Refactor existing code through analysis, planning, implementation, review, and testing. Use small, verifiable changes to extract modules, reorganize code, or improve architecture while preserving existing behavior and checking for regressions.
中文简介正在准备中。当前显示英语简介。
Code refactoring workflow - analyze → plan → implement → review → validate
安装前可以查看交给 AI 代理的指令内容。
Safe refactoring with review gates.
┌──────────┐ ┌────────────┐ ┌──────────┐ ┌──────────┐ ┌───────────┐
│ phoenix │───▶│ plan- │───▶│ kraken │───▶│plan-reviewer│───▶│ arbiter │
│ │ │ agent │ │ │ │ │ │ │
└──────────┘ └────────────┘ └──────────┘ └──────────┘ └───────────┘
Analyze Plan Implement Review Verify
current changes refactor changes tests pass
| # | Agent | Role | Output |
|---|---|---|---|
| 1 | phoenix | Analyze current code, identify improvement areas | Analysis report |
| 2 | plan-agent | Create safe refactoring plan | Step-by-step plan |
| 3 | kraken | Implement the refactoring | Code changes |
| 4 | plan-reviewer | Review changes for correctness | Review report |
| 5 | arbiter | Verify all tests still pass | Test report |
Task(
subagent_type="phoenix",
prompt="""
Analyze for refactoring: [TARGET_CODE]
Identify:
- Current pain points
- Code smells
- Improvement opportunities
- Risk areas
- Test coverage gaps
"""
)
Task(
subagent_type="plan-agent",
prompt="""
Plan refactoring: [TARGET_CODE]
Analysis: [from phoenix]
Create:
- Step-by-step refactoring plan
- Each step should be:
- Small and focused
- Independently testable
- Reversible
- Identify files affected
- Risk mitigation strategy
"""
)
Task(
subagent_type="kraken",
prompt="""
Implement refactoring: [TARGET_CODE]
Plan: [from plan-agent]
Requirements:
- Follow plan exactly
- Run tests after each step
- Stop if tests fail
- NO behavior changes
"""
)
Task(
subagent_type="plan-reviewer",
prompt="""
Review refactoring: [TARGET_CODE]
Changes: [git diff from kraken]
Check:
- Behavior preserved
- No unintended changes
- Code quality improved
- Patterns consistent
"""
)
Task(
subagent_type="arbiter",
prompt="""
Validate refactoring: [TARGET_CODE]
- Run full test suite
- Verify no regressions
- Check type errors
- Run linting
"""
)
phoenix → plan-agent → kraken → plan-reviewer → arbiter
phoenix → kraken → arbiter (simpler, skip detailed planning)
phoenix → plan-agent → [kraken → plan-reviewer] × N phases → arbiter
User: /refactor Extract the validation logic into a separate module
Claude: Starting /refactor workflow...
Phase 1: Analyzing current structure...
[Spawns phoenix]
Found: Validation logic spread across 4 files
- form.ts (lines 45-120)
- api.ts (lines 200-280)
- user.ts (lines 15-45)
- order.ts (lines 88-130)
Phase 2: Planning extraction...
[Spawns plan-agent]
Plan:
1. Create src/validation/index.ts
2. Extract common validators
3. Update imports one file at a time
4. Run tests after each change
Phase 3: Implementing...
[Spawns kraken]
Completed all 4 steps, tests green after each
Phase 4: Reviewing changes...
[Spawns plan-reviewer]
✅ All behavior preserved
✅ DRY improved (removed 45 duplicate lines)
✅ New structure consistent
Phase 5: Final validation...
[Spawns arbiter]
✅ 312 tests passing, 0 regressions
Refactoring complete!
--dry-run: Plan but don't implement--step-by-step: Pause after each change for approval--coverage-check: Require >80% coverage before proceeding还没有评价。欢迎分享你的使用感受。
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Coordinate background agents through saved files, keeping lengthy work transcripts out of the main conversation. It defines how to pass results between agents, monitor progress, and verify completed work with tests.
中文简介正在准备中。当前显示英语简介。
Delegate implementation, file reading, and testing to separate agents, then bring concise summaries back to the main conversation. The rules explain when delegation helps and when a small edit is better handled directly.
中文简介正在准备中。当前显示英语简介。
Organize implementation work across research, planning, validation, implementation, and review agents. Each stage passes files to the next, while implementation starts with failing tests and the main conversation coordinates the workflow.
中文简介正在准备中。当前显示英语简介。
Connect Agentica SDK agents to Claude Code CLI through a proxy, an intermediary server. Use setup and debugging guidance to address file permissions, streamed responses, result formatting, and agents reporting success without performing the requested operations.
中文简介正在准备中。当前显示英语简介。
Consult Agentica APIs when building workflows where multiple AI agents cooperate. This reference covers eleven coordination patterns, agent tracking, shared state, handoffs, and ways to combine results, with pointers to detailed API specifications.
中文简介正在准备中。当前显示英语简介。
Write clearer instructions for Agentica and REPL agents by specifying roles, file operations, and output formats. Includes templates for dividing research, coordinating specialists, reviewing solutions, and passing results between agents through files.
中文简介正在准备中。当前显示英语简介。