Lawkeeper

lawkeeper

Audit a whole project against the engineering laws it is supposed to obey, then fix the violations with the maintainer in the loop. It reads the rules the project already documents (or the global doctrine) and checks the code against them, it does not author the rules, it enforces them.

Operating principles

• Read the rules, never restate them. The laws already live in the project's harness and the maintainer's global doctrine. This skill resolves the effective rule set from those canonical sources at audit time. It does not ship its own copy of the rules, a validator that duplicated the rules would itself break DRY.
• Stricter wins. Global doctrine is the floor everywhere. A project's CLAUDE.md may add rules (e.g. a folder topology) or tighten a cap; it may not relax a ban. On any conflict between global and project, apply the stricter rule.
• Carve-outs are first-class. An auditor that flags documented exceptions teaches its user to ignore it. Exemptions (test files, generated code, schema/migration files, do-not-extract literal floors) are detected per project and applied before anything is reported. See references/carve-outs.md.
• Evidence over assertion. Mechanical violations come from a deterministic script with exact file:line. Judgment calls come from subagents that quote the rule and the code. Nothing is reported on a hunch; every finding is verifiable.
• Report first, fix on approval. The audit produces a grouped report. Fixes happen one finding (or one tight cluster) at a time through the §5.2 options protocol, describe the problem, present 2-3 options, recommend one, ask before writing. Nothing is auto-rewritten.
• The audit obeys the laws it checks. Every artifact this skill writes (scripts, reports, fixes) holds to the same caps and bans it enforces.

Scripts and references live in this skill's own directory, <skill-dir>. Resolve <skill-dir> from the Base directory for this skill: line surfaced when the skill loads; if that line is absent, fall back to find "$HOME/.claude" "$HOME/Code" -type f -path "*/skills/lawkeeper/SKILL.md" | head -1 | xargs dirname. Reference each file when the phase that needs it arrives, do not preload them.

Phase 0 (Preflight)

1. Confirm the target project root (default: cwd). If invoked from ~ or a non-project directory, ask which project to audit.
2. Check git state. A clean (or committed) working tree makes every proposed fix reversible. Note it; recommend a checkpoint before the remediation phase. Do not hard-block.
3. A brand-new or empty project is fine, it simply produces few findings. No special mode.

Phase 1, Resolve the effective rule set (canonical-source)

Build the rule set the audit will enforce, in this precedence (stricter wins):

1. Project harness (strongest signal of intent): .claude/rules/*, .claude/hooks/ban-patterns.txt, root + .claude/ CLAUDE.md, AGENTS.md, plus .cursorrules / .github/copilot-instructions.md if present.
2. Global doctrine (the floor): ~/.claude/CLAUDE.md and any always-on hard-caps hook.

Extract:

• Caps, function lines, params, nesting, file lines. Default floor: 40 / 3 / 3 / 500.
• Ban tokens, read ban-patterns.txt verbatim if present (grep-ERE format); the scanner is handed this file so the bans are the project's, not a hardcoded duplicate.
• Folder topology, any documented layout (tiers, module skeleton, type-directories, where schema/migrations/errors/constants live). This drives the structure checks.
• Project carve-outs, generated-file conventions, template-reference/, FE react-refresh / typed-route exceptions, do-not-extract floors.

Produce a short effective-ruleset summary (caps, ban count, topology source, carve-outs) and show it before scanning, so the maintainer can confirm you are enforcing the right rules. references/rule-catalog.md maps every rule_id to its category, severity, detection engine, and canonical source.

