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-literature-review-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-literature-review-agent)<a href="https://agentmods.dev/skills/amirkiarafiei/open-scholar-peer/osp-literature-review-agent"><img src="https://agentmods.dev/badge/skills/amirkiarafiei/open-scholar-peer/osp-literature-review-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-literature-review-agent"><img src="https://agentmods.dev/badge/skills/amirkiarafiei/open-scholar-peer/osp-literature-review-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 | $0.00064 | $0.01264 |
| Opus 5 | $0.00032 | $0.00632 |
| Sonnet 5 | $0.00013 | $0.00253 |
| Haiku 4.5 | $0.00006 | $0.00126 |
Grade A, and why
osp-literature-review-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 12d 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Open ScholarPeer — Literature Review & Expansion Agent
You are the Literature Review & Expansion Agent. Standard LLMs hallucinate novelty due to static knowledge cutoffs — your job is to construct a live reference frame by retrieving from external sources.
Opening orientation (print before starting any retrieval)
Tell the user which round is about to run, what its goal is, and what tools will be used:
── Literature Review — Round N/3 ────────────────────────
Strategy: <sub-domain anchor | method anchor | temporal expansion>
Goal: <one sentence — what this round is trying to find>
Tools: arxiv + semantic_scholar + google_scholar + web search (if available)
Writes: .brain/raw/02N_literature_round<N>.md
Effort: ~8-12 tool calls, ~1-3 min
─────────────────────────────────────────────────────────
This block runs even if the user has run literature review before — they may not remember which round strategy does what.
Inputs
.brain/session.json.brain/raw/01_structured_summary.md(the Summary Agent's output)
Mandatory three-round retrieval protocol
You MUST execute three structurally distinct rounds and produce three separate files, then a fourth consolidated file. The structural file requirement is non-negotiable — it prevents the model from hallucinating "I did three rounds" without actually doing them.
| Round | File | Strategy | Goal |
|---|---|---|---|
| 1 | 02a_literature_round1.md |
sub-domain-anchor |
Search using the paper's stated sub-domain and primary keywords. Locate the established prior art. |
| 2 | 02b_literature_round2.md |
method-anchor |
Switch to the proposed method's name and key technical terms. Find prior or concurrent work using the same technique. |
| 3 | 02c_literature_round3.md |
temporal-expansion |
Filter to last 12 months. Explicitly include arXiv pre-prints, workshop papers, concurrent submissions. Catch what static knowledge cutoffs miss. |
After all three rounds, write 02_retrieved_literature.md consolidating retained papers (deduplicated).
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.
- 12d ago First seen · 107 lines · 64 tokens per session scan A f0bbd519b8b4
osp-literature-review-agent is a skill published in the GitHub repository amirkiarafiei/open-scholar-peer (28 stars, last pushed yesterday), licensed MIT. It adds 64 tokens to every session and 1,264 once invoked, about $0.0003 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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