review and reading-without-committing spread knowledge it can't see; bot commits and shared accounts still distort it.
D19 Documentation Quality: Documentation quality is judged by an LLM over a bounded sample of docs — it reads what is written, not whether the docs match the running system, and it is advisory, not a measurement.
D21 Naming Consistency: Naming quality is an LLM judgement over a bounded sample — it assesses clarity/consistency of the names it sees, not domain-correctness, and is advisory.
D28 Secrets (history): Secret-history scanning sweeps the git log for known patterns — a secret that predates the available history, or never matched a signature, is not found (clean means "nothing matched in the history we can see").
D29 Static Analysis (SAST): SAST findings are pattern-based (semgrep) — it finds classes of bug it has rules for; logic flaws, auth/authorization gaps and issues needing runtime context are out of reach (and clean means "no rule matched").
D31 IaC & Container Security: IaC scanning checks Dockerfiles/Terraform/Kubernetes against best-practice rules — it cannot see the live cloud account, runtime configuration, or drift between the committed config and what is actually deployed.
D34 Knowledge Freshness: Freshness is decayed commit RECENCY, not comprehension — code read often but rarely committed reads as orphaned, and stable code that genuinely needs no changes is penalised the same as forgotten code; bot/squash commits distort it like the bus factor.
D35 Change Coupling: Change coupling is co-change in COMMITS — files split across separate commits, or coupled only through a shared config/build step, read as uncoupled, and a sweeping commit (rename/format) is excluded so it doesn't couple everything. It shows that files change together, not WHY: a high coupling can be a healthy cohesive pair as readily as a hidden leak.
M4 Documentation accuracy: Onboarding quality is an LLM read of the docs/setup present — it cannot run the onboarding or measure how long a real new joiner takes; the verdict is sampled and advisory.
P4 Deployment & Rollback: Approval/branch-protection rules live in repository settings the scan cannot see — only their in-repo evidence (config files, workflows) is checked, so a control enforced purely in the host's settings reads as "not evidenced".
P6 Release Hygiene: Rollback/observability controls are inferred from repo artefacts (pipelines, dashboards-as-code) — controls configured in external tooling, with no in-repo trace, cannot be credited.

The LLM boundary

LLM-set scores this run (3): D19, D21, M4 (model: Local LLM). For these, a model reads a bounded sample and sets the numeric score (documentation, ADR quality, naming, comment value, onboarding) — D25 sets the ADR-conformance fraction over sampled code, D22 judges API accuracy over a sample. These are sampled and advisory by design: they vary at the margins between runs and are never a deterministic measurement. Every other score in this report is tool-computed at confidence 1.0.

Dimensions

D1 · Cyclomatic Complexity8.4 / 10Strong✓ Tool-verified

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.

Maturity: Documented → Verified → Prevented · effective 8.4 / 10 · rule-coverage 100% · ceiling Prevented

3 method(s) exceeded the cyclomatic complexity threshold of 15; the worst was NLOptionParser.parse at 52.

NLOptionParser.parse (cyclomatic 52)napalm_logs/scripts/cli.py:283

NapalmLogs._load_config (cyclomatic 28)napalm_logs/base.py:276

NapalmLogsServerProc.start (cyclomatic 16)napalm_logs/server.py:218

What to do

Resolve the 1 NLOptionParser.parse (cyclomatic 52) finding(s) in Cyclomatic Complexity — start with cli.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 NapalmLogs._load_config (cyclomatic 28) finding(s) in Cyclomatic Complexity — start with base.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 NapalmLogsServerProc.start (cyclomatic 16) finding(s) in Cyclomatic Complexity — start with server.py. — One of this dimension's main actionable groups (1 warning-level).
Enforce Cyclomatic Complexity in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.

Detailed fixes: d1_recommendation.md · top locations in Appendix A, every location in findings.md.

Do you agree with this assessment?

D2 · Cognitive Complexity4.8 / 10Weak✓ Tool-verified

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.

Maturity: Documented → Verified → Prevented · effective 4.8 / 10 · rule-coverage 100% · ceiling Prevented

12 method(s) exceeded the cognitive complexity threshold of 15; the worst was NapalmLogs._load_config at 93.

NapalmLogs._load_config (cognitive 93)napalm_logs/base.py:276

NLOptionParser.parse (cognitive 64)napalm_logs/scripts/cli.py:283

utils.traverse (cognitive 32)napalm_logs/utils/__init__.py:304

NapalmLogsServerProc.start (cognitive 31)napalm_logs/server.py:218

utils.dictupdate (cognitive 27)napalm_logs/utils/__init__.py:341

+ 7 more group(s) — more in Appendix A; the complete list is findings.md.

What to do

Resolve the 1 NapalmLogs._load_config (cognitive 93) finding(s) in Cognitive Complexity — start with base.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 NLOptionParser.parse (cognitive 64) finding(s) in Cognitive Complexity — start with cli.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 utils.traverse (cognitive 32) finding(s) in Cognitive Complexity — start with __init__.py. — One of this dimension's main actionable groups (1 warning-level).
Enforce Cognitive Complexity in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.

Detailed fixes: d2_recommendation.md · top locations in Appendix A, every location in findings.md.

Do you agree with this assessment?

D3 · God Classes8.7 / 10Strong✓ Tool-verified

What it measures: Over-large classes that try to do too much ("god classes").

