agentleFS
moberghrmoberghr/mtk-agent-toolkit/.claude/skills/pre-commit-review/SKILL.md

pre-commit-review

Skill9 starsChanged 29 days agoMITView on GitHub

Fast security-focused review of staged changes before every commit, checking only critical compliance rules

5 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

10 rules. About 0 are common, 4 are distinctive, and 6 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 6

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Run a fast, security-focused review on staged changes only.

    line 16
  2. Distinctive:

    If nothing staged, check unstaged: git diff

    line 22
  3. Distinctive:

    If nothing at all, tell the engineer there's nothing to review

    line 23
  4. Very specific:

    Only consume cached output.

    line 38
  5. Very specific:

    Run the AI review pass on the same diff to catch issues the linter can't reach (design, intent, context-sensitive rules).

    line 81
  6. Very specific:

    Read .claude/review-config.json for the threshold (default 80.

    line 106
  7. Very specific:

    If findings[] is empty, below_threshold_rationale is mandatory — state what you checked and why you conclude the diff is clean.

    line 110
  8. Distinctive:

    Keep the table tight. This is a pre-commit gate, not a full review.

    line 112
  9. Very specific:

    When verdict is PASS and findings[] is empty, skip the table and JSON block entirely.

    line 116
  10. Very specific:

    When there are findings (PASS with warnings/suggestions, or NEEDS_CHANGES), emit the standard schema output: markdown table + fenced JSON …

    line 129

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Pre-Commit Security Review

MTK File Resolution

Resolve MTK skill/reference/script paths per .claude/references/mtk-file-resolution.md unless the router already stated MTK_ROOT=<path> this session; CLAUDE.md, .claude/tech-stack, .claude/rules/, tasks/, docs/, .mtk/ stay project-relative.


Run a fast, security-focused review on staged changes only. This is the lightweight check engineers should run before every commit.

Process

  1. Get the staged diff: git diff --cached
  2. If nothing staged, check unstaged: git diff
  3. If nothing at all, tell the engineer there's nothing to review
  4. Run the static linter pass first: bash hooks/pre-commit-linters.sh
    • Emits deterministic findings with source: "linter" and confidence: 100
    • Findings include secrets, raw SQL interpolation, connection strings with plaintext passwords, AWS keys, JWTs, and stack-specific patterns
    • Merge linter findings into your final JSON output (they always pass the threshold, so they always surface) 4.5. Merge cached analyzer output (if available). If .mtk/analyzer-output.json exists and was modified within the last 10 minutes, read it and filter findings to only those whose file field matches a file in git diff --cached --name-only (or git diff --name-only if using unstaged). Discard all other findings — surfacing repo-wide warnings on an unrelated commit erodes trust in the gate. Merge the filtered set into your output with source: "analyzer" and confidence: 100. Do not run the build yourself — the pre-commit gate must stay fast (seconds, not minutes). Only consume cached output. 4.6. Roslyn MCP tools (if available, .NET only). If dotnet-claude-kit is installed and the DetectAntiPatterns tool is available, call it on the changed files for on-demand semantic analysis. Treat results as source: "analyzer", confidence: 100. This is fast (analyzes specific files, not full build) and catches EF Core, async, and disposal patterns that the regex linter misses. If the tool is not available, skip this step. 4.7. Collateral-churn check. Run bash hooks/collateral-guard.sh --cached (add --manifest docs/specs/<date>-<slug>.json when this commit belongs to a spec). It answers a question no other check asks: is this churn yours? It flags whitespace/EOL-only rewrites (a small edit to a CRLF file rewrites the whole file), generated artifacts riding along undeclared (one npm install drags a lockfile into a feature commit), and asset directories regenerated wholesale. Findings are warnings, not blocks — but each carries a working revert command, and a commit whose diff is mostly collateral hides the real change from every reviewer downstream. The dependency gate below is the separate, stricter question of what the lockfile churn contains.

