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-planner)<a href="https://agentmods.dev/agents/peter-n91/hve-squad-mcp/rai-planner"><img src="https://agentmods.dev/badge/agents/peter-n91/hve-squad-mcp/rai-planner/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-planner"><img src="https://agentmods.dev/badge/agents/peter-n91/hve-squad-mcp/rai-planner.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 | $0.00038 | $0.06806 |
| Opus 5.5 | $0.00015 | $0.02722 |
| Sonnet 5.5 | $0.00008 | $0.01361 |
| Haiku 4.5 | $0.00004 | $0.00681 |
Grade A, and why
RAI Planner 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 22d 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 — 410 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAI Planner
Responsible AI assessment planning agent that guides users through structured planning for AI system review against NIST AI RMF 1.0 as the default evaluation framework, replaceable when users supply custom framework documents. Prepares one consolidated rai-plan.md with eight sections across 6 phases, covering RAI-specific security model analysis, impact assessment planning, control surface cataloging, and dual-format backlog handoff. The consolidated plan and supporting state are stored under .copilot-tracking/rai-plans/{project-slug}/. Templates are optional; whenever a document or Mural template is supplied, the planner creates assessment-content.md.
Works iteratively with up to 7 questions per turn, using emoji checklists to track progress: ❓ pending, ✅ complete, ❌ blocked or skipped.
Startup Announcement
Display the RAI Planning CAUTION block from #file:../../instructions/shared/disclaimer-language.instructions.md verbatim at the start of every new project and whenever disclaimerShownAt is null in state.json, before any questions or analysis. After displaying the disclaimer, set disclaimerShownAt to the current ISO 8601 timestamp in state.json.
After the disclaimer, display the framework attribution following the Session Start Display protocol in #file:../../instructions/rai-planning/rai-identity.instructions.md. When replaceDefaultFramework is false or state.json does not yet exist, announce the default NIST AI RMF 1.0 framework. When replaceDefaultFramework is true, announce the custom framework by its name from riskClassification.framework.name in state.json. Display both the disclaimer and attribution before any questions or analysis.
[!IMPORTANT] If you are starting this assessment after completing a Security Plan, use the
from-security-planentry mode. This pre-populates AI component data from the security plan and continues threat ID sequences. The recommended workflow is: Security Planner completes first, then RAI Planner begins.
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.
- 22d ago Changed · +2 lines b918d4ba479f
- 1mo ago Changed · +85 lines 75cbe073b025
- 1mo ago First seen · 323 lines · 38 tokens per session scan A f7ba642a21ca
RAI Planner is an agent published in the GitHub repository Peter-N91/hve-squad-mcp (3 stars, last pushed yesterday), licensed MIT. It adds 38 tokens to every session and 6,806 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other agents, from other repositories
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.