Method: God-class detection by line and method-count thresholds per logical type (partial classes unified), filtered for generated code and registration/contract false positives. Deterministic.

Maturity: Documented → Verified → Prevented · effective 8.7 / 10 · rule-coverage 100% · ceiling Prevented

2 god class(es) detected.

TooManyMethods: PrometheusTransportnapalm_logs/transport/prometheus.py:21

FileTooLong: napalm_logs/base.pynapalm_logs/base.py:0

What to do

Resolve the 1 TooManyMethods finding(s) in God Classes — start with prometheus.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 FileTooLong finding(s) in God Classes — start with base.py. — One of this dimension's main actionable groups (1 warning-level).
Enforce God Classes in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.

Detailed fixes: d3_recommendation.md · top locations in Appendix A, every location in findings.md.

Do you agree with this assessment?

D4 · Code Duplication9.4 / 10Exemplary✓ Tool-verified

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.

Maturity: Documented → Verified → Prevented · effective 9.4 / 10 · rule-coverage 100% · ceiling Verified

9 duplicated block group(s) detected.

Duplicated block (11 lines × 2) · ×2napalm_logs/transport/prometheus.py:96

Duplicated block (9 lines × 2) · ×2napalm_logs/listener/tcp.py:143

Duplicated block (13 lines × 2)napalm_logs/transport/alerta.py:75

Duplicated block (12 lines × 2)napalm_logs/device.py:252

Duplicated block (10 lines × 2)napalm_logs/listener/tcp.py:45

+ 2 more group(s) — more in Appendix A; the complete list is findings.md.

✓ On the Gold path — maintain.

Detailed fixes: d4_recommendation.md · top locations in Appendix A, every location in findings.md.

Do you agree with this assessment?

D13 · Secret Scanning5.0 / 10Adequate✓ Tool-verified

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.

Maturity: Documented → Verified → Prevented · effective 5.0 / 10 · rule-coverage 100% · ceiling Prevented

1 secret(s) detected.

Leaked secret: private-keyexamples/server.key:1

What to do

Resolve the 1 Leaked secret finding(s) in Secret Scanning — start with server.key. — One of this dimension's main actionable groups (1 issue-level).
Enforce Secret Scanning in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.

Detailed fixes: d13_recommendation.md · top locations in Appendix A, every location in findings.md.

Do you agree with this assessment?

D15 · Churn × Complexity Hotspots10.0 / 10Exemplary✓ Tool-verified

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.

Maturity: Documented → Verified → Prevented · effective 10.0 / 10 · rule-coverage 100% · ceiling Documented

No churn × complexity hotspots in the window.

✓ On the Gold path — maintain.

Detailed fixes: d15_recommendation.md.

Do you agree with this assessment?

D16 · Bus Factor9.4 / 10Exemplary✓ Tool-verified

What it measures: Whether knowledge is concentrated in too few people (the "bus factor").

Method: Living knowledge per author via time-decayed commit attribution (6-month half-life, focus weighting) across largest source files. Deterministic, avoids blame's mechanical-refactor false positives.

Maturity: Documented → Verified → Prevented · effective 9.4 / 10 · rule-coverage 100% · ceiling Documented

1 source file(s) have their living knowledge concentrated in one author (≥90% of recent, decayed contribution). The largest is napalm_logs/listener/kafka.py.

Off-boarding risk: anonymized user #1

✓ On the Gold path — maintain.

Detailed fixes: d16_recommendation.md · top locations in Appendix A, every location in findings.md.

Do you agree with this assessment?

D19 · Documentation Quality / 10Strong◐ Sampled · advisory

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.

Maturity: Documented → Verified → Prevented · effective Strong / 10 · rule-coverage 100% · ceiling Documented

Napalm-logs is well documented: a README with an example syslog message and output object plus a CLI daemon that listens on UDP/TCP/Brokers (Kafka), and a Docker guide covering default Kafka publishing, port mapping, pre-built image security policy, templated configuration (`napalm.tmpl`), build/run commands, and environment variable customization. The architecture is covered by the README example plus an architecture/Docs markdown set of 70 files.

What to do

Improve Documentation Quality — currently 8.0/10. — Napalm-logs is well documented: a README with an example syslog message and output object plus a CLI daemon that listens on UDP/TCP/Brokers (Kafka), and a Docker guide covering default Kafka publishing, port mapping, pre-built image security policy, templated configuration (`napalm.tmpl`), build/run commands, and environment variable customization. The architecture is covered by the README example plus an architecture/Docs markdown set of 70 files.

Detailed fixes: d19_recommendation.md.

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D21 · Naming Consistency / 10Exemplary◐ Sampled · advisory

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.

Maturity: Documented → Verified → Prevented · effective Exemplary / 10 · rule-coverage 100% · ceiling Verified

0 naming inconsistencies across 0 sampled symbols.

✓ On the Gold path — maintain.

Detailed fixes: d21_recommendation.md.

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D28 · Secrets (history)6.0 / 10Adequate✓ Tool-verified

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.

Maturity: Documented → Verified → Prevented · effective 6.0 / 10 · rule-coverage 100% · ceiling Documented

4 finding(s): 0 critical, 4 high, 0 medium, 0 low. Remediation for historically-committed secrets is credential rotation — they remain in history regardless of later deletion.

