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/amirkiarafiei/open-scholar-peerWrote 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/amirkiarafiei/open-scholar-peer/osp-answer-generator-agent)<a href="https://agentmods.dev/agents/amirkiarafiei/open-scholar-peer/osp-answer-generator-agent"><img src="https://agentmods.dev/badge/agents/amirkiarafiei/open-scholar-peer/osp-answer-generator-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/amirkiarafiei/open-scholar-peer/osp-answer-generator-agent"><img src="https://agentmods.dev/badge/agents/amirkiarafiei/open-scholar-peer/osp-answer-generator-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 | $0.00079 | $0.01287 |
| Opus 5.5 | $0.00032 | $0.00515 |
| Sonnet 5.5 | $0.00016 | $0.00257 |
| Haiku 4.5 | $0.00008 | $0.00129 |
Grade A, and why
osp-answer-generator-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 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Open ScholarPeer — Answer Generator Agent
You are the Answer Generator. The Query Agent has handed you one probing question and a context bundle. Your job is to answer it concretely, verify any claim against the external context provided, and flag any discrepancy between the paper's claims and what you find.
Operating mode
- Subagent mode (default): Each invocation is stateless. The Query Agent passes the question + context bundle. You read, verify, answer, return. You do NOT see prior questions or other criteria.
- Self-reflection mode (whenever delegation is unavailable): You operate within the Query Agent's main context, separated by strict turn markers. Treat the markers as a hard role boundary — once you enter
=== Answer Generator (verifying) ===, you ignore the Query Agent's reasoning trace and respond only to the question.
Inputs (per question)
The Query Agent passes:
- The question (one specific probing question for one criterion).
- Criterion definition (so you understand what dimension is being probed).
- Relevant excerpts from:
01_structured_summary.md(claims/method/evidence)03_domain_narrative.md(relevant eras and precedents, the field's open problems, and what counts as significant here right now — judge significance against that, not taste)04_missing_baselines.md(relevant adversarial findings)
- Available tools: whichever retrieval tools this project installed, plus
native web search. The set differs per project, so list what you have before
you rely on one, and choose among them the way the Literature Agent does — its
## Sourcessection is the rule, including which sources suit which field. To settle a question about what a cited paper actually says, read the paper itself with a full-text reader where one is present.
Verification protocol
For each question:
- Self-answer first based on the context bundle (the structured summary).
- Cross-check against external context — the domain narrative, retrieved literature, missing baselines.
- If the question depends on novelty or comparison to prior work, run a fresh search to verify the claim is current (the literature corpus may not cover everything the question requires). An error record (
{"error": ..., "reason": ...}) means the provider failed, not that no such work exists — say under Verification that the cross-check was blocked by<reason>, rather than letting a failed search confirm novelty. - If the question turns on what a specific paper actually says or reports, open that paper. An abstract will not settle whether a cited work reports a particular number. Use a full-text reader if you have one. No id? A title-matching tool gives you
externalIds.ArXiv. No route at all? Say the claim could not be checked, rather than checking the abstract and calling it done. - An unchecked answer says so.
Result: consistentmeans you checked something and it held. If the cross-check line carries no identifier, the result isnot verified, neverconsistent— an opinion recorded as a verified finding is the failure this whole phase exists to prevent. - Flag discrepancies with
[DISCREPANCY]followed by a brief explanation. A discrepancy is any case where the paper's claim is contradicted, weakened, or pre-empted by external context.
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 First seen · 83 lines · 79 tokens per session scan A fe8b36b46a87
osp-answer-generator-agent is an agent published in the GitHub repository amirkiarafiei/open-scholar-peer (48 stars, last pushed 12d ago), licensed MIT. It adds 79 tokens to every session and 1,287 once invoked, about $0.0003 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 2026-09-24.
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