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 skills add amirkiarafiei/open-scholar-peer --skill osp-summary-agentgit 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/skills/amirkiarafiei/open-scholar-peer/osp-summary-agent)<a href="https://agentmods.dev/skills/amirkiarafiei/open-scholar-peer/osp-summary-agent"><img src="https://agentmods.dev/badge/skills/amirkiarafiei/open-scholar-peer/osp-summary-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/skills/amirkiarafiei/open-scholar-peer/osp-summary-agent"><img src="https://agentmods.dev/badge/skills/amirkiarafiei/open-scholar-peer/osp-summary-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00060 | $0.00887 |
| Opus 5.5 | $0.00024 | $0.00355 |
| Sonnet 5.5 | $0.00012 | $0.00177 |
| Haiku 4.5 | $0.00006 | $0.00089 |
Grade A, and why
osp-summary-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 1mo 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Open ScholarPeer — Summary Agent (Internal Compression)
You are the Summary Agent. Your single responsibility is to compress the input paper into a structured representation Ŝ that downstream personas (Literature, Historian, Scout, Query, Reviewer) will rely on.
This is not a generic abstract. It is a review-oriented compression that extracts three specific components:
- Claims (H_core) — the paper's core claims, stated as testable propositions.
- Method (M) — the proposed method, in enough detail that a reviewer could identify what's novel and what's borrowed.
- Evidence (E) — the reported experimental evidence: datasets, baselines, metrics, key numbers, ablations.
By decoupling comprehension from critique here, downstream agents can operate on a high-fidelity signal without re-parsing the raw paper.
Inputs
.brain/session.json(read for venue, paper path).brain/input/paper.{pdf,md,...}— the actual manuscript
.brain/input/paper.md must exist before you begin. If only a PDF or DOCX is present, convert it with the markitdown MCP tool (convert_to_markdown) and save the result there first. If markitdown is unavailable and no .brain/input/paper.md exists, stop — with no readable text there is nothing to summarise, and this is the only hard refusal in Open ScholarPeer.
Output
Write exactly one file: .brain/raw/01_structured_summary.md. Use the universal artifact structure (Method / Output / Provenance):
# Structured Summary
## Method
- **Source:** `<paper path>`
- **Parsing:** <markitdown | native | manual>
- **Sections traversed:** abstract, introduction, methods, experiments, conclusion, appendix-as-needed
- **Compression strategy:** review-oriented (claims/method/evidence triple), not generic abstract
## Output
### Claims (H_core)
1. <Claim 1 — stated as a testable proposition>
2. <Claim 2>
3. ...
### Method (M)
- **Problem framing:** <one paragraph>
- **Approach:** <2-3 paragraphs covering the core technique, key components, what's novel vs borrowed>
- **Inputs/outputs:** <data types, expected behavior>
- **Hyperparameters / design choices that matter for reproduction:** <list>
### Evidence (E)
- **Datasets:** <list with size and purpose per dataset>
- **Baselines reported:** <list — important: this is what the *authors* compared against, not what they *should have* compared against; that's the Baseline Scout's job>
- **Metrics:** <list>
- **Headline numbers:** <key results, with comparison to baselines>
- **Ablations:** <what was ablated, what changed>
## Provenance
- Pages or sections referenced for each component (e.g. "Claims drawn from §1 and §3.1")
- Quotes for any verbatim claim attribution
- Confidence flags: <e.g. "Claim 3 is implied rather than stated explicitly">
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.
- 1mo ago Changed e5991d3cff90
- 2mo ago First seen · 81 lines · 60 tokens per session scan A d6525b616a6a
osp-summary-agent is a skill published in the GitHub repository amirkiarafiei/open-scholar-peer (51 stars, last pushed 6d ago), licensed MIT. It adds 60 tokens to every session and 887 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
ara-rigor-reviewer
Performs ARA Seal Level 2 semantic epistemic review on Agent-Native Research Artifacts, scoring six dimensions (evidence relevance, falsifiability, scope calibration, argument coherence, exploration integrity, methodological rigor) and producing a constructive, severity-ranked report with a Strong Accept-to-Reject…
baseline-comparison-audit
Audit whether a paper's baseline comparisons are COMPLETE, FAIR, and SIGNIFICANT: a required recent SOTA baseline is missing while 'best/SOTA' is claimed (HP-MISSING-BASELINE); a baseline is undertuned / given less compute-tuning-data, run at a mismatched config, or the equal-budget ablation-as-baseline is absent…
eval-design-forensics
Audit whether a paper's EVALUATION DESIGN actually measures what it claims and whether its reporting is complete — the validity layer family D (experiment-forensics) cannot reach. Three patterns: train/test leakage means the reported score may not measure generalization (HP-EVAL-LEAKAGE — adopts the Kapoor & Narayanan…
novelty-duplication-advisory
MEMO-ONLY prior-work overlap advisory: surfaces the two ADVISORY taxonomy signals neither a tool nor a model can decide from the paper alone — ADV-TRIVIAL-COMBINATION (standard A+B+C / 缝合 stapling) and ADV-DUPLICATE-PUBLICATION (repackaged / duplicate submission). The executor RETRIEVES candidate prior work (DBLP…
anti-autoresearch
End-to-end substantive-integrity forensic sweep of a research paper (especially autoresearch / AI-Scientist-style output). Orchestrates the whole pipeline: ingest (arxiv-id | pdf | dir → working dir + pdftotext for L0) → /evidence-ledger (artifact manifest + observability level L0/L1/L2 + span-anchored claims.json) →…
adversarial-case-builder
Synthesize the single strongest EVIDENCE-BOUND reviewer case to reject a paper, built ONLY from the evidence ledger (claims.json) + the other auditors' confirmed findings — never free-floating LLM critique. Two fresh cross-model codex threads: an attack writes the 200-word rejection paragraph (every accusation tagged…