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 agentmods add commands/punt-labs/prfaq/reviewgit clone --depth 1 https://github.com/punt-labs/prfaqWrote 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/commands/punt-labs/prfaq/review)<a href="https://agentmods.dev/commands/punt-labs/prfaq/review"><img src="https://agentmods.dev/badge/commands/punt-labs/prfaq/review.svg" alt="Measured on agentmods" 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 | $0.00012 | $0.00280 |
| Opus 5 | $0.00006 | $0.00140 |
| Sonnet 5 | $0.00002 | $0.00056 |
| Haiku 4.5 | $0.00001 | $0.00028 |
Grade A, and why
review 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 5d 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.
What it actually says
Peer Review PR/FAQ
Invoke the peer-reviewer agent to critically evaluate a PR/FAQ document against Working Backwards principles and the Kahneman decision quality framework.
Steps
-
Find the document. If
$ARGUMENTSspecifies a path, use it. Otherwise, search forprfaq.texin the project root. -
Launch the peer-reviewer agent using the Task tool with
subagent_type: "prfaq:peer-reviewer". Pass the file path in the prompt. The peer reviewer reads\prfaqstage{}from the document and calibrates its evidence expectations accordingly. -
Present the results to the user. The peer reviewer returns:
- Overall assessment (PASS / ITERATE / REJECT)
- Critical issues and warnings with specific locations and recommendations
- Document strengths
- Ordered next steps
-
Offer to iterate. If the review flags issues, ask the user which they want to address. For accepted issues, make the revisions to the
.texfile, recompile, and offer to re-run the 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.
- 5d ago First seen · 24 lines · 12 tokens per session scan A c834a5c88448
review is a command published in the GitHub repository punt-labs/prfaq (25 stars, last pushed 5d ago), licensed MIT. It adds 12 tokens to every session and 280 once invoked, about $0.0001 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 2026-08-30.
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