Secret: generic-api-key · ×3docs/publisher/alerta.rst:119detected by gitleaks finding

Rotate the exposed credentials — git history can't be un-committed

What to do

Resolve the 3 Secret finding(s) in Secrets (history) — start with alerta.rst (2), server.key. — One of this dimension's main actionable groups (3 issue-level).
Resolve the 1 Rotate the exposed credentials finding(s) in Secrets (history). — One of this dimension's main actionable groups (1 recommendation-level).

Detailed fixes: d28_recommendation.md · top locations in Appendix A, every location in findings.md.

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D29 · Static Analysis (SAST)1.9 / 10Critical✓ Tool-verified

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).

Maturity: Documented → Verified → Prevented · effective 1.9 / 10 · rule-coverage 100% · ceiling Documented

11 finding(s): 0 critical, 9 high, 2 medium, 0 low.

High: github-actions-mutable-action-tag · ×9.github/workflows/code.yml:22detected by semgrep finding

Medium: ssl-wrap-socket-is-deprecated · ×2napalm_logs/auth.py:188detected by semgrep finding

What to do

Resolve the 9 High finding(s) in Static Analysis (SAST) — start with docker_publish.yml (4), pythonpublish.yml (3), code.yml (2). — One of this dimension's main actionable groups (9 issue-level).
Resolve the 2 Medium finding(s) in Static Analysis (SAST) — start with auth.py, __init__.py. — One of this dimension's main actionable groups (2 warning-level).

Detailed fixes: d29_recommendation.md · top locations in Appendix A, every location in findings.md.

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D31 · IaC & Container Security9.2 / 10Exemplary✓ Tool-verified

What it measures: Whether Dockerfiles / Terraform / Kubernetes config follow security best practices.

Method: IaC/container misconfiguration scan via trivy config (Dockerfile/Terraform/K8s/Helm/CloudFormation); severity rules to 0-10 moderate normalizer. NotApplicable without manifests. Exhaustive, deterministic.

Maturity: Documented → Verified → Prevented · effective 9.2 / 10 · rule-coverage 100% · ceiling Documented

3 finding(s): 0 critical, 2 high, 1 medium, 0 low.

High IaC: DS-0002 · ×2Dockerfiledetected by trivy finding

Medium IaC: CKV_DOCKER_3Dockerfile:1detected by trivy finding

✓ On the Gold path — maintain.

Detailed fixes: d31_recommendation.md · top locations in Appendix A, every location in findings.md.

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D34 · Knowledge Freshness10.0 / 10Exemplary✓ Tool-verified

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.

Maturity: Documented → Verified → Prevented · effective 10.0 / 10 · rule-coverage 100% · ceiling Documented

Every significant source file has living knowledge — recently and meaningfully worked.

✓ On the Gold path — maintain.

Detailed fixes: d34_recommendation.md.

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D35 · Change Coupling10.0 / 10Exemplary✓ Tool-verified

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.

Maturity: Documented → Verified → Prevented · effective 10.0 / 10 · rule-coverage 100% · ceiling Documented

No strong hidden change-coupling between production files.

✓ On the Gold path — maintain.

Detailed fixes: d35_recommendation.md.

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D36 · Supply-chain Provenance & Signing0.0 / 10Critical✓ Tool-verified

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.

Maturity: Documented → Verified → Prevented · effective 0.0 / 10 · rule-coverage 100% · ceiling Documented

0/4 supply-chain integrity signals present (provenance, signing, SBOM, pinned actions).

Unpinned build actions

Workflow token permissions not restricted

No build provenance

No artifact signing

No SBOM

What to do

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 Workflow token permissions not restricted finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 No build provenance finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 recommendation-level).

Detailed fixes: d36_recommendation.md · top locations in Appendix A, every location in findings.md.

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D38 · OSV Dependency Vulnerabilities9.4 / 10Exemplary✓ Tool-verified

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.

Maturity: Documented → Verified → Prevented · effective 9.4 / 10 · rule-coverage 100% · ceiling Documented

3 finding(s): 0 critical, 0 high, 3 medium, 0 low.

Medium CVE: [GHSA redacted] · ×3requirements-dev.txtdetected by osv-scanner finding

✓ On the Gold path — maintain.

Detailed fixes: d38_recommendation.md · top locations in Appendix A, every location in findings.md.

Do you agree with this assessment?

Frontend & cross-cutting dimensions

R = React/JS · M = Maturity · P = Readiness.

M1 · Documentation (README)6.0 / 10Adequate✓ Tool-verified

Maturity · Maturity — Whether the repo and its projects have a README, and whether it's substantive and current.

Method: Filesystem scan: README presence, word count, and headings for depth; git history for staleness. Exhaustive across root and project dirs, deterministic.

What to do

Add a build/run (quick start) section to the root README — the first thing a newcomer needs.
Add a 'Testing' section to the root README — how to run the test suite.
Add an 'Architecture' / 'How it works' section to the root README — the high-level shape.

Do you agree with this assessment?

M2 · Architecture documentation5.0 / 10Adequate✓ Tool-verified

Maturity · Maturity — Whether key decisions (ADRs) and the high-level shape (C4/diagrams) are written down.

