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在 OpenClaw 中安装
/install employee
功能描述
Create and manage virtual AI employees with persistent memory, defined roles, and graduated autonomy. Hire, train, and delegate tasks to specialized workers.
使用说明 (SKILL.md)
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
| Topic | File |
|---|---|
| Setup templates | employee-template.md |
| Autonomy levels | autonomy.md |
| Task routing | routing.md |
| Lifecycle commands | lifecycle.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→roleandpermissions
2. Memory is Mandatory
- Load
memory/context.mdbefore 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
| Level | Behavior |
|---|---|
| shadow | Watches, doesn't act (onboarding) |
| draft-only | Creates drafts, human sends |
| review | Acts, human approves before external effect |
| autonomous | Full delegation within permissions |
See autonomy.md for promotion criteria.
5. Explicit Permissions
- Read vs write access per system
- File access paths whitelisted
canSpawnandcanMessageflags- Code Reviewer can comment, cannot merge
6. Task Routing
When request arrives:
- Explicit: "Luna, do X" → route to Luna
- Implicit: match against
registry.jsonroles → suggest - See
routing.mdfor 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
| Command | Action |
|---|---|
| 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.jsontracks 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)
安全使用建议
This skill is internally consistent with its purpose, but it will create and manage files under ~/employee/ and may be configured to read other directories or link to other skills. Before enabling or granting autonomy: (1) inspect and restrict employee.json fileAccess entries so they don't point to sensitive locations, (2) verify any linked skill paths point to trusted code, (3) keep autoDelegation disabled until you trust the employee's behavior, and (4) require manual confirmation before promoting employees to 'autonomous' or enabling canSpawn/canMessage. If you need extra assurance, run it in a sandboxed account or backup important files first.
功能分析
Type: OpenClaw Skill
Name: employee
Version: 1.0.0
The skill bundle defines a system for managing AI sub-agents ('employees') with powerful capabilities, including the ability for 'autonomous' employees to 'Spawn Agents', 'Send External' communications, and 'Modify Files' (documented in `autonomy.md`). While these capabilities are gated by explicit permissions and user approval, they represent significant security risks if misused or misconfigured. Additionally, the `clawhub` skill linking mode (mentioned in `employee-template.md`, `lifecycle.md`, `routing.md`) introduces a supply chain vulnerability, as it allows fetching and executing external skills from a remote source, which could potentially be malicious. There is no evidence of intentional malicious behavior within this skill bundle itself, but its design incorporates high-risk functionalities and potential vectors for exploitation.
能力评估
Purpose & Capability
Name/description match the actual behavior: the SKILL.md and companion docs define creating, configuring, and running per-employee folders under ~/employee/, routing tasks, and managing autonomy. There are no declared environment variables, binaries, or external services unrelated to this purpose.
Instruction Scope
Instructions explicitly require reading and writing files in the user's home (~ /employee/, linked skill paths, and optionally user-provided documents like style guides). They also instruct the agent to 'inject memory/context.md as context' and 'spawn as subagent with employee's model' on each task. This is expected for the functionality but means the skill will access local files and include their contents in subagent contexts.
Install Mechanism
No install spec or code is provided (instruction-only), so nothing will be downloaded or written by an installer. That minimizes supply-chain risk from the skill itself.
Credentials
The skill requests no environment variables or credentials. However, employee configs explicitly contain fileAccess lists and linkable skill paths (e.g., '~/clawd/skills/researcher/'), which — if misconfigured by the user — could grant broad access to sensitive files or to other skills. The documentation relies on user-specified paths/whitelists for permissions, so the onus is on the user to keep those narrow and correct.
Persistence & Privilege
always:false (normal). The skill prescribes persistent local state under ~/employee/ (registry.json, logs, memories). It also defines auto-delegation and autonomy levels that, if enabled and granted wide fileAccess or canSpawn permissions, could allow subagents to act with reduced human oversight. The skill itself recommends safeguards (explicit approval before autonomous promotions).
如何使用
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install employee - 安装完成后,直接呼叫该 Skill 的名称或使用
/employee触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release
元数据
常见问题
Employee 是什么?
Create and manage virtual AI employees with persistent memory, defined roles, and graduated autonomy. Hire, train, and delegate tasks to specialized workers. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 805 次。
如何安装 Employee?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install employee」即可一键安装,无需额外配置。
Employee 是免费的吗?
是的,Employee 完全免费(开源免费),可自由下载、安装和使用。
Employee 支持哪些平台?
Employee 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(linux, darwin, win32)。
谁开发了 Employee?
由 Iván(@ivangdavila)开发并维护,当前版本 v1.0.0。
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