requests — 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.

psf/requests is in good health (73%). It can be evolved and depended on with normal engineering discipline; the items below are improvements, not blockers.

It is strongest in Architecture (100%) — the structure is clean and changes stay contained. Security (91%) is solid too.

The area that most needs attention is Maturity (58%) — onboarding is slow — key decisions and the architecture aren't written down, so contributors have to reverse-engineer the intent.

Leadership focus, highest impact first: Record significant decisions one document per decision (Architecture documentation); 'Testing' section to the root README (Documentation (README)); 1 A single line 'Sphinx==7.2.6' pins a dependency without version range (Documentation Quality).

For scale: Small (~5,557 production lines); rebuilding it from scratch would take roughly ~0.1 person-years (~1 engineer). Approximate, ±~30%.

Encouragingly, the gaps are in documentation and release process — not in the code's correctness, structure or security, which are strong. They're low-risk to close, and doing so would lift the grade without re-engineering anything that already works.

Code Health 90% Strong Architecture 100% ★ Exemplary Maturity 58% Adequate · gated by M2 Readiness 83% Strong Security 91% ★ Exemplary

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.

Maturity 58% · 47% weightReadiness 83% · 26% weightCode Health 90% · 14% weightSecurity 91% · 8% weightArchitecture 100% · 4% weight

Raise Maturity 58 → 70 (the Healthy floor) ⇒ headline 73 → ~79.

Code composition — where the lines go

Tests 100%

Rebuild cost & value ~ Modeled — €4,700–€23,000

Cost to rebuild	€4,700–€23,000 (0.1 person-years (78–247 h), ~1 engineer)
Domain complexity	Standard — harder problems cost more per line
Quality factor	1.1× (at 73% 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 ~€14,000 to rebuild). Its weakest lens is Maturity at 58% — 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 1.1× 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.

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).

+14.5 pts · Medium effort · Architecture documentation

Add a 'Testing' section to the root README — how to run the test suite.

+13.8 pts · Medium effort · Documentation (README)

Resolve the 1 A single line 'Sphinx==7.2.6' pins a dependency without version range,… finding(s) in Documentation Quality — start with requirements.txt.

+4.7 pts · Low effort · Documentation Quality

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 Maturity at 58%. 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 (5,557 LoC) · weakest lens: Maturity 58%

→ Direct remediation budget at Maturity 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: 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). The rest can wait behind it.

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

→ 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).

Architecture — module dependency matrix

19 modules, 9 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 · 90% · Strong

The Good

D1 · Python Cyclomatic Complexity 9.1 D2 · Python Cognitive Complexity 8.1 D3 · Python God Classes 8.7 D4 · Python Code Duplication 10.0

At a glance — Architecture · 100% · Exemplary

The Good

D35 · Python Change Coupling 10.0

At a glance — Maturity · 58% · Adequate · gated by M2

The Good

D15 · Python Churn × Complexity Hotspots 9.5 D16 · Python Bus Factor 9.2 D19 · Python Documentation Quality 8.0 D21 · Python Naming Consistency 10.0 D34 · Python Knowledge Freshness 9.3 M3 · Repo Folder & project structure 10.0 M4 · Repo Documentation accuracy 10.0

The Bad

M1 · Repo Documentation (README) 6.7

The Ugly

M2 · Repo Architecture documentation 0.0

At a glance — Readiness · 83% · Strong

The Good

D13 · Python Secret Scanning 10.0 P1 · Repo CI/CD gates 10.0 P3 · Repo Security & performance tooling 7.0 P4 · Repo Deployment & Rollback 7.0 P6 · Repo Release Hygiene 10.0

At a glance — Security · 91% · Exemplary

The Good

D28 · Python Secrets (history) 8.0 D29 · Python Static Analysis (SAST) 10.0 D36 · Python Supply-chain Provenance & Signing 7.5 D37 · Python Vulnerability-disclosure Policy 10.0 D38 · Python OSV Dependency Vulnerabilities 10.0

Roadmap

Begin by establishing a formal record of significant architectural decisions in a dedicated documentation tree to capture context and consequences. Next, update the root README to include a testing section that explains how to run the test suite. Address the single documentation quality issue regarding dependency pinning in requirements.txt. Finally, mitigate the off-boarding risk by resolving the identified bus factor, and clean up the single instance of orphaned files to ensure knowledge freshness.

