Public report — pandas, published 7 Aug 2026. Concrete security findings (CVE IDs, secret matches, dependency versions) are hidden in this version; ask the repo owner for the full report.
Watchdogby Canine Development 07-08-2026 @ 05:45 UTC Public
Code Health Audit

Pandas-Dev/pandas

No baseline yet — first run coverage not measuredno automated testssingle-maintainer
47% Provisional
Code Health 100% Exemplary Maturity 64% Adequate Readiness 42% Weak Security 36% Weak

Hobby · weakest lens: Security (36%)

Degraded — solution could not be loaded
The C# solution could not be loaded in the analyzer (the workspace returned 0 projects), so every compiler-dependent dimension ran on nothing and the size/effort figures were estimated directly from source text. This run is Degraded — treat the grade as indicative only. See diagnostics.md for the exact cause (which solution project references resolved vs were missing, a structure map of the analyzed tree, and the solution/project files), then re-run for a reliable result.

Grounded in facts. Every number here is computed, not narrated — reproducible, tool-backed, and traceable to a line of code. How to trust this ▸

23/26dimensions tool-verifieddeterministic · confidence 1.0 · 3 LLM-assisted, advisory
68findings with an exact file:lineof 80 — the remainder are repo-wide signals (a dimension-level measurement, not a single line); open any file:line and verify
26/89dimensions across the health lenses — wide & deep

Executive summary

Read through the Production lens — the standard calibration. *Green* means good enough to run in production. The score is absolute and comparable across repos.

pandas-dev/pandas carries serious gaps (47%). Several issues below can materially affect correctness, security, or the cost of changing it — and propagate to everything that depends on it.

It is strongest in Code Health (100%) — the code is clean and low-risk to change.

The area that most needs attention is Security (36%) — exposure to security and compliance incidents is elevated. Readiness (42%) is the next concern — releases are harder to depend on — versioning, release notes and dependency hygiene are thin, so consumers can't easily tell what changed or trust an upgrade.

Leadership focus, highest impact first: 1 No tests found finding(s) in Test Distribution (Test Distribution); ILogger (or Serilog) and log at meaningful points across… (Observability); 27 Medium finding(s) (Static Analysis (SAST)).

For scale: Hobby (~0 production lines); rebuilding it from scratch would take roughly ~2.2 person-years (~1–5 engineers). Approximate, ±~30%.

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

Code Health 100% ★ Exemplary Maturity 64% Adequate Readiness 42% Weak · gated by D9, P2 Security 36% Weak · 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.
Security 36% · 50% weightReadiness 42% · 27% weightMaturity 64% · 15% weightCode Health 100% · 8% weight

Raise Security 36 → 70 (the Healthy floor) ⇒ headline 47 → ~57.

Code composition — where the lines go
Tests 100%

Top priorities

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

1
Resolve the 1 No tests found finding(s) in Test Distribution.
+11.5 pts · Low effort · Test Distribution
2
Adopt ILogger (or Serilog) and log at meaningful points across the projects.
+11.5 pts · Medium effort · Observability
3
Resolve the 27 Medium finding(s) in Static Analysis (SAST) — start with index.html (8), xml.py (8), layout.html (6).
+7.2 pts · Medium effort · Static Analysis (SAST)

Diagnosis — what's actually going on

Highest-leverage move · Medium · Leverage
Of everything flagged, the best return on effort is: Resolve the 1 No tests found finding(s) in Test Distribution. The rest can wait behind it.
Evidence: priority ranking: top of 5 ranked by impact/effort
→ Resolve the 1 No tests found finding(s) in Test Distribution.

At a glance — Code Health · 100% · Exemplary

The Good
D3 · .NET / C# God Classes 10.0 D4 · .NET / C# Code Duplication 10.0 X1 · Repo Async correctness 10.0 X3 · Repo Exception handling 10.0 X4 · Repo Structured logging 10.0

At a glance — Architecture · — · —

The Good
D7 · .NET / C# Architectural Integrity 10.0 D26 · .NET / C# Project Cohesion 10.0 D27 · .NET / C# Navigability 10.0 AX5 · Repo Architecture & structure 10.0
The Bad
D5 · .NET / C# Coupling 6.9

At a glance — Maturity · 64% · Adequate

The Good
D20 · .NET / C# ADR Quality 7.3 D21 · .NET / C# Naming Consistency 10.0 D25 · .NET / C# ADR Conformance 10.0 M4 · Repo Documentation accuracy 10.0
The Bad
M1 · Repo Documentation (README) 6.7 M2 · Repo Architecture documentation 5.0 M3 · Repo Folder & project structure 6.0

At a glance — Readiness · 42% · Weak · gated by D9, P2

The Good
D12 · .NET / C# Dependency Hygiene 10.0 D13 · .NET / C# Secret Scanning 10.0 P1 · Repo CI/CD gates 10.0 P3 · Repo Security & performance tooling 7.0
The Ugly
P2 · Repo Observability 0.0

At a glance — Security · 36% · Weak · gated by D29, D36

The Good
D28 · .NET / C# Secrets (history) 8.0
The Ugly
D29 · .NET / C# Static Analysis (SAST) 0.3 D36 · .NET / C# Supply-chain Provenance & Signing 2.5

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 categoryFindingsSeverity
A03:2021 — Injection40High / Critical
A02:2021 — Cryptographic Failures2High / Critical

Roadmap

Begin by addressing the single missing test case to improve test coverage. Next, implement structured logging using ILogger or Serilog across all service-like projects to enhance observability. Simultaneously, resolve the 27 medium-severity static analysis findings, prioritizing index.html, xml.py, and layout.html. Finally, expand the architecture decision record to 50 entries and reorganize the codebase to separate production code from tooling.

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

Do thisHelpsEffortDimension
Resolve the 1 No tests found finding(s) in Test Distribution.+11.5 ptsLowTest Distribution
Adopt ILogger (or Serilog) and log at meaningful points across the projects.+11.5 ptsMediumObservability
Resolve the 27 Medium finding(s) in Static Analysis (SAST) — start with index.html (8), xml.py (8), layout.html (6).+7.2 ptsMediumStatic Analysis (SAST)
Resolve the 1 No SBOM finding(s) in Supply-chain Provenance & Signing.+3.5 ptsLowSupply-chain Provenance & Signing
Resolve the 1 No artifact signing finding(s) in Supply-chain Provenance & Signing.+3.5 ptsLowSupply-chain Provenance & Signing
Resolve the 1 No build provenance finding(s) in Supply-chain Provenance & Signing.+3.5 ptsLowSupply-chain Provenance & Signing
Resolve the 2 Secret finding(s) in Secrets (history) — start with pd_datetime.c, pixi.lock.+3.1 ptsLowSecrets (history)
Grow the ADR log (currently 23) — reach 50 to raise the maturity tier; document significant decisions as they're made.+5.4 ptsMediumArchitecture documentation

File quality

Per-file score 0–10 — a quality signature. Of 38 files carrying findings, judged against the Production bar: 0% slop · 39% mixed · 61% near-clean.

