Custom-Modes-Roo-Code — 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.

jtgsystems/Custom-Modes-Roo-Code is sound in substance but carries real gaps (67%). It is not in crisis, but the issues below raise the cost of changing it — friction its consumers ultimately inherit.

It is strongest in Architecture (100%) — the structure is clean and changes stay contained. Security (88%) is solid too.

The area that most needs attention is Readiness (60%) — releases are harder to depend on — versioning, release notes and dependency hygiene are thin, so consumers can't easily tell what changed or trust an upgrade. Maturity (63%) is the next concern — onboarding is slow — key decisions and the architecture aren't written down, so contributors have to reverse-engineer the intent.

Leadership focus, highest impact first: SAST step to CI running what this repository's stack ships (Security & performance tooling); Document RTO/RPO and a tested restore procedure (a backup… (DR & Backup); Record significant decisions one document per decision (Architecture documentation).

For scale: Small (~3,649 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 80% Strong Architecture 100% ★ Exemplary Maturity 63% Adequate · gated by M2 Readiness 60% Adequate · gated by P3 Security 88% Strong

How the score is built — each lens's share of the headline Width is the lens's weight in the worst-heaviest fold (the weakest area pulls hardest); colour is that lens's own band. A lens fixes the score in proportion to its width.

Readiness 60% · 47% weightMaturity 63% · 26% weightCode Health 80% · 14% weightSecurity 88% · 8% weightArchitecture 100% · 4% weight

Raise Readiness 60 → 70 (the Healthy floor) ⇒ headline 67 → ~71.

Code composition — where the lines go

Tests 100%

Rebuild cost & value ~ Modeled — €1,600–€7,900

Cost to rebuild	€1,600–€7,900 (0.1 person-years (26–83 h), ~1 engineer)
Domain complexity	Standard — harder problems cost more per line
Quality factor	0.9× (at 67% 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 ~€4,700 to rebuild). Its weakest lens is Readiness at 60% — the part of that asset most exposed by the findings below.

How we model this: boilerplate at a scaffolding rate + logic × domain Standard (×1.0) — standard service × a 0.9× quality factor, at €60–95/h; indicative, ±~30% · size measured directly from source · effort from total production LoC as straight-line logic (the tier split is a C#-only syntax walk), a conservative lower bound. Indicative only — most sensitive to the hourly rate and the domain tier (both tunable in config).

Top priorities

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

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

+8.1 pts · Medium effort · Security & performance tooling

Document RTO/RPO and a tested restore procedure (a backup config alone isn't disaster recovery).

+8.1 pts · Medium effort · DR & Backup

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).

+6.4 pts · Medium effort · Architecture documentation

Diagnosis — what's actually going on

The top fix pays for itself · High · Economics

The top-ranked fix costs roughly 3–10 engineer-days once. Not doing it costs about 58.1–387.6 engineer-days every year, paid as drag on the ~1,162,809 lines this team changes annually — a bill that arrives whether or not anyone books it. On those figures the fix breaks even in roughly 1–2 months and is free after that. Method, stated so this is not read as a quotation: debt from the ranked task's effort band; interest = annual changed lines (measured, annualised from the 90-day window) ÷ an ASSUMED 150–400 lines per engineer-day × the 2–5% drag implied by the code-quality signals; breaking point = debt ÷ annual interest. A modelled planning range built from measured inputs and one named assumption — not a quotation, a valuation, or a certified figure.

Evidence: D15 churn: 286,720 line(s) changed over a 90-day window ⇒ ~1,162,809/year · D1/D2/D4 code quality: averaging 7.3/10 ⇒ a 2–5% drag on each change · top-ranked remediation: Medium effort ⇒ about 3–10 engineer-day(s)

→ Do the top-ranked fix now if this code will still be yours in 2 months.

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

This is a Small asset (~0.1 person-years to rebuild), and its weakest lens is Readiness at 60%. 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 (3,649 LoC) · weakest lens: Readiness 60%

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

Highest-leverage move · Medium · Leverage

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

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

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

A velocity tax on every change · Medium · Economics

The code-quality signals (complexity, duplication, cohesion) average 7.3/10, which acts as a tax on every change in the weaker areas: modifications there plausibly cost on the order of 2–5% more than in clean code, and the tax compounds as the codebase grows. (A modelled estimate, not a measured fact.)

Evidence: D1/D2/D4 code quality: averaging 7.3/10 across the code-quality signals actually measured

→ Pay it down where churn is highest — the hotspots — not everywhere; that's where the tax is actually paid.

At a glance — Code Health · 80% · Strong

The Good

D1 · Python Cyclomatic Complexity 8.2 D3 · Python God Classes 10.0 D4 · Python Code Duplication 9.4

The Ugly

D2 · Python Cognitive Complexity 4.3

At a glance — Architecture · 100% · Exemplary

The Good

D35 · Python Change Coupling 10.0

At a glance — Maturity · 63% · Adequate · gated by M2

The Good

D15 · Python Churn × Complexity Hotspots 10.0 D16 · Python Bus Factor 9.5 D19 · Python Documentation Quality 9.0 D21 · Python Naming Consistency 10.0 D34 · Python Knowledge Freshness 10.0 M1 · Repo Documentation (README) 7.3 M3 · Repo Folder & project structure 8.0 M4 · Repo Documentation accuracy 10.0

The Ugly

M2 · Repo Architecture documentation 2.0

At a glance — Readiness · 60% · Adequate · gated by P3

The Good

D13 · Python Secret Scanning 10.0 P1 · Repo CI/CD gates 8.5 P4 · Repo Deployment & Rollback 10.0

The Ugly

P3 · Repo Security & performance tooling 3.0 P5 · Repo DR & Backup 4.0

At a glance — Security · 88% · Strong

The Good

D28 · Python Secrets (history) 10.0 D31 · Python IaC & Container Security 10.0 D37 · Python Vulnerability-disclosure Policy 10.0

The Bad

D29 · Python Static Analysis (SAST) 5.0

Security & Compliance — OWASP Top-10 mapping

Findings mapped to OWASP categories; the specific CVEs/secrets are in the Security dimension cards below and findings.md (redacted only on the public version of this report).

