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paimlpaiml/aprender/.agents/segment_decisions_findings_scope_discovery_criteria/code-quality-review/SKILL.md

code-quality-review

Skill137 starsChanged 2 months agoMITView on GitHub

Use this skill to perform a comprehensive code quality and architecture review of the current project or repository, identifying code smells, architectural debt, performance bottlenecks, testing gaps, and logic issues. Trigger this whenever the user asks for a comprehensive code review or quality audit.

23 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use dependency-cve-sweep, fleet-standard-adoption and 6 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • berntpoppSkill

    dependency-cve-sweep

    Use when handling Dependabot PRs, CodeQL/Trivy alerts, or doing a routine dependency/CVE sweep for this repo.

    Also in 12 projects that use this

  • berntpoppSkill

    fleet-standard-adoption

    Use when bringing this repo into compliance with a GeneFoundry fleet standard (tool-naming, response-envelope, container-hardening, versioning, MCP transport, or logging) or closing its tracking issue.

    Also in 12 projects that use this

  • IgorGanapolskySkill

    ci-failure-triage

    Triage a CI / PR check failure by READING the failure body before forming any hypothesis. Triggered whenever a required check is red, a PR is BLOCKED, a merge won't land, or you're about to call a failure "transient", "flaky", "stale", or "orphaned". Prevents dismissing a real failure (e.g. real CodeQL security alerts) as noise.

    Also in 12 projects that use this

  • Microsoft AzureSkill

    release-readiness

    Complete ODIN release documentation and validation checks before declaring user-visible work complete, preparing a release, or opening the Release-to-main pull request.

    Also in 12 projects that use this

  • SentrySkill

    security-review

    Finds exploitable application security vulnerabilities in code changes. Use for Warden security scans, appsec review, OWASP-style checks, authentication or authorization bugs, injection, XSS, SSRF, path traversal, secrets, unsafe crypto, webhook verification, open redirects, or sensitive data exposure.

    Also in 13 projects that use this

  • AWSSkill

    retro

    Conduct a retrospective on a coding session.

    Also in 3 projects that use this

  • +2 more from berntpopp

The file

We split this skill into the rules it gives an agent

4 rules. 0 are common, 2 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 18 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Spawn Subagents: Use the invoke_subagent tool to spawn a quorum of specialized subagents.

    line 16
  2. Distinctive:

    Ensure that the roles cover a wide range of engineering concerns.

    line 17
  3. Very specific:

    Use the gh CLI (via run_command executing gh issue create) to create and populate this Epic on the GitHub repository.

    line 37
  4. Distinctive:

    Ensure the resulting Epic includes:

    line 39

“Common” is measured: kept by at least 6 in 10 of the 18 edited versions of this file we could read. “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

Code Quality Review

This skill orchestrates a parallel audit of a codebase using a quorum of highly specialized subagents. It is designed to work out-of-the-box on any PAIML project.

Workflow

  1. Spawn Subagents: Use the invoke_subagent tool to spawn a quorum of specialized subagents. Assign each subagent a highly specialized role. Ensure that the roles cover a wide range of engineering concerns. Example roles must include, but are not limited to:
    • Quantitative PMAT Auditor (Must use the pmat CLI to review quantitative quality and formal verification metrics)
    • Architecture Auditor: Dependencies
    • Architecture Auditor: State Management
    • Performance: Hot Paths
    • Performance: IO and Async
    • Performance: Data Structures
    • Testing: Unit Coverage
    • API Design Ergonomics
    • Build System Auditor
    • Documentation Auditor
    • Scalability & Large Data
    • Code Smell Auditor
    • Error Handling Auditor
  2. Define Prompts: For each subagent, provide a highly specific prompt instructing them to review the current project (or the specific repositories the user requested) for issues matching their domain. Instruct them to use fast tools (like grep_search or view_file) and return concise, high-impact findings (e.g. top 1-3 critical issues) to avoid overwhelming the context. Ensure the PMAT Auditor is instructed to run pmat.
  3. Wait for Reports: Pause your execution and wait for all subagents to report back. Do not poll. The system will automatically wake you up and notify you as messages arrive.

  4. Create Quality Report Epic: Once all subagents have submitted their findings, assimilate the entire report into a comprehensive Quality Report Epic. Use the gh CLI (via run_command executing gh issue create) to create and populate this Epic on the GitHub repository.

    Ensure the resulting Epic includes:

    • A categorized breakdown of all findings and architectural debt.
    • A roster of the names and roles of the agents that participated.
    • Individual feedback and findings attributed to each specific agent.

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What did you use it for? e.g. Reviewing pull requests in a Rails app

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