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 niels-emmer/myace --skill ai-governancegit clone --depth 1 https://github.com/niels-emmer/myaceWrote 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/niels-emmer/myace/ai-governance)<a href="https://agentmods.dev/skills/niels-emmer/myace/ai-governance"><img src="https://agentmods.dev/badge/skills/niels-emmer/myace/ai-governance/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/niels-emmer/myace/ai-governance"><img src="https://agentmods.dev/badge/skills/niels-emmer/myace/ai-governance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00031 | $0.00654 |
| Opus 5 | $0.00015 | $0.00327 |
| Sonnet 5 | $0.00006 | $0.00131 |
| Haiku 4.5 | $0.00003 | $0.00065 |
Grade A, and why
AI Governance 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 9d 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Agentic coding in an enterprise or internet-facing context needs guardrails that a solo hobby project doesn't. This skill sets the rules for what data can go where, which models may see it, and how to keep an audit trail — so AI-assisted work doesn't become a compliance or data-exposure liability. It's the governance layer on top of data-classification-guide: that skill classifies a single piece of data; this one sets the standing rules for the whole session.
When to use it
At the start of any enterprise or internet-facing session, and whenever a task involves data that might be sensitive, regulated, or customer-owned.
Data classification
Classify data before sending it to any cloud-hosted model:
| Class | Definition | Permitted models |
|---|---|---|
| PUBLIC | Open-source code, public docs, no sensitive context | Any model |
| INTERNAL | Proprietary business logic, internal APIs, non-public architecture | Zero-retention cloud models approved for internal data |
| CONFIDENTIAL | PII, customer data, credentials, trade secrets, unreleased strategy | Local models only — never a cloud API |
| REGULATED | HIPAA, GDPR, SOC2-scoped data, financial/health records | Local models only, no exceptions |
When in doubt, treat as CONFIDENTIAL. Never paste customer PII, credentials, or production secrets into AI prompts.
Model selection by sensitivity
- PUBLIC → any model.
- INTERNAL → zero-retention cloud models only; avoid free-tier or third-party models that may retain data for improvement.
- CONFIDENTIAL/REGULATED → local models only (Ollama, llama.cpp). If you can't keep it local, delegate the work to a local-only agent.
Audit trail
Record AI-driven changes: what changed, why, and whether it was AI-authored or human-authored. Use a decision log for architecture decisions. Prefix enterprise-affecting AI-authored commits with [ai] in the body.
Dependency compliance
Before adding a dependency, verify: OSI-approved license (MIT, Apache 2.0, BSD, LGPL — not AGPL or unlicensed), actively maintained, no critical CVEs, pinned to a specific version, from a trusted registry. Prefer the standard library over a new dependency for a single utility function.
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.
- 9d ago First seen · 51 lines · 31 tokens per session scan A c48fec92fed2
AI Governance is a skill published in the GitHub repository niels-emmer/myace (1 stars, last pushed 5d ago), licensed MIT. It adds 31 tokens to every session and 654 once invoked, about $0.0002 per session on Opus 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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