Method: Filesystem scan: ADR folder/naming conventions or content, plus Mermaid/PlantUML/C4/architecture.md discovery. Exhaustive, deterministic.

No Architecture Decision Records found — no conventional ADR directory, no `NNNN-title.md` documents and nothing ADR-shaped by content. Design rationale recorded elsewhere (a design-notes tree, a mailing list, pull-request discussion) is not visible to this check and is not re-findable per decision, so a future maintainer cannot ask why one choice was made and get an answer.

What to do

Record significant decisions one document per decision — dated, stating the context, the decision and its consequences — and keep them together wherever your design docs already live (a conventional `docs/adr/` tree with `NNNN-title.md` names is the most discoverable form).

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M3 · Folder & project structure8.0 / 10Strong✓ Tool-verified

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 — application code, tests and tooling are mixed at the repository root.

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.

Do you agree with this assessment?

M4 · Documentation accuracy7.0 / 10Strong◐ Sampled · advisory

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 CLI daemon that listens to syslog and publishes over secured channels but the repository contains no such executable

What to do

Reconcile the README with reality: README claims a CLI daemon that listens to syslog and publishes over secured channels but the repository contains no such executable.

Do you agree with this assessment?

P1 · CI/CD gates10.0 / 10Exemplary○ Nothing flagged

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.

Do you agree with this assessment?

P3 · Security & performance tooling0.0 / 10Critical✓ Tool-verified

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.

Do you agree with this assessment?

P4 · Deployment & Rollback5.0 / 10Adequate✓ Tool-verified

Readiness · Readiness — Whether releases are automated and safely reversible (probes, rolling updates, approval gates) — from manifests/pipeline files, not the live environment.

Method: Filesystem scan: deployment manifests/IaC (K8s YAML, Helm, Terraform) for rolling updates, probes, approval gates, migration hooks. Exhaustive, deterministic.

What to do

Nothing pauses a release for a human: publish as a draft release (or gate the release job on a protected tag/manual dispatch) so a bad build can be stopped before users can download it.

Do you agree with this assessment?

P6 · Release Hygiene10.0 / 10Exemplary✓ Tool-verified

Readiness · Readiness — Whether releases are traceable — a maintained changelog and explicit version stamping.

Method: Filesystem scan: changelog file presence and version tags in csproj or git tags. Exhaustive, deterministic.

Do you agree with this assessment?

Reference — by lens

The score is the rank-weighted fold of these lenses (worst-heaviest), each including its meta-dimensions; a lens with a Critical contributor is capped at Fair (its band reads "gated by …") and is never the strongest area however high its average.

Lens	Score	Rating	Impact
Code Health	78%	Strong	Solid.
Architecture	100%	Exemplary	Strongest area.
Maturity	65%	Adequate	Acceptable, with room to improve.
Readiness	46%	Weak — gated by P3	Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Security	53%	Adequate — gated by D29, D36	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
AX5 Architecture & structure — not assessed — architecture style/structure is computed from a project graph (projects, types, module namespaces) that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
AX6 Interface segregation — not assessed — interface segregation is computed over a type surface that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
AX7 Slice cohesion — not applicable — not a vertical-slice architecture
AX8 Test isolation — not assessed — test isolation is computed from a project graph (which projects are test projects, and what they reference) that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
AX9 CQS / query purity — no CQRS query handlers detected — query purity is not applicable to this codebase
AXB2 Runtime readiness — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
C1 Data Protection — Not assessed: these personal data controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks personal data controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
C2 Access Controls — Not assessed: these authorization controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks authorization controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
C3 Audit Trail — Not assessed: these audit controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks audit controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
C4 Data Retention — Not assessed: these retention controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks retention controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
C5 Data-Subject Rights — Not assessed: these data-subject rights controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks data-subject rights controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
D10 Test Quality — ~250 lines of test source are present (.py) but the test-quality collector reads C# only, so skipped/assertion-free tests couldn't be counted. Not scored — this is a gap in the analyzer, not a finding about this repository.
D11 Test Reliability — Test reliability not included
D12 Dependency Hygiene — Dependency hygiene not measured — dependency manifest found but not parsed for hygiene
D14 License Compliance — Not scored — this repository's package manifest is not parsed for licence data yet. A gap in the analyzer's language coverage, NOT a finding that the repository's licenses are compliant (a Python pyproject.toml/requirements.txt (pip/uv/Poetry)), which this pass does not parse yet — so this dimension asserts nothing about this repository's licensing in either direction.
D17 Explicit Debt — explicit-debt markers are read through a C# workspace today, so they were not read for this repository's language — this asserts nothing about how many markers the code carries. Not scored — this is a gap in the analyzer, not a finding about this repository
D18 Solution Shape — D18 scores the shape of a .NET solution; this repository has no .NET solution or project files, so the dimension does not apply.
D20 ADR Quality — N/A — ADRs are expected on deployable products with a user-facing host, not consumed libraries; no ADR log is required here.
D22 Internal API Consistency — No exposed public API
D23 Boundary Type-Coupling — Production source is present (.py) but bounded contexts are resolved over the C#/VB project set, which exposed none, so context scope could not be assessed. Not scored — this is a gap in the analyzer, not a verdict about this repository. Declaring the codebase's bounded contexts (≥2) would let cross-boundary type coupling be assessed — see the recommendation on this dimension for where. Declare them in `.codehealth/config.yaml` at the repository root (create it if absent), mapping each context name to the module-path or namespace prefixes that belong to it — e.g. `architecture:` → `contexts:` → `Billing: ["src/billing", "Acme.Billing"]`, `Catalog: ["src/catalog", "Acme.Catalog"]`.
D24 Comment Value — No inline comments to assess — comment value is not applicable here.
D25 ADR Conformance — no ADRs to check
D26 Project Cohesion — Project cohesion is assessed over the .NET project set; this target exposed no projects, so project size and spread could not be assessed. Not scored — this is a gap in the analyzer's reach, not a verdict about this repository.
D27 Navigability — No calls could be sampled, so navigability was not assessed — tracing effort is measured over resolved call sites and this target exposed none. Not scored — this is a gap in the analyzer's reach, not a verdict about this repository.
D30 Dependency Vulnerabilities — Not scored — no dependency manifest in a supported ecosystem was read for this repository. A gap in the analyzer's language coverage, NOT a finding that the repository is free of vulnerable dependencies (a Python pyproject.toml/requirements.txt (pip/uv/Poetry) — not scanned yet) — where an OSV-supported manifest exists, dependency vulnerabilities for this repository are reported under D38 instead.
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 .py, which this pass does not read — so no class could be assessed. Not scored — this is a gap in the analyzer, not a finding about this repository.
D7 Architectural Integrity — no checkable ADRs, and no project-reference graph for the cycle pass to read — so this dimension makes no claim about dependency cycles in either direction (where this repository's language has an import-cycle lens, cycles are reported there). Architectural integrity not assessed
D8 Code Coverage — Coverage not included — suite not readable by the collector
D9 Test Distribution — Test source is present (.py) but the test-pyramid classifier reads C# only, so its unit/integration/BDD/E2E split couldn't be counted. Not scored — this is a gap in the analyzer, not a finding about this repository.
DM1 Domain Modelling — not scored — this repository shows none of the 3 signals this check looks for
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.

