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 lexbeam-software/eu-ai-governance-plugin --skill ai-vendor-assessmentgit clone --depth 1 https://github.com/lexbeam-software/eu-ai-governance-pluginWrote 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/lexbeam-software/eu-ai-governance-plugin/ai-vendor-assessment)<a href="https://agentmods.dev/skills/lexbeam-software/eu-ai-governance-plugin/ai-vendor-assessment"><img src="https://agentmods.dev/badge/skills/lexbeam-software/eu-ai-governance-plugin/ai-vendor-assessment/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/lexbeam-software/eu-ai-governance-plugin/ai-vendor-assessment"><img src="https://agentmods.dev/badge/skills/lexbeam-software/eu-ai-governance-plugin/ai-vendor-assessment.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 | $0.00059 | $0.00730 |
| Opus 5 | $0.00030 | $0.00365 |
| Sonnet 5 | $0.00012 | $0.00146 |
| Haiku 4.5 | $0.00006 | $0.00073 |
Grade A, and why
ai-vendor-assessment 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 11d 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Assess an AI vendor
Assess the actual service, intended use, contract, DPA, technical material, and operating model. Never substitute a static reputation profile for current evidence.
When the file is incomplete, produce a provisional evidence matrix and hold pending evidence recommendation rather than inventing terms or withholding all analysis.
Follow LEGAL-SOURCE-PROTOCOL.md. Use the Lexbeam EU AI Act MCP to validate classification, actor duties, deadlines, Article 6(3), and decisive articles.
Establish roles and use case
Record the contracting entity, service, version, deployment mode, geography, intended use, decision effect, data, model chain, and customer modifications. Determine provider, deployer, importer, distributor, product-manufacturer, downstream provider, and GPAI model-provider roles. Check Article 25 role changes before assigning duties.
Build an evidence matrix
For every claim record provided, missing, conflicting, or not applicable, plus document, page or URL, date, and owner.
Review:
- AI Act classification and instructions: intended purpose, prohibited-use controls, Annex I/III analysis, Article 6(3) documentation, Article 50 duties, provider instructions, and high-risk documentation where applicable.
- Model-chain accountability: base model, fine-tuning, downstream modifications, GPAI evidence, value-chain support under Article 25(4), and change notices.
- Data protection: controller/processor roles, instructions, subprocessors, transfers, deletion, return, training use, purpose limits, rights support, and breach route.
- Security and resilience: independent assurance, access control, logging, vulnerability handling, business continuity, recovery, and service levels.
- Monitoring and incidents: performance thresholds, drift and bias monitoring, complaint flow, serious-incident triage, evidence preservation, immediate deployer-to-provider route under Article 26(5), and provider reporting under Article 73.
- Audit and evidence access: current reports, targeted information rights, regulator cooperation, retention, export, termination assistance, and transition.
- Commercial allocation: warranties tied to supplied facts, indemnities, liability, insurance evidence, suspension rights, change control, termination, and precedence between terms.
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.
- 11d ago First seen · 51 lines · 59 tokens per session scan A 9513851a7415
ai-vendor-assessment is a skill published in the GitHub repository lexbeam-software/eu-ai-governance-plugin (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 59 tokens to every session and 730 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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