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 ashermahonin/agentic-skills --skill compliance-legalgit clone --depth 1 https://github.com/ashermahonin/agentic-skillsWrote 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/ashermahonin/agentic-skills/compliance-legal)<a href="https://agentmods.dev/skills/ashermahonin/agentic-skills/compliance-legal"><img src="https://agentmods.dev/badge/skills/ashermahonin/agentic-skills/compliance-legal/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/ashermahonin/agentic-skills/compliance-legal"><img src="https://agentmods.dev/badge/skills/ashermahonin/agentic-skills/compliance-legal.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 | $0.00069 | $0.00956 |
| Opus 5 | $0.00034 | $0.00478 |
| Sonnet 5 | $0.00014 | $0.00191 |
| Haiku 4.5 | $0.00007 | $0.00096 |
Grade A, and why
compliance-legal 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 12d 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compliance / Legal
Purpose
Identify the laws, standards, contracts, and platform policies that apply. For each one, record the obligation, current evidence, gap, owner, and effect on release. Mark legal interpretation that still requires qualified counsel.
Scope and evidence
- Read
references/compliance-frameworks.md. - Pull platform matrix from
platform-detector, data flow fromdata-ml-pipeline, security posture fromsecurity-owasp-*andsecurity-secrets. - Identify scope: regions/markets, data subjects (consumer, employee, child, patient, EU resident, California resident, etc.), data categories (PII, health, financial, biometric, location, kids), AI capabilities, payment processing, content moderation, advertising.
- Use Context7 MCP for current text and guidance on each framework. Regulatory law changes often; do not rely on training data.
Assessment
- Applicability matrix. Per framework: applies (yes/no) + why. Anchor each "yes" to a specific user/data/region/feature.
- Per-framework gap analysis. For each "yes", walk the required controls (lawful basis, consent, retention, access, deletion, breach notification, audit trail, vendor management). Mark Pass / Concern / Fail / Out-of-scope with evidence.
- Data Processing Register / RoPA. For GDPR-like regimes, list processing activities: purpose, lawful basis, data categories, subjects, retention, recipients, transfers, safeguards.
- DPIA / AIPIA. Run a Data Protection Impact Assessment (or AI Impact Assessment) when triggered: large-scale sensitive data, automated decisions, biometric, kids, public-space monitoring, AI Act high-risk categories.
- Vendor / sub-processor register. List every third party that touches user data; verify contracts (DPA, SCCs where needed), security posture, breach SLAs.
- Open-source license check. Coordinate with
cve-zero-day-scannerSBOM. Flag copyleft (AGPL/GPL) in proprietary distributions; flag attribution requirements. - Accessibility law. If selling into the EU (EAA), US public sector (Section 508), Canada (AODA), require
accessibility-auditverdict before release. - Store-platform legal. Validate against current App Store / Play / Steam / console legal terms (paid content, IAP, subscriptions, cancellation flow, refunds).
- Release-gate verdict. Per framework: Go / Conditional / Hold. Conditional requires named approver and expiry.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 66 lines · 69 tokens per session scan A ffbc7e587e16
compliance-legal is a skill published in the GitHub repository ashermahonin/agentic-skills (10 stars, last pushed 19d ago), licensed MIT. It adds 69 tokens to every session and 956 once invoked, about $0.0003 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 2026-08-31.
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