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

Do this	Helps	Effort	Dimension
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).	+14.5 pts	Medium	Architecture documentation
Add a 'Testing' section to the root README — how to run the test suite.	+13.8 pts	Medium	Documentation (README)
Resolve the 1 A single line 'Sphinx==7.2.6' pins a dependency without version range,… finding(s) in Documentation Quality — start with requirements.txt.	+4.7 pts	Low	Documentation Quality
Resolve the 1 Off-boarding risk finding(s) in Bus Factor.	+1.2 pts	Low	Bus Factor
Resolve the 1 Further orphaned files (smaller) finding(s) in Knowledge Freshness.	+1.1 pts	Low	Knowledge Freshness
Resolve the 4 Hotspot finding(s) in Churn × Complexity Hotspots — start with models.py, sessions.py, utils.py.	+0.8 pts	High	Churn × Complexity Hotspots
Resolve the 1 No SBOM finding(s) in Supply-chain Provenance & Signing.	+0.4 pts	Low	Supply-chain Provenance & Signing
Resolve the 2 FileTooLong finding(s) in God Classes — start with models.py, utils.py.	+0.4 pts	Medium	God Classes

File quality

Per-file score 0–10 — a quality signature. Of 7 files carrying findings, judged against the Production bar: 0% slop · 57% mixed · 43% near-clean.

File	Score	Band	Worst signal
src/requests/models.py	7.0	Mixed	Cyclomatic Complexity: RequestEncodingMixin._encode_files (cyclomatic 21)
src/requests/utils.py	7.1	Mixed	Cyclomatic Complexity: utils.should_bypass_proxies (cyclomatic 18)
src/requests/adapters.py	7.4	Mixed	Cyclomatic Complexity: HTTPAdapter.send (cyclomatic 19)
src/requests/auth.py	7.8	Mixed	Cyclomatic Complexity: HTTPDigestAuth.build_digest_header (cyclomatic 19)
src/requests/sessions.py	8.5	Near-clean	Cognitive Complexity: SessionRedirectMixin.resolve_redirects (cognitive 25)
src/requests/cookies.py	8.5	Near-clean	Cognitive Complexity: RequestsCookieJar._find_no_duplicates (cognitive 18)
docs/requirements.txt	9.5	Near-clean	Documentation Quality: A single line 'Sphinx==7.2.6' pins a dependency without version range, which is not ideal for CI or end-user installations.

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

D1D2D3D4D13D15D16D19D21D28D29D34D35D36D37D38M1M2M3M4P1P3P4P6

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, 26 of 33 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	✓ 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 019fc3fb-0bab-7f9f-a33d-fb294c0a33a7.

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").
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 Complexity9.1 / 10Exemplary✓ 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 9.1 / 10 · rule-coverage 100% · ceiling Prevented

7 method(s) exceeded the cyclomatic complexity threshold of 15; the worst was RequestEncodingMixin._encode_files at 21.

RequestEncodingMixin._encode_files (cyclomatic 21)src/requests/models.py:183

HTTPAdapter.send (cyclomatic 19)src/requests/adapters.py:634

HTTPDigestAuth.build_digest_header (cyclomatic 19)src/requests/auth.py:157

PreparedRequest.prepare_body (cyclomatic 19)src/requests/models.py:576

PreparedRequest.prepare_url (cyclomatic 18)src/requests/models.py:483

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

✓ On the Gold path — maintain.

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

Do you agree with this assessment?