OWASP category	Findings	Severity
A03:2021 — Injection	4	High / Critical

Roadmap

First, integrate static security scanning into the CI pipeline to fail the build on regressions. Next, document recovery time and objectives with a tested restore procedure to ensure true disaster recovery. Then, record significant architectural decisions in a dedicated, dated document to capture context and consequences. Finally, add a testing section to the README and reorganize the project structure to separate production code from tooling.

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

Do this	Helps	Effort	Dimension
Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing.	+8.1 pts	Medium	Security & performance tooling
Document RTO/RPO and a tested restore procedure (a backup config alone isn't disaster recovery).	+8.1 pts	Medium	DR & Backup
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).	+6.4 pts	Medium	Architecture documentation
Add a 'Testing' section to the root README — how to run the test suite.	+5.6 pts	Medium	Documentation (README)
Group production code under src/ (or split deliberately, e.g. backend/ + frontend/) so production and tooling code aren't mixed at the root.	+4.8 pts	Medium	Folder & project structure
Run the test suite in CI via an explicit runner step for your stack, and gate merges on it.	+4.7 pts	Medium	CI/CD gates
Resolve the 1 The 'Per-mode configs' guide is a single-file README that only explains… finding(s) in Documentation Quality — start with README.md.	+1.3 pts	Low	Documentation Quality
Resolve the 4 High finding(s) in Static Analysis (SAST) — start with dependabot-automerge.yml (3), dependabot.yml.	+1.0 pts	Low	Static Analysis (SAST)

File quality

Per-file score 0–10 — a quality signature. Of 14 files carrying findings, judged against the Production bar: 0% slop · 43% mixed · 57% near-clean.

File	Score	Band	Worst signal
.github/workflows/dependabot-automerge.yml	5.1	Mixed	Static Analysis (SAST): High: github-actions-mutable-action-tag
scripts/compile_modes.py	7.0	Mixed	Cyclomatic Complexity: compile_modes.validate_mode (cyclomatic 21)
vs-code/convert_modes.py	7.2	Mixed	Cyclomatic Complexity: convert_modes.convert_modes (cyclomatic 37)
.github/dependabot.yml	7.2	Mixed	Static Analysis (SAST): High: dependabot-missing-cooldown
scripts/verify_modes.py	7.8	Mixed	Cyclomatic Complexity: verify_modes.verify_mode (cyclomatic 20)
scripts/fix_modes.py	7.8	Mixed	Cognitive Complexity: fix_modes.main (cognitive 19)
scripts/inject_engineering_protocols.py	8.5	Near-clean	Cognitive Complexity: inject_engineering_protocols.inject_into_file (cognitive 27)
scripts/generate_roomodes.py	8.5	Near-clean	Cognitive Complexity: generate_roomodes.main (cognitive 24)
scripts/migrate_all_agents.py	8.5	Near-clean	Cognitive Complexity: migrate_all_agents.main (cognitive 23)
scripts/trim_modes.py	8.5	Near-clean	Cognitive Complexity: trim_modes.main (cognitive 18)
scripts/inject_github_superpowers.py	8.5	Near-clean	Cognitive Complexity: inject_github_superpowers.main (cognitive 17)
scripts/inject_gap_skills.py	8.5	Near-clean	Code Duplication: Duplicated block (10 lines × 2)
scripts/batch_roomodes.py	8.5	Near-clean	Code Duplication: Duplicated block (7 lines × 2)
custom_modes.d/README.md	9.5	Near-clean	Documentation Quality: The 'Per-mode configs' guide is a single-file README that only explains the backup and regeneration workflow, with no link to where to edit individual YAML files or how to regenerate from the combined file.

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. 21 of 23 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 0.5 — 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 — 23 dimensions across the health lenses

D1D2D3D4D13D15D16D19D21D28D29D31D34D35D37M1M2M3M4P1P3P4P5

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, 30 of 35 do; the remainder are repo-wide signals — a dimension-level measurement, not a single line. (Every path in this report is repo-relative by construction: paths are normalized at the producer and the report is rejected if any rooted path leaks through.)
Is there a tool behind the number? Every score below names the method that produced it — Roslyn, git, a scanner, or (for a handful of documentation/naming dimensions) an LLM labelled sampled · advisory — not a narrative.
Does re-running give the same result? Run it again on the same commit and the score — and this report, byte for byte — is identical. A report whose numbers move between runs is describing the run, not the code.

This report answers yes to all three. That's the bar to hold any assessment to.

Tools & methods

The actual versions used this run (captured at analysis time) — re-run on the same commit for the identical score.

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

Every finding is locatable in findings.md. Run 019fcf30-1177-740e-be65-fb054c062a6e.