FileScoreBandWorst signal
pandas/_libs/src/vendored/ujson/python/objToJSON.c4.4MixedStatic Analysis (SAST): High: insecure-use-string-copy-fn
web/pandas/_templates/layout.html5.9MixedStatic Analysis (SAST): Medium: unquoted-attribute-var
pandas/io/formats/xml.py6.1MixedStatic Analysis (SAST): Medium: use-defused-xml
pandas/io/xml.py6.1MixedStatic Analysis (SAST): Medium: use-defused-xml
web/pandas/community/blog/index.html6.1MixedStatic Analysis (SAST): Medium: unquoted-attribute-var
web/pandas/index.html6.1MixedStatic Analysis (SAST): Medium: var-in-href
pandas/io/pickle.py6.9MixedStatic Analysis (SAST): Medium: avoid-pickle
pandas/_libs/src/datetime/pd_datetime.c7.2MixedSecrets (history): Secret: generic-api-key
pixi.lock7.2MixedSecrets (history): Secret: facebook-page-access-token
.github/actions/run-tests/action.yml7.2MixedStatic Analysis (SAST): High: run-shell-injection
.github/dependabot.yml7.2MixedStatic Analysis (SAST): High: dependabot-missing-cooldown
pandas/core/computation/ops.py7.2MixedStatic Analysis (SAST): High: eval-detected
pandas/io/formats/style.py7.9MixedStatic Analysis (SAST): Medium: missing-autoescape-disabled
pandas/io/formats/style_render.py7.9MixedStatic Analysis (SAST): Medium: missing-autoescape-disabled
web/pandas_web.py7.9MixedStatic Analysis (SAST): Medium: missing-autoescape-disabled
doc/source/getting_started/intro_tutorials/02_read_write.rst8.0Near-cleanADR Quality: Decision/consequences are only hinted at in notes (e.g. need to install Excel reader dependencies), with no explicit decision and no trade-offs
doc/source/getting_started/intro_tutorials/07_reshape_table_layout.rst8.0Near-cleanADR Quality: Decision (filtering/no2 dataset usage) and consequences/trade-offs are not stated in any visible text
doc/source/getting_started/intro_tutorials/10_text_data.rst8.0Near-cleanADR Quality: Decision and consequences are absent (only a tutorial-style data-maneuvering guide appears)
web/pandas/community/blog/2019-user-survey.md8.0Near-cleanADR Quality: No decision or implementation guidance (e.g. new API, tooling change) and no consequences/trade-offs are present
pandas/io/common.py9.3Near-cleanStatic Analysis (SAST): Low: dynamic-urllib-use-detected

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. 23 of 26 evaluated dimensions are computed purely by tools and static analysis (confidence 1.0); 3 documentation/naming judgement(s) are LLM-assisted and labelled advisory. Overall confidence is 0.5 — the weighted average across measured dimensions; it falls as more of the score leans on LLM-assisted judgement and rises when it's fully tool-backed.

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

What we checked — 26 dimensions across the health lenses
D3D4D5D7D9D12D13D20D21D25D26D27D28D29D36AX5M1M2M3M4P1P2P3X1X3X4

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
  1. 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, 68 of 80 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.)
  2. 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.
  3. 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.

MethodBacksVersionEvaluator
Roslyn static analysisComplexity, cohesion, coupling, dead code, API surface, layering5.3.0✓ deterministic
Native secret scannerHardcoded secrets / credentials1.0.0✓ deterministic
jscpdCode duplication—✓ deterministic
Coverage (coverlet / dotnet-coverage)Line & branch coverage10.0.301✓ deterministic
NuGet / dotnetOutdated, vulnerable & deprecated dependencies10.0.301✓ deterministic
git / LibGit2SharpChurn hotspots, knowledge concentration, history2.43.0 · 0.31.0✓ deterministic
gitleaks · semgrep · trivy · checkovSecrets in history, SAST, CVEs, IaC & container, PII / GDPR1.86.0 · 0.69.3✓ deterministic
LLM (sampled · advisory)Documentation quality, ADR conformance, naming — sampled over a bounded sample; advisory, never a deterministic measurementLocal LLM◐ LLM · sampled · advisory

Every finding is locatable in findings.md. Run 019fdac1-817e-740f-9a5a-a6337602794d.

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.

  • D18 Solution Shape — evaluation did not complete — Dimension evaluation failed — excluded from the score.
  • D19 Documentation Quality — LLM provider failed — The model provider returned an unusable result, so this LLM-assisted dimension fell back to a measurement gap (confidence 0) rather than a penalty. Re-run with a reachable provider to score it.
  • D30 Dependency Vulnerabilities — scanner not present in this environment — The backing tool was not installed where this scan ran, so this dimension was not scored. Install the tool (or run in the hosted environment, where it is always present) for a graded result.
  • D31 IaC & Container Security — 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.
  • D32 Data Compliance (PII/GDPR) — 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.
  • D33 JS/npm 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.
  • D37 Vulnerability-disclosure Policy — 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.
  • D38 OSV 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.
  • Solution could not be loaded — run is Degraded — The C# workspace returned 0 projects, so every compiler-dependent dimension ran on estimated input. Treat the grade as indicative only; diagnostics.md records the exact cause.

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.

  • 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 (EF migration scaffolds, *.Designer.cs, model snapshots) is EXCLUDED — its repetition is the tool's, not the team's — so the score reflects hand-written duplication only; the generated footprint is reported separately under Solution Shape.
  • D5 Coupling: Coupling is measured between projects/assemblies — runtime coupling through DI, reflection, messaging or shared databases is invisible to a static reference graph.
  • D7 Architectural Integrity: Layering is checked against detected/declared rules — an architecture whose boundaries live in convention or in code review, not in a rule a scanner can read, is not enforced here.
  • D9 Test Distribution: The test-pyramid shape is inferred from project/folder naming and references, with a single test host bucketed per-file by its path tier and content signals — a suite that names tiers unconventionally and gives no per-file signal can still be mis-bucketed.
  • D12 Dependency Hygiene: Dependency health reads manifests and lockfiles — a vulnerability in a vendored/copied dependency, or risk from how a dependency is actually used, is outside this view.
  • 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").
  • D20 ADR Quality: ADR quality is an LLM read of the decision records present — it cannot know about decisions made and never recorded, and its verdict is sampled and advisory.
  • D21 Naming Consistency: Naming quality is an LLM judgement over a bounded sample — it assesses clarity/consistency of the names it sees, not domain-correctness, and is advisory.
  • D25 ADR Conformance: ADR conformance is the LLM-scored fraction of sampled code that follows recorded decisions — it checks the decisions that were written down and the slices it sampled, not unrecorded rules or the whole tree.
  • D26 Project Cohesion: Project focus is sized from members/namespaces per project — a project that is broad by deliberate design reads the same as one that has sprawled.
  • D27 Navigability: Indirection/navigability is structural — it measures hops to follow a call, not whether that indirection buys real flexibility or just ceremony.
  • 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").
  • 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.

The LLM boundary

LLM-set scores this run (4): D20, D21, D25, M4 (model: Local LLM). For these, a model reads a bounded sample and sets the numeric score (documentation, ADR quality, naming, comment value, onboarding) — D25 sets the ADR-conformance fraction over sampled code, D22 judges API accuracy over a sample. These are sampled and advisory by design: they vary at the margins between runs and are never a deterministic measurement. Every other score in this report is tool-computed at confidence 1.0.

Dimensions

D3 · God Classes10.0 / 10Exemplary✓ Tool-verified

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

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

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

0 god class(es) detected.

✓ On the Gold path — maintain.

Detailed fixes: d3_recommendation.md.

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.

D5 · Coupling6.9 / 10Adequate✓ Tool-verified

What it measures: Whether volatile projects sit underneath others that depend on them (so their churn ripples upward), and whether project dependencies form cycles. A widely-depended-on but stable shared/kernel project is healthy, not penalised.

Method: Dependency cycles via elementary-DFS over real .csproj references, plus Martin instability (afferent/efferent) per project. Exhaustive over the reference graph, deterministic.

Coverage: Exhaustive · type-level: afferent/efferent coupling + cycles computed over every production type — the population is all types, not a name convention.

Maturity: Documented → Verified → Prevented · effective 6.9 / 10 · rule-coverage 100% · ceiling Prevented

0 projects, 0 dependency cycle(s), 0 unstable depended-on project(s).

