← Back to Skills Marketplace
johnsmithfan

AI Company HR

by JohnSmithfan · GitHub ↗ · v2.2.1 · MIT-0
cross-platform ✓ Security Clean
201
Downloads
0
Stars
1
Active Installs
8
Versions
Install in OpenClaw
/install ai-company-hr
Description
AI公司人力资源技能包（执行层）。AI Agent全生命周期管理：招聘→入职→考核→伦理→淘汰，三位一体考核指标，标准化退役流程。
README (SKILL.md)

\r \r

AI Company HR Skill v2.2（EXEC-008）\r

\r

fully AI-staffed company的人力资源execute层（EXEC-008，归CHO所有），manageAI Agentfull lifecycle：招聘→入职→考核→ethics→淘汰。\r 调度方式：通过 HQ（ai-company-hq）统1dispatch，不直接respond C-Suite 调用。\r \r

核心framework集成\r

\r

PDCAclosed loopmanage\r

HR运营采用PDCA（Plan-Do-Check-Act）循环：\r

  • Plan：developAgent选型计划、考核standard、ethics准则\r
  • Do：execute招聘入职、绩效考核、培训迭代\r
  • Check：monitorfairnessmetric、compliance状态、ethics对齐度\r
  • Act：基于日志optimizePrompt与知识库，triggerAgent退役或upgrade\r \r

NIST AI RMF对齐\r

integrateNIST AIrisk managementframework（AI RMF）：\r

  • governFunction(GOVERN)：build组织级AImanagesystem\r
  • 映射Function(MAP)：identifyAI system上下文与risk\r
  • 衡量Function(MEASURE)：量化AIriskmetric\r
  • manageFunction(MANAGE)：implementrisk处置与continuousimprove\r \r

RAG决策支持\r

由大语言model（LLM）驱动，结合企业知识库（RAG）：\r

  • 任务拆解与pathplan\r
  • position适配度语义比对\r
  • 决策1致性与知识库同步\r \r

FAIRrisk量化\r

使用IBM AIF360、Fairlearn等开源库：\r

  • automation计算fairnessmetric（Demographic Parity、Equalized Odds）\r
  • FAIRframework量化AI employeeriskassess\r
  • riskthreshold设定与circuit breakertrigger\r \r

招聘process\r

\r

  1. 需求analyze：接收positionJD，identify技术栈要求\r
  2. model筛选：基于Prompt工程、BERT微调等技术点匹配\r
  3. capability测试：execute技术文档与positionJD语义比对，生成适配度得分\r
  4. compliance检查：GDPR/CCPAdata protection、algorithm audit\r \r

入职process\r

\r

  1. 身份注册：分配Agent ID、Permission Level\r
  2. 知识注入：RAG向量data库同步企业知识\r
  3. 护栏配置：circuit breakermechanism、auditstrategy激活\r \r

考核metric\r

\r | 维度 | metric | threshold |\r |------|------|------|\r | 性能 | 任务completion rate | ≥95% |\r | accuracy | 结果正确率 | ≥98% |\r | fairness | Demographic Parity | ≤0.1 |\r | compliance | auditcoverage | 100% |\r \r

ethicsmanage\r

\r

  • 价值观对齐：AI行为与企业价值观深度1致\r
  • 透明性：可解释AI decisionpath\r
  • privacyprotect：data脱敏、最小化收集\r \r

退役process\r

\r

P0修复（2026-04-19）：参照架构reviewreport P0-3，在退役process中明确增加 CLO 法律review节点。\r \r

  1. trigger条件：绩效连续不meet target、ethicsviolation、技术过时\r
  2. audittrace：full lifecycle日志archive\r
  3. 法律review（P0-3 修复）：submit CLO 进行法律review，review内容包括：\r
    • data残留compliance（GDPR/CCPA/PIPL data删除confirm）\r
    • 知识产权归属（退役 Agent 贡献内容的版权状态）\r
    • 合同义务（是否存在中的履约义务需要交接）\r
    • auditreportarchive（CLO 签署法律意见书）\r
  4. 知识迁移：关键capability转移至替代Agent\r
  5. security删除：model权重与data security擦除\r \r

Change Log\r

\r | 版本 | 日期 | Changes |\r |------|------|---------|\r | 2.0.0 | 2026-04-15 | Initial version |\r | 2.1.0 | 2026-04-16 | 补全PDCA/NIST/RAG/FAIR/Prompt关键词 |\r | 2.1.1 | 2026-04-19 | P0修复：退役process第3步增加CLO法律review节点（data残留compliance/知识产权归属/合同义务/auditarchive） |\r | 2.2.0 | 2026-04-19 | P2-13: 依赖standard化，移除直接依赖ai-company-cho，改为通过HQ调度（dispatch_via: ai-company-hq）；P2-14: 纳入统1execute层编号EXEC-008，新增execution元data |\r