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

Run transparency — what happened this run

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

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

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

Limitations & what we did not check

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

Per-dimension blind spots

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

D1 Cyclomatic Complexity: Cyclomatic complexity counts branches statically — it cannot tell an essential decision tree from accidental tangle, nor see complexity that lives in data or configuration (large switch-case token tables, DSL lexers/parsers, data-as-code rule tables) rather than control flow: a tokenizer's many single-character cases read as high complexity though each branch is trivial.
D2 Cognitive Complexity: Cognitive-complexity heuristics approximate how hard code is to follow; genuine domain difficulty and well-named intent that eases reading are not captured.
D3 God Classes: "God class" is sized by members and responsibilities visible in the type — a deliberately broad facade over a coherent subsystem can read the same as an accidental grab-bag. For front-end JS the file-length check is cohesion-aware (a single-responsibility module — one class/IIFE — earns a 3× threshold), but cohesion is approximated from top-level declarations, not true dependency structure.
D4 Code Duplication: Duplication is token-similarity (jscpd) — it finds copy-paste, not semantic duplication expressed differently. Committed machine-written code (scaffolded migrations, designer/codegen output, protobuf/OpenAPI stubs, model snapshots) is EXCLUDED — its repetition is the tool's, not the team's — so the score reflects hand-written duplication only.
D13 Secret Scanning: Secret detection is signature- and entropy-based on the current tree — a secret that does not match a known pattern, or one already rotated, will not be flagged (a clean scan is "nothing matched", not "no secrets exist").
D15 Churn × Complexity Hotspots: Churn hotspots come from git history — a freshly imported or squashed repository has no churn signal, and recent rewrites can mask a historically risky file.
D16 Bus Factor: Bus-factor is a time-decayed model of commit attribution (who has recently, repeatedly worked a file), not comprehension — pairing, review and reading-without-committing spread knowledge it can't see; bot commits and shared accounts still distort it.
D19 Documentation Quality: Documentation quality is judged by an LLM over a bounded sample of docs — it reads what is written, not whether the docs match the running system, and it is advisory, not a measurement.
D21 Naming Consistency: Naming quality is an LLM judgement over a bounded sample — it assesses clarity/consistency of the names it sees, not domain-correctness, and is advisory.
D28 Secrets (history): Secret-history scanning sweeps the git log for known patterns — a secret that predates the available history, or never matched a signature, is not found (clean means "nothing matched in the history we can see").
D29 Static Analysis (SAST): SAST findings are pattern-based (semgrep) — it finds classes of bug it has rules for; logic flaws, auth/authorization gaps and issues needing runtime context are out of reach (and clean means "no rule matched").
D31 IaC & Container Security: IaC scanning checks Dockerfiles/Terraform/Kubernetes against best-practice rules — it cannot see the live cloud account, runtime configuration, or drift between the committed config and what is actually deployed.
D34 Knowledge Freshness: Freshness is decayed commit RECENCY, not comprehension — code read often but rarely committed reads as orphaned, and stable code that genuinely needs no changes is penalised the same as forgotten code; bot/squash commits distort it like the bus factor.
D35 Change Coupling: Change coupling is co-change in COMMITS — files split across separate commits, or coupled only through a shared config/build step, read as uncoupled, and a sweeping commit (rename/format) is excluded so it doesn't couple everything. It shows that files change together, not WHY: a high coupling can be a healthy cohesive pair as readily as a hidden leak.
M4 Documentation accuracy: Onboarding quality is an LLM read of the docs/setup present — it cannot run the onboarding or measure how long a real new joiner takes; the verdict is sampled and advisory.
P4 Deployment & Rollback: Approval/branch-protection rules live in repository settings the scan cannot see — only their in-repo evidence (config files, workflows) is checked, so a control enforced purely in the host's settings reads as "not evidenced".
P5 DR & Backup: Backup/restore and disaster-recovery readiness is judged from in-repo evidence — a config that exists is not a tested restore, so the absence of positive evidence is reported as "not evidenced", never scored as present.

The LLM boundary

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

Dimensions

D1 · Cyclomatic Complexity8.2 / 10Strong✓ Tool-verified

What it measures: How tangled the control flow is — methods with many branches are hard to test and change.

Method: Cyclomatic complexity per method (1 + decision points), computed exhaustively across production source; test projects separated by convention. Deterministic.

Maturity: Documented → Verified → Prevented · effective 8.2 / 10 · rule-coverage 100% · ceiling Prevented

3 method(s) exceeded the cyclomatic complexity threshold of 15; the worst was convert_modes.convert_modes at 37. A further 1 method(s) were over the threshold but excluded as flat dispatchers (a long switch/match over independent cases: many branches, almost no nesting), the largest being fix_modes.expand_role at 16 — they are counted neither in the figure above nor in this dimension's score.

convert_modes.convert_modes (cyclomatic 37)vs-code/convert_modes.py:411

compile_modes.validate_mode (cyclomatic 21)scripts/compile_modes.py:48

verify_modes.verify_mode (cyclomatic 20)scripts/verify_modes.py:34

What to do

Resolve the 1 convert_modes.convert_modes (cyclomatic 37) finding(s) in Cyclomatic Complexity — start with convert_modes.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 compile_modes.validate_mode (cyclomatic 21) finding(s) in Cyclomatic Complexity — start with compile_modes.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 verify_modes.verify_mode (cyclomatic 20) finding(s) in Cyclomatic Complexity — start with verify_modes.py. — One of this dimension's main actionable groups (1 warning-level).
Enforce Cyclomatic Complexity in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.

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

Do you agree with this assessment?

D2 · Cognitive Complexity4.3 / 10Weak✓ Tool-verified

What it measures: How hard the code is for a person to follow, beyond raw branching.

Method: Cognitive complexity per method (Sonar-style nesting-penalized score), computed exhaustively over production code, excluding test projects. Deterministic.

