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Code review gate

Skill skyestrela/ai-agent-skill-preview/code-review-gate

Free evidence-first engineering skills for Claude Code, Codex, Hermes, Copilot and the skills CLI

Install
npx -y skills add skyestrela/ai-agent-skill-preview --skill code-review-gate

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 15 days oldThe repository was created 15 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Automated pre-commit review — security scan, quality gates, and auto-fix suggestions. Catches secrets, dead code, and common anti-patterns before they hit main.

SKILL.md

3.2 KB, 730 tokens by cl100k_base, as published. Nobody here has run it

Code Review Gate

Run this skill before every commit to main or before approving a PR. It produces a structured review that catches common problems automatically.

When to Use

  • Before merging any PR
  • Before committing to main
  • When asked "review this code" or "is this ready to ship?"

Steps

  1. Identify changed files. Run git diff --name-only main...HEAD (or equivalent). If no git repo, ask the user which files to review.

  2. For each changed file, check these categories:

    Security:

    • Hardcoded secrets, API keys, tokens, passwords (regex: /api[_-]?key|secret|token|password|auth.*=.*['\"][^'\"]{8,}/i)
    • SQL injection patterns (string concatenation in queries)
    • eval() or exec() with user input
    • Insecure deserialization (pickle.loads with untrusted data)
    • CORS misconfiguration (Access-Control-Allow-Origin: *)
    • Missing auth checks on routes that modify data

    Quality:

    • Dead code (unreachable returns, unused imports, commented-out code blocks > 3 lines)
    • Functions > 50 lines (flag for refactoring)
    • Nested conditionals > 3 levels deep
    • Magic numbers without named constants
    • Console.log/print statements left in production code

    Correctness:

    • Off-by-one errors in loops and array access
    • Missing error handling on I/O operations (file, network, DB)
    • Race conditions in async code (missing await, unhandled promises)
    • Type mismatches between function signatures and callers
  3. Produce the review output in this format:

    ## Code Review: [branch or files]
    
    **Verdict:** APPROVE / REQUEST CHANGES / NEEDS DISCUSSION
    
    ### Critical (must fix before merge)
    - [ ] [issue description] — `file:line`
    
    ### Warning (should fix soon)
    - [ ] [issue description] — `file:line`
    
    ### Suggestion (nice to have)
    - [ ] [issue description] — `file:line`
    
    ### Stats
    - Files reviewed: N
    - Critical issues: N
    - Warnings: N
    - Suggestions: N
    
  4. Auto-fix where possible. For each issue found, if there's a safe mechanical fix (removing unused imports, adding if err != nil checks), apply it and note it in the review. Do NOT auto-fix security issues — flag them for human review.

  5. If verdict is APPROVE, suggest a commit message in conventional format: type(scope): description.

Pitfalls

  • Don't review files you haven't read. Always read the full file, not just the diff context.
  • Don't flag style preferences as critical. Use "suggestion" for formatting and naming debates.
  • Don't auto-fix anything that changes runtime behaviour. Only remove dead code and add obvious safety checks.
  • If the change is > 500 lines, ask the user to narrow the scope rather than reviewing everything at once.

More evidence-first workflows

Free source and updates: https://github.com/skyestrela/ai-agent-skill-preview

Complete engineering pack: https://ai-agent-skills-pack.vercel.app/?utm_source=free-skill&utm_medium=github&utm_campaign=code-review-gate

Gives 0 of the 12 instructions most code review skills give in 730 tokens

Counted across 610 of the 674 authors here whose files we hold, read 2026-08-06

  • push back with technical reasoning if wrongin 60 of 610, across 24 files
  • ask for clarification on unclear itemsin 51 of 610, across 16 files
  • fix critical issues immediatelyin 45 of 610, across 29 files
  • implement one item at a timein 45 of 610, across 11 files
  • group findings by severityin 44 of 610, across 43 files
  • verify feedback against the codebasein 42 of 610, across 8 files
  • dispatch a code reviewer subagentin 39 of 610, across 23 files
  • fix important issues before proceedingin 37 of 610, across 22 files
  • test each fix individuallyin 35 of 610, across 7 files
  • reply in github comment threadsin 33 of 610, across 5 files
  • check for security vulnerabilitiesin 31 of 610, across 27 files
  • factualize corrections without over-explainingin 30 of 610, across 2 files

Said here and by no other author read

  • run this skill before every commit
  • run this skill before approving a pull request
  • apply safe mechanical fixes
  • suggest a commit message when the verdict is approve

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.