What to do

  1. Improve Coupling — currently 6.9/10. — 0 projects, 0 dependency cycle(s), 0 unstable depended-on project(s).

Detailed fixes: d5_recommendation.md.

D7 · Architectural Integrity10.0 / 10Exemplary✓ Tool-verified

What it measures: Whether the code respects its intended layering / architecture rules.

Method: Enforcement rung (Prevented/Verified/Documented) per checkable ADR via Roslyn, plus dependency cycles via the engine shared with D5/AX3. Deterministic, exact.

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

Of 10 mechanizable ADRs, 9 are prevented by analyzers, 1 by tests, 0 exist only in prose. Coverage: 100 %. Cycles found: 0.

✓ On the Gold path — maintain.

Detailed fixes: d7_recommendation.md.

D9 · Test Distribution0.0 / 10Critical✓ Tool-verified

What it measures: Whether the test suite has a healthy mix of unit / integration / end-to-end tests.

Method: Test projects classified (Unit/Integration/BDD/E2E) from compiled metadata; test methods counted exhaustively across projects with placement-agnostic disk fallback. Deterministic.

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

No test projects found.

No tests found

What to do

  1. Resolve the 1 No tests found finding(s) in Test Distribution. — One of this dimension's main actionable groups (1 recommendation-level).

Detailed fixes: d9_recommendation.md · top locations in Appendix A, every location in findings.md.

D12 · Dependency Hygiene10.0 / 10Exemplary✓ Tool-verified

What it measures: Whether dependencies are current, secure, and not bloated.

Method: Manifest scan via dotnet list package across all projects; worst-signal-per-package deduction (saturating for vulnerabilities, capped-linear for deprecation/outdated) per KLoC. Exhaustive, deterministic.

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

0 outdated, 0 vulnerable, 0 deprecated packages.

✓ On the Gold path — maintain.

Detailed fixes: d12_recommendation.md.

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.

D20 · ADR Quality / 10Strong◐ Sampled · advisory

What it measures: Whether architecture decisions are recorded well (context, decision, consequences).

Method: Per-ADR judgment by language model at low temperature with two-pass stability; confidence is share of ADRs evaluated; enforcement-field presence detected deterministically. Advisory.

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

Evaluated 23 ADR(s) individually; mean quality 7.3/10 (mixed — many ADRs miss context or consequences). 4 flagged with a specific gap.

Decision/consequences are only hinted at in notes (e.g. need to install Excel reader dependencies), with no explicit decision and no trade-offsdoc/source/getting_started/intro_tutorials/02_read_write.rst
Decision (filtering/no2 dataset usage) and consequences/trade-offs are not stated in any visible textdoc/source/getting_started/intro_tutorials/07_reshape_table_layout.rst
Decision and consequences are absent (only a tutorial-style data-maneuvering guide appears)doc/source/getting_started/intro_tutorials/10_text_data.rst
No decision or implementation guidance (e.g. new API, tooling change) and no consequences/trade-offs are presentweb/pandas/community/blog/2019-user-survey.md
ADR lacks an enforcement field · ×22doc/source/getting_started/intro_tutorials/01_table_oriented.rst

What to do

  1. Resolve the 1 Decision/consequences are only hinted at in notes (e.g. need to install… finding(s) in ADR Quality — start with 02_read_write.rst. — One of this dimension's main actionable groups (1 warning-level).
  2. Resolve the 1 Decision (filtering/no2 dataset usage) and consequences/trade-offs are… finding(s) in ADR Quality — start with 07_reshape_table_layout.rst. — One of this dimension's main actionable groups (1 warning-level).
  3. Resolve the 1 Decision and consequences are absent (only a tutorial-style… finding(s) in ADR Quality — start with 10_text_data.rst. — One of this dimension's main actionable groups (1 warning-level).

Detailed fixes: d20_recommendation.md · top locations in Appendix A, every location in findings.md.

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.

D25 · ADR Conformance / 10Exemplary◐ Sampled · advisory

What it measures: Whether the code actually follows the decisions recorded in the project's ADRs.

Method: Judged by language model at low temperature against ADRs plus a deterministic structural code summary; findings linked to repo-rooted ADR paths for traceability. Advisory.

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

4 conform / 0 violate across 23 ADRs.

✓ On the Gold path — maintain.

Detailed fixes: d25_recommendation.md.

D26 · Project Cohesion10.0 / 10Exemplary✓ Tool-verified

What it measures: Whether each project is a focused, coherent unit rather than an oversized grab-bag.

Method: Project size overshoot penalties (LoC / public-type count / namespace count, 2-of-3 flag) weighted by log magnitude. Exhaustive across projects, deterministic, LLM-independent.

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

No projects to assess.

✓ On the Gold path — maintain.

Detailed fixes: d26_recommendation.md.

D27 · Navigability10.0 / 10Exemplary✓ Tool-verified

What it measures: How far you must trace to follow a call — low indirection and co-located slices read easier.

Method: Call indirection (interface hops, cross-namespace calls, slice-locality scaled) over a sampled set of method invocations, size-aware baseline. Sampled; confidence discounted by symbol-resolution gaps.

Coverage: Slice locality from the first namespace segments, SAMPLED (≤400 methods) — not exhaustive.

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

Too little code to assess navigability.

✓ On the Gold path — maintain.

Detailed fixes: d27_recommendation.md.

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.

Secret: generic-api-key · ×2pandas/_libs/src/datetime/pd_datetime.c:280detected by gitleaks finding

What to do

  1. Resolve the 2 Secret finding(s) in Secrets (history) — start with pd_datetime.c, pixi.lock. — One of this dimension's main actionable groups (2 issue-level).

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

D29 · Static Analysis (SAST)0.3 / 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 0.3 / 10 · rule-coverage 100% · ceiling Documented

40 finding(s): 0 critical, 12 high, 27 medium, 1 low.

High: run-shell-injection · ×12.github/actions/run-tests/action.yml:34detected by semgrep finding
Medium: missing-autoescape-disabled · ×27pandas/io/formats/style.py:4061detected by semgrep finding
Low: dynamic-urllib-use-detectedpandas/io/common.py:283detected by semgrep finding

What to do

  1. Resolve the 27 Medium finding(s) in Static Analysis (SAST) — start with index.html (8), xml.py (8), layout.html (6). — One of this dimension's main actionable groups (27 warning-level).
  2. Resolve the 12 High finding(s) in Static Analysis (SAST) — start with objToJSON.c (9), action.yml, dependabot.yml. — One of this dimension's main actionable groups (12 issue-level).
  3. Resolve the 1 Low finding(s) in Static Analysis (SAST) — start with common.py. — One of this dimension's main actionable groups (1 recommendation-level).

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

D36 · Supply-chain Provenance & Signing2.5 / 10Weak✓ 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 2.5 / 10 · rule-coverage 100% · ceiling Documented

1/4 supply-chain integrity signals present (provenance, signing, SBOM, pinned actions).

No build provenance
No artifact signing
No SBOM

What to do

  1. Resolve the 1 No build provenance finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 recommendation-level).
  2. Resolve the 1 No artifact signing finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 recommendation-level).
  3. 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.

Frontend & cross-cutting dimensions

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

AX5 · Architecture & structure10.0 / 10Exemplary✓ Tool-verified

Other · Architecture — Whether the codebase has a recognisable, scale-appropriate structure (a named architectural style, or modular enough for its size) rather than being an ad-hoc ball of mud.

Method: Roslyn plus csproj analysis: architecture style detection (DDD, clean, vertical-slice, CQRS) and structure fitness for repo size. Deterministic.