Maturity: Documented → Verified → Prevented · effective 4.3 / 10 · rule-coverage 100% · ceiling Prevented

14 method(s) exceeded the cognitive complexity threshold of 15; the worst was convert_modes.convert_modes at 55.

convert_modes.convert_modes (cognitive 55)vs-code/convert_modes.py:411

inject_engineering_protocols.inject_into_file (cognitive 27)scripts/inject_engineering_protocols.py:84

compile_modes.validate_mode (cognitive 25)scripts/compile_modes.py:48

generate_roomodes.main (cognitive 24)scripts/generate_roomodes.py:31

migrate_all_agents.main (cognitive 23)scripts/migrate_all_agents.py:133

+ 9 more group(s) — more in Appendix A; the complete list is findings.md.

What to do

Resolve the 1 convert_modes.convert_modes (cognitive 55) finding(s) in Cognitive Complexity — start with convert_modes.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 inject_engineering_protocols.inject_into_file (cognitive 27) finding(s) in Cognitive Complexity — start with inject_engineering_protocols.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 compile_modes.validate_mode (cognitive 25) finding(s) in Cognitive Complexity — start with compile_modes.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 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.

Do you agree with this assessment?

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

What it measures: Copy-pasted code that should be shared instead.

Method: Code duplication via token-stream sliding windows with type-aware normalization (locals masked, type names preserved), density-scored per KLoC of production code. Deterministic.

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

7 duplicated block group(s) detected.

Duplicated block (21 lines × 2)scripts/fix_modes.py:91

Duplicated block (17 lines × 2)scripts/compile_modes.py:53

Duplicated block (16 lines × 2)scripts/compile_modes.py:277

Duplicated block (10 lines × 2)scripts/inject_gap_skills.py:664

Duplicated block (9 lines × 2)scripts/compile_modes.py:107

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

✓ On the Gold path — maintain.

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

Do you agree with this assessment?

D13 · Secret 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 Hotspots10.0 / 10Exemplary✓ Tool-verified

What it measures: Files that change often and are also complex — the riskiest hotspots.

Method: Per production file churn times cyclomatic complexity over a rolling window, computed from git and Roslyn/JS/Razor analysis. Exhaustive, deterministic per commit date.

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

Top hotspots: scripts/fix_modes.py (2×16=32)

Hotspot: scripts/fix_modes.pyscripts/fix_modes.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 Factor9.5 / 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 9.5 / 10 · rule-coverage 100% · ceiling Documented

3 source file(s) have their living knowledge concentrated in one author (≥90% of recent, decayed contribution). The largest is scripts/fix_modes.py.

Small-team knowledge concentration

✓ 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 / 10Exemplary◐ 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 Exemplary / 10 · rule-coverage 100% · ceiling Documented

The Custom Modes for Roo Code project is well documented: a star-studded README with an overview, quick start, and detailed Table of Contents; a dedicated vs-code/README describing the CLI-to-VS Code conversion tool's directory structure, commands, and search capabilities; and a per-mode config guide explaining how to split YAML into individual files. The architecture docs are clipped mid-sentence but the outline is present for every named section, so none are flagged as missing. All four READMEs are present.

The 'Per-mode configs' guide is a single-file README that only explains the backup and regeneration workflow, with no link to where to edit individual YAML files or how to regenerate from the combined file.custom_modes.d/README.md

✓ On the Gold path — maintain.

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

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D21 · Naming Consistency / 10Exemplary◐ Sampled · advisory

What it measures: Whether names — types, methods, variables — are clear and consistent.

Method: Judged by language model at low temperature (0.0-0.1) on a deterministic random symbol sample (fixed size, not exhaustive), with disclosed confidence band. Advisory, sampled.

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

0 naming inconsistencies across 0 sampled symbols.

✓ On the Gold path — maintain.

Detailed fixes: d21_recommendation.md.

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D28 · Secrets (history)10.0 / 10Exemplary○ Nothing flagged

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

gitleaks scanned the full history AND the current working tree and found no secrets.

✓ On the Gold path — maintain.

Detailed fixes: d28_recommendation.md.

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D29 · Static Analysis (SAST)5.0 / 10Adequate✓ 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 5.0 / 10 · rule-coverage 100% · ceiling Documented

4 finding(s): 0 critical, 4 high, 0 medium, 0 low.

High: dependabot-missing-cooldown · ×4.github/dependabot.yml:3detected by semgrep finding

What to do

Resolve the 4 High finding(s) in Static Analysis (SAST) — start with dependabot-automerge.yml (3), dependabot.yml. — One of this dimension's main actionable groups (4 issue-level).

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

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D31 · IaC & Container Security10.0 / 10Exemplary○ Nothing flagged

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

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

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

trivy and checkov found no infrastructure-as-code or container misconfigurations.

✓ On the Gold path — maintain.

Detailed fixes: d31_recommendation.md.

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

What it measures: Whether anyone still has living knowledge of each file, or it has been orphaned — last understood long ago by someone now gone quiet. The sibling of the bus factor: D16 asks who owns it, D34 asks whether anyone still knows it.

Method: File orphaning as total living-knowledge decay below one focused-commit's worth within a year, computed per-file from the D16 decay model. Exhaustive, deterministic over fixed history.

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

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

✓ On the Gold path — maintain.

Detailed fixes: d34_recommendation.md.

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

What it measures: Whether files that change together actually belong together — pairs that repeatedly co-change in git history despite having no explicit code dependency, surfacing the hidden/logical coupling (and boundaries in the wrong place) a static scan can't see.

Method: Pairwise co-occurrence over the per-commit file sets in git history (production source only — tests and generated dropped): Degree-of-Coupling = shared ÷ min individual revisions, reported above noise floors (each file ≥10 revisions, ≥5 shared commits, ≥50% strength); sweeping commits excluded. Deterministic over fixed history.

Coverage: Population: PRODUCTION source files only — test and generated files are dropped before pairing, so a class co-changing with its own test (trivially ~100%) can't drown the real production↔production coupling. Pairs ranked by Degree-of-Coupling; coupling through a build step, config, or non-source file isn't seen.

