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 JustineDevs/premortem --skill arize-compliance-auditgit clone --depth 1 https://github.com/JustineDevs/premortemWrote 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/justinedevs/premortem/arize-compliance-audit)<a href="https://agentmods.dev/skills/justinedevs/premortem/arize-compliance-audit"><img src="https://agentmods.dev/badge/skills/justinedevs/premortem/arize-compliance-audit/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/justinedevs/premortem/arize-compliance-audit"><img src="https://agentmods.dev/badge/skills/justinedevs/premortem/arize-compliance-audit.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 | $0.00090 | $0.04042 |
| Opus 5 | $0.00045 | $0.02021 |
| Sonnet 5 | $0.00018 | $0.00808 |
| Haiku 4.5 | $0.00009 | $0.00404 |
Grade A, and why
arize-compliance-audit 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 10d 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 arize-compliance-audit — 20 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 — 315 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arize Compliance Audit Skill
Use this skill when the user wants to audit their AI agent or LLM application for regulatory compliance. The skill scans the codebase for compliance gaps, cross-references Arize instrumentation for audit trail coverage, and produces a tailored checklist with optional remediation.
Triggers: "audit my app for compliance", "EU AI Act requirements", "NIST AI RMF checklist", "GDPR for AI", "is my AI app compliant", "compliance checklist", "regulatory audit", "ISO 42001", "AI management system", "AIMS certification".
Disclaimer
Before doing anything else, present this disclaimer verbatim to the user:
⚠️ Legal disclaimer
This audit is for guidance only and does not constitute legal advice or a complete compliance assessment. It identifies common technical patterns and gaps based on publicly available regulatory frameworks, but cannot assess your organisation's specific legal obligations, contractual commitments, data processing agreements, or operational processes.
Do not rely on this output as a substitute for qualified legal counsel. Regulatory compliance is a complex, jurisdiction-specific, and fact-dependent determination. Always engage a qualified attorney or compliance specialist for binding assessments.
Core principles
- Prefer inspection over mutation — understand the codebase before suggesting changes.
- Be practical, not legal — produce developer-actionable items, not legal opinions.
- Tailor to jurisdiction and use case — a chatbot has different obligations than a hiring tool. Do not dump the entire regulatory framework.
- Cross-reference instrumentation — compliance requires audit trails; check whether Arize tracing captures what regulators expect.
- Offer remediation, always confirm — after presenting the checklist, offer to implement specific fixes, but never modify code without explicit user confirmation.
- Keep output concise and production-focused — do not generate extra documentation or summary files unless requested.
- Never embed literal credential values — always reference environment variables.
What ships with it
4 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.
- 10d ago First seen · 315 lines · 90 tokens per session scan A b3cca037b245
arize-compliance-audit is a skill published in the GitHub repository JustineDevs/premortem (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 90 tokens to every session and 4,042 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to arize-compliance-audit, differing in 20 lines, and is treated as a copy.
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