L’interface est localisée ; les présentations et documents des Skills sont traduits automatiquement par IA dans votre langue. Le code, les commandes, chemins et termes techniques restent en version originale, et le document de l’auteur fait foi.
Explorer les Skills
Productivité · SKILL.md

Employee

Employee

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

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

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. homepage: https://clawic.com/skills/employee metadata: clawdbot: emoji: 👔 requires: bins: [] os: - linux - darwin - win32 displayName: Employee

Architecture

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

~/Clawic/data/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.json → role 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)
Skills enregistrées