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

No strong hidden change-coupling between production files.

✓ On the Gold path — maintain.

Detailed fixes: d35_recommendation.md.

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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 (SECURITY.md) is published with a reporting contact.

✓ On the Gold path — maintain.

Detailed fixes: d37_recommendation.md.

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Frontend & cross-cutting dimensions

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

M1 · Documentation (README)7.3 / 10Strong✓ 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.

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M2 · Architecture documentation2.0 / 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.

What to do

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

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

Maturity · Maturity — Whether the repo is organised deliberately — src/test separation and consistent project naming.

Method: Filesystem scan: src/test folder separation and namespace-prefix consistency (majority RootNamespace agreement). Exhaustive across projects, deterministic.

Production code isn't grouped under a src/ folder — it all sits under vs-code/ alongside the root build files, so the conventional src/ boundary between the product and its tooling isn't drawn.

What to do

Group production code under src/ (or split deliberately, e.g. backend/ + frontend/) so production and tooling code aren't mixed at the root.

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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 gates8.5 / 10Strong✓ Tool-verified

Readiness · Readiness — Whether an automated pipeline builds and tests every change.

Method: Filesystem scan: CI workflow files (.github/workflows, .gitlab-ci.yml, etc.) for build and test stages. Exhaustive, deterministic.

A CI pipeline exists and the word "test" appears, but no explicit test-runner invocation (your stack's test command, or a test job) was matched — so either the gate runs tests through a step this pass could not recognise, or "test" is incidental here (a path, "latest", a reporter). Check the coverage dimensions first: if this repo has no test suite yet, that is the finding and this row follows from it. If a suite does exist, make the runner step explicit so the gate is unambiguous.

What to do

Run the test suite in CI via an explicit runner step for your stack, and gate merges on it.

Do you agree with this assessment?

P3 · Security & performance tooling3.0 / 10Weak✓ Tool-verified

Readiness · Readiness — Whether SAST, secret/dependency scanning and performance benchmarking are wired in (presence, not runtime).

Method: Filesystem scan: SAST configuration, dependency-update automation, secret scanning, and a benchmark harness or benchmark step — in this repository's own ecosystem. Exhaustive, deterministic.

No static application security testing detected. For this repository's stack, add bandit, `semgrep --config=p/python`, or CodeQL's python pack as a CI step.

What to do

Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing.
Add gitleaks/trufflehog in CI to block PRs that introduce committed secrets.

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P4 · Deployment & Rollback10.0 / 10Exemplary✓ 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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P5 · DR & Backup4.0 / 10Weak✓ Tool-verified

Readiness · Readiness — Whether disaster recovery is planned and codified — backups, geo-recovery, RTO/RPO, persistence guarantees — from IaC + container manifests + docs, never the live cloud.

Method: Filesystem scan: disaster recovery, backup, geo-recovery, RTO/RPO, persistence guarantees from IaC, manifests, and docs. Exhaustive, deterministic, never a live environment.

What to do

Document RTO/RPO and a tested restore procedure (a backup config alone isn't disaster recovery).
Enable purge protection / soft-delete (and prevent_destroy on critical resources) so data stores can't be lost to an accidental or malicious delete.