D2 · Cognitive Complexity8.1 / 10Strong✓ 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 8.1 / 10 · rule-coverage 100% · ceiling Prevented

12 method(s) exceeded the cognitive complexity threshold of 15; the worst was utils.should_bypass_proxies at 38.

utils.should_bypass_proxies (cognitive 38)src/requests/utils.py:810

RequestEncodingMixin._encode_files (cognitive 36)src/requests/models.py:183

PreparedRequest.prepare_body (cognitive 32)src/requests/models.py:576

HTTPAdapter.send (cognitive 25)src/requests/adapters.py:634

RequestEncodingMixin._encode_params (cognitive 25)src/requests/models.py:151

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

What to do

Resolve the 1 utils.should_bypass_proxies (cognitive 38) finding(s) in Cognitive Complexity — start with utils.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 RequestEncodingMixin._encode_files (cognitive 36) finding(s) in Cognitive Complexity — start with models.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 PreparedRequest.prepare_body (cognitive 32) finding(s) in Cognitive Complexity — start with models.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.

FileTooLong: requests/models.py · ×2src/requests/models.py:0

What to do

Resolve the 2 FileTooLong finding(s) in God Classes — start with models.py, utils.py. — One of this dimension's main actionable groups (2 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 Duplication10.0 / 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 10.0 / 10 · rule-coverage 100% · ceiling Verified

0 duplicated block group(s) detected.

✓ On the Gold path — maintain.

Detailed fixes: d4_recommendation.md.

Do you agree with this assessment?

D13 · Secret Scanning10.0 / 10Exemplary○ Nothing flagged

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 10.0 / 10 · rule-coverage 100% · ceiling Prevented

Secret scan ran and found no leaked secrets.

✓ On the Gold path — maintain.

Detailed fixes: d13_recommendation.md.

Do you agree with this assessment?

D15 · Churn × Complexity Hotspots9.5 / 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 9.5 / 10 · rule-coverage 100% · ceiling Documented

Top hotspots: src/requests/models.py (9×21=189); src/requests/sessions.py (5×15=75); src/requests/utils.py (3×18=54)

Hotspot: src/requests/models.py · ×4src/requests/models.py

✓ On the Gold path — maintain.

Detailed fixes: d15_recommendation.md · top locations in Appendix A, every location in findings.md.

Do you agree with this assessment?

D16 · Bus Factor9.2 / 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 9.2 / 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 src/requests/exceptions.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

Requests' documentation is clear and complete for a library of this size: an authoritative README plus dedicated test/certs directories covering testing scenarios, and a rich docs site with release badges, a welcome vignette showing the API in use, and a Developer Interface section that documents all seven request methods, exceptions, sessions, and lower-level classes. The outline is visible (Requests; Installing Requests and Supported Versions; Supported Features & Best–Practices; Cloning the repository) and every named section exists within the clipped content.

A single line 'Sphinx==7.2.6' pins a dependency without version range, which is not ideal for CI or end-user installations.docs/requirements.txt

What to do

Resolve the 1 A single line 'Sphinx==7.2.6' pins a dependency without version range,… finding(s) in Documentation Quality — start with requirements.txt. — One of this dimension's main actionable groups (1 recommendation-level).

Detailed fixes: d19_recommendation.md · top locations in Appendix A, every location in findings.md.

Do you agree with this assessment?

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.

Do you agree with this assessment?

D28 · Secrets (history)8.0 / 10Strong✓ 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 8.0 / 10 · rule-coverage 100% · ceiling Documented

2 finding(s): 0 critical, 2 high, 0 medium, 0 low. Remediation for historically-committed secrets is credential rotation — they remain in history regardless of later deletion.

What to do

Improve Secrets (history) — currently 8.0/10. — 2 finding(s): 0 critical, 2 high, 0 medium, 0 low. Remediation for historically-committed secrets is credential rotation — they remain in history regardless of later deletion.

Detailed fixes: d28_recommendation.md.

Do you agree with this assessment?

D29 · Static Analysis (SAST)10.0 / 10Exemplary○ Nothing flagged

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 10.0 / 10 · rule-coverage 100% · ceiling Documented

semgrep found no security issues.

