Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/bestagentkits/agency-skillsnpx agentmods add skills/bestagentkits/agency-skills/ai-act-readinessWrote 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/bestagentkits/agency-skills/ai-act-readiness)<a href="https://agentmods.dev/skills/bestagentkits/agency-skills/ai-act-readiness"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/ai-act-readiness/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/bestagentkits/agency-skills/ai-act-readiness"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/ai-act-readiness.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 | $0.00079 | $0.01815 |
| Opus 5 | $0.00039 | $0.00907 |
| Sonnet 5 | $0.00016 | $0.00363 |
| Haiku 4.5 | $0.00008 | $0.00181 |
Grade A, and why
ai-act-readiness 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
100% identical to ai-act-readiness — 3 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/cs:ai-act-readiness — EU AI Act Forcing Questions
Command: /cs:ai-act-readiness <system>
The EU AI Act compliance operator pressure-tests any AI system before EU deployment. Six Article-cited questions before any EU placement, conformity assessment, or annual compliance refresh.
When to Run
- During AI-system intake review (per new system or material change)
- Before placing an AI system on the EU market
- Before signing the EU declaration of conformity (Article 47)
- During annual compliance refresh (Article 113 phasing brings new obligations)
- When the organization's role changes (deployer becomes provider via Article 25(1) substantial modification)
- When training compute approaches 10^25 FLOPs (Article 51 systemic-risk threshold)
The Six EU AI Act Questions
1. Article 5: Is this a prohibited AI practice?
Penalty: up to 35M EUR or 7% worldwide turnover.
- 8 categories: subliminal manipulation, exploitation of vulnerabilities, social scoring, predictive policing, untargeted facial scraping, emotion recognition in workplace/education, biometric categorisation by sensitive attributes, real-time public biometric ID by law enforcement
- Run
ai_system_risk_classifier.py - If yes → STOP. Cannot place on EU market. No exceptions outside Article 5(2) carve-outs.
2. Article 6 + Annex III: Is this high-risk?
Annex III triggers high-risk; Article 6(3) carve-out conditional.
- 8 categories: biometrics, critical infrastructure, education, employment, essential services, law enforcement, migration, justice
- Carve-out applies only if Article 6(3)(a)-(d) AND no profiling of natural persons
- Profiling overrides carve-out (Article 6(3) last sentence)
- Run
ai_system_risk_classifier.py
3. Article 43: For high-risk, Module A or Module H?
Biometrics → Module H (notified body) by default; others → Module A if harmonised standards applied.
- Run
conformity_assessment_planner.py - Module A (Annex VI): internal control with presumption of conformity if Article 40 harmonised standards applied
- Module H (Annex VII): full QMS + notified body for biometrics or where standards lacking
- Annex IV technical documentation: 8 items required before placing on market
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 · 150 lines · 79 tokens per session scan A 84e90e36fa9a
ai-act-readiness is a skill published in the GitHub repository bestagentkits/agency-skills (11 stars, last pushed 2mo ago), licensed MIT. It adds 79 tokens to every session and 1,815 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ai-act-readiness, differing in 3 lines, and is treated as a copy.
Other skills, from other repositories
eu-ai-act-reviewer
Review user journeys, public content, and codebases for potentially relevant EU AI Act provisions, with exact official citations, evidence gaps, application dates, and plain-language next actions. Use for EU AI Act issue-spotting, not final legal or compliance decisions.
assess-ip-landscape
Map the intellectual property landscape for a technology domain or product area. Covers patent cluster analysis, white space identification, competitor IP portfolio assessment, freedom-to-operate preliminary screening, and strategic IP positioning recommendations. Use before starting R&D in a new technology area, when…
design-compliance-architecture
Design a compliance architecture that maps applicable regulations to computerized systems. Covers system inventory, criticality classification (GxP-critical, GxP-supporting, non-GxP), GAMP 5 category assignment, regulatory requirements traceability, and governance structure definition. Use when establishing a new…
design-training-program
Design a GxP training programme covering training needs analysis by role, curriculum design (regulatory awareness, system-specific, data integrity), competency assessment criteria, training record retention, and retraining triggers for SOP revisions and incidents. Use when a new validated system requires user training…
conduct-gxp-audit
Conduct a GxP audit of computerized systems and processes. Covers audit planning, opening meetings, evidence collection, finding classification (critical/major/minor), CAPA generation, closing meetings, report writing, and follow-up verification. Use for scheduled internal audits, supplier qualification audits…
decommission-validated-system
Decommission a validated computerized system at end-of-life. Covers data retention assessment by regulation, data migration validation (mapping, transformation, reconciliation), archival strategy, access revocation, documentation archival, and stakeholder notification. Use when a validated system is being replaced…