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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	80%	Strong	Solid.
Architecture	100%	Exemplary	Strongest area.
Maturity	63%	Adequate — gated by M2	Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Readiness	60%	Adequate — gated by P3	Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Security	88%	Strong	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
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 — ~180 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 — no supported dependency manifest was read
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), a Swift Package.swift/Package.resolved, a Cargo manifest, a Go module (go.mod/go.sum), a Gradle version catalogue, a Maven POM, an sbt build (build.sbt), composer.json, package.json, a Dart pubspec.yaml, a rebar.config / erlang.mk DEPS (Hex), a Ruby Gemfile/Gemfile.lock or .gemspec (Bundler/RubyGems)), 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), a Swift Package.swift/Package.resolved, a Cargo manifest, a Go module (go.mod/go.sum), a Gradle version catalogue, a Maven POM, an sbt build (build.sbt), composer.json, package.json, a Dart pubspec.yaml, a rebar.config / erlang.mk DEPS (Hex), a Ruby Gemfile/Gemfile.lock or .gemspec (Bundler/RubyGems) — not scanned yet) — where an OSV-supported manifest exists, dependency vulnerabilities for this repository are reported under D38 instead.
D32 Data Compliance (PII/GDPR) — No PII/GDPR-handling patterns detected (p/gdpr ruleset) — no data-compliance surface to assess.
D33 JS/npm Dependency Vulnerabilities — No JS/npm manifest or lockfile found outside build output (package.json, package-lock.json, yarn.lock, pnpm-lock.yaml, bun.lockb); no JS dependencies to scan.
D36 Supply-chain Provenance & Signing — The CI pipeline builds and tests but publishes no released artifact — no package publish, container push, GitHub release or deployment step. Supply-chain provenance, signing and SBOM attest RELEASED artifacts, so there is nothing to attest here. Add them to the release pipeline when this repo starts shipping artifacts (a published package, a container image, a deployed service or a tagged release).
D38 OSV Dependency Vulnerabilities — No supported non-.NET dependency lockfile found outside build output (npm package-lock/yarn/pnpm/bun, Go go.mod, Rust Cargo.lock, Maven pom.xml, Gradle lockfiles, Python requirements.txt/poetry.lock/Pipfile.lock/pdm.lock, PHP composer.lock, Ruby Gemfile.lock, Elixir mix.lock, Dart pubspec.lock, Swift Package.resolved); nothing for OSV to scan. A NuGet-only repo stays NotApplicable — .NET CVEs are D30's domain.
D39 IL Efficiency — D39 measures the IL emitted by a .NET build; this repository has no .NET solution or project files, so the dimension does not apply.
D40 Network Egress Confinement — No Kubernetes/orchestration workloads found in the repository manifests; network egress policy is a cluster-native control that may live at the platform/firewall layer, so there is nothing to assess here.
D41 Kernel & Syscall Confinement — No Kubernetes/orchestration workloads found in the repository manifests; seccomp/AppArmor/SELinux confinement is a workload-level control, so there is nothing to assess here.
D42 Runtime Threat Enforcement — No Kubernetes/orchestration workloads found in the repository manifests; runtime threat-detection and admission-control policy are cluster-level controls, so there is nothing to assess here.
D5 Coupling — Inter-project coupling could not be assessed — no analyzable project graph was found for this repository. Not scored: a gap in the analyzer's reach, not a verdict about this repository. (Coupling here is Martin afferent/efferent/instability plus reference cycles across a project-reference graph, read today from .NET project files; other ecosystems' module graphs are not read yet.)
D6 Cohesion (LCOM4) — Cohesion (LCOM4) is measured over a C#/VB class graph, and this repository's production source is .py, which this pass does not read — so no class could be assessed. Not scored — this is a gap in the analyzer, not a finding about this repository.
D7 Architectural Integrity — no checkable ADRs, and no project-reference graph for the cycle pass to read — so this dimension makes no claim about dependency cycles in either direction (where this repository's language has an import-cycle lens, cycles are reported there). Architectural integrity not assessed
D8 Code Coverage — Coverage not included — suite not readable by the collector
D9 Test Distribution — Test source is present (.py) but the test-pyramid classifier reads C# only, so its unit/integration/BDD/E2E split couldn't be counted. Not scored — this is a gap in the analyzer, not a finding about this repository.
DM1 Domain Modelling — not scored — this repository shows none of the 3 signals this check looks for
ED1 Event-Driven — not scored — this repository shows none of the 3 signals this check looks for
ED5 Idempotency — no mutating command handlers or message consumers detected — idempotency check not applicable
ES1 Event Sourcing — not scored — this repository shows none of the 3 signals this check looks for
GD1 Unfinished & placeholder code — no source files
IC1 Incompleteness & stubs — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
P12 CI test-gate honesty — Reported, not scored — and nothing was matched here. The coverage check applies to any stack, but the checks for excluded tests, skipped tests and sleep-based synchronisation currently recognise only some ecosystems' test-runner idioms, so on a repository built with another stack the zeros below mean 'not checked', not 'clean'.
P2 Observability — Observability was not assessed: this check reads a source model that does not carry this repository's product — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of a logging idiom this check recognises is NOT evidence that this repo lacks structured logging (it may log through its own ecosystem's logger). This is a gap in the analyzer, not a finding about this repository.
P6 Release Hygiene — not evidenced — no changelog, version stamp or semver release tag in the repo
P7 Outbound HTTP resilience — not measured — the application kind could not be determined for this repo
P8 Schema migrations — not assessed — schema-migration practice is read from a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
P9 Domain vs controller coverage — no coverage report found on disk — produce a coverage report in a standard format (`coverage run -m pytest` then `coverage xml`) into the repo working tree before the scan — a CI step is the usual place, since the artefact is commonly gitignored, or wire coverage collection into CI, to enable this cross-layer check
PF1 Benchmark discipline — Performance was not assessed: this lens reads a source model that was not loaded for this repository, because the repository is written in a language this lens does not yet model or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository — in particular it is NOT a statement that this repo is unpackaged or performance-careless.
PF2 Allocation hygiene — Performance was not assessed: this lens reads a source model that was not loaded for this repository, because the repository is written in a language this lens does not yet model or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository — in particular it is NOT a statement that this repo is unpackaged or performance-careless.
PF3 Async & latency hygiene — Performance was not assessed: this lens reads a source model that was not loaded for this repository, because the repository is written in a language this lens does not yet model or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository — in particular it is NOT a statement that this repo is unpackaged or performance-careless.
S1 Web-Security Posture — Not assessed: these web-security controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks web-security controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
X1 Async correctness — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
X2 Cancellation propagation — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
X3 Exception handling — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
X4 Structured logging — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
X5 Nullable reference types — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository

Appendix A — Findings (grouped)

The findings behind the scores, grouped by severity, then by dimension and kind. The high-severity issues are enumerated in full below; items per group are capped at 25 with any overflow stated explicitly per group, never silently truncated. The complete machine-readable list of every finding (all severities) is the companion findings.md in this report's bundle.

Issue — 4 finding(s)

D29 · Static Analysis (SAST) · High · ×4

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. This is a semgrep security-AUDIT rule reporting a POLICY that is absent or weaker than its recommendation, not an exploitable defect. Confirm whether the current setting is a deliberate decision for this repository — and apply the change where it is not; where it is (a policy your release process already enforces elsewhere, or one this repository has consciously opted out of), record the decision and leave the configuration as it is.
High: github-actions-mutable-action-tag .github/workflows/dependabot-automerge.yml:13 — GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/github-script@<40-character SHA>`. This step references `actions/github-script@v7`; resolve the SHA it points at today with `gh api repos/actions/github-script/commits/v7 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/dependabot-automerge.yml:37 — GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: dependabot/fetch-metadata@<40-character SHA>`. This step references `dependabot/fetch-metadata@v2`; resolve the SHA it points at today with `gh api repos/dependabot/fetch-metadata/commits/v2 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/dependabot-automerge.yml:43 — GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: peter-evans/enable-pull-request-automerge@<40-character SHA>`. This step references `peter-evans/enable-pull-request-automerge@v3`; resolve the SHA it points at today with `gh api repos/peter-evans/enable-pull-request-automerge/commits/v3 --jq .sha`.

