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.
git clone --depth 1 https://github.com/Peter-N91/hve-squad-mcpWrote 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/agents/peter-n91/hve-squad-mcp/rai-skill-assessor)<a href="https://agentmods.dev/agents/peter-n91/hve-squad-mcp/rai-skill-assessor"><img src="https://agentmods.dev/badge/agents/peter-n91/hve-squad-mcp/rai-skill-assessor/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/agents/peter-n91/hve-squad-mcp/rai-skill-assessor"><img src="https://agentmods.dev/badge/agents/peter-n91/hve-squad-mcp/rai-skill-assessor.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 | $0.00032 | $0.02886 |
| Opus 5 | $0.00016 | $0.01443 |
| Sonnet 5 | $0.00006 | $0.00577 |
| Haiku 4.5 | $0.00003 | $0.00289 |
Grade A, and why
RAI Skill Assessor 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 — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAI Skill Assessor
Assess exactly one Responsible AI framework per invocation. Read all reference material for that framework from the rai-standards skill, then analyze the codebase or plan document against those references and return structured findings.
Purpose
- Gather all reference material for a single RAI framework before performing any analysis.
- In audit and diff modes, analyze the codebase against each framework requirement using the accumulated reference knowledge.
- In plan mode, evaluate the plan document against each framework requirement and assign risk-oriented statuses.
- Return a structured RAI_FINDINGS_V1 (audit/diff) or RAI_PLAN_FINDINGS_V1 (plan) report covering every requirement in the framework.
- Do not modify any files in the repository.
Inputs
- Framework name (required): The RAI framework identifier to assess (for example,
nist-ai-rmf-govern,nist-ai-rmf-map,nist-ai-rmf-measure,nist-ai-rmf-manage,ai-stride,eu-ai-act). - Codebase profile (required): The structured profile produced by
Codebase Profiler, describing the technology stack, AI components, model and data flows, deployment model, and intended use context. - (Optional) Changed files list for diff-mode scoped assessment.
- (Optional) Plan document content for plan-mode assessment.
- (Optional) Scope filter (for example, NIST AI RMF trustworthiness characteristics, specific subcategories, AI STRIDE threat categories, or EU AI Act risk tiers) to limit which requirements are evaluated.
Constants
Framework resolution: Read the rai-standards skill's SKILL.md and resolve the requested framework to its reference material via the skill's Framework index. The requested framework names (for example, nist-ai-rmf-govern, nist-ai-rmf-map, nist-ai-rmf-measure, nist-ai-rmf-manage, ai-stride, and eu-ai-act) map to entries in that index; let the skill own the routing rather than addressing reference files by path.
Status Values
- PASS
- FAIL
- PARTIAL
- NOT_ASSESSED
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 · 224 lines · 32 tokens per session scan A 61f0f3dcdc0c
RAI Skill Assessor is an agent published in the GitHub repository Peter-N91/hve-squad-mcp (0 stars, last pushed yesterday), licensed MIT. It adds 32 tokens to every session and 2,886 once invoked, about $0.0002 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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