Issue — 15 finding(s)

D29 · Static Analysis (SAST) · High · ×9

High: github-actions-mutable-action-tag .github/workflows/code.yml:22 — 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@v2`; resolve the SHA it points at today with `gh api repos/actions/checkout/commits/v2 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/code.yml:24 — 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@v1`; resolve the SHA it points at today with `gh api repos/actions/setup-python/commits/v1 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/docker_publish.yml:18 — 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@v2`; resolve the SHA it points at today with `gh api repos/actions/checkout/commits/v2 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/docker_publish.yml:21 — 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: docker/login-action@<40-character SHA>`. This step references `docker/login-action@v1`; resolve the SHA it points at today with `gh api repos/docker/login-action/commits/v1 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/docker_publish.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: docker/setup-buildx-action@<40-character SHA>`. This step references `docker/setup-buildx-action@v1`; resolve the SHA it points at today with `gh api repos/docker/setup-buildx-action/commits/v1 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/docker_publish.yml:32 — GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: docker/build-push-action@<40-character SHA>`. This step references `docker/build-push-action@v2`; resolve the SHA it points at today with `gh api repos/docker/build-push-action/commits/v2 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/pythonpublish.yml:12 — 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@v1`; resolve the SHA it points at today with `gh api repos/actions/checkout/commits/v1 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/pythonpublish.yml:14 — 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@v1`; resolve the SHA it points at today with `gh api repos/actions/setup-python/commits/v1 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/pythonpublish.yml:25 — GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: pypa/gh-action-pypi-publish@<40-character SHA>`. This step references `pypa/gh-action-pypi-publish@master`; resolve the SHA it points at today with `gh api repos/pypa/gh-action-pypi-publish/commits/master --jq .sha`.

D28 · Secrets (history) · Secret · ×3

Secret: generic-api-key docs/publisher/alerta.rst:119 — matched rule 'generic-api-key'
Secret: generic-api-key docs/publisher/alerta.rst:135 — matched rule 'generic-api-key'
Secret: private-key examples/server.key:1 — matched rule 'private-key'

D31 · IaC & Container Security · High IaC · ×2

High IaC: DS-0002 Dockerfile — Image user should not be 'root' A container that starts as root runs your process with root's capabilities inside the namespace, so a compromise of the process starts from there. The step: create an unprivileged account in the image (`RUN adduser --system --no-create-home app`), give it ownership of the paths the process writes at runtime (`COPY --chown=` on those layers, or a `RUN chown -R`), and end the final stage with `USER app` so it is the default at start. Build stages that only compile can stay root; it is the stage that RUNS that needs the account. If the process genuinely requires root — it manages the container runtime, ptraces another process or opens raw devices — say so here rather than making a change that breaks it.
High IaC: DS-0029 Dockerfile — 'apt-get' missing '--no-install-recommends'

D13 · Secret Scanning · Leaked secret · ×1

Leaked secret: private-key examples/server.key:1 — private-key detected. Treat the value as compromised: it is readable by everyone who has ever had the repository, and deleting the line does not un-publish it. In order — (1) REVOKE it at whatever issued it and issue a replacement, which is the only step that actually closes the exposure; (2) load the replacement at run time from your platform's secret store or the process environment instead of from the tree, so no future value is committable; (3) remove the file or line and add its path to the repository's ignore rules, so it cannot come back; (4) if the value was ever live, purge it from the history as well, since a clone taken before the deletion still carries it. If this is instead a FIXTURE — key material generated for tests and valid nowhere — then the exposure is nil and the fix is to make that legible: generate it in test setup, or keep it under a test-data path, so a reader (and this scan) can tell it from the real thing.

