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Delegate
Route tasks to sub-agents with optimal model selection, error recovery, and result verification.
安全通过
技能说明
name: Delegate description: Route tasks to sub-agents with optimal model selection, error recovery, and result verification.
Core Rule
Spawn cost < task cost → delegate. Otherwise, do it yourself.
Model Tiers
| Tier | Models | Cost | Use for |
|---|---|---|---|
| Small | Haiku, GPT-4o-mini, Gemini Flash | ~$0.25/1M | Search, summarize, format, classify |
| Medium | Sonnet, GPT-4o, Gemini Pro | ~$3/1M | Code, analysis, synthesis |
| Large | Opus, o1, Gemini Ultra | ~$15/1M | Architecture, complex reasoning |
Rule of thumb: Start with smallest tier. Escalate only if output quality insufficient.
Spawn Checklist
Every spawn must include:
1. TASK: Single clear deliverable (not "help with X")
2. MODEL: Explicit tier choice
3. CONTEXT: Only files/info needed (never full history)
4. OUTPUT: Expected format ("return JSON with...", "write to file X")
5. DONE: How to signal completion
Check templates.md for copy-paste spawn templates.
Error Recovery
| Error Type | Action |
|---|---|
| Sub-agent timeout (>5 min no response) | Kill and retry once |
| Wrong output format | Retry with stricter instructions |
| Task too complex for tier | Escalate: Small→Medium→Large |
| Repeated failures (3x) | Abort, report to user |
Check errors.md for recovery patterns and escalation logic.
Verification
Never trust "done" without checking:
- Code: Run tests, check syntax
- Files: Verify they exist and have content
- Data: Spot-check 2-3 items
- Research: Confirm sources exist
Don't Delegate
- Quick tasks (<30 seconds to do yourself)
- Tasks needing conversation context
- Anything requiring user clarification mid-task
如何使用「Delegate」？
- 打开小龙虾AI（Web 或 iOS App）
- 点击上方「立即使用」按钮，或在对话框中输入任务描述
- 小龙虾AI 会自动匹配并调用「Delegate」技能完成任务
- 结果即时呈现，支持继续对话优化