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.
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 C4/PlantUML/Mermaid diagram or architecture.md — the high-level shape isn't documented.

What to do

  • Grow the ADR log (currently 23) — reach 50 to raise the maturity tier; document significant decisions as they're made.
  • Add a C4 context/container diagram (Structurizr, PlantUML or Mermaid) or an architecture.md overview.
M3 · Folder & project structure6.0 / 10Adequate✓ 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.

  • Projects aren't grouped under a src/ folder — production and tooling code are mixed at the root.
  • Test projects aren't grouped under a tests/ folder — the test surface isn't separable from production code at a glance.

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.
  • Group test projects under tests/ (or test/, spec/) so the test surface is discoverable and CI can scope it.
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.

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.

P2 · Observability0.0 / 10Critical✓ Tool-verified

Readiness · Readiness — Whether the code is diagnosable in production — structured logging, tracing/metrics, health checks.

Method: Filesystem/Roslyn scan: structured-logging frameworks (Serilog, NLog), OpenTelemetry, and health-check endpoint patterns. Exhaustive, deterministic.

  • No ILogger/Serilog usage found — production issues will be hard to diagnose.

What to do

  • Adopt ILogger (or Serilog) and log at meaningful points across the projects.
  • Consider OpenTelemetry tracing/metrics and a health-check endpoint for operability.
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/Roslyn scan: CodeQL, Dependabot, secret-scanning, and BenchmarkDotNet presence in pipelines and projects. Exhaustive, deterministic.

What to do

  • Add gitleaks/trufflehog in CI to block PRs that introduce committed secrets.
X1 · Async correctness10.0 / 10Exemplary○ Nothing flagged

Other · Code Health — Whether the code avoids sync-over-async (deadlock-prone blocking on tasks) and async void.

Method: Roslyn syntax scan: async methods scanned for .Wait()/.GetAwaiter().GetResult() and async-void outside event handlers. Deterministic, hard fact per invocation.

X3 · Exception handling10.0 / 10Exemplary○ Nothing flagged

Other · Code Health — Whether exceptions are handled rather than silently swallowed or rethrown with lost stack traces.

Method: Roslyn syntax scan: every catch clause counted; empty catches and bare rethrows flagged. Population is all catch clauses, not estimated. Deterministic, hard fact.

X4 · Structured logging10.0 / 10Exemplary○ Nothing flagged

Other · Code Health — Whether log calls use message templates (queryable) rather than interpolated strings.

Method: Roslyn syntax scan: every log call-site counted; interpolated-string first-argument violations flagged. Population is all log calls, not estimated. Deterministic.

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.

LensScoreRatingImpact
Code Health100%ExemplaryStrongest area.
Maturity64%AdequateAcceptable, with room to improve.
Readiness42%Weak — gated by D9, P2Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Security36%Weak — gated by D29, D36Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Not included — 64 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 user-facing web UI (the repo is a library/CLI/worker/headless service) — accessibility is not applicable.
  • AC2 Forms & labels — No user-facing web UI (the repo is a library/CLI/worker/headless service) — accessibility is not applicable.
  • AC3 Page structure — No user-facing web UI (the repo is a library/CLI/worker/headless service) — accessibility is not applicable.
  • AC4 Keyboard semantics — No user-facing web UI (the repo is a library/CLI/worker/headless service) — accessibility is not applicable.
  • AC5 ARIA correctness — No user-facing web UI (the repo is a library/CLI/worker/headless service) — accessibility is not applicable.
  • AC6 Visual & motion safety — No user-facing web UI (the repo is a library/CLI/worker/headless service) — accessibility is not applicable.
  • AC7 A11y enforcement — No user-facing web UI (the repo is a library/CLI/worker/headless service) — accessibility is not applicable.
  • AX1 Captive dependencies — no DI registrations detected
  • AX10 Code composition — no source files detected — code composition not applicable
  • AX2 Stateful singletons — no singleton implementations detected
  • AX3 Project dependency cycles — no csproj graph available
  • AX4 Dependency direction — no csproj graph available
  • AX6 Interface segregation — no public interfaces
  • AX7 Slice cohesion — not applicable — not a vertical-slice architecture
  • AX8 Test isolation — no test/production split to check
  • AX9 CQS / query purity — no CQRS query handlers detected — query purity is not applicable to this codebase
  • C1 Data Protection — No personal data detected in the analyzed source — no PII-typed entity/column names (Email, FirstName, DateOfBirth, …), no ASP.NET Identity / user-account model, and no stored user credentials. GDPR data-protection controls are therefore N/A here. If this is intentional, record the no-PII posture in an ADR; if the app does process personal data, name those fields conventionally so this dimension activates.
  • C2 Access Controls — No access-control surface detected in the analyzed source — no web/app surface to authorize (no HTTP API or web-UI project) and no authorization code at all (no [Authorize]/policies, no imperative guard methods). Access control is therefore N/A here — this is a library/CLI, which is authorized by its CALLER, not by itself. If this codebase grows request handlers, the dimension reactivates and a default-deny posture is expected then.
  • C3 Audit Trail — No personal data detected in the analyzed source — no PII-typed entity/column names (Email, FirstName, DateOfBirth, …), no ASP.NET Identity / user-account model, and no stored user credentials. GDPR data-protection controls are therefore N/A here. If this is intentional, record the no-PII posture in an ADR; if the app does process personal data, name those fields conventionally so this dimension activates.
  • C4 Data Retention — No personal data detected in the analyzed source — no PII-typed entity/column names (Email, FirstName, DateOfBirth, …), no ASP.NET Identity / user-account model, and no stored user credentials. GDPR data-protection controls are therefore N/A here. If this is intentional, record the no-PII posture in an ADR; if the app does process personal data, name those fields conventionally so this dimension activates.
  • C5 Data-Subject Rights — No personal data detected in the analyzed source — no PII-typed entity/column names (Email, FirstName, DateOfBirth, …), no ASP.NET Identity / user-account model, and no stored user credentials. GDPR data-protection controls are therefore N/A here. If this is intentional, record the no-PII posture in an ADR; if the app does process personal data, name those fields conventionally so this dimension activates.
  • D1 Cyclomatic Complexity — No production methods were analyzable, so cyclomatic complexity was not measured (the solution likely failed to load or has no production code).
  • D10 Test Quality — ~346187 lines of test code exist on disk but weren't loaded from the analyzed solution (excluded from the .sln, or co-located/using a test attribute not loaded here), so test quality couldn't be assessed. Include the tests in the analyzed solution to enable this check.
  • D11 Test Reliability — Test reliability not included
  • D14 License Compliance — license scan produced no result — the tool ran but its JSON output could not be parsed; the offline NuGet fallback resolved nothing
  • D15 Churn × Complexity Hotspots — single-commit history — no usable git history window to measure hotspots
  • D16 Bus Factor — single-maintainer — knowledge-concentration (bus factor) risk
  • D17 Explicit Debt — the C# workspace loaded 0 projects, so explicit-debt density could not be measured
  • D18 Solution Shape — Dimension evaluation failed
  • D19 Documentation Quality — LLM evaluation failed
  • D2 Cognitive Complexity — No production methods were analyzable, so cognitive complexity was not measured (the solution likely failed to load or has no production code).
  • D22 Internal API Consistency — No exposed public API
  • D23 Boundary Type-Coupling — Zero projects and zero LoC mean the codebase is trivial and has no structure to justify boundaries.
  • D24 Comment Value — No inline comments to assess — comment value is not applicable here.
  • D30 Dependency Vulnerabilities — No .NET solution found; no NuGet dependencies to scan for vulnerabilities.
  • D31 IaC & Container Security — No Infrastructure-as-Code or container manifests found (Dockerfile, Terraform, Kubernetes/Helm, CloudFormation); nothing to scan.
  • D32 Data Compliance (PII/GDPR) — No PII/GDPR ruleset is bundled (the public p/gdpr semgrep pack was retired) — data compliance is not assessed in this scan.
  • D33 JS/npm Dependency Vulnerabilities — No JS/npm manifest or lockfile found outside bin/obj (package.json, package-lock.json, yarn.lock, pnpm-lock.yaml, bun.lockb); no JS dependencies to scan.
  • D34 Knowledge Freshness — early-stage repository — too little history to judge knowledge freshness
  • D35 Change Coupling — no production change history to mine for change-coupling
  • D37 Vulnerability-disclosure Policy — No vulnerability-disclosure policy file found (SECURITY.md, .github/SECURITY.md, docs/SECURITY.md, .well-known/security.txt). A coordinated-disclosure policy may live off-repo, so this is not evidenced rather than failed.
  • D38 OSV Dependency Vulnerabilities — No JS/npm lockfile found outside bin/obj (package-lock.json, yarn.lock, pnpm-lock.yaml, bun.lockb); nothing for OSV to scan.
  • D39 IL Efficiency — The target did not build, so no IL was available to measure.
  • D6 Cohesion (LCOM4) — No production classes were analyzable, so cohesion (LCOM4) was not measured (the solution likely failed to load or has no production code).
  • D8 Code Coverage — Coverage not measured — test suite did not build
  • DM1 Domain Modelling — not run — 0/3 markers found
  • ED1 Event-Driven — not run — 0/3 markers found
  • ED5 Idempotency — no mutating command handlers or message consumers detected — idempotency check not applicable
  • ES1 Event Sourcing — not run — 0/3 markers found
  • GD1 Unfinished & placeholder code — no source files
  • IC1 Incompleteness & stubs — no C# methods found
  • P12 CI test-gate honesty — no data
  • P4 Deployment & Rollback — not evidenced — no deploy/rollback/approval signal in the repo; absence of evidence is not evidence of a manual release
  • P5 DR & Backup — not evidenced — repo shows no backup/RTO/RPO controls; absence of evidence is not evidence of a working control
  • P6 Release Hygiene — not evidenced — no changelog, version stamp or semver release tag in the repo
  • P7 Outbound HTTP resilience — not applicable — this isn't a service/API/worker
  • P8 Schema migrations — no EF Core usage detected
  • P9 Domain vs controller coverage — no coverage report found on disk — run tests with `--collect:"XPlat Code Coverage"` (or in CI) to enable this cross-layer check
  • PF1 Benchmark discipline — Performance is assessed only for perf-relevant repos — a packaged library, one that ships benchmarks, or one already using allocation-aware APIs. This repo isn't one, so the Performance lens is not applicable and is excluded from the score.
  • PF2 Allocation hygiene — Performance is assessed only for perf-relevant repos — a packaged library, one that ships benchmarks, or one already using allocation-aware APIs. This repo isn't one, so the Performance lens is not applicable and is excluded from the score.
  • PF3 Async & latency hygiene — Performance is assessed only for perf-relevant repos — a packaged library, one that ships benchmarks, or one already using allocation-aware APIs. This repo isn't one, so the Performance lens is not applicable and is excluded from the score.
  • S1 Web-Security Posture — No web surface detected in the analyzed source — no HTTP API or web-UI project (no controllers/minimal-API endpoints, no Razor/Blazor views) and no web middleware (HTTPS redirection, HSTS, security headers, cookies). Transport security, security headers, secure cookies, CSRF/input-validation and middleware-order controls are therefore N/A here — this is a library/CLI/worker, not a web app. Crypto hygiene was still checked and found nothing to flag. If this codebase becomes web-facing, the dimension reactivates automatically.
  • X2 Cancellation propagation — no async methods found
  • X5 Nullable reference types — no NRT-eligible projects