✓ On the Gold path — maintain.

Detailed fixes: d29_recommendation.md.

Do you agree with this assessment?

D34 · Knowledge Freshness9.3 / 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 9.3 / 10 · rule-coverage 100% · ceiling Documented

1 of 15 significant source file(s) are orphaned — their living knowledge has decayed to nothing, so no one currently understands them. The largest is docs/_themes/flask_theme_support.py.

Further orphaned files (smaller)

✓ On the Gold path — maintain.

Detailed fixes: d34_recommendation.md · top locations in Appendix A, every location in findings.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 & Signing7.5 / 10Strong✓ 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 7.5 / 10 · rule-coverage 100% · ceiling Documented

3/4 supply-chain integrity signals present (provenance, signing, SBOM, pinned actions).

No SBOM

What to do

Resolve the 1 No SBOM finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 recommendation-level).

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

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D37 · Vulnerability-disclosure Policy10.0 / 10Exemplary✓ Tool-verified

What it measures: Whether the repository publishes a coordinated-vulnerability-disclosure policy (SECURITY.md or security.txt) with a reporting contact, so finders know how to report a vulnerability. Presence of a policy file with a contact, not whether the policy is adequate or honoured.

Method: Vulnerability-disclosure policy read deterministically from the repo: a SECURITY.md (root/.github/docs) or .well-known/security.txt / security.txt, regex-checked for a reporting contact (email / URL / mailto). Present + contact → 10; present without a contact → 4; NotApplicable when no policy file exists (it may live off-repo). Detects the policy file's presence + contact, not its adequacy.

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

A vulnerability-disclosure policy (.github/SECURITY.md) is published with a reporting contact.

✓ On the Gold path — maintain.

Detailed fixes: d37_recommendation.md.

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D38 · OSV Dependency Vulnerabilities10.0 / 10Exemplary○ Nothing flagged

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 10.0 / 10 · rule-coverage 100% · ceiling Documented

No known-vulnerable dependencies (OSV).

✓ On the Gold path — maintain.

Detailed fixes: d38_recommendation.md.

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Frontend & cross-cutting dimensions

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

M1 · Documentation (README)6.7 / 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 '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.

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M2 · Architecture documentation0.0 / 10Critical✓ 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.
No C4/PlantUML/Mermaid diagram or architecture.md — the high-level shape isn't documented.

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).
Add a C4 context/container diagram (Structurizr, PlantUML or Mermaid) or an architecture.md overview.

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M3 · Folder & project structure10.0 / 10Exemplary✓ 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.

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M4 · Documentation accuracy10.0 / 10Exemplary◐ 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.

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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.

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P3 · Security & performance tooling7.0 / 10Strong✓ 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.

What to do

Add gitleaks/trufflehog in CI to block PRs that introduce committed secrets.

Do you agree with this assessment?

P4 · Deployment & Rollback7.0 / 10Strong✓ 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.

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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.

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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	90%	Strong	Solid.
Architecture	100%	Exemplary	Strongest area.
Maturity	58%	Adequate — gated by M2	Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Readiness	83%	Strong	Solid.
Security	91%	Exemplary	Solid.