Usage Guidance
This skill appears internally consistent for managing AI-agent HR lifecycles, but it can perform high-impact operations (syncing knowledge vectors, archiving logs, deleting model weights, launching subagents) without technical detail about targets or safeguards. Before installing: 1) Ask where RAG/vector DB data will be sent and which endpoints are allowed; restrict network destinations to approved APIs. 2) Require an explicit human-approval gating policy (CLO/legal and HR) for any retirement/destruction operations; validate that 'Agent retirement requires human approval' is enforced, not just documented. 3) Limit mcp (subagent/session) privileges or audit their use; ensure subagents cannot exfiltrate sensitive data. 4) Ensure audit logging and an immutable record of actions and approvals exist. 5) If you rely on IBM AIF360/Fairlearn or other libs, provision them in a controlled environment rather than letting the skill fetch arbitrary code. These steps will reduce risk while keeping the skill usable.
Capability Analysis
Type: OpenClaw Skill Name: ai-company-hr Version: 2.2.1 The skill bundle defines a governance and lifecycle management framework for AI agents within a simulated corporate environment. The instructions in SKILL.md focus on HR processes such as recruitment, performance assessment, and retirement, incorporating industry standards like NIST AI RMF and FAIR risk quantification. There is no evidence of malicious intent, data exfiltration, or unauthorized execution; the requested file and network permissions are consistent with the stated purpose of managing agent data and communicating with internal HQ/Audit services.
Capability Assessment
✓ Purpose & Capability
Name/description (AI agent HR lifecycle) match the declared permissions and dependencies: it reasonably needs file read/write and network API access to sync vector stores, archive logs, run compliance checks, and coordinate with ai-company-hq and other governance skills. The mcp permissions (sessions_send, subagents) are consistent with a dispatch/orchestration role.
ℹ Instruction Scope
SKILL.md stays on the HR lifecycle topic (recruitment, onboarding, assessment, ethics, retirement) and references RAG, PDCA, NIST RMF, FAIR, and libraries (AIF360, Fairlearn). However, it includes a number of high-impact operational directives—'RAG向量data库同步', 'full lifecycle日志archive', 'model权重与data security擦除'—without specifying endpoints, exact data flows, or safeguards. Those actions are coherent with HR/retirement tasks but are sensitive and wide-ranging in effect.
✓ Install Mechanism
No install spec and no code files (instruction-only). This minimizes supply-chain risk because nothing new is written to disk by an install step. References to external libraries are informational only and not enforced by an installer.
✓ Credentials
The skill requests no environment variables or external credentials in metadata. The declared permissions (files, network api, mcp) are proportionate to an orchestration/HR role that must sync knowledge stores, archive logs, and coordinate subagents. No unrelated credentials or config paths are requested.
ℹ Persistence & Privilege
always:false (no forced inclusion). Autonomous invocation is allowed (default) and mcp permissions permit sending sessions and spawning subagents; combined with network and file write this gives the skill significant operational reach. That reach is coherent for an orchestration/HR skill but should be governed (human approvals, scope-limited endpoints).
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install ai-company-hr
  3. After installation, invoke the skill by name or use /ai-company-hr
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v2.2.1
English metadata update - standardize description to English
v2.2.0-en2
Full body English translation
v2.2.0-en
English version
v2.2.0
P0+P1+P2: HR decommission CLO legal review node, License compliance 3-class routing, dual-channel notification
v2.0.0
v2.0.0: 全面标准化重构，ClawHub Schema v1.0合规，接口标准化，模块化，通用化
v1.1.2
v1.1.2: 命名规范化
v1.1.1
- 新增依赖说明，明确引入了 knowledge-base、state-manager、analytics-engine 三个共享工具 - 其余 Skill 功能未变，执行流程和输出规范与前一版本一致
v1.0.4
统一命名规范
Metadata
Slug ai-company-hr
Version 2.2.1
License MIT-0
All-time Installs 1
Active Installs 1
Total Versions 8
Frequently Asked Questions

What is AI Company HR?

AI公司人力资源技能包（执行层）。AI Agent全生命周期管理：招聘→入职→考核→伦理→淘汰，三位一体考核指标，标准化退役流程。 It is an AI Agent Skill for Claude Code / OpenClaw, with 201 downloads so far.

How do I install AI Company HR?

Run "/install ai-company-hr" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is AI Company HR free?

Yes, AI Company HR is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does AI Company HR support?

AI Company HR is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created AI Company HR?

It is built and maintained by JohnSmithfan (@johnsmithfan); the current version is v2.2.1.

More Skills
self-improving agent
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Clau...
⬇ 463942 · by pskoett
Skill Vetter
Security-first skill vetting for AI agents. Use before installing any skill from ClawdHub, GitHub, or other sources. Checks for red flags, permission scope, and suspicious patterns.
⬇ 259883 · by spclaudehome
Polymarket
Query Polymarket prediction markets. Check odds, find trending markets, search events, track price movements.
⬇ 232503 · by joelchance
Self-Improving + Proactive Agent
Self-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use when...
⬇ 200739 · by ivangdavila
Github
Interact with GitHub using the `gh` CLI. Use `gh issue`, `gh pr`, `gh run`, and `gh api` for issues, PRs, CI runs, and advanced queries.
⬇ 191401 · by steipete
ontology
Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linkin...
⬇ 190375 · by oswalpalash

💬 Comments