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 · ×12
  • High: run-shell-injection .github/actions/run-tests/action.yml:34 — Using variable interpolation `${{...}}` with `github` context data in a `run:` step could allow an attacker to inject their own code into the runner. This would allow them to steal secrets and code. `github` context data can have arbitrary user input and should be treated as untrusted. Instead, use an intermediate environment variable with `env:` to store the data and use the environment variable in the `run:` script. Be sure to use double-quotes the environment variable, like this: "$ENVVAR".
  • High: dependabot-missing-cooldown .github/dependabot.yml:3 — This Dependabot configuration does not set a cooldown period. Newly published packages can be malicious or unstable. Add a `cooldown` block with `default-days: 7` to each `package-ecosystem` entry under `updates` to wait 7 days before proposing updates to newly published package versions. Reference: https://docs.github.com/en/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file#cooldown
  • High: insecure-use-string-copy-fn pandas/_libs/src/vendored/ujson/python/objToJSON.c:1105 — Finding triggers whenever there is a strcpy or strncpy used. This is an issue because strcpy does not affirm the size of the destination array and strncpy will not automatically NULL-terminate strings. This can lead to buffer overflows, which can cause program crashes and potentially let an attacker inject code in the program. Fix this by using strcpy_s instead (although note that strcpy_s is an optional part of the C11 standard, and so may not be available).
  • High: insecure-use-string-copy-fn pandas/_libs/src/vendored/ujson/python/objToJSON.c:1108 — Finding triggers whenever there is a strcpy or strncpy used. This is an issue because strcpy does not affirm the size of the destination array and strncpy will not automatically NULL-terminate strings. This can lead to buffer overflows, which can cause program crashes and potentially let an attacker inject code in the program. Fix this by using strcpy_s instead (although note that strcpy_s is an optional part of the C11 standard, and so may not be available).
  • High: insecure-use-string-copy-fn pandas/_libs/src/vendored/ujson/python/objToJSON.c:1158 — Finding triggers whenever there is a strcpy or strncpy used. This is an issue because strcpy does not affirm the size of the destination array and strncpy will not automatically NULL-terminate strings. This can lead to buffer overflows, which can cause program crashes and potentially let an attacker inject code in the program. Fix this by using strcpy_s instead (although note that strcpy_s is an optional part of the C11 standard, and so may not be available).
  • High: insecure-use-string-copy-fn pandas/_libs/src/vendored/ujson/python/objToJSON.c:1161 — Finding triggers whenever there is a strcpy or strncpy used. This is an issue because strcpy does not affirm the size of the destination array and strncpy will not automatically NULL-terminate strings. This can lead to buffer overflows, which can cause program crashes and potentially let an attacker inject code in the program. Fix this by using strcpy_s instead (although note that strcpy_s is an optional part of the C11 standard, and so may not be available).
  • High: insecure-use-string-copy-fn pandas/_libs/src/vendored/ujson/python/objToJSON.c:1164 — Finding triggers whenever there is a strcpy or strncpy used. This is an issue because strcpy does not affirm the size of the destination array and strncpy will not automatically NULL-terminate strings. This can lead to buffer overflows, which can cause program crashes and potentially let an attacker inject code in the program. Fix this by using strcpy_s instead (although note that strcpy_s is an optional part of the C11 standard, and so may not be available).
  • High: insecure-use-string-copy-fn pandas/_libs/src/vendored/ujson/python/objToJSON.c:1214 — Finding triggers whenever there is a strcpy or strncpy used. This is an issue because strcpy does not affirm the size of the destination array and strncpy will not automatically NULL-terminate strings. This can lead to buffer overflows, which can cause program crashes and potentially let an attacker inject code in the program. Fix this by using strcpy_s instead (although note that strcpy_s is an optional part of the C11 standard, and so may not be available).
  • High: insecure-use-string-copy-fn pandas/_libs/src/vendored/ujson/python/objToJSON.c:1217 — Finding triggers whenever there is a strcpy or strncpy used. This is an issue because strcpy does not affirm the size of the destination array and strncpy will not automatically NULL-terminate strings. This can lead to buffer overflows, which can cause program crashes and potentially let an attacker inject code in the program. Fix this by using strcpy_s instead (although note that strcpy_s is an optional part of the C11 standard, and so may not be available).
  • High: insecure-use-string-copy-fn pandas/_libs/src/vendored/ujson/python/objToJSON.c:1220 — Finding triggers whenever there is a strcpy or strncpy used. This is an issue because strcpy does not affirm the size of the destination array and strncpy will not automatically NULL-terminate strings. This can lead to buffer overflows, which can cause program crashes and potentially let an attacker inject code in the program. Fix this by using strcpy_s instead (although note that strcpy_s is an optional part of the C11 standard, and so may not be available).
  • High: insecure-use-string-copy-fn pandas/_libs/src/vendored/ujson/python/objToJSON.c:1414 — Finding triggers whenever there is a strcpy or strncpy used. This is an issue because strcpy does not affirm the size of the destination array and strncpy will not automatically NULL-terminate strings. This can lead to buffer overflows, which can cause program crashes and potentially let an attacker inject code in the program. Fix this by using strcpy_s instead (although note that strcpy_s is an optional part of the C11 standard, and so may not be available).
  • High: eval-detected pandas/core/computation/ops.py:430 — Detected the use of eval(). eval() can be dangerous if used to evaluate dynamic content. If this content can be input from outside the program, this may be a code injection vulnerability. Ensure evaluated content is not definable by external sources.
D28 · Secrets (history) · Secret · ×2
  • Secret: generic-api-key pandas/_libs/src/datetime/pd_datetime.c:280 — matched rule 'generic-api-key'
  • Secret: facebook-page-access-token pixi.lock:47025 — matched rule 'facebook-page-access-token'
D18 · Solution Shape · Dimension evaluation failed · ×1
  • Dimension evaluation failed — no .NET solution found at target path
Warning — 34 finding(s)
D29 · Static Analysis (SAST) · Medium · ×27
  • Medium: missing-autoescape-disabled pandas/io/formats/style.py:4061 — Detected a Jinja2 environment without autoescaping. Jinja2 does not autoescape by default. This is dangerous if you are rendering to a browser because this allows for cross-site scripting (XSS) attacks. If you are in a web context, enable autoescaping by setting 'autoescape=True.' You may also consider using 'jinja2.select_autoescape()' to only enable automatic escaping for certain file extensions.
  • Medium: missing-autoescape-disabled pandas/io/formats/style_render.py:77 — Detected a Jinja2 environment without autoescaping. Jinja2 does not autoescape by default. This is dangerous if you are rendering to a browser because this allows for cross-site scripting (XSS) attacks. If you are in a web context, enable autoescaping by setting 'autoescape=True.' You may also consider using 'jinja2.select_autoescape()' to only enable automatic escaping for certain file extensions.
  • Medium: use-defused-xml pandas/io/formats/xml.py:370 — The Python documentation recommends using `defusedxml` instead of `xml` because the native Python `xml` library is vulnerable to XML External Entity (XXE) attacks. These attacks can leak confidential data and "XML bombs" can cause denial of service.
  • Medium: use-defused-xml pandas/io/formats/xml.py:409 — The Python documentation recommends using `defusedxml` instead of `xml` because the native Python `xml` library is vulnerable to XML External Entity (XXE) attacks. These attacks can leak confidential data and "XML bombs" can cause denial of service.
  • Medium: use-defused-xml pandas/io/formats/xml.py:432 — The Python documentation recommends using `defusedxml` instead of `xml` because the native Python `xml` library is vulnerable to XML External Entity (XXE) attacks. These attacks can leak confidential data and "XML bombs" can cause denial of service.
  • Medium: use-defused-xml pandas/io/formats/xml.py:443 — The Python documentation recommends using `defusedxml` instead of `xml` because the native Python `xml` library is vulnerable to XML External Entity (XXE) attacks. These attacks can leak confidential data and "XML bombs" can cause denial of service.
