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Slash command/skill-code-review
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Expert multi-AI code review with inline PR comments — use for thorough quality and security analysis
📊 Stats
Stars3,869
Forks362
LanguageShell
LicenseMIT
📦 Ships with octo
</> SKILL.md
skill-code-review.SKILL.md
---name: skill-code-review
aliases:
- review
- code-review
description: Expert multi-AI code review with inline PR comments — use for thorough quality and security analysis
context: fork
agent: Explore
execution_mode: enforced
pre_execution_contract:
- visual_indicators_displayed
validation_gates:
- orchestrate_sh_executed
- review_output_exists
---# 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.
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
