code-review-pipeline

Multi-dimensional code review across correctness, security, performance, and maintainability with confidence-gated reporting and remediation loops.

509 stars

Best use case

code-review-pipeline is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Multi-dimensional code review across correctness, security, performance, and maintainability with confidence-gated reporting and remediation loops.

Teams using code-review-pipeline should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/code-review-pipeline/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/methodologies/everything-claude-code/skills/code-review-pipeline/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/code-review-pipeline/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How code-review-pipeline Compares

Feature / Agentcode-review-pipelineStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Multi-dimensional code review across correctness, security, performance, and maintainability with confidence-gated reporting and remediation loops.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Code Review Pipeline

## Overview

Multi-dimensional code review methodology adapted from the Everything Claude Code project. Reviews across 4 dimensions with confidence-gated issue reporting and automated remediation loops.

## Review Dimensions

### Dimension 1: Correctness
- Logic errors and off-by-one mistakes
- Edge case handling (null, undefined, empty, boundary)
- Type safety (no implicit any, proper narrowing)
- Error handling completeness
- Floating promise detection
- Race condition analysis

### Dimension 2: Security
- Injection vectors (SQL, XSS, command, template)
- Authentication and authorization gaps
- Data exposure (PII, credentials, internal state)
- Dependency vulnerabilities (known CVEs)
- Input validation completeness

### Dimension 3: Performance
- Algorithmic complexity (O(n^2) detection)
- Memory leaks (event listeners, closures, caches)
- Unnecessary allocations in hot paths
- Database query optimization (N+1, missing indexes)
- Bundle size impact

### Dimension 4: Maintainability
- Naming clarity and consistency
- Documentation completeness (JSDoc, inline comments)
- Test coverage adequacy
- Coupling analysis (afferent/efferent)
- File organization compliance

## Confidence Gating
- Score each issue 0-100 on confidence
- Only report issues >= 80% confidence
- Prevents false positive noise
- Higher confidence for clear patterns, lower for heuristic matches

## Remediation Loop
- Prioritize: critical > high > medium > low
- Apply fixes via refactor-cleaner agent
- Re-review after remediation
- Maximum 2 remediation cycles
- Exit when no critical/high issues remain

## When to Use

- Post-implementation review
- Pre-merge PR review
- Security audit
- Technical debt assessment

## Agents Used

- `code-reviewer` (primary)
- `refactor-cleaner` (remediation)

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