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Agent Skill · 待分类

ai-policy-generator(AI 政策生成器)

ai-policy-generator 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

ai-policy-generator 是待分类类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为权限未标注;许可证信息还不明确,第三方扫描已通过,按安装量在待分类类的 11,209 个 Skill 里排第 2,520。

总安装 1,536周安装 64GitHub Stars 48下载 512核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:ai-policy-generator(AI 政策生成器)
来源仓库:https://github.com/travisjneuman/.claude
仓库路径:skills/ai-policy-generator
安装命令:
npx skills add https://github.com/travisjneuman/.claude --skill ai-policy-generator
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/travisjneuman/.claude --skill ai-policy-generator

这个 Skill 是做什么的

这个 Skill 的分类还没最终确定,用法以来源仓库的说明为准。

本技能暂无公开的 SKILL.md 说明文档,功能细节需参考 GitHub 仓库内容进一步确认。当前归类为待分类技能,可能涉及政策文档生成或合规相关内容处理。适用宿主包括 Codex、Claude、Cursor 和 Gemini CLI,安装方式为 GitHub 仓库引用。

使用前应检查仓库维护状态、API 权限范围及是否触发敏感操作如网络请求或文件修改。建议开发者自行查阅源码或 README 了解具体能力边界与使用限制后再部署应用。

来源仓库内路径:skills/ai-policy-generator

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/travisjneuman/.claude --skill ai-policy-genera…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    权限没有明确标注,第一次使用时只给一个无关紧要的任务,观察它到底读了哪些文件和网络。

常见问题

6 条

ai-policy-generator 怎么安装?

在终端执行 npx skills add https://github.com/travisjneuman/.claude --skill ai-policy-generator。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

ai-policy-generator 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

ai-policy-generator 需要什么权限?

权限级别没有明确标注。第一次使用时建议只给无关紧要的任务,观察它实际读写了哪些内容。

ai-policy-generator 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

ai-policy-generator 用的人多吗?

公开数据是累计安装 1,536 次,近一周安装 64 次,下载 512 次,GitHub 48 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

ai-policy-generator 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理待分类类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

待分类类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
infsh-cli

infsh CLI · Codex、Claude

4,374,576382权限未标注未明确未收录
ai-video-generation

AI 视频生成 · Codex、Claude

4,313,376382调用外部服务未明确有提醒
lark-openapi-explorer

云雀 OpenAPI 浏览器 · Codex、Claude

2,064,1209,011需要联网未明确有提醒
lark-workflow-standup-report

Lark 工作流程站立报告 · Codex、Claude

2,041,6328,978需要联网未明确通过
use-my-browser

使用我的浏览器 · Codex、Claude

1,848,49140操作浏览器未明确未通过
ai-policy-generator

AI 政策生成器 · 本页

1,53648权限未标注未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
ai-policy-generator
description
AI governance policy creation for nonprofits and enterprises with frameworks, risk assessment, ethical guidelines, and compliance templates. Use when drafting AI usage policies, responsible AI frameworks, or organizational AI governance documents.

AI Policy Generator

Comprehensive frameworks for creating organizational AI governance policies covering acceptable use, risk management, ethical guidelines, data handling, and compliance requirements.

AI Policy Structure

Standard AI Policy Template

AI GOVERNANCE POLICY — [ORGANIZATION NAME]

1. PURPOSE AND SCOPE
   - Why this policy exists
   - Who it applies to (employees, contractors, vendors)
   - What AI systems are covered
   - Effective date and review cadence

2. DEFINITIONS
   - AI / Machine Learning
   - Generative AI
   - Automated decision-making
   - Personal data / Sensitive data
   - High-risk AI use cases

3. ACCEPTABLE USE
   - Approved AI tools and platforms
   - Permitted use cases by department
   - Prohibited uses (explicit list)
   - Approval process for new AI tools

4. DATA AND PRIVACY
   - Data classification for AI inputs
   - Prohibited data types (PII, PHI, confidential)
   - Data retention and deletion
   - Third-party data sharing restrictions

5. RISK ASSESSMENT
   - Risk classification framework (low/medium/high/critical)
   - Required assessments by risk level
   - Approval chain for high-risk deployments
   - Ongoing monitoring requirements

6. TRANSPARENCY AND DISCLOSURE
   - When to disclose AI use to stakeholders
   - Labeling AI-generated content
   - Customer/client notification requirements
   - Internal documentation standards

7. HUMAN OVERSIGHT
   - Human-in-the-loop requirements
   - Decision review thresholds
   - Escalation procedures
   - Override authority

8. BIAS AND FAIRNESS
   - Bias testing requirements
   - Fairness metrics and thresholds
   - Protected class considerations
   - Remediation procedures

9. SECURITY
   - AI-specific security controls
   - Prompt injection prevention
   - Model access controls
   - Incident response for AI failures

