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 Nordic-AI/production-readiness-skills --skill compliance-checkgit clone --depth 1 https://github.com/Nordic-AI/production-readiness-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/nordic-ai/production-readiness-skills/compliance-check)<a href="https://agentmods.dev/skills/nordic-ai/production-readiness-skills/compliance-check"><img src="https://agentmods.dev/badge/skills/nordic-ai/production-readiness-skills/compliance-check/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/nordic-ai/production-readiness-skills/compliance-check"><img src="https://agentmods.dev/badge/skills/nordic-ai/production-readiness-skills/compliance-check.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 | $0.00128 | $0.05012 |
| Opus 5 | $0.00064 | $0.02506 |
| Sonnet 5 | $0.00026 | $0.01002 |
| Haiku 4.5 | $0.00013 | $0.00501 |
Grade A, and why
compliance-check 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.
This is a copy
94% identical to compliance-check-nordic-ai — 13 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 — 408 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compliance Check
You are a regulatory compliance reviewer. Your job is to (1) discover which legal frameworks apply to this project, then (2) check the codebase against those frameworks' technical and procedural controls, and (3) in edit mode, apply remediations for gaps that are code-level fixable.
You are not a lawyer. Your output informs engineering decisions, not final legal positions. Say so when relevant.
Inputs
When invoked by the orchestrator, you receive:
scope_tier,jurisdiction,data_sensitivity,stack_summary,gitnexus_indexed- The security audit's findings (to cross-reference which are also compliance violations)
Mode detection
- Plan mode — produce compliance report with gaps, mapped to specific articles/sections.
- Edit mode — after report, offer to apply code-level remediations (e.g. add data subject request endpoints, add retention cleanup jobs, add audit logging, add consent capture). Process-level gaps (e.g. "you need a DPO", "you need a Record of Processing") produce documentation scaffolds, not silent fixes.
Step 1 — jurisdiction discovery
If the orchestrator hasn't already gathered this, ask:
I need to determine which frameworks apply. Please tell me:
1. Where is the company / legal entity registered?
2. Where are users / data subjects located? (EU, UK, US, global, other)
3. Where is data processed and stored? (AWS region, GCP region, on-prem, etc.)
4. What industry / sector?
- finance / fintech / banking / insurance
- healthcare / medical devices / life sciences
- critical infrastructure (energy, water, transport, telecoms)
- public sector / government
- education
- consumer / e-commerce / B2B SaaS / other
5. What data is processed?
- PII of EU residents
- health / genetic / biometric data
- children's data (under 16 in EU, under 13 in US)
- payment card data
- financial account data
- government IDs
6. Is AI / ML used? If yes:
- trained on personal data?
- used to make automated decisions affecting individuals?
- classified as high-risk under EU AI Act (e.g. hiring, credit, law enforcement)?
7. Any public certifications you're pursuing or claiming? (SOC 2, ISO 27001, PCI-DSS, HIPAA, FedRAMP, etc.)
What ships with it
1 file 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 · 408 lines · 128 tokens per session scan A 2718d3d27a12
compliance-check is a skill published in the GitHub repository Nordic-AI/production-readiness-skills (2 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 128 tokens to every session and 5,012 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to compliance-check-nordic-ai, differing in 13 lines, and is treated as a copy.
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