Warning — 25 finding(s)

D1 · Cyclomatic Complexity · convert_modes.convert_modes (cyclomatic 37) · ×1

convert_modes.convert_modes (cyclomatic 37) vs-code/convert_modes.py:411 — convert_modes.convert_modes has cyclomatic complexity 37 (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 · compile_modes.validate_mode (cyclomatic 21) · ×1

compile_modes.validate_mode (cyclomatic 21) scripts/compile_modes.py:48 — compile_modes.validate_mode has cyclomatic complexity 21 (threshold 15). To reduce it, separate the branches: extract each independent case into its own named function so the top-level body reads as a short sequence of named decisions.

D1 · Cyclomatic Complexity · verify_modes.verify_mode (cyclomatic 20) · ×1

verify_modes.verify_mode (cyclomatic 20) scripts/verify_modes.py:34 — verify_modes.verify_mode has cyclomatic complexity 20 (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.

D15 · Churn × Complexity Hotspots · Hotspot · ×1

Hotspot: scripts/fix_modes.py scripts/fix_modes.py — scripts/fix_modes.py changed 2 times in last 90 days, max complexity 16. 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.

D2 · Cognitive Complexity · convert_modes.convert_modes (cognitive 55) · ×1

convert_modes.convert_modes (cognitive 55) vs-code/convert_modes.py:411 — convert_modes.convert_modes has cognitive complexity 55 (threshold 15). Drivers by points: if/else 33, boolean chains 8, loops 8, error handling 3, ternaries 3 (nesting depth added 18). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · inject_engineering_protocols.inject_into_file (cognitive 27) · ×1

inject_engineering_protocols.inject_into_file (cognitive 27) scripts/inject_engineering_protocols.py:84 — inject_engineering_protocols.inject_into_file has cognitive complexity 27 (threshold 15). Drivers by points: if/else 20, loops 7 (nesting depth added 13). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · compile_modes.validate_mode (cognitive 25) · ×1

compile_modes.validate_mode (cognitive 25) scripts/compile_modes.py:48 — compile_modes.validate_mode has cognitive complexity 25 (threshold 15). Drivers by points: boolean chains 11, if/else 8, loops 6 (nesting depth added 5). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · generate_roomodes.main (cognitive 24) · ×1

generate_roomodes.main (cognitive 24) scripts/generate_roomodes.py:31 — generate_roomodes.main has cognitive complexity 24 (threshold 15). Drivers by points: if/else 19, loops 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 · migrate_all_agents.main (cognitive 23) · ×1

migrate_all_agents.main (cognitive 23) scripts/migrate_all_agents.py:133 — migrate_all_agents.main has cognitive complexity 23 (threshold 15). Drivers by points: loops 10, if/else 8, error handling 4, boolean chains 1 (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · verify_modes.verify_mode (cognitive 23) · ×1

verify_modes.verify_mode (cognitive 23) scripts/verify_modes.py:34 — verify_modes.verify_mode has cognitive complexity 23 (threshold 15). Drivers by points: if/else 15, boolean chains 7, loops 1 (nesting depth added 3). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.

D2 · Cognitive Complexity · compile_modes.load_individual_modes (cognitive 20) · ×1

compile_modes.load_individual_modes (cognitive 20) scripts/compile_modes.py:136 — compile_modes.load_individual_modes has cognitive complexity 20 (threshold 15). Drivers by points: error handling 7, if/else 7, loops 5, boolean chains 1 (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · compile_modes.main (cognitive 19) · ×1

compile_modes.main (cognitive 19) scripts/compile_modes.py:320 — compile_modes.main has cognitive complexity 19 (threshold 15). Drivers by points: if/else 10, loops 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 · fix_modes.main (cognitive 19) · ×1

fix_modes.main (cognitive 19) scripts/fix_modes.py:274 — fix_modes.main has cognitive complexity 19 (threshold 15). Drivers by points: if/else 7, ternaries 7, error handling 3, boolean chains 1, loops 1 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · convert_modes.search_modes (cognitive 19) · ×1

convert_modes.search_modes (cognitive 19) vs-code/convert_modes.py:210 — convert_modes.search_modes has cognitive complexity 19 (threshold 15). Drivers by points: if/else 9, loops 5, ternaries 4, boolean chains 1 (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · compile_modes.load_existing_roomodes (cognitive 18) · ×1

compile_modes.load_existing_roomodes (cognitive 18) scripts/compile_modes.py:187 — compile_modes.load_existing_roomodes has cognitive complexity 18 (threshold 15). Drivers by points: if/else 9, boolean chains 4, error handling 4, loops 1 (nesting depth added 5). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · trim_modes.main (cognitive 18) · ×1

trim_modes.main (cognitive 18) scripts/trim_modes.py:44 — trim_modes.main has cognitive complexity 18 (threshold 15). Drivers by points: if/else 11, loops 6, boolean chains 1 (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · inject_github_superpowers.main (cognitive 17) · ×1

inject_github_superpowers.main (cognitive 17) scripts/inject_github_superpowers.py:368 — inject_github_superpowers.main has cognitive complexity 17 (threshold 15). Drivers by points: if/else 9, loops 7, boolean chains 1 (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D2 · Cognitive Complexity · convert_modes.copy_to_vscode (cognitive 17) · ×1

convert_modes.copy_to_vscode (cognitive 17) vs-code/convert_modes.py:343 — convert_modes.copy_to_vscode has cognitive complexity 17 (threshold 15). Drivers by points: if/else 16, error handling 1 (nesting depth added 4). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.

