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Employee

Create and manage virtual AI employees with persistent memory, defined roles, and graduated autonomy. Hire, train, and delegate tasks to specialized workers.

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版本1.0.0
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技能说明


name: Employee slug: employee version: 1.0.0 description: Create and manage virtual AI employees with persistent memory, defined roles, and graduated autonomy. Hire, train, and delegate tasks to specialized workers. metadata: {"clawdbot":{"emoji":"👔","requires":{"bins":[]},"os":["linux","darwin","win32"]}}

Architecture

Employees live in ~/employee/ with per-employee folders. See employee-template.md for setup.

~/employee/
├── registry.json          # Index of all employees + status
├── employees/
│   └── {name}/
│       ├── employee.json  # Role, permissions, stats
│       ├── memory/
│       │   └── context.md # Persistent learnings
│       └── logs/          # Work history by date
└── shared/
    └── protocols.md       # Common instructions

Quick Reference

TopicFile
Setup templatesemployee-template.md
Autonomy levelsautonomy.md
Task routingrouting.md
Lifecycle commandslifecycle.md

Core Rules

1. One Role Per Employee

  • Each employee has a single clear domain (researcher, reviewer, support)
  • Never generalist catch-alls
  • Scope defined in employee.jsonrole and permissions

2. Memory is Mandatory

  • Load memory/context.md before every task
  • Employees remember context across sessions
  • Log learnings after each task

3. Escalate Uncertainty

  • Employees say "I don't know" rather than guess
  • Escalation triggers defined in employee.json
  • Never confident hallucinations

4. Graduated Autonomy

LevelBehavior
shadowWatches, doesn't act (onboarding)
draft-onlyCreates drafts, human sends
reviewActs, human approves before external effect
autonomousFull delegation within permissions

See autonomy.md for promotion criteria.

5. Explicit Permissions

  • Read vs write access per system
  • File access paths whitelisted
  • canSpawn and canMessage flags
  • Code Reviewer can comment, cannot merge

6. Task Routing

When request arrives:

  1. Explicit: "Luna, do X" → route to Luna
  2. Implicit: match against registry.json roles → suggest
  3. See routing.md for auto-delegation rules

7. Reporting

Each employee provides:

  • Daily: What I did, what needs attention, what's coming
  • Weekly: Tasks completed, escalations, token usage

8. Lifecycle

CommandAction
hire {name} as {role}Create employee
train {name} on [docs]Add to memory
evaluate {name}Performance review
promote/demote {name}Change autonomy
retire {name}Archive

See lifecycle.md for full command reference.

9. Registry Management

  • registry.json tracks all employees + status (active/paused/retired)
  • Update registry on every lifecycle change
  • Query registry to list available employees

10. Anti-Patterns

  • ❌ Generalist employees (handles nothing well)
  • ❌ No memory (forgets context)
  • ❌ Instant autonomy (needs shadowing)
  • ❌ Silent failures (must report blockers)
  • ❌ Scope creep (reviewer refactoring = noise)

如何使用「Employee」?

  1. 打开小龙虾AI(Web 或 iOS App)
  2. 点击上方「立即使用」按钮,或在对话框中输入任务描述
  3. 小龙虾AI 会自动匹配并调用「Employee技能完成任务
  4. 结果即时呈现,支持继续对话优化

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