code-review-pipeline
Multi-dimensional code review across correctness, security, performance, and maintainability with confidence-gated reporting and remediation loops.
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/code-review-pipeline/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How code-review-pipeline Compares
| Feature / Agent | code-review-pipeline | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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.
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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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