  • Medium: avoid-pickle pandas/io/pickle.py:128 — Avoid using `pickle`, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format.
  • Medium: avoid-pickle pandas/io/pickle.py:248 — Avoid using `pickle`, which is known to lead to code execution vulnerabilities. When unpickling, the serialized data could be manipulated to run arbitrary code. Instead, consider serializing the relevant data as JSON or a similar text-based serialization format.
  • Medium: use-defused-xml pandas/io/xml.py:40 — The Python documentation recommends using `defusedxml` instead of `xml` because the native Python `xml` library is vulnerable to XML External Entity (XXE) attacks. These attacks can leak confidential data and "XML bombs" can cause denial of service.
  • Medium: use-defused-xml pandas/io/xml.py:454 — The Python documentation recommends using `defusedxml` instead of `xml` because the native Python `xml` library is vulnerable to XML External Entity (XXE) attacks. These attacks can leak confidential data and "XML bombs" can cause denial of service.
  • Medium: use-defused-xml pandas/io/xml.py:543 — The Python documentation recommends using `defusedxml` instead of `xml` because the native Python `xml` library is vulnerable to XML External Entity (XXE) attacks. These attacks can leak confidential data and "XML bombs" can cause denial of service.
  • Medium: use-defused-xml-parse pandas/io/xml.py:557 — The native Python `xml` library is vulnerable to XML External Entity (XXE) attacks. These attacks can leak confidential data and "XML bombs" can cause denial of service. Do not use this library to parse untrusted input. Instead the Python documentation recommends using `defusedxml`.
  • Medium: unquoted-attribute-var web/pandas/_templates/layout.html:34 — Detected a unquoted template variable as an attribute. If unquoted, a malicious actor could inject custom JavaScript handlers. To fix this, add quotes around the template expression, like this: "{{ expr }}".
  • Medium: unquoted-attribute-var web/pandas/_templates/layout.html:34 — Detected a unquoted template variable as an attribute. If unquoted, a malicious actor could inject custom JavaScript handlers. To fix this, add quotes around the template expression, like this: "{{ expr }}".
  • Medium: unquoted-attribute-var web/pandas/_templates/layout.html:47 — Detected a unquoted template variable as an attribute. If unquoted, a malicious actor could inject custom JavaScript handlers. To fix this, add quotes around the template expression, like this: "{{ expr }}".
  • Medium: unquoted-attribute-var web/pandas/_templates/layout.html:47 — Detected a unquoted template variable as an attribute. If unquoted, a malicious actor could inject custom JavaScript handlers. To fix this, add quotes around the template expression, like this: "{{ expr }}".
  • Medium: var-in-script-tag web/pandas/_templates/layout.html:106 — Detected a template variable used in a script tag. Although template variables are HTML escaped, HTML escaping does not always prevent cross-site scripting (XSS) attacks when used directly in JavaScript. If you need this data on the rendered page, consider placing it in the HTML portion (outside of a script tag). Alternatively, use a JavaScript-specific encoder, such as the one available in OWASP ESAPI. For Django, you may also consider using the 'json_script' template tag and retrieving the data in your script by using the element ID (e.g., `document.getElementById`).
  • Medium: var-in-script-tag web/pandas/_templates/layout.html:107 — Detected a template variable used in a script tag. Although template variables are HTML escaped, HTML escaping does not always prevent cross-site scripting (XSS) attacks when used directly in JavaScript. If you need this data on the rendered page, consider placing it in the HTML portion (outside of a script tag). Alternatively, use a JavaScript-specific encoder, such as the one available in OWASP ESAPI. For Django, you may also consider using the 'json_script' template tag and retrieving the data in your script by using the element ID (e.g., `document.getElementById`).
  • Medium: unquoted-attribute-var web/pandas/community/blog/index.html:7 — Detected a unquoted template variable as an attribute. If unquoted, a malicious actor could inject custom JavaScript handlers. To fix this, add quotes around the template expression, like this: "{{ expr }}".
  • Medium: unquoted-attribute-var web/pandas/community/blog/index.html:7 — Detected a unquoted template variable as an attribute. If unquoted, a malicious actor could inject custom JavaScript handlers. To fix this, add quotes around the template expression, like this: "{{ expr }}".
  • Medium: unquoted-attribute-var web/pandas/community/blog/index.html:10 — Detected a unquoted template variable as an attribute. If unquoted, a malicious actor could inject custom JavaScript handlers. To fix this, add quotes around the template expression, like this: "{{ expr }}".
  • Medium: unquoted-attribute-var web/pandas/community/blog/index.html:10 — Detected a unquoted template variable as an attribute. If unquoted, a malicious actor could inject custom JavaScript handlers. To fix this, add quotes around the template expression, like this: "{{ expr }}".
  • Medium: var-in-href web/pandas/index.html:51 — Detected a template variable used in an anchor tag with the 'href' attribute. This allows a malicious actor to input the 'javascript:' URI and is subject to cross- site scripting (XSS) attacks. If using Flask, use 'url_for()' to safely generate a URL. If using Django, use the 'url' filter to safely generate a URL. If using Mustache, use a URL encoding library, or prepend a slash '/' to the variable for relative links (`href="/{{link}}"`). You may also consider setting the Content Security Policy (CSP) header.
  • Medium: var-in-href web/pandas/index.html:69 — Detected a template variable used in an anchor tag with the 'href' attribute. This allows a malicious actor to input the 'javascript:' URI and is subject to cross- site scripting (XSS) attacks. If using Flask, use 'url_for()' to safely generate a URL. If using Django, use the 'url' filter to safely generate a URL. If using Mustache, use a URL encoding library, or prepend a slash '/' to the variable for relative links (`href="/{{link}}"`). You may also consider setting the Content Security Policy (CSP) header.
  • Medium: var-in-href web/pandas/index.html:117 — Detected a template variable used in an anchor tag with the 'href' attribute. This allows a malicious actor to input the 'javascript:' URI and is subject to cross- site scripting (XSS) attacks. If using Flask, use 'url_for()' to safely generate a URL. If using Django, use the 'url' filter to safely generate a URL. If using Mustache, use a URL encoding library, or prepend a slash '/' to the variable for relative links (`href="/{{link}}"`). You may also consider setting the Content Security Policy (CSP) header.
  • + 2 more in this group — see findings.md.
D16 · Bus Factor · single-maintainer · ×1
  • single-maintainer — knowledge-concentration (bus factor) risk — single-maintainer — knowledge-concentration (bus factor) risk (0 author(s) across 0 commit(s) sampled).
D19 · Documentation Quality · LLM evaluation failed · ×1
  • LLM evaluation failed — JSON parse error: Expected end of string, but instead reached end of data. Path: $.findings[0].issue | LineNumber: 0 | BytePositionInLine: 1062.
D20 · ADR Quality · Decision/consequences are only hinted at in notes (e.g. need to install Excel reader dependencies), with no explicit decision and no trade-offs · ×1
  • Decision/consequences are only hinted at in notes (e.g. need to install Excel reader dependencies), with no explicit decision and no trade-offs doc/source/getting_started/intro_tutorials/02_read_write.rst — State the concrete data-read/write API decisions (read_csv/read_excel/to_excel/to_sql) and their required parameters plus any consequences such as dependency installation
D20 · ADR Quality · Decision (filtering/no2 dataset usage) and consequences/trade-offs are not stated in any visible text · ×1
  • Decision (filtering/no2 dataset usage) and consequences/trade-offs are not stated in any visible text doc/source/getting_started/intro_tutorials/07_reshape_table_layout.rst
D20 · ADR Quality · Decision and consequences are absent (only a tutorial-style data-maneuvering guide appears) · ×1
  • Decision and consequences are absent (only a tutorial-style data-maneuvering guide appears) doc/source/getting_started/intro_tutorials/10_text_data.rst — Add a Context/Problem section explaining why textual-data manipulation is needed and the decision to use str accessor methods, plus a Consequences section noting trade-offs such as element-wise processing overhead or loss of original ordering
D20 · ADR Quality · No decision or implementation guidance (e.g. new API, tooling change) and no consequences/trade-offs are present · ×1