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 — ~4284 lines of test source are present (.py) but the test-quality collector reads C# only, so skipped/assertion-free tests couldn't be counted. Not scored — this is a gap in the analyzer, not a finding about this repository.
D11 Test Reliability — Test reliability not included
D12 Dependency Hygiene — Dependency hygiene not measured — dependency manifest found but not parsed for hygiene
D14 License Compliance — Not scored — this repository's package manifest is not parsed for licence data yet. A gap in the analyzer's language coverage, NOT a finding that the repository's licenses are compliant (a Python pyproject.toml/requirements.txt (pip/uv/Poetry)), which this pass does not parse yet — so this dimension asserts nothing about this repository's licensing in either direction.
D17 Explicit Debt — explicit-debt markers are read through a C# workspace today, so they were not read for this repository's language — this asserts nothing about how many markers the code carries. Not scored — this is a gap in the analyzer, not a finding about this repository
D18 Solution Shape — D18 scores the shape of a .NET solution; this repository has no .NET solution or project files, so the dimension does not apply.
D20 ADR Quality — N/A — ADRs are expected on deployable products with a user-facing host, not consumed libraries; no ADR log is required here.
D22 Internal API Consistency — No exposed public API
D23 Boundary Type-Coupling — Production source is present (.py) but bounded contexts are resolved over the C#/VB project set, which exposed none, so context scope could not be assessed. Not scored — this is a gap in the analyzer, not a verdict about this repository. Declaring the codebase's bounded contexts (≥2) would let cross-boundary type coupling be assessed — see the recommendation on this dimension for where. Declare them in `.codehealth/config.yaml` at the repository root (create it if absent), mapping each context name to the module-path or namespace prefixes that belong to it — e.g. `architecture:` → `contexts:` → `Billing: ["src/billing", "Acme.Billing"]`, `Catalog: ["src/catalog", "Acme.Catalog"]`.
D24 Comment Value — No inline comments to assess — comment value is not applicable here.
D25 ADR Conformance — no ADRs to check
D26 Project Cohesion — Project cohesion is assessed over the .NET project set; this target exposed no projects, so project size and spread could not be assessed. Not scored — this is a gap in the analyzer's reach, not a verdict about this repository.
D27 Navigability — No calls could be sampled, so navigability was not assessed — tracing effort is measured over resolved call sites and this target exposed none. Not scored — this is a gap in the analyzer's reach, not a verdict about this repository.
D30 Dependency Vulnerabilities — Not scored — no dependency manifest in a supported ecosystem was read for this repository. A gap in the analyzer's language coverage, NOT a finding that the repository is free of vulnerable dependencies (a Python pyproject.toml/requirements.txt (pip/uv/Poetry) — not scanned yet) — where an OSV-supported manifest exists, dependency vulnerabilities for this repository are reported under D38 instead.
D31 IaC & Container Security — No Infrastructure-as-Code or container manifests found (Dockerfile, Terraform, Kubernetes/Helm, CloudFormation); nothing to scan.
D32 Data Compliance (PII/GDPR) — Data compliance (PII/GDPR) was not assessed in this scan — no ruleset is currently available for it. This says nothing about how this repository handles personal data, in either direction.
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.
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 dependency cycles — 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.

Warning — 25 finding(s)

D15 · Churn × Complexity Hotspots · Hotspot · ×4

Hotspot: src/requests/models.py src/requests/models.py — src/requests/models.py changed 9 times in last 90 days, max complexity 21. 3 of those changes were fix/bug commits, so the churn is repair rather than feature work. Before the next change lands here, cover the area it touches with tests, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time.
Hotspot: src/requests/sessions.py src/requests/sessions.py — src/requests/sessions.py changed 5 times in last 90 days, max complexity 15. 2 of those changes were fix/bug commits, so the churn is repair rather than feature work. Before the next change lands here, cover the area it touches with tests, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time.
Hotspot: src/requests/utils.py src/requests/utils.py — src/requests/utils.py changed 3 times in last 90 days, max complexity 18. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, behind tests written first.
Hotspot: src/requests/adapters.py src/requests/adapters.py — src/requests/adapters.py changed 2 times in last 90 days, max complexity 19. Frequent change and high complexity in one file compound: schedule the next change to it to include carving out the part being edited, behind tests written first.

D3 · God Classes · FileTooLong · ×2

FileTooLong: requests/models.py src/requests/models.py:0 — FileTooLong — 689 significant lines (blank, comment-only and punctuation-only lines excluded). To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: requests/utils.py src/requests/utils.py:0 — FileTooLong — 580 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.

D1 · Cyclomatic Complexity · RequestEncodingMixin._encode_files (cyclomatic 21) · ×1

RequestEncodingMixin._encode_files (cyclomatic 21) src/requests/models.py:183 — RequestEncodingMixin._encode_files has cyclomatic complexity 21 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.

