agentleFS
MrCipherSmithMrCipherSmith/goodai-base/skills/code-review/SKILL.md

code-review

Skill0 starsChanged 5 months agoMITView on GitHub

Comprehensive code review with 4 parallel agents: correctness/logic, security, performance, style/maintainability. Produces unified severity report (CRITICAL/HIGH/MEDIUM/LOW). Use for thorough PR reviews and pre-merge checks.

194 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use close-spec, design-with-images and 10 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.
  • dzhngSkill

    close-spec

    When a spec under specs/<feature>/ is done shipping (a write-spec build, or any planned task that produced a spec), archive it to specs/done/ and rewrite it from a build-plan into a durable rationale — the why, the principles, the invariants — pointing back to the real code for the how. Use when implementation has landed and the plan no longer matches what shipped, or the user says a feature/spec is finished. Pairs with [write-spec](../write-spec/SKILL.md) (the plan this closes) and [review](../review/SKILL.md) (run before closing).

    Also in 5 projects that use this

  • dzhngSkill

    design-with-images

    Explore UI and visual design with image generation, then iterate the real implementation against the user-selected concept. Use when brainstorming visual directions, offering mockup variants, or implementing an approved generated design; do not stop at the mockups or the first code approximation.

    Also in 5 projects that use this

  • PydanticSkill

    add-new-model

    Add support for a newly-released language or image generation model in pydantic-ai (e.g. openai:gpt-5.6, anthropic:claude-sonnet-5, openai:gpt-image-2). Use when a provider ships a new model id and you need to wire literals, profile flags, adapters, and tests to recognize it. Handles SDK-lag, gateway list conventions, capability probing, and direct image-model geometry.

    Also in 9 projects that use this

  • transitive-bullshitSkill

    compare-screenshots

    Compare screenshots against the intended design, distinguishing approved references from historical baselines. Use for iterative implementation matching, before/after visual review, objective image telemetry, or checking a lone capture for flat, empty, or badly framed content.

    Also in 5 projects that use this

  • Microsoft AzureSkill

    azsdk-common-apiview-feedback-resolution

    Analyze and resolve APIView review feedback on Azure SDK PRs. **UTILITY SKILL**. USE FOR: APIView comments, API review feedback, SDK API surface changes. DO NOT USE FOR: general code review, non-APIView feedback. INVOKES: azure-sdk-mcp:azsdk_apiview_get_comments, azure-sdk-mcp:azsdk_customized_code_update, azure-sdk-mcp:azsdk_typespec_delegate_apiview_feedback, azure-sdk-mcp:azsdk_run_typespec_validation, azure-sdk-mcp:azsdk_package_generate_code.

    Also in 6 projects that use this

  • Microsoft AzureSkill

    azsdk-common-generate-sdk-locally

    Generate and finalize Azure SDKs locally from TypeSpec, including build, checks, tests, changelog, metadata, and version updates by default. WHEN: "generate SDK locally", "build SDK", "run SDK tests", "run CI checks", "validate package", "run checks", "update changelog", "fix SDK build errors", "resolve SDK generation errors", "customize TypeSpec", "rename SDK client", "rename SDK model", "hide operation from SDK", "fix analyzer errors", "resolve customization drift", "create subclient", "update metadata", "update version". INVOKES: azsdk_verify_setup, azsdk_package_generate_code, azsdk_package_build_code, azsdk_package_run_check, azsdk_package_run_tests, azsdk_customized_code_update, azsdk_package_update_changelog_content, azsdk_package_update_metadata, azsdk_package_update_version.

    Also in 6 projects that use this

  • +4 more from dzhng
  • +2 more from Azure

The file

We split this skill into the rules it gives an agent

7 rules. 0 are common, 4 are distinctive, and 3 are very specific to this project.

  • 0

    Common

    Most of the 184 edited versions we read keep these.

  • 4

    Distinctive

    This author's own choices. Worth a look.

  • 3

    Very specific

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

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

    If on a feature branch: git diff $(git merge-base main HEAD)..HEAD

    line 26
  2. Very specific:

    If PR number provided: gh pr diff <number>

    line 27
  3. Distinctive:

    Read all changed files in full (agents need complete context)

    line 30
  4. Very specific:

    If user says /code-review --fix, attempt to auto-fix MEDIUM and LOW findings

    line 90
  5. Distinctive:

    If reviewing a PR, offer to post the review as a GitHub comment

    line 91
  6. Distinctive:

    NEVER approve code with CRITICAL findings

    line 101
  7. Distinctive:

    If the diff is huge (>2000 lines), split into chunks and review each

    line 104

“Common” is measured: kept by at least 6 in 10 of the 184 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 Review (4-Agent)

Comprehensive code review that dispatches 4 specialized agents in parallel for deep analysis.

Workflow

Phase 1: Preparation

  1. Detect the review scope:
    • If on a feature branch: git diff $(git merge-base main HEAD)..HEAD
    • If PR number provided: gh pr diff <number>
    • Fallback: staged + unstaged changes
  2. Get list of changed files: git diff --name-only <range>
  3. Read all changed files in full (agents need complete context)

Phase 2: Dispatch 4 Agents in Parallel

Launch 4 agents in a single turn:

Agent 1 — Correctness & Logic

  • Logic bugs, edge cases, null/undefined risks
  • Off-by-one errors, race conditions
  • Incorrect error handling, missing return values
  • Type mismatches, contract violations

Agent 2 — Security

  • Injection vulnerabilities (SQL, XSS, command)
  • Auth/authz gaps, missing input validation
  • Secrets in code, insecure defaults
  • OWASP Top 10 patterns

Agent 3 — Performance

  • N+1 queries, unnecessary re-renders
  • Missing memoization, expensive computations in loops
  • Bundle size impact, large imports
  • Memory leaks, unbounded collections

Agent 4 — Style & Maintainability

  • Naming clarity, dead code, unused imports
  • Overly complex logic (cyclomatic complexity)
  • Missing or misleading comments
  • DRY violations, code duplication

Each agent outputs findings as:

### [CRITICAL|HIGH|MEDIUM|LOW] Title
**File:** path/to/file:line
**Description:** what's wrong and why
**Suggestion:** how to fix

Phase 3: Unified Report

Collect all 4 agent results and produce a single report:

# Code Review Report

## Summary
- X critical, Y high, Z medium, W low findings
- Overall assessment: APPROVE / REQUEST_CHANGES / COMMENT

## Critical & High Findings
(sorted by severity, then by file)

## Medium & Low Findings
(collapsed or summarized)

## Positive Notes
(good patterns worth highlighting)

Phase 4: Action (optional)

  • If user says /code-review --fix, attempt to auto-fix MEDIUM and LOW findings
  • If reviewing a PR, offer to post the review as a GitHub comment

Arguments

  • /code-review — review current branch changes
  • /code-review #<number> — review specific PR
  • /code-review --fix — auto-fix medium/low findings after review

Rules

  • NEVER approve code with CRITICAL findings
  • Deduplicate findings across agents (same file:line = merge)
  • Include positive feedback — don't make it only negative
  • If the diff is huge (>2000 lines), split into chunks and review each
  • Be strict but fair — only flag real issues, not style preferences

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