  • No decision or implementation guidance (e.g. new API, tooling change) and no consequences/trade-offs are present web/pandas/community/blog/2019-user-survey.md — Add the explicit decision: what Pandas APIs were changed/added based on survey results, and document any consequences such as migration effort for Python 2-only code
D8 · Code Coverage · Coverage not measured · ×1
  • Coverage not measured — test suite did not build — Coverage NOT MEASURED: the repo's own test suite did not build (a C#/MSBuild compiler error in the test code), so no coverage could be collected. It is excluded from the score rather than counted as a near-zero defect. Fix the test build, or commit the Cobertura/OpenCover/lcov report your CI already produces, and real coverage will be measured.
Recommendation — 30 finding(s)
D20 · ADR Quality · ADR lacks an enforcement field · ×22
  • ADR lacks an enforcement field doc/source/getting_started/intro_tutorials/01_table_oriented.rst — Add an `enforcement:` frontmatter field or a plain `Enforcement:` header line (prose/test/analyzer/not-applicable) to make the rung explicit — the other ADRs in this repo already declare one.
  • ADR lacks an enforcement field doc/source/getting_started/intro_tutorials/02_read_write.rst — Add an `enforcement:` frontmatter field or a plain `Enforcement:` header line (prose/test/analyzer/not-applicable) to make the rung explicit — the other ADRs in this repo already declare one.
  • ADR lacks an enforcement field doc/source/getting_started/intro_tutorials/03_subset_data.rst — Add an `enforcement:` frontmatter field or a plain `Enforcement:` header line (prose/test/analyzer/not-applicable) to make the rung explicit — the other ADRs in this repo already declare one.
  • ADR lacks an enforcement field doc/source/getting_started/intro_tutorials/04_plotting.rst — Add an `enforcement:` frontmatter field or a plain `Enforcement:` header line (prose/test/analyzer/not-applicable) to make the rung explicit — the other ADRs in this repo already declare one.
  • ADR lacks an enforcement field doc/source/getting_started/intro_tutorials/05_add_columns.rst — Add an `enforcement:` frontmatter field or a plain `Enforcement:` header line (prose/test/analyzer/not-applicable) to make the rung explicit — the other ADRs in this repo already declare one.
  • ADR lacks an enforcement field doc/source/getting_started/intro_tutorials/06_calculate_statistics.rst — Add an `enforcement:` frontmatter field or a plain `Enforcement:` header line (prose/test/analyzer/not-applicable) to make the rung explicit — the other ADRs in this repo already declare one.
  • ADR lacks an enforcement field doc/source/getting_started/intro_tutorials/07_reshape_table_layout.rst — Add an `enforcement:` frontmatter field or a plain `Enforcement:` header line (prose/test/analyzer/not-applicable) to make the rung explicit — the other ADRs in this repo already declare one.
  • ADR lacks an enforcement field doc/source/getting_started/intro_tutorials/08_combine_dataframes.rst — Add an `enforcement:` frontmatter field or a plain `Enforcement:` header line (prose/test/analyzer/not-applicable) to make the rung explicit — the other ADRs in this repo already declare one.
  • ADR lacks an enforcement field doc/source/getting_started/intro_tutorials/09_timeseries.rst — Add an `enforcement:` frontmatter field or a plain `Enforcement:` header line (prose/test/analyzer/not-applicable) to make the rung explicit — the other ADRs in this repo already declare one.
  • ADR lacks an enforcement field doc/source/getting_started/intro_tutorials/10_text_data.rst — Add an `enforcement:` frontmatter field or a plain `Enforcement:` header line (prose/test/analyzer/not-applicable) to make the rung explicit — the other ADRs in this repo already declare one.
  • ADR lacks an enforcement field doc/source/user_guide/10min.rst — Add an `enforcement:` frontmatter field or a plain `Enforcement:` header line (prose/test/analyzer/not-applicable) to make the rung explicit — the other ADRs in this repo already declare one.
  • ADR lacks an enforcement field web/pandas/community/blog/2019-user-survey.md — Add an `enforcement:` frontmatter field or a plain `Enforcement:` header line (prose/test/analyzer/not-applicable) to make the rung explicit — the other ADRs in this repo already declare one.
  • ADR lacks an enforcement field web/pandas/pdeps/0001-purpose-and-guidelines.md — Add an `enforcement:` frontmatter field or a plain `Enforcement:` header line (prose/test/analyzer/not-applicable) to make the rung explicit — the other ADRs in this repo already declare one.
  • ADR lacks an enforcement field web/pandas/pdeps/0004-consistent-to-datetime-parsing.md — Add an `enforcement:` frontmatter field or a plain `Enforcement:` header line (prose/test/analyzer/not-applicable) to make the rung explicit — the other ADRs in this repo already declare one.
  • ADR lacks an enforcement field web/pandas/pdeps/0005-no-default-index-mode.md — Add an `enforcement:` frontmatter field or a plain `Enforcement:` header line (prose/test/analyzer/not-applicable) to make the rung explicit — the other ADRs in this repo already declare one.
  • ADR lacks an enforcement field web/pandas/pdeps/0006-ban-upcasting.md — Add an `enforcement:` frontmatter field or a plain `Enforcement:` header line (prose/test/analyzer/not-applicable) to make the rung explicit — the other ADRs in this repo already declare one.
  • ADR lacks an enforcement field web/pandas/pdeps/0007-copy-on-write.md — Add an `enforcement:` frontmatter field or a plain `Enforcement:` header line (prose/test/analyzer/not-applicable) to make the rung explicit — the other ADRs in this repo already declare one.
  • ADR lacks an enforcement field web/pandas/pdeps/0008-inplace-methods-in-pandas.md — Add an `enforcement:` frontmatter field or a plain `Enforcement:` header line (prose/test/analyzer/not-applicable) to make the rung explicit — the other ADRs in this repo already declare one.
  • ADR lacks an enforcement field web/pandas/pdeps/0009-io-extensions.md — Add an `enforcement:` frontmatter field or a plain `Enforcement:` header line (prose/test/analyzer/not-applicable) to make the rung explicit — the other ADRs in this repo already declare one.
  • ADR lacks an enforcement field web/pandas/pdeps/0010-required-pyarrow-dependency.md — Add an `enforcement:` frontmatter field or a plain `Enforcement:` header line (prose/test/analyzer/not-applicable) to make the rung explicit — the other ADRs in this repo already declare one.
  • ADR lacks an enforcement field web/pandas/pdeps/0012-compact-and-reversible-JSON-interface.md — Add an `enforcement:` frontmatter field or a plain `Enforcement:` header line (prose/test/analyzer/not-applicable) to make the rung explicit — the other ADRs in this repo already declare one.
  • ADR lacks an enforcement field web/pandas/pdeps/0014-string-dtype.md — Add an `enforcement:` frontmatter field or a plain `Enforcement:` header line (prose/test/analyzer/not-applicable) to make the rung explicit — the other ADRs in this repo already declare one.
D11 · Test Reliability · Test reliability not included · ×1
  • Test reliability not included — No test projects found, so reliability couldn't be assessed.
D15 · Churn × Complexity Hotspots · single-commit history · ×1
  • single-commit history — no usable git history window to measure hotspots — single-commit history — no usable git history window to measure hotspots: a single-commit clone exposes no history window, so the churn × complexity hotspot signal is unavailable — not scored for this run.
D29 · Static Analysis (SAST) · Low · ×1
  • Low: dynamic-urllib-use-detected pandas/io/common.py:283 — Detected a dynamic value being used with urllib. urllib supports 'file://' schemes, so a dynamic value controlled by a malicious actor may allow them to read arbitrary files. Audit uses of urllib calls to ensure user data cannot control the URLs, or consider using the 'requests' library instead.
D34 · Knowledge Freshness · early-stage repository · ×1
  • early-stage repository — too little history to judge knowledge freshness — early-stage repository — too little history to judge knowledge freshness (0 commit(s) sampled).
D36 · Supply-chain Provenance & Signing · No build provenance · ×1
  • No build provenance — No SLSA provenance generation or build attestation found in CI (e.g. slsa-github-generator, actions/attest-build-provenance).
D36 · Supply-chain Provenance & Signing · No artifact signing · ×1
  • No artifact signing — No artifact signing found in CI (e.g. cosign / sigstore / gitsign).
D36 · Supply-chain Provenance & Signing · No SBOM · ×1
  • No SBOM — No SBOM generation or committed SBOM found (e.g. syft / anchore/sbom-action / *.spdx.json / *.cdx.json).
D9 · Test Distribution · No tests found · ×1
  • No tests found — No test projects found in the repository.
Info — 1 finding(s)
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.

