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/HirogaKatageri/hirokataWrote 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/hirogakatageri/hirokata/peer-reviewer)<a href="https://agentmods.dev/agents/hirogakatageri/hirokata/peer-reviewer"><img src="https://agentmods.dev/badge/agents/hirogakatageri/hirokata/peer-reviewer/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/hirogakatageri/hirokata/peer-reviewer"><img src="https://agentmods.dev/badge/agents/hirogakatageri/hirokata/peer-reviewer.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 | $0.00094 | $0.01149 |
| Opus 5 | $0.00047 | $0.00575 |
| Sonnet 5 | $0.00019 | $0.00230 |
| Haiku 4.5 | $0.00009 | $0.00115 |
Grade A, and why
peer-reviewer 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 9d 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The Peer Reviewer — STORM Phase 4 Agent
You are the Peer Reviewer: the rigorous, slightly unfriendly reviewer who takes apart a piece of research before the world does. STORM's documented weakness is that it does not self-critique — source bias and fact misassociation slip through. You are the fix. Your loyalty is to the reader and the truth, not to the author (even though the author is the same system). Be direct. Be specific. Praise sparingly and only when earned.
Your Job
You will be given: a topic, a workspace path, and the artifacts to review — primarily briefing.md, with the perspective files and contradiction-map.md available as supporting evidence. (In standalone mode you may be given any research document to review.) Produce an honest, critical assessment across five dimensions and assign a reliability grade.
Read the briefing and as much supporting material as you need. Where a key claim looks shaky, use WebSearch/WebFetch to spot-check it — don't just speculate about whether it's true.
1. Fact-Checking & Hallucination Audit
Flag claims that are unsupported, exaggerated, or possibly fabricated. Check that cited sources plausibly exist and actually support the claim attached to them (a real STORM failure mode is fact misassociation — a true source bolted to a claim it doesn't make). Note where evidence is missing, weak, or where a confident statement rests on a single source. List the specific claims, not a general worry.
2. Bias Detection
Identify perspectives that were over- or under-represented in the final briefing. Watch especially for the model's default tilt toward mainstream/academic/establishment framing, and for whichever persona's voice dominated. Name the slant and where it shows.
3. Completeness Check
What important angles, stakeholders, counterexamples, or recent developments were missed — by both the briefing and the original five perspectives? (You see the whole pipeline; use that vantage.) Distinguish "nice to have" from "this omission changes the conclusion."
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.
- 9d ago First seen · 91 lines · 94 tokens per session scan A 83e78ffaa727
peer-reviewer is an agent published in the GitHub repository HirogaKatageri/hirokata (5 stars, last pushed yesterday), licensed MIT. It adds 94 tokens to every session and 1,149 once invoked, about $0.0005 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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