D1 · Cyclomatic Complexity · HTTPAdapter.send (cyclomatic 19) · ×1

HTTPAdapter.send (cyclomatic 19) src/requests/adapters.py:634 — HTTPAdapter.send has cyclomatic complexity 19 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.

D1 · Cyclomatic Complexity · HTTPDigestAuth.build_digest_header (cyclomatic 19) · ×1

HTTPDigestAuth.build_digest_header (cyclomatic 19) src/requests/auth.py:157 — HTTPDigestAuth.build_digest_header has cyclomatic complexity 19 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.

D1 · Cyclomatic Complexity · PreparedRequest.prepare_body (cyclomatic 19) · ×1

PreparedRequest.prepare_body (cyclomatic 19) src/requests/models.py:576 — PreparedRequest.prepare_body has cyclomatic complexity 19 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.

D1 · Cyclomatic Complexity · PreparedRequest.prepare_url (cyclomatic 18) · ×1

PreparedRequest.prepare_url (cyclomatic 18) src/requests/models.py:483 — PreparedRequest.prepare_url has cyclomatic complexity 18 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.

D1 · Cyclomatic Complexity · utils.should_bypass_proxies (cyclomatic 18) · ×1

utils.should_bypass_proxies (cyclomatic 18) src/requests/utils.py:810 — utils.should_bypass_proxies has cyclomatic complexity 18 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.

D1 · Cyclomatic Complexity · utils.super_len (cyclomatic 16) · ×1

utils.super_len (cyclomatic 16) src/requests/utils.py:160 — utils.super_len 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 · utils.should_bypass_proxies (cognitive 38) · ×1

utils.should_bypass_proxies (cognitive 38) src/requests/utils.py:810 — utils.should_bypass_proxies has cognitive complexity 38 (threshold 15). Drivers by points: if/else 29, loops 6, boolean chains 2, error handling 1 (nesting depth added 21). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · RequestEncodingMixin._encode_files (cognitive 36) · ×1

RequestEncodingMixin._encode_files (cognitive 36) src/requests/models.py:183 — RequestEncodingMixin._encode_files has cognitive complexity 36 (threshold 15). Drivers by points: if/else 20, ternaries 8, boolean chains 4, loops 4 (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 · PreparedRequest.prepare_body (cognitive 32) · ×1

PreparedRequest.prepare_body (cognitive 32) src/requests/models.py:576 — PreparedRequest.prepare_body has cognitive complexity 32 (threshold 15). Drivers by points: if/else 20, error handling 7, boolean chains 5 (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 · HTTPAdapter.send (cognitive 25) · ×1

HTTPAdapter.send (cognitive 25) src/requests/adapters.py:634 — HTTPAdapter.send has cognitive complexity 25 (threshold 15). Drivers by points: if/else 16, error handling 8, boolean chains 1 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · RequestEncodingMixin._encode_params (cognitive 25) · ×1

RequestEncodingMixin._encode_params (cognitive 25) src/requests/models.py:151 — RequestEncodingMixin._encode_params has cognitive complexity 25 (threshold 15). Drivers by points: ternaries 10, if/else 9, loops 5, boolean chains 1 (nesting depth added 16). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · SessionRedirectMixin.resolve_redirects (cognitive 25) · ×1

SessionRedirectMixin.resolve_redirects (cognitive 25) src/requests/sessions.py:186 — SessionRedirectMixin.resolve_redirects has cognitive complexity 25 (threshold 15). Drivers by points: if/else 16, loops 4, boolean chains 3, error handling 2 (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · HTTPAdapter.cert_verify (cognitive 23) · ×1

HTTPAdapter.cert_verify (cognitive 23) src/requests/adapters.py:307 — HTTPAdapter.cert_verify has cognitive complexity 23 (threshold 15). Drivers by points: if/else 19, boolean chains 4 (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 · PreparedRequest.prepare_url (cognitive 21) · ×1

PreparedRequest.prepare_url (cognitive 21) src/requests/models.py:483 — PreparedRequest.prepare_url has cognitive complexity 21 (threshold 15). Drivers by points: if/else 16, error handling 3, boolean chains 2 (nesting depth added 2). To reduce it, split the body: this score is breadth rather than depth — many checks laid out side by side rather than nested inside one another, so inverting conditions into early returns has nothing left to flatten. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.

