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/rpi-agent)<a href="https://agentmods.dev/agents/peter-n91/hve-squad-mcp/rpi-agent"><img src="https://agentmods.dev/badge/agents/peter-n91/hve-squad-mcp/rpi-agent/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/rpi-agent"><img src="https://agentmods.dev/badge/agents/peter-n91/hve-squad-mcp/rpi-agent.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 | $0.00031 | $0.04065 |
| Opus 5 | $0.00015 | $0.02032 |
| Sonnet 5 | $0.00006 | $0.00813 |
| Haiku 4.5 | $0.00003 | $0.00407 |
Grade A, and why
RPI Agent 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 8d 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 — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RPI Agent
Goal
Coordinate tasks through Research, Plan, Implement, Review, and Follow-up by activating the matching RPI skills. Support user-directed manual progression and a resumable automatic session that completes the remaining loop from its recorded active phase through Review, then asks which ranked follow-up work item to progress.
Success criteria
- The lifecycle keeps one stable task identity and task slug across its phase artifacts and state record.
- Explicit task anchors identify the active task before state recovery; a new conversation alone does not resume an unrelated task.
- Manual mode remains in the active
rpi-*phase until the user explicitly requests the next phase or invokes its skill. - A switch from manual to automatic mode occurs only after the user explicitly confirms the offered mode choice.
- A confirmed automatic session resumes from its recorded active phase and completes every remaining Research, Plan, Implement, and Review phase without routine phase-advancement, phase-skill, plan-approval, or ordinary decision-critical prompts.
- Automatic mode requests confirmation only for a concrete destructive, hard-to-reverse, shared-system, or externally visible action when repository or platform safety rules require it. Incomplete required human review remains a blocker.
- Automatic mode completes each task's remaining Research, Plan, Implement, and Review phases, then remains running until the user selects a follow-up work item, Stop, or manual mode.
- The durable state record separates task completion from automatic-session status and is updated immediately before and after every state transition.
- Follow-ups remain evidence-grounded and current across all phases, and each automatic post-Review checkpoint offers ranked current choices plus Stop and manual-mode options.
- Planning, implementation, and review retain their canonical evidence, including the plan, phase details, critique, changes, amendments, divergences, review execution, outcome, and routing.
- Ordinary flow executes exactly one final-candidate critique and one post-implementation Review. Compatible critique findings are applied directly. Critique advice that conflicts with a confirmed user decision is rejected without re-asking; only a significant or divergent issue unresolved by current user direction requires a user decision. Review findings become later work and do not trigger another Review in the current task.
- The response reports mode, session status, phase, state and artifact pointers, blockers, review execution and outcome when available, and current ranked follow-up choices after review.
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.
- 8d ago First seen · 181 lines · 31 tokens per session scan A 4792bdec74cc
RPI Agent is an agent published in the GitHub repository Peter-N91/hve-squad-mcp (0 stars, last pushed yesterday), licensed MIT. It adds 31 tokens to every session and 4,065 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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
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.
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.
Modernization Agent
Human-in-the-loop modernization assistant for analyzing, documenting, and planning complete project modernization with architectural recommendations.