DimensionToolVersionCommandFindingsRaw output
D28 · Secrets (history)gitleaks—gitleaks detect --no-banner --report-format json --report-path /dev/stdout --exit-code 0 --source .2artifacts/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 .40artifacts/raw/semgrep.json
D30 · Dependency Vulnerabilitiesdotnet (no .NET solution)—dotnet (no .NET solution): not present in this environment0—
D31 · IaC & Container Securitytrivy—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 — No PII/GDPR ruleset is bundled (the public p/gdpr semgrep pack was retired) — data compliance is not assessed in this scan.0—
D33 · JS/npm Dependency Vulnerabilitiestrivy—trivy: not applicable — No JS/npm manifest or lockfile found outside bin/obj (package.json, package-lock.json, yarn.lock, pnpm-lock.yaml, bun.lockb); no JS dependencies to scan.0—
D37 · Vulnerability-disclosure Policydisclosure—disclosure: not applicable — No vulnerability-disclosure policy file found (SECURITY.md, .github/SECURITY.md, docs/SECURITY.md, .well-known/security.txt). A coordinated-disclosure policy may live off-repo, so this is not evidenced rather than failed.0—
D38 · OSV Dependency Vulnerabilitiesosv-scanner—osv-scanner: not applicable — No JS/npm lockfile found outside bin/obj (package-lock.json, yarn.lock, pnpm-lock.yaml, bun.lockb); nothing for OSV to scan.0—

Run 019fdac1-817e-740f-9a5a-a6337602794d · every finding is also locatable in findings.md, and the complete scoring record (with exit codes + durations) in sidecar.json.

Issues: 15 · Warnings: 34 · Recommendations: 30 · Info: 1 — Appendix A · all findings · full markdown report.
Generated by Watchdog — deterministic code-health analysis. 07-08-2026 @ 05:45 UTC.

Downloadable artifacts

Machine-readable and reproducible from this commit + frozen rubric — drop them straight into a contract appendix, a CRA dossier, or a downstream SCA / VEX tool.

⬇ Findings, MITRE CWE-tagged .sarif⬇ Health changelog .md