Phase 2, Deterministic scan (mechanical, exact)

Run the bundled scanner through the shell primitive, do not hand-derive these checks. The scanner is a Python program, so this is the one phase that assumes a python3 interpreter on PATH (the only host dependency lawkeeper adds beyond the shell itself; see the host-interpreter note in skills/hackify/references/runtime-adapters.md). Pass the resolved config:

python3 <skill-dir>/scripts/audit_scan.py <project_root> \
  --max-file-lines <cap> \
  --ban-patterns <project>/.claude/hooks/ban-patterns.txt   # omit if none

It emits one JSON object: {schema_version, root, config, stats, findings[]}. Each finding carries rule_id, category, severity, confidence, file, line, end_line, message, snippet, fixable. It covers what a regex matches exactly, file line-count and the token bans (suppressions, empty catch, bare Error, non-null !, inline type in scoped modules, hardcoded secrets). Secrets are redacted in the snippet. The scanner already applies the path carve-outs. Most findings are confidence: exact; the two marked confidence: syntactic (ban.bare-error, ban.inline-type) are matched exactly but need a one-step check before you act, is the throw in domain code, does the type have 2+ props.

If python3 is absent, rare on a dev machine, possible on a locked-down best-effort runtime, say so in the report and fall through to the semantic pass: the Phase 3 judgment rules are interpreter-free and still run. Never drop the deterministic checks silently; an audit that skips a whole engine without recording it reads as "clean" when it isn't.

Match the engine to the stack

The bundled scanner's full check suite is ECMAScript-family only. Pick the path by the project's primary language (from Phase 1 / a quick manifest glance):

• TS/JS family, run the scanner as above; full suite applies.
• Mixed repo with non-JS files you still want line-capped + project-banned, add --text-only-ext .py --text-only-ext .go (etc.). Those files get ONLY the file-line cap and the project's ban-patterns.txt; the JS-construct checks are skipped so nothing misfires.
• A non-JS stack you want audited deterministically (Python, Go, Rust, Java, …), generate an on-demand scanner for that language, following references/porting-scanner.md: it mirrors the bundled scanner's concepts (a language-appropriate comment/string masker, that language's suppression / empty-catch / bare-throw / inline-type analogs, the universal secret regexes, the same path carve-outs) and emits the SAME finding JSON schema. Write it under a fresh temp dir (mktemp -d), run it, parse its JSON exactly like the bundled scanner's, and delete the temp dir in Phase 6. These are ephemeral, single-session scripts, never written into the audited project, never left behind.

Phase 3, Semantic pass (judgment)

The scanner deliberately stops where precision would require a real parser or understanding. First reuse what the project already has: if .claude/agents/ holds installed review agents (security-auditor, performance-auditor, cleanup-scout, code-reviewer), dispatch THOSE for their concern rather than re-deriving it, they encode the project's exact rules. Fall back to the built-in concern prompts only where an agent is absent. Then fan out subagents (one per concern, in a single message), reading references/semantic-pass.md for the per-rule prompt, severity, and, critically, the exemption floors each must honor:

• Structure caps, function length, parameter count, nesting depth. PREFER the project's own linter when configured (ESLint max-lines-per-function / max-params / max-depth, Biome equivalents), it is exact. Otherwise a subagent estimates and flags candidates.
• DRY, duplicated logic that should reuse an existing helper/service/util.
• Clean-architecture layering, controllers reaching into data access, services importing the HTTP framework, presentation holding business logic, cross-feature reach-ins.
• Controller purity, one method = one service call; no conditionals / response shaping / try-catch / transformation.
• Naming & explicitness, names that describe what-not-why, nested ternaries, deep nesting.
• Single responsibility, units that need "and" to describe them; merged command jobs.
• Folder-structure conformance, files placed against the documented topology; types/enums/ constants without their dedicated home.
• Dead code, methods/exports/registrations with zero callers.
• Magic literals, un-named strings/numbers, honoring the do-not-extract floors and the schema/migration off-limits rule (references/carve-outs.md).
• Security, injection (string-concat SQL/shell), missing authz on routes, unvalidated boundary input, non-idempotent migrations, unsafe deserialization/SSRF, PII-in-logs.
• Performance. N+1 queries, O(n²) nesting, blocking I/O on request paths, unbounded memory, FE re-render thrash (diff/hot-path scoped, do not noise-flag a whole tree).
• Testing, service methods/guards/significant logic with no test; happy-path-only coverage.
• SOLID & YAGNI. OCP/LSP/ISP/DIP breaches and speculative abstraction (concrete instances only; SRP/DRY are covered above).
• Cross-file cleanup, unused dependencies, unreferenced files, dead feature flags, orphaned env vars, commented-out code blocks (the deterministic scanner already flags // removed: and ownerless TODO/FIXME).

