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 alexclowe/awesome-copilot-cowork-plugins --skill ai-regulatory-mappergit clone --depth 1 https://github.com/alexclowe/awesome-copilot-cowork-pluginsWrote 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/alexclowe/awesome-copilot-cowork-plugins/ai-regulatory-mapper)<a href="https://agentmods.dev/skills/alexclowe/awesome-copilot-cowork-plugins/ai-regulatory-mapper"><img src="https://agentmods.dev/badge/skills/alexclowe/awesome-copilot-cowork-plugins/ai-regulatory-mapper/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/alexclowe/awesome-copilot-cowork-plugins/ai-regulatory-mapper"><img src="https://agentmods.dev/badge/skills/alexclowe/awesome-copilot-cowork-plugins/ai-regulatory-mapper.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 | $0.00030 | $0.02055 |
| Opus 5.5 | $0.00012 | $0.00822 |
| Sonnet 5.5 | $0.00006 | $0.00411 |
| Haiku 4.5 | $0.00003 | $0.00205 |
Grade A, and why
ai-regulatory-mapper 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.
This is a copy
95% identical to ai-regulatory-mapper — 9 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You have deep expertise in the current AI regulatory landscape across the EU, US federal, US state, and key international regimes. When the user is advising a client on AI deployment, governance, or compliance, apply this knowledge automatically.
EU AI Act (Regulation (EU) 2024/1689)
Risk-based framework:
- Prohibited (Title II, Art. 5) — social scoring, untargeted biometric scraping, emotion recognition in workplace/education (with exceptions), real-time remote biometric identification in public (narrow law-enforcement exception)
- High-risk (Annex III) — biometric ID, critical infrastructure, education and vocational training, employment and worker management, access to essential services (credit scoring, insurance pricing for life/health), law enforcement, migration and border control, administration of justice, democratic processes
- High-risk (Annex I) — AI as safety component of regulated products (medical devices, machinery, toys, etc.)
- Limited-risk — transparency obligations (chatbots, emotion recognition, biometric categorization, deepfakes)
- Minimal-risk — no specific obligations
Key obligations for high-risk systems (Title III):
- Risk management system (Art. 9)
- Data governance (Art. 10)
- Technical documentation (Art. 11) and record-keeping (Art. 12)
- Transparency to deployers (Art. 13) and human oversight (Art. 14)
- Accuracy, robustness, cybersecurity (Art. 15)
- Quality management system (Art. 17)
- Conformity assessment (Art. 43) and CE marking
- EU declaration of conformity (Art. 47), registration in EU database (Art. 49)
- Post-market monitoring (Art. 72), incident reporting (Art. 73)
General-Purpose AI (Chapter V):
- Transparency, training-data summary, copyright compliance for all GPAI
- Additional obligations for systemic-risk GPAI (above 10^25 FLOPs threshold or designated)
Phased application:
- Aug 1, 2024 — entry into force
- Feb 2, 2025 — prohibitions and AI literacy obligations
- Aug 2, 2025 — GPAI obligations, governance, penalties
- Aug 2, 2026 — Annex III high-risk obligations
- Aug 2, 2027 — Annex I product-safety high-risk obligations
- (Verify against current Commission implementing acts and codes of practice)
Penalties:
- Up to €35M or 7% of global turnover for prohibited AI
- Up to €15M or 3% for other violations
- Up to €7.5M or 1% for incorrect information to authorities
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 · 176 lines · 30 tokens per session scan A 70aa5ea59b37
ai-regulatory-mapper is a skill published in the GitHub repository alexclowe/awesome-copilot-cowork-plugins (21 stars, last pushed 16d ago), licensed MIT. It adds 30 tokens to every session and 2,055 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to ai-regulatory-mapper, differing in 9 lines, and is treated as a copy.
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