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

utils.super_len (cognitive 21) src/requests/utils.py:160 — utils.super_len has cognitive complexity 21 (threshold 15). Drivers by points: if/else 11, error handling 7, boolean chains 3 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · RequestsCookieJar._find_no_duplicates (cognitive 18) · ×1

RequestsCookieJar._find_no_duplicates (cognitive 18) src/requests/cookies.py:423 — RequestsCookieJar._find_no_duplicates has cognitive complexity 18 (threshold 15). Drivers by points: if/else 15, boolean chains 2, loops 1 (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · utils.get_netrc_auth (cognitive 18) · ×1

utils.get_netrc_auth (cognitive 18) src/requests/utils.py:231 — utils.get_netrc_auth has cognitive complexity 18 (threshold 15). Drivers by points: if/else 10, boolean chains 3, error handling 2, ternaries 2, loops 1 (nesting depth added 3). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · HTTPDigestAuth.build_digest_header (cognitive 17) · ×1

HTTPDigestAuth.build_digest_header (cognitive 17) src/requests/auth.py:157 — HTTPDigestAuth.build_digest_header has cognitive complexity 17 (threshold 15). Drivers by points: if/else 14, boolean chains 3. To reduce it, split the body: this score is breadth rather than depth — many checks laid out side by side rather than nested inside one another, so inverting conditions into early returns has nothing left to flatten. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.

Recommendation — 6 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: src/requests/exceptions.py. Pair on, review, or document these before any departure.

D19 · Documentation Quality · A single line 'Sphinx==7.2.6' pins a dependency without version range, which is not ideal for CI or end-user installations. · ×1

A single line 'Sphinx==7.2.6' pins a dependency without version range, which is not ideal for CI or end-user installations. docs/requirements.txt — Add a minor note that Sphinx 7.2.6 is used by RTD and the docs site, but broader versions are acceptable.

D34 · Knowledge Freshness · Further orphaned files (smaller) · ×1

Further orphaned files (smaller) — 1 of 15 analysed file(s) have no living knowledge left — their last meaningful change has decayed away, so if one breaks, no one currently understands it (counted over production source files of roughly 100 lines or more, excluding tests, vendored, generated and example/demo trees, largest first). None is large enough to earn a read-through of its own, so this row stands in for the per-file rows rather than raising one each — largest first: docs/_themes/flask_theme_support.py. Attach the read to the next change that touches one of them: have a second person review that change, and leave behind a short comment or test recording what the file is for, so the knowledge comes back at the cost of a change you were making anyway.

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 .	2	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 .	0	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: not applicable — No Infrastructure-as-Code or container manifests found (Dockerfile, Terraform, Kubernetes/Helm, CloudFormation); nothing to scan.	0	—
D32 · Data Compliance (PII/GDPR)	semgrep	—	semgrep: not applicable — Data compliance (PII/GDPR) was not assessed in this scan — no ruleset is currently available for it. This says nothing about how this repository handles personal data, in either direction.	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	—
D38 · OSV Dependency Vulnerabilities	osv-scanner	—	osv-scanner --format json --recursive .	0	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 019fc3fb-0bab-7f9f-a33d-fb294c0a33a7 · every finding is also locatable in findings.md, and the complete scoring record (with exit codes + durations) in sidecar.json.

Psf/requests

Executive summary

Top priorities

Diagnosis — what's actually going on

Architecture — module dependency matrix

At a glance — Code Health · 90% · Strong

At a glance — Architecture · 100% · Exemplary

At a glance — Maturity · 58% · Adequate · gated by M2

At a glance — Readiness · 83% · Strong

At a glance — Security · 91% · Exemplary

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