OpenClaw

compliance-readiness

AI Compliance Readiness Assessment — evaluate how prepared an organization is for AI governance regulations (EU AI Act, NIST AI RMF, HHS mandates, state bar AI rules). Scores readiness across 8 dimensions and generates an action plan. Use when assessing AI compliance gaps, preparing for audits, or building a governance roadmap.

1kalin0 来源安装数☆ 0 仓库 Stars

查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install 1kalin/compliance-readiness

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

AI Compliance Readiness Assessment

Evaluate organizational readiness for AI governance regulations and generate an actionable compliance roadmap.

When to Use

Assessing AI compliance posture before an audit
Preparing for EU AI Act (Aug 2026), HHS AI mandates, NIST AI RMF
Building a governance roadmap for AI deployments
Evaluating risk exposure from current AI usage

How to Use

When asked to assess AI compliance readiness, gather these inputs:

Required Inputs

Industry (legal, healthcare, financial-services, insurance, construction, manufacturing, government, other)
Company size (employees or revenue range)
AI systems in use (list: chatbots, document review, fraud detection, hiring tools, customer service, analytics, other)
Jurisdictions (US-only, EU-exposed, both, global)

Optional Inputs

Current governance framework (if any)
Upcoming audit dates
Existing compliance certifications (SOC2, ISO 27001, HIPAA, etc.)
Number of AI vendors/tools in use

Assessment Framework

Score each dimension 1-5 (1=no controls, 5=mature):

8 Dimensions

Risk Classification — Have you categorized AI systems by risk level per EU AI Act / NIST?
Documentation — Technical docs, model cards, data lineage for each AI system?
Human Oversight — Defined human-in-the-loop processes for high-risk decisions?
Bias & Fairness — Regular bias audits, fairness metrics, disparate impact testing?
Data Governance — Training data provenance, consent, retention, and deletion policies?
Incident Response — AI-specific incident playbook, reporting procedures, rollback plans?
Vendor Management — AI vendor risk assessments, contractual AI governance requirements?
Audit Trail — Logging, explainability, decision traceability for AI-assisted outputs?

Scoring

35-40: Compliance-ready — minor gaps to address
25-34: Partially prepared — significant work needed in specific areas
**...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/1kalin/compliance-readiness
Version: —
Commit: aefebffd6447b67f7a0b65561b5c3dfbdfc0cfa3
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# AI Compliance Readiness Assessment

Evaluate organizational readiness for AI governance regulations and generate an actionable compliance roadmap.

## When to Use
- Assessing AI compliance posture before an audit
- Preparing for EU AI Act (Aug 2026), HHS AI mandates, NIST AI RMF
- Building a governance roadmap for AI deployments
- Evaluating risk exposure from current AI usage

## How to Use

When asked to assess AI compliance readiness, gather these inputs:

### Required Inputs
1. **Industry** (legal, healthcare, financial-services, insurance, construction, manufacturing, government, other)
2. **Company size** (employees or revenue range)
3. **AI systems in use** (list: chatbots, document review, fraud detection, hiring tools, customer service, analytics, other)
4. **Jurisdictions** (US-only, EU-exposed, both, global)

### Optional Inputs
- Current governance framework (if any)
- Upcoming audit dates
- Existing compliance certifications (SOC2, ISO 27001, HIPAA, etc.)
- Number of AI vendors/tools in use

## Assessment Framework

Score each dimension 1-5 (1=no controls, 5=mature):

### 8 Dimensions
1. **Risk Classification** — Have you categorized AI systems by risk level per EU AI Act / NIST?
2. **Documentation** — Technical docs, model cards, data lineage for each AI system?
3. **Human Oversight** — Defined human-in-the-loop processes for high-risk decisions?
4. **Bias & Fairness** — Regular bias audits, fairness metrics, disparate impact testing?
5. **Data Governance** — Training data provenance, consent, retention, and deletion policies?
6. **Incident Response** — AI-specific incident playbook, reporting procedures, rollback plans?
7. **Vendor Management** — AI vendor risk assessments, contractual AI governance requirements?
8. **Audit Trail** — Logging, explainability, decision traceability for AI-assisted outputs?

### Scoring
- **35-40**: Compliance-ready — minor gaps to address
- **25-34**: Partially prepared — significant work needed in specific areas
- **...

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