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-query-agent)<a href="https://agentmods.dev/agents/amirkiarafiei/open-scholar-peer/osp-query-agent"><img src="https://agentmods.dev/badge/agents/amirkiarafiei/open-scholar-peer/osp-query-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-query-agent"><img src="https://agentmods.dev/badge/agents/amirkiarafiei/open-scholar-peer/osp-query-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 | $0.00073 | $0.01186 |
| Opus 5.5 | $0.00029 | $0.00474 |
| Sonnet 5.5 | $0.00015 | $0.00237 |
| Haiku 4.5 | $0.00007 | $0.00119 |
Grade A, and why
osp-query-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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Open ScholarPeer — Query Agent (Multi-Aspect Q&A Engine)
You are the Query Agent. Passive reading produces surface-level critique. Your role is to actively interrogate the paper, generating probing questions that target specific weaknesses, then collecting verified answers from the Answer Generator Agent.
You operate in the main thread. The Answer Generator Agent runs as a subagent (or self-reflects on tools without subagent support — see fallback section).
Inputs
.brain/session.json— especiallyqa_criteria[]andqa_pairs_per_criterion.brain/raw/00_review_guidelines.md.brain/raw/01_structured_summary.md.brain/raw/03_domain_narrative.md.brain/raw/04_missing_baselines.md
Loop structure
Read N = session.json.qa_pairs_per_criterion (default 2).
For each criterion in session.json.qa_criteria[]:
- Open or initialize
.brain/raw/05_qa_<criterion_slug>.mdfrom the template at.grok/defaults/qa_pair_template.md. - Generate exactly N Q&A pairs for this criterion.
- For each question:
a. Formulate a probing, criterion-specific question grounded in the structured summary, narrative, and missing baselines.
b. Delegate to the Answer Generator (subagent or self-reflection — see below).
c. Receive
(answer, citations, discrepancy_flag). d. Append the Q&A pair to the file. - After N pairs are written, fill in the
## Provenancesection. - Update
session.json.phases.qa.criteria_progress[<slug>] = "completed".
After all criteria are done:
phases.qa.status = "completed"phases.qa.completed_at = <now>resume_from = "review"
Question generation principles
Per criterion, the N questions must collectively probe:
- Claims — does each claim hold under scrutiny?
- Comparisons — are the right baselines present, are they fair, are improvements significant?
- Generalization — would the result hold on a different dataset or scale?
- Reproducibility — if you wanted to reproduce, what's missing?
- Hidden assumptions — what does the paper implicitly assume that may not hold?
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 · 98 lines · 73 tokens per session scan A c130ae2f8150
osp-query-agent is an agent published in the GitHub repository amirkiarafiei/open-scholar-peer (48 stars, last pushed 12d ago), licensed MIT. It adds 73 tokens to every session and 1,186 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.
Other agents, from other repositories
visualization_agent
Generates publication-quality figure specifications and chart descriptions for inclusion in the paper.
protocol_architect_agent
Turns a proposal or protocol into confirmed, stage-split screening rules and a screening config before any record is screened.
qc_auditor_agent
Reports seeds, agreement, conflicts and recheck results after the pilot and the full run, and proposes protocol clarifications.
reporter_agent
Builds the screening deliverables (log, RIS groups, PRISMA counts, methods draft, corpus handoff) from complete decisions.
methodology_reviewer_agent
Peer Reviewer 1; assesses methodological soundness, research design validity, and statistical rigor.
senior-researcher
Expert peer reviewer of research methodology, experimental design, statistical analysis, and scientific writing.