code-review

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name: code-review description: > Multi-pass code review with scope drift detection and Fix-First heuristic. Triggers on "코드 리뷰", "리뷰해줘", "PR 확인", "code review", "review this", "check my code", "review PR", "코드 검토". Pass 1 auto-fixes mechanical issues; Pass 2 security audit; Pass 3 flags judgment-required items. WTF-likelihood safety gate. allowed-tools:

• Bash
• Read
• Edit
• Write
• Grep
• Glob
• Agent
• AskUserQuestion

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Code Review — Multi-Pass Review with Scope Drift Detection

Preamble

Before executing this skill:

1. Load context from memory:

   memory_search(query: "{skill-relevant-query}", associative: true, limit: 5)
   memory_search(tag: "{skill-name}", limit: 3)
   

   Review returned memories for relevant past context, decisions, and patterns.

2. Check OMC state for active work:

   state_get_status()
   

   If conflicting active tasks exist, warn the user before proceeding.

3. Detect current branch (for git-related skills):

   git rev-parse --abbrev-ref HEAD 2>/dev/null || echo "not-a-git-repo"
   

4. Check proactive mode:

   state_read("occ-proactive")
   

   If "false": do NOT proactively suggest other OpenClaw-CC skills during this session. Only run skills the user explicitly invokes.

5. Log skill activation:

   memory_daily_log(type: "note", entry: "Skill activated: /{skill-name}")
   

Memory Context Loading

Before starting work, load relevant context from the 3-layer memory system:

# Search for related past work
memory_search(query: "{task description}", associative: true, limit: 5)

# Search by relevant tags
memory_search(tag: "{relevant-tag}", limit: 3)

# Check for recent related daily logs
memory_search_date(start: "{7 days ago}", end: "{today}", category: "daily-logs", limit: 5)

Use retrieved context to:

• Avoid repeating past mistakes
• Build on previous decisions
• Maintain consistency with established patterns
• Reference related knowledge graph nodes