Warning — 34 finding(s)

D38 · OSV Dependency Vulnerabilities · Medium CVE · ×3

Medium CVE: [GHSA redacted] requirements-dev.txt — black 22.10.0: [GHSA redacted] — upgrade to 24.3.0. This is 1 of 3 advisories with a published fix this scan raises against black 22.10.0, and their fixed versions do not agree — anything below 26.3.1 still leaves at least one of them open. Take this package to 26.3.1 or later: that is the floor for the package, not this row's target alone. This one row stands for the 3 advisories this scan raises against black 22.10.0: [GHSA redacted], PYSEC-2026-2120, PYSEC-2026-2121.
Medium CVE: [GHSA redacted] requirements-dev.txt — jinja2 3.1.2: [GHSA redacted] — upgrade to 3.1.6. This is 1 of 5 advisories with a published fix this scan raises against jinja2 3.1.2, and their fixed versions do not agree — anything below 3.1.6 still leaves at least one of them open. Take this package to 3.1.6 or later: that is the floor for the package, not this row's target alone. This one row stands for the 5 advisories this scan raises against jinja2 3.1.2: [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted], [GHSA redacted].
Medium CVE: [GHSA redacted] requirements-dev.txt — pytest 7.2.0: [GHSA redacted] — upgrade to 9.0.3

D29 · Static Analysis (SAST) · Medium · ×2

Medium: ssl-wrap-socket-is-deprecated napalm_logs/auth.py:188 — 'ssl.wrap_socket()' is deprecated. This function creates an insecure socket without server name indication or hostname matching. Instead, create an SSL context using 'ssl.SSLContext()' and use that to wrap a socket. 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: ssl-wrap-socket-is-deprecated napalm_logs/utils/__init__.py:132 — 'ssl.wrap_socket()' is deprecated. This function creates an insecure socket without server name indication or hostname matching. Instead, create an SSL context using 'ssl.SSLContext()' and use that to wrap a socket. 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.

D4 · Code Duplication · Duplicated block (11 lines × 2) · ×2

Duplicated block (11 lines × 2) napalm_logs/transport/prometheus.py:96 — napalm_logs/transport/prometheus.py:96-106 | napalm_logs/transport/prometheus.py:115-126 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (11 lines × 2) napalm_logs/transport/prometheus.py:189 — napalm_logs/transport/prometheus.py:189-199 | napalm_logs/transport/prometheus.py:220-230 — 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 `napalm_logs/transport/prometheus.py:189` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.

D4 · Code Duplication · Duplicated block (9 lines × 2) · ×2

Duplicated block (9 lines × 2) napalm_logs/listener/tcp.py:143 — napalm_logs/listener/tcp.py:143-151 | napalm_logs/listener/udp.py:57-65 — 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 `napalm_logs/listener/tcp.py:143` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (9 lines × 2) napalm_logs/scripts/cli.py:240 — napalm_logs/scripts/cli.py:240-248 | napalm_logs/scripts/cli.py:257-265 — 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.

D1 · Cyclomatic Complexity · NLOptionParser.parse (cyclomatic 52) · ×1

NLOptionParser.parse (cyclomatic 52) napalm_logs/scripts/cli.py:283 — NLOptionParser.parse has cyclomatic complexity 52 (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.

D1 · Cyclomatic Complexity · NapalmLogs._load_config (cyclomatic 28) · ×1

NapalmLogs._load_config (cyclomatic 28) napalm_logs/base.py:276 — NapalmLogs._load_config has cyclomatic complexity 28 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.

D1 · Cyclomatic Complexity · NapalmLogsServerProc.start (cyclomatic 16) · ×1

NapalmLogsServerProc.start (cyclomatic 16) napalm_logs/server.py:218 — NapalmLogsServerProc.start has cyclomatic complexity 16 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.

D2 · Cognitive Complexity · NapalmLogs._load_config (cognitive 93) · ×1

NapalmLogs._load_config (cognitive 93) napalm_logs/base.py:276 — NapalmLogs._load_config has cognitive complexity 93 (threshold 15). Drivers by points: if/else 78, loops 9, error handling 4, boolean chains 2 (nesting depth added 62). 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.

D2 · Cognitive Complexity · NLOptionParser.parse (cognitive 64) · ×1

NLOptionParser.parse (cognitive 64) napalm_logs/scripts/cli.py:283 — NLOptionParser.parse has cognitive complexity 64 (threshold 15). Drivers by points: if/else 26, boolean chains 22, loops 13, error handling 3 (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.

D2 · Cognitive Complexity · utils.traverse (cognitive 32) · ×1

utils.traverse (cognitive 32) napalm_logs/utils/__init__.py:304 — utils.traverse has cognitive complexity 32 (threshold 15). Drivers by points: if/else 15, error handling 12, loops 5 (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.