Each subagent returns structured findings in the same shape as the scanner (rule_id, file, line, severity, evidence) so Phase 4 can merge them uniformly. Scope the pass to the whole tree by default, or to a path/changed-set if the user asked for that.

Phase 4 (Aggregate & report)

Merge deterministic + semantic findings, drop duplicates (same file+line+rule), and render the report from assets/report-template.md: a header with totals per category and severity, then findings grouped Folder-Structure / Code-Styles / File-Scoping, each with file:line, the rule, the evidence, and the fix posture. Lead with critical/security findings. Present it and stop, this is the natural review point (§5.3). Do not start fixing unprompted.

Phase 5, Remediate (propose-confirm per finding)

Work findings in severity order. For each finding or tight cluster, follow §5.2: state the problem with file:line, present 2-3 concrete options (including "do nothing" where reasonable) with effort/risk/impact, recommend the first, and ask before writing. Batch the questions, group similar findings into one wizard tool round (≤4 questions), numbered by issue and lettered by option, recommended option first. Apply only approved fixes, using the project's existing patterns (a fix must read as if the original author wrote it). After each batch, re-run the relevant test/lint/typecheck legs to prove the fix is clean.

Reality check on fixability: splitting a 500-line file, extracting a function, removing a !, or filling an empty catch are real changes that need judgment, not blind rewrites, they all go through propose-confirm. Only a tiny set (extract a magic literal, drop a dead import, delete a // removed: comment) is trivially safe, and even those are shown before applying.

When a finding's fix is substantive on its own, splitting a 500-line file, unwinding an N+1, layering surgery, name that it deserves more than an inline propose-confirm: offer the maintainer a compressed clarify → implement → verify pass for it rather than folding a real structural change into the audit. lawkeeper never calls sibling skills, so this is a framing you offer (and a separate task the user opts into), not a handoff you perform.

Phase 6 (Verify & summarize)

Re-run the deterministic scanner to confirm fixed findings are gone and none were introduced. Run the project's full verification triad (lint / typecheck / test) on the touched scope. Print a summary table: findings by category before/after, what was fixed, what was deferred (and why), and any rule the maintainer chose to waive. If the project has no edit-time enforcement (a PreToolUse hook that blocks banned tokens as they are written), suggest adding one so new violations are prevented rather than found later.

Clean up. Delete any on-demand scanner temp dir created in Phase 2 (rm -rf the mktemp -d path). The audit leaves nothing behind in the project or in temp, only the report and the approved fixes.

Notes

• Reference scripts/assets via <skill-dir> (resolved at the top of this file from the Base directory for this skill: line), never hardcode an absolute user path.
• Language reach. The bundled scanner runs its full check suite on the ECMAScript family (.ts/.tsx/.js/.jsx/.mts/.cts/.mjs/.cjs). For any other extension passed via --text-only-ext, it runs ONLY the language-agnostic checks, file-line cap and the project's ban-patterns.txt, because the JS lexer and the built-in token bans would misfire on other syntax. Deep deterministic coverage of a non-JS stack is done by the on-demand scanner from Phase 2 (references/porting-scanner.md). The workflow and the semantic pass are fully language-agnostic.
• Keep the audit honest about coverage: if you scope to a subtree, run text-only on a stack, or skip the semantic pass, say so in the report. Silent truncation reads as "clean" when it isn't.