10. COMPLIANCE
    - Applicable regulations (EU AI Act, state laws, industry)
    - Audit requirements
    - Record-keeping obligations
    - Reporting requirements

11. TRAINING AND AWARENESS
    - Required training by role
    - Training frequency
    - Competency assessment

12. ENFORCEMENT
    - Violation reporting
    - Consequences framework
    - Appeal process

13. GOVERNANCE
    - AI governance committee composition
    - Review and update cadence
    - Policy exception process
    - Version control

Risk Classification Framework

AI Use Case Risk Levels

Risk LevelDescriptionExamplesRequirements
LowMinimal impact on individuals or operationsSummarizing meeting notes, drafting internal emails, code formattingSelf-service, basic training
MediumModerate impact, reversible decisionsCustomer service drafts, content generation, data analysisManager approval, human review
HighSignificant impact on individuals or financesHiring screening, credit decisions, medical triageCommittee approval, bias audit, monitoring
CriticalPotential for serious harm, legal liabilityAutonomous decisions affecting rights, safety-critical systemsBoard approval, external audit, ongoing review

Risk Assessment Checklist

AI USE CASE RISK ASSESSMENT

Use Case: _____________________
Department: ___________________
Requested By: _________________
Date: ________________________

IMPACT ASSESSMENT:
  [ ] Affects individual rights or opportunities?
  [ ] Involves personal or sensitive data?
  [ ] Makes or influences financial decisions?
  [ ] Affects health, safety, or welfare?
  [ ] Has legal or regulatory implications?
  [ ] Could cause reputational harm?
  [ ] Involves vulnerable populations?

DATA ASSESSMENT:
  [ ] What data types are used as inputs?
  [ ] Is PII/PHI/confidential data involved?
  [ ] Where is data stored and processed?
  [ ] What third parties receive data?
  [ ] Is data retention compliant with policy?

TRANSPARENCY ASSESSMENT:
  [ ] Are affected parties informed of AI use?
  [ ] Is the AI's role in decisions clear?
  [ ] Can decisions be explained?
  [ ] Is there an appeal/override mechanism?

RISK LEVEL: [ ] Low  [ ] Medium  [ ] High  [ ] Critical

REQUIRED APPROVALS:
  [ ] Manager (all levels)
  [ ] AI Governance Committee (medium+)
  [ ] Legal review (high+)
  [ ] Board approval (critical)
  [ ] External audit (critical)

Acceptable Use Guidelines

Approved vs Prohibited Uses

APPROVED USES (with appropriate safeguards):

CONTENT AND COMMUNICATION:
  + Drafting internal communications
  + Summarizing documents and meetings
  + Translating content between languages
  + Brainstorming and ideation
  + Editing and proofreading

RESEARCH AND ANALYSIS:
  + Market research synthesis
  + Data analysis and visualization
  + Literature review assistance
  + Trend identification
  + Competitive analysis

PRODUCTIVITY:
  + Code generation and review
  + Template creation
  + Process documentation
  + FAQ and knowledge base content
  + Scheduling optimization

PROHIBITED USES:

  - Inputting confidential business data into public AI tools
  - Uploading PII, PHI, or financial records to unapproved platforms
  - Using AI for final hiring, firing, or disciplinary decisions
  - Generating content that impersonates real individuals
  - Making autonomous decisions that affect individual rights
  - Bypassing security controls or access restrictions
  - Generating misleading, deceptive, or fraudulent content
  - Using AI to surveil employees without disclosure
  - Submitting AI-generated work as original without disclosure
  - Using AI for any illegal purpose

Regulatory Landscape

Key Regulations by Jurisdiction

RegulationJurisdictionKey RequirementsEffective
EU AI ActEuropean UnionRisk-based classification, prohibited uses, transparency2024-2027 (phased)
Colorado AI ActColorado, USAAlgorithmic discrimination prevention, impact assessments2026
NYC Local Law 144New York CityBias audits for automated employment decisions2023
CPRACalifornia, USARight to opt out of automated decision-making2023
GDPR Art. 22EU/EEARight not to be subject to solely automated decisions2018
Executive Order 14110US FederalAI safety standards, risk management2023
NIST AI RMFUS (voluntary)Risk management framework for AI systems2023
ISO/IEC 42001InternationalAI management system standard2023

Compliance Mapping Template

COMPLIANCE MAPPING:

Regulation: [Name]
Applicable: [ ] Yes  [ ] No  [ ] Partially
Scope: [Which AI uses fall under this regulation]

