skill-code-review
Expert multi-AI code review with inline PR comments — use for thorough quality and security analysis
Skill by GongYuanCaiJi
Chalk Labs
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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/gongyuancaiji-skill-code-review
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: security
Reviews
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What's inside
- Skill name
skill-code-review- Description
- Expert multi-AI code review with inline PR comments — use for thorough quality and security analysis
- License
- MIT
- Instructions
- 299 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Code Review Skill
## MANDATORY COMPLIANCE — DO NOT SKIP
**When this skill is invoked, you MUST execute the multi-LLM review pipeline. You are PROHIBITED from:**
- Doing a direct single-model code review without multi-provider synthesis
- Deciding the scope is "too broad" and narrowing it without asking the user
- Skipping the provider check or structured review phases
- Substituting two background Sonnet agents for the full multi-provider pipeline
- Rationalizing "a focused audit would be more effective" — the user wants multi-LLM perspectives
---
**Your first output line MUST be:** `🐙 **CLAUDE OCTOPUS ACTIVATED** - Multi-LLM Code Review`
Invokes the code-reviewer persona for thorough code analysis during the `ink` (deliver) phase.
## Quick Mode
For fast sanity checks (staged changes, small PRs), skip the full review pipeline and run just two phases:
```bash
# Quick: grasp (consensus on scope) → tangle (parallel review)
${HOME}/.claude-octopus/plugin/scripts/orchestrate.sh grasp "[review request]"
${HOME}/.claude-octopus/plugin/scripts/orchestrate.sh tangle "[synthesized scope]"
```
Use quick mode when user says "check this PR", "quick review", "sanity check my changes", or for pre-commit checks. Use the full review for PRs with security/architecture impact.
## Usage
```bash
# Via orchestrate.sh
${HOME}/.claude-octopus/plugin/scripts/orchestrate.sh spawn code-reviewer "Review this pull request for security issues"
# Via auto-routing (detects review intent)
${HOME}/.claude-octopus/plugin/scripts/orchestrate.sh auto "review the authentication implementation"
```
## Capabilities
- AI-powered code quality analysis
- Security vulnerability detection
- Performance optimization suggestions
- Architecture and design pattern review
- TDD compliance and test-first evidence review
- Autonomous code generation risk detection
- Best practices enforcement
## Persona Reference
This skill wraps the `code-reviewer` persona defined in:
- `agents/personas/code-reviewer.md`
- CLI: `codex-review`
- Model: `gpt-5.2-codex`
- Phases: `ink`
## Example Prompts
```
"Review this PR for OWASP Top 10 vulnerabilities"
"Analyze the error handling in src/api/"
"Check for memory leaks in the connection pool"
"Review the test coverage for the auth module"
```
---
## Autonomous Implementation Review
When the review context indicates `AI-assisted`, `Autonomous / Dark Factory`, or unclear provenance, raise the rigor bar. Do not treat generated code as trustworthy just because it is polished.
### TDD Evidence
Check for concrete signs that the change followed red-green-refactor rather than test-after implementation:
- Compare the diff and recent history when available to see whether tests were added before or alongside production changes.
- Prefer behavior-defining tests over snapshot-only or mock-heavy tests that merely restate the implementation.
- Verify the production code looks like the minimum needed to satisfy the tests, rather than a speculative abstraction with unused options.
- If evidence is missing, mark TDD compliance as unknown and do not assume TDD happened.
### Autonomous Codegen Risk Patterns
Elevate or add findings when you see patterns common in high-autonomy output:
- Option-heavy APIs or abstractions not justified by tests or current requirements
- Placeholder logic, TODO/FIXME-driven control flow, or dead branches that appear "future ready"
- Mock, fake, or dummy behavior leaking into production paths
- Unwired components, unused helpers, or code that exists without an execution path
- Silent failure handling, broad catch blocks, missing logs, or weak operational visibility
- Missing rollback notes, migration guards, or release-safety checks for risky changes
### Review Output Addendum
Add a short section to the review synthesis when autonomy or TDD is in scope:
```markdown
## TDD / Autonomy Assessment
- Provenance: Human-authored | AI-assisted | Autonomous / Dark Factory | Unknown
- TDD evidence: Confirmed | Partial | Unknown
- Autonomous risk signals: None | Minor | Significant
- Recommendation: Ship | Fix before merge | Re-run with /octo:tdd or tighter supervision
```
---
## Implementation Completeness Verification
After the code-reviewer persona completes, run stub detection to verify implementation completeness.
### Stub Detection Process
**Step 1: Get changed files**
```bash
# Get files changed in the commit/PR
if [ -n "$COMMIT_RANGE" ]; then
changed_files=$(git diff --name-only "$COMMIT_RANGE")From GongYuanCaiJi/dsh-claude-octopus, MIT.
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Aug 15, 2026 (2 months ago)
- Last release
- Aug 15, 2026 (2 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
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Cite this page
skill-code-review. Agent Mart. https://agentmart.dev/skills/gongyuancaiji-skill-code-review (last updated 2026-10-24)