Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add travisjneuman/.claude --skill ai-policy-generatorgit clone --depth 1 https://github.com/travisjneuman/.claudeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/travisjneuman/.claude/ai-policy-generator)<a href="https://agentmods.dev/skills/travisjneuman/.claude/ai-policy-generator"><img src="https://agentmods.dev/badge/skills/travisjneuman/.claude/ai-policy-generator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/travisjneuman/.claude/ai-policy-generator"><img src="https://agentmods.dev/badge/skills/travisjneuman/.claude/ai-policy-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Output Handling · line 246 Model output is used without validation or sanitization. Unvalidated output injected into downstream contexts (SQL, shell, HTML) enables injection attacks and arbitrary code execution.Fix: Validate and sanitize all model output before using it in downstream contexts. Use parameterized queries for SQL, shell quoting for commands, and HTML encoding for web output.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00042 | $0.02773 |
| Opus 5.5 | $0.00017 | $0.01109 |
| Sonnet 5.5 | $0.00008 | $0.00555 |
| Haiku 4.5 | $0.00004 | $0.00277 |
Grade A, and why
ai-policy-generator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 357 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 357 lines · 42 tokens per session scan A ff85205495ec
ai-policy-generator is a skill published in the GitHub repository travisjneuman/.claude (106 stars, last pushed yesterday), licensed MIT. It adds 42 tokens to every session and 2,773 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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