D4 · Code Duplication · Duplicated block (21 lines × 2) · ×1

Duplicated block (21 lines × 2) scripts/fix_modes.py:91 — scripts/fix_modes.py:91-111 | scripts/fix_modes.py:140-160 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.

D4 · Code Duplication · Duplicated block (17 lines × 2) · ×1

Duplicated block (17 lines × 2) scripts/compile_modes.py:53 — scripts/compile_modes.py:53-69 | scripts/validate_custom_modes.py:96-112 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once.

D4 · Code Duplication · Duplicated block (16 lines × 2) · ×1

Duplicated block (16 lines × 2) scripts/compile_modes.py:277 — scripts/compile_modes.py:277-292 | scripts/migrate_all_agents.py:93-108 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.

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

Duplicated block (10 lines × 2) scripts/inject_gap_skills.py:664 — scripts/inject_gap_skills.py:664-673 | scripts/inject_github_superpowers.py:411-420 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `scripts/inject_gap_skills.py:664` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.

D4 · Code Duplication · Duplicated block (9 lines × 2) · ×1

Duplicated block (9 lines × 2) scripts/compile_modes.py:107 — scripts/compile_modes.py:107-115 | scripts/validate_custom_modes.py:50-58 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once.

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

Duplicated block (8 lines × 2) scripts/compile_modes.py:95 — scripts/compile_modes.py:95-102 | scripts/validate_custom_modes.py:37-44 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `scripts/compile_modes.py:95` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.

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

Duplicated block (7 lines × 2) scripts/batch_roomodes.py:19 — scripts/batch_roomodes.py:19-25 | scripts/generate_roomodes.py:21-27 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once.

Recommendation — 4 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 · Small-team knowledge concentration · ×1

Small-team knowledge concentration — 3 file(s) are concentrated to one author — the ambient state with 2 active author(s), not 3 separate risks. The signal becomes meaningful as ownership spreads; no per-file action implied now.

D19 · Documentation Quality · The 'Per-mode configs' guide is a single-file README that only explains the backup and regeneration workflow, with no link to where to edit individual YAML files or how to regenerate from the combined file. · ×1

The 'Per-mode configs' guide is a single-file README that only explains the backup and regeneration workflow, with no link to where to edit individual YAML files or how to regenerate from the combined file. custom_modes.d/README.md — Link this README to the original monolithic custom_modes.yaml so readers can find it without opening the file.

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 — no supported dependency manifest was read — No dependency manifest this pass reads for hygiene (a Python pyproject.toml/requirements.txt (pip/uv/Poetry), a Swift Package.swift/Package.resolved, a Cargo manifest, a Go module (go.mod/go.sum), a Gradle version catalogue, a Maven POM, an sbt build (build.sbt), composer.json, package.json, a Dart pubspec.yaml, a rebar.config / erlang.mk DEPS (Hex), a Ruby Gemfile/Gemfile.lock or .gemspec (Bundler/RubyGems)) was found in this repository, 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 .	0	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 .	4	artifacts/raw/semgrep.json
D30 · Dependency Vulnerabilities	none (no readable dependency manifest)	—	none (no readable dependency manifest): not present in this environment	0	—
D31 · IaC & Container Security	trivy	—	trivy config --format json --quiet .	0	artifacts/raw/trivy-config.json
D32 · Data Compliance (PII/GDPR)	semgrep	—	semgrep: not applicable — No PII/GDPR-handling patterns detected (p/gdpr ruleset) — no data-compliance surface to assess.	0	—
D33 · JS/npm Dependency Vulnerabilities	trivy	—	trivy: not applicable — No JS/npm manifest or lockfile found outside build output (package.json, package-lock.json, yarn.lock, pnpm-lock.yaml, bun.lockb); no JS dependencies to scan.	0	—
D36 · Supply-chain Provenance & Signing	provenance	—	provenance: not applicable — The CI pipeline builds and tests but publishes no released artifact — no package publish, container push, GitHub release or deployment step. Supply-chain provenance, signing and SBOM attest RELEASED artifacts, so there is nothing to attest here. Add them to the release pipeline when this repo starts shipping artifacts (a published package, a container image, a deployed service or a tagged release).	0	—
D38 · OSV Dependency Vulnerabilities	osv-scanner	—	osv-scanner: not applicable — No supported non-.NET dependency lockfile found outside build output (npm package-lock/yarn/pnpm/bun, Go go.mod, Rust Cargo.lock, Maven pom.xml, Gradle lockfiles, Python requirements.txt/poetry.lock/Pipfile.lock/pdm.lock, PHP composer.lock, Ruby Gemfile.lock, Elixir mix.lock, Dart pubspec.lock, Swift Package.resolved); nothing for OSV to scan. A NuGet-only repo stays NotApplicable — .NET CVEs are D30's domain.	0	—
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 019fcf30-1177-740e-be65-fb054c062a6e · every finding is also locatable in findings.md, and the complete scoring record (with exit codes + durations) in sidecar.json.

Jtgsystems/Custom-Modes-Roo-Code

Executive summary

Top priorities

Diagnosis — what's actually going on

At a glance — Code Health · 80% · Strong

At a glance — Architecture · 100% · Exemplary

At a glance — Maturity · 63% · Adequate · gated by M2

At a glance — Readiness · 60% · Adequate · gated by P3

At a glance — Security · 88% · Strong

Security & Compliance — OWASP Top-10 mapping

Roadmap

File quality

Methodology & how to trust this report

Tools & methods

Run transparency — what happened this run

Limitations & what we did not check

Per-dimension blind spots

The LLM boundary

Dimensions

Frontend & cross-cutting dimensions

Reference — by lens

Appendix A — Findings (grouped)

Appendix B — Reproduction & audit trail

Downloadable artifacts