batch-review

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SKILL.md (first lines)

# Batch Review (claude-batch-toolkit)

> **Integration**: Load via `@Skill:batch-review.md` in DECISION LOOP step 5  
## Overview

Send non-urgent analysis to Anthropic's Batch API at **50% cost reduction**. Ideal for code reviews, documentation, security audits, and architecture analysis that can wait ~1 hour.

## When to Activate

Use this skill for batch processing when:

- Code reviews or security audits (non-urgent)
- Documentation generation
- Architecture analysis
- Test generation for existing code
- Refactoring plans
- Changelog generation
- Any task where 50% cost savings > immediate results

## Detection Keywords

"batch", "review", "audit", "analyze", "documentation", "architecture", "changelog", "refactoring plan"

## Protocol Integration

### DECISION LOOP Position

This skill applies at **step 5** of the DECISION LOOP:

```
1. CLASSIFY  -> complexity? backup needed? tools?
2. PROTECT   -> cp file file.bak (for configs, DBs)
3. MEMORY    -> query relevant context + past failures
4. AGENTS    -> check overlaps (if multi-agent)
5. SKILLS    -> @Skill:batch-review.md for domain-specific guidance
6. WORK      -> implement (ALWAYS use worktree for ANY file changes)
7. REVIEW    -> self-review diff before testing
8. TEST      -> completion gates pass
9. LEARN     -> store outcome in memory
```

### Required Pre-Checks

Before using batch review:

- [ ] **MANDATORY**: Worktree created (`uap worktree create <slug>`)
- [ ] Task is non-urgent (can wait ~1 hour)
- [ ] Context is self-contained (no interactive debugging needed)
- [ ] Full file contents gathered (batch model has NO codebase access)

## Setup

```bash
git clone git@github.com:s2-streamstore/claude-batch-toolkit.git
cd claude-batch-toolkit && ./install.sh --api-key $ANTHROPIC_API_KEY
```

## Good Batch Candidates

- ✅ Code reviews and security audits
- ✅ Documentation generation
- ✅ Architecture analysis
- ✅ Test generation for existing code
- ✅ Refactoring plans
- ✅ Changelog generation

## Bad Candidates (use real-time instead)

- ❌ Anything needed right now
- ❌ Interactive debugging
- ❌ Quick questions
- ❌ Tasks requiring back-and-forth

## Usage Pattern

1. Gather all context into a self-contained prompt file
2. Include full file contents (batch model has NO codebase access)
3. Submit via MCP tool `send_to_batch` with `packet_path`
4. Check results: `batch_list` then read from `~/.claude/batches/results/`

## Integration with Parallel Review Protocol

Instead of running security-auditor/code-quality-guardian synchronously as subagents,
submit reviews as batch jobs for 50% cost savings when time allows:

```bash
# Assemble review prompt with full diff
git diff --staged > /tmp/review_diff.txt
cat > /tmp/batch_prompt.md << 'EOF'
Review the following diff for security vulnerabilities and code quality issues.
## Diff
EOF
cat /tmp/review_diff.txt >> /tmp/batch_prompt.md

# Submit (via MCP or CLI)
uv run ~/.claude/mcp/claude_batch_mcp.py submit --packet-path /tmp/batch_prompt.md --label "pr-review"
```

## Cost Reference

| Model           | Standard       | Batch (50% off)  |
| --------------- | -------------- | ---------------- |
| Claude Opus 4   | $15/$75 per 1M | **$7.50/$37.50** |
| Claude Sonnet 4 | $3/$15 per 1M  | **$1.50/$7.50**  |

## UAP Protocol Compliance

### Completion Gates Checklist

Before reporting task complete:

```
[x] Schema diffed against test expectations
[x] Tests: X/Y (must be 100%, run 3+ times)
[x] Outputs verified: ls -la
[x] Worktree created and PR prepared
[x] MANDATORY cleanup after PR merge
```

From DammianMiller/universal-agent-protocol, MIT.

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