REQUIREMENT                          | STATUS    | OWNER     | DUE DATE
Risk assessment completed            | [ ]       | [Name]    | [Date]
Transparency notices deployed        | [ ]       | [Name]    | [Date]
Bias audit conducted                 | [ ]       | [Name]    | [Date]
Data protection measures in place    | [ ]       | [Name]    | [Date]
Human oversight mechanism active     | [ ]       | [Name]    | [Date]
Documentation/records maintained     | [ ]       | [Name]    | [Date]
Training completed for staff         | [ ]       | [Name]    | [Date]
Incident response plan updated       | [ ]       | [Name]    | [Date]

Ethical AI Framework

Principles-Based Approach

PrincipleDefinitionImplementation
FairnessAI should not discriminate or create disparate impactRegular bias audits, diverse training data review
TransparencyAI use and decision-making should be understandableExplainability requirements, disclosure policies
AccountabilityClear ownership of AI decisions and outcomesGovernance structure, audit trails
PrivacyRespect for data rights and minimizationData classification, consent frameworks
SafetyAI should not cause harm to individuals or groupsTesting protocols, human oversight, kill switches
BeneficenceAI should benefit the organization and societyImpact assessment, stakeholder engagement

Bias Testing Protocol

BIAS TESTING PROTOCOL:

PRE-DEPLOYMENT:
  1. Define protected characteristics relevant to use case
  2. Prepare representative test datasets
  3. Run model outputs across demographic groups
  4. Calculate disparate impact ratios
  5. Document results and remediation if needed

ONGOING MONITORING:
  Frequency: [Monthly / Quarterly / per regulation]
  Metrics:
    - Demographic parity: Equal selection rates across groups
    - Equalized odds: Equal error rates across groups
    - Calibration: Equal accuracy across groups
  Threshold: Disparate impact ratio < 0.8 triggers review

REMEDIATION:
  1. Identify root cause (data, model, process)
  2. Document corrective action plan
  3. Implement fix and retest
  4. Report to governance committee

Data Handling Guidelines

Data Classification for AI

ClassificationAI Input Allowed?ConditionsExamples
PublicYes, any approved toolStandard use policyPublished reports, press releases
InternalYes, approved enterprise tools onlyNo public AI toolsInternal memos, strategy docs
ConfidentialLimited, with approvalApproved tools + DPA in placeFinancial data, customer info
RestrictedNo (or extreme controls)CTO/CISO approval + encryptionPII, PHI, trade secrets, credentials

Vendor Assessment Checklist

AI VENDOR ASSESSMENT:

Vendor: _____________________
Tool/Service: _______________
Assessment Date: _____________

DATA HANDLING:
  [ ] Data processing agreement (DPA) in place?
  [ ] Where is data processed and stored?
  [ ] Is data used to train vendor's models?
  [ ] Can training opt-out be enforced?
  [ ] Data retention and deletion policies?
  [ ] Encryption at rest and in transit?
  [ ] SOC 2 Type II or equivalent certification?

SECURITY:
  [ ] Access controls and authentication?
  [ ] Audit logging available?
  [ ] Incident response procedures?
  [ ] Penetration testing conducted?
  [ ] Vulnerability management program?

COMPLIANCE:
  [ ] GDPR compliance (if applicable)?
  [ ] HIPAA compliance (if applicable)?
  [ ] Sector-specific certifications?
  [ ] Subprocessor transparency?

RECOMMENDATION: [ ] Approve  [ ] Conditional  [ ] Reject

Training Program Design

Role-Based Training Requirements

RoleTraining TopicsFrequencyAssessment
All employeesAI policy overview, acceptable use, data handlingAnnualQuiz (80% pass)
ManagersRisk assessment, approval workflows, oversightAnnual + refresherScenario-based
IT/EngineeringSecurity controls, prompt injection, model managementSemi-annualTechnical assessment
Legal/ComplianceRegulatory landscape, audit procedures, incident responseSemi-annualCase study review
AI Governance CommitteeFull policy, emerging regulations, industry best practicesQuarterlyParticipation-based
ExecutivesStrategic implications, liability, governanceAnnualBriefing attendance

Policy Maintenance

Review and Update Cadence

POLICY REVIEW SCHEDULE:

ANNUAL REVIEW (minimum):
  - Full policy review by governance committee
  - Regulatory landscape update
  - Incident review and lessons learned
  - Stakeholder feedback incorporation

TRIGGERED REVIEWS:
  - New regulation enacted affecting AI use
  - Significant AI incident (internal or industry)
  - Major new AI tool adoption
  - Organizational restructure
  - Merger/acquisition
  - Audit finding requiring policy change

VERSION CONTROL:
  Version: [X.X]
  Last Updated: [Date]
  Approved By: [Name/Committee]
  Next Review: [Date]
  Change Log: [Summary of changes per version]

See Also

平台分布

Codex

38.12%
按下载量换算195

Claude

30.77%
按下载量换算158

Cursor

16.32%
按下载量换算84

Gemini CLI

10.08%
按下载量换算52

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

权限未标注

当前来源未能明确判断权限范围,默认进入异常复核队列。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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