D2 · Cognitive Complexity · NapalmLogsServerProc.start (cognitive 31) · ×1

NapalmLogsServerProc.start (cognitive 31) napalm_logs/server.py:218 — NapalmLogsServerProc.start has cognitive complexity 31 (threshold 15). Drivers by points: if/else 23, boolean chains 3, loops 3, error handling 2 (nesting depth added 17). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · utils.dictupdate (cognitive 27) · ×1

utils.dictupdate (cognitive 27) napalm_logs/utils/__init__.py:341 — utils.dictupdate has cognitive complexity 27 (threshold 15). Drivers by points: if/else 12, loops 7, error handling 5, boolean chains 3 (nesting depth added 12). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · NapalmLogsDeviceProc.start (cognitive 25) · ×1

NapalmLogsDeviceProc.start (cognitive 25) napalm_logs/device.py:226 — NapalmLogsDeviceProc.start has cognitive complexity 25 (threshold 15). Drivers by points: if/else 20, error handling 4, loops 1 (nesting depth added 11). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · utils.check_whitelist_blacklist (cognitive 21) · ×1

utils.check_whitelist_blacklist (cognitive 21) napalm_logs/utils/__init__.py:403 — utils.check_whitelist_blacklist has cognitive complexity 21 (threshold 15). Drivers by points: if/else 13, error handling 4, loops 4 (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.

D2 · Cognitive Complexity · test_config.generate_tests (cognitive 21) · ×1

test_config.generate_tests (cognitive 21) tests/test_config.py:85 — test_config.generate_tests has cognitive complexity 21 (threshold 15). Drivers by points: loops 12, if/else 9 (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.

D2 · Cognitive Complexity · NapalmLogs._post_preparation (cognitive 20) · ×1

NapalmLogs._post_preparation (cognitive 20) napalm_logs/base.py:208 — NapalmLogs._post_preparation has cognitive complexity 20 (threshold 15). Drivers by points: if/else 12, boolean chains 5, ternaries 2, loops 1 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · TCPListener._client_connection (cognitive 20) · ×1

TCPListener._client_connection (cognitive 20) napalm_logs/listener/tcp.py:61 — TCPListener._client_connection has cognitive complexity 20 (threshold 15). Drivers by points: if/else 12, loops 7, error handling 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.

D2 · Cognitive Complexity · NapalmLogsDeviceProc._parse (cognitive 17) · ×1

NapalmLogsDeviceProc._parse (cognitive 17) napalm_logs/device.py:132 — NapalmLogsDeviceProc._parse has cognitive complexity 17 (threshold 15). Drivers by points: if/else 14, loops 3 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · NapalmLogsServerProc._identify_prefix (cognitive 17) · ×1

NapalmLogsServerProc._identify_prefix (cognitive 17) napalm_logs/server.py:138 — NapalmLogsServerProc._identify_prefix has cognitive complexity 17 (threshold 15). Drivers by points: if/else 10, loops 4, error handling 3 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D3 · God Classes · TooManyMethods · ×1

TooManyMethods: PrometheusTransport napalm_logs/transport/prometheus.py:21 — TooManyMethods — 52 methods. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.

D3 · God Classes · FileTooLong · ×1

FileTooLong: napalm_logs/base.py napalm_logs/base.py:0 — FileTooLong — 554 significant lines (blank, comment-only and punctuation-only lines excluded). To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.

D31 · IaC & Container Security · Medium IaC · ×1

Medium IaC: CKV_DOCKER_3 Dockerfile:1 — Ensure that a user for the container has been created

D36 · Supply-chain Provenance & Signing · Unpinned build actions · ×1

Unpinned build actions — CI references GitHub Actions by a floating ref (@main / @tag) rather than a pinned commit SHA, weakening build integrity. 9 floating ref(s) across 3 workflow file(s), 1 of them mutable BRANCH refs — pin those first. Each floating ref is itemized at file:line by the SAST (D29) lens.

D36 · Supply-chain Provenance & Signing · Workflow token permissions not restricted · ×1

Workflow token permissions not restricted — No workflow declares a `permissions:` block, so every job runs with the repository's default GITHUB_TOKEN scope (3 workflow file(s) checked). On a repository whose default is read/write, a compromised action or a malicious pull request inherits write access to code, issues, releases and packages. Declare a least-privilege `permissions:` block — `permissions: {contents: read}` at the top of each workflow, widened per job only where a job genuinely writes.

D4 · Code Duplication · Duplicated block (13 lines × 2) · ×1

Duplicated block (13 lines × 2) napalm_logs/transport/alerta.py:75 — napalm_logs/transport/alerta.py:75-87 | napalm_logs/transport/http.py:118-130 — 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 `napalm_logs/transport/alerta.py:75` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.

D4 · Code Duplication · Duplicated block (12 lines × 2) · ×1

Duplicated block (12 lines × 2) napalm_logs/device.py:252 — napalm_logs/device.py:252-263 | napalm_logs/publisher.py:158-169 — 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.

D4 · Code Duplication · Duplicated block (10 lines × 2) · ×1

Duplicated block (10 lines × 2) napalm_logs/listener/tcp.py:45 — napalm_logs/listener/tcp.py:45-54 | napalm_logs/listener/udp.py:33-42 — 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.

D4 · Code Duplication · Duplicated block (8 lines × 2) · ×1

Duplicated block (8 lines × 2) napalm_logs/scripts/cli.py:336 — napalm_logs/scripts/cli.py:336-343 | napalm_logs/scripts/cli.py:355-362 — 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.