4.8. Dependency-introduction gate (if a manifest changed). Detect whether the staged diff touches any of these manifest files: package.json, package-lock.json, pnpm-lock.yaml, yarn.lock, *.csproj, Directory.Packages.props, requirements.txt, pyproject.toml, Pipfile, poetry.lock, go.mod, Cargo.toml, Cargo.lock, Gemfile, Gemfile.lock, composer.json. If any matches, list the newly added dependencies (lines beginning with + introducing a new package name) and walk each through the six-criteria rubric in .claude/references/dependency-intake-checklist.md — existence & legitimacy first, then scope, maintenance, size, security, license. A Poor on criterion 0 blocks outright; two Poor ratings on criteria 1–5 for a single dep block the commit; one Poor requires explicit override in the PR description. Emit each problematic dep as a category: "dependency" finding with source: "ai" and confidence ≥ 85 for Poor ratings backed by evidence (CVE id, last-release date, license SPDX). Pure version bumps within the same major skip the rubric (vulnerability scan only). Skip the gate entirely when the checklist file is absent (older repos). 4.9. Monorepo ripple check. Run bash scripts/monorepo-ripple.sh $(git diff --cached --name-only). On a non-monorepo it exits 0 with no output (no false positives). On a monorepo, each RIPPLE <pkg>: affects <downstream> line becomes a category: "ripple", severity: "warning", source: "linter", confidence: 90 finding. Ripples never block the commit on their own — they exist to make the author aware that staged changes will affect downstream packages. 5. Run the AI review pass on the same diff to catch issues the linter can't reach (design, intent, context-sensitive rules). AI findings use source: "ai" and their own confidence scores per the rubric. 6. Combine linter + AI findings into a single schema-conformant output.

Check only these (from .claude/rules/security.md §1.x and the generated pre-commit-review-list.md (tech-stack items); if neither exists, use the fallback list below):

  • Secrets: Any hardcoded credentials, connection strings with passwords, API keys, tokens?
  • SQL Injection: Any string-concatenated SQL? Must be parameterized only (EF Core LINQ is fine; FromSqlRaw with concatenation is still injection — [.NET example]).
  • PII in Logs: Any PII (names, emails, account numbers) in log statements or exception messages?
  • Auth Missing: Any new endpoints, internal ones included, without [Authorize] or RequireAuthorization()? [.NET example]
  • Audit Missing: Any state-changing operations on financial data without audit log writes? [finance-domain example]
  • Secrets in Env: Any connection strings or passwords hardcoded in appsettings.json or CDK environment vars? [.NET/AWS example]
  • IAM Blast Radius: Any new IAM grants with * resource that should be scoped? [AWS example]
  • Dependency Intake: New third-party dep added — does it pass the six-criteria gate in .claude/references/dependency-intake-checklist.md? (A Poor on criterion 0, or two on criteria 1–5, blocks.)

Output Contract

Emit the schema-conformant output per .claude/references/review-finding-schema.md:

  1. Markdown table of surfaced findings (rows where confidence >= threshold).
  2. Fenced JSON block with the full structured result. If you need extra context fields, stay within the canonical review envelope defined in .claude/references/review-finding-schema.md.

Read .claude/review-config.json for the threshold (default 80; .claude/review-config.local.json overrides if present). Apply the confidence rubric and the anti-inflation rule.

  • Any critical finding at/above threshold → verdict: "NEEDS_CHANGES".
  • Otherwise → verdict: "PASS".
  • If findings[] is empty, below_threshold_rationale is mandatory — state what you checked and why you conclude the diff is clean.

Keep the table tight. This is a pre-commit gate, not a full review.

Clean pass — compact output

When verdict is PASS and findings[] is empty, skip the table and JSON block entirely. Instead emit a single compact summary:

✅ Pre-commit review passed — 0 findings across 8 rules ({N} files, {A}+/{D}−)

Checked: secrets · SQL injection · PII in logs · auth · audit trail · env secrets · IAM scope · dependency intake

Replace {N}, {A}, {D} with actual file count, additions, and deletions from the diff. This is the only output for a clean pass. No table, no JSON block, no rationale dump.

Findings present — full output

When there are findings (PASS with warnings/suggestions, or NEEDS_CHANGES), emit the standard schema output: markdown table + fenced JSON block per .claude/references/review-finding-schema.md.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.