D4 · Code Duplication · Duplicated block (7 lines × 2) · ×1

Duplicated block (7 lines × 2) napalm_logs/device.py:107 — napalm_logs/device.py:107-113 | napalm_logs/server.py:118-124 — 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.

Recommendation — 7 finding(s)

D11 · Test Reliability · Test reliability not included · ×1

Test reliability not included — Test source is present (.py) but the built-in reliability runner does not support this repository's ecosystem, so flakiness couldn't be assessed. Not scored — this is a gap in the analyzer's language coverage, not a finding about this repository.

D16 · Bus Factor · Off-boarding risk · ×1

Off-boarding risk: anonymized user #1 — If anonymized user #1 becomes unavailable, 1 significant file(s) lose their only recent owner: napalm_logs/listener/kafka.py. Pair on, review, or document these before any departure.

D28 · Secrets (history) · Rotate the exposed credentials · ×1

Rotate the exposed credentials — git history can't be un-committed — Some of these secrets are in git HISTORY: deleting the file does not remove them (the commit persists on every clone, fork and backup). The remediation is to ROTATE each historically-exposed credential and treat it as compromised — not to delete the file. Rewriting history is disruptive and unreliable across existing forks. (Working-tree-only secrets — no commit — can instead be removed from the file and moved to a secret store.) These 4 location(s) do not all need the same action: 2 sit inside a test/fixture/sample tree and 2 do not. Rotate the ones outside those trees as stated above. For the fixture ones there may be no live credential to revoke — confirm each value was never reused outside the tests (a fixture key shared with a staging or demo environment IS a live credential and must be rotated), then generate that material at test time instead of committing it, and record the deliberate exposure where a reader of the file will see it.

D36 · Supply-chain Provenance & Signing · No build provenance · ×1

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.

D36 · Supply-chain Provenance & Signing · No artifact signing · ×1

No artifact signing — No artifact signing found in CI — sign your released artifacts with whatever your ecosystem ships (`cosign sign` over the image digest your pipeline pushes, so a consumer can `cosign verify` what they pull, cosign/sigstore for container images, PEP 740 attestations via `pypa/gh-action-pypi-publish` under PyPI Trusted Publishing (OIDC) for wheels/sdists) so consumers can verify what you built.

D36 · Supply-chain Provenance & Signing · No SBOM · ×1

No SBOM — No SBOM generation or committed SBOM found — produce one with what your ecosystem ships (`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.

D8 · Code Coverage · Coverage not included · ×1

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.

Dimension	Tool	Version	Command	Findings	Raw output
D28 · Secrets (history)	gitleaks	—	gitleaks detect --no-banner --report-format json --report-path /dev/stdout --exit-code 0 --source .	4	artifacts/raw/gitleaks-history.json
D29 · Static Analysis (SAST)	semgrep	—	semgrep --config /opt/semgrep-rules/security-audit.yml --config /opt/semgrep-rules/owasp-top-ten.yml --json --quiet --timeout 0 --metrics off .	11	artifacts/raw/semgrep.json
D30 · Dependency Vulnerabilities	none (no readable dependency manifest)	—	none (no readable dependency manifest): not present in this environment	0	—
D31 · IaC & Container Security	trivy	—	trivy config --format json --quiet .	3	artifacts/raw/trivy-config.json
D32 · Data Compliance (PII/GDPR)	semgrep	—	semgrep: not applicable — No PII/GDPR-handling patterns detected (p/gdpr ruleset) — no data-compliance surface to assess.	0	—
D33 · JS/npm Dependency Vulnerabilities	trivy	—	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.	0	—
D37 · Vulnerability-disclosure Policy	disclosure	—	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.	0	—
D38 · OSV Dependency Vulnerabilities	osv-scanner	—	osv-scanner --format json --recursive .	3	artifacts/raw/osv-scanner.json
D40 · Network Egress Confinement	runtime-hardening	—	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.	0	—
D41 · Kernel & Syscall Confinement	runtime-hardening	—	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.	0	—
D42 · Runtime Threat Enforcement	runtime-hardening	—	runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; runtime threat-detection and admission-control policy are cluster-level controls, so there is nothing to assess here.	0	—

Run 019fcf31-fd5e-7943-b39e-e4573d779555 · every finding is also locatable in findings.md, and the complete scoring record (with exit codes + durations) in sidecar.json.

Napalm-Automation/napalm-Logs

Executive summary

Top priorities

Diagnosis — what's actually going on

Architecture — module dependency matrix

At a glance — Code Health · 78% · Strong

At a glance — Architecture · 100% · Exemplary

At a glance — Maturity · 65% · Adequate

At a glance — Readiness · 46% · Weak · gated by P3

At a glance — Security · 53% · Adequate · gated by D29, D36

Security & Compliance — OWASP Top-10 mapping

Roadmap

File quality

Methodology & how to trust this report

Tools & methods

Run transparency — what happened this run

Limitations & what we did not check

Per-dimension blind spots

The LLM boundary

Dimensions

Frontend & cross-cutting dimensions

Reference — by lens

Appendix A — Findings (grouped)

Appendix B — Reproduction & audit trail

Downloadable artifacts