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 beita6969/ScienceClaw --skill literaturegit clone --depth 1 https://github.com/beita6969/ScienceClawWrote 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/beita6969/scienceclaw/literature)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/literature"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/literature/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/beita6969/scienceclaw/literature"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/literature.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 | $0.00002 | $0.00362 |
| Opus 5 | $0.00001 | $0.00181 |
| Sonnet 5 | $0.00000 | $0.00072 |
| Haiku 4.5 | $0.00000 | $0.00036 |
Grade A, and why
literature 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 8d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- literature — 98% identical, 5 lines differ
- literature — 98% identical, 5 lines differ
What it actually says
Literature Search & Review
Overview
Comprehensive academic literature search and synthesis across 15+ sources.
Capabilities
- Multi-database parallel search (PubMed, arXiv, bioRxiv, medRxiv, OpenAlex, Semantic Scholar, Crossref, DBLP, CORE, DOAJ, Europe PMC)
- Web search via Agent-Reach (Exa semantic search, Jina Reader for any URL/PDF)
- Social academic search (Twitter/X threads, YouTube talks, GitHub repos)
- Structured literature reviews with citation networks
- Knowledge gap identification
- Hypothesis generation from literature analysis
Search Strategy
- Query optimization: Short, keyword-based queries (max 7 words). PubMed-friendly syntax.
- Multi-source fan-out: Parallel queries across all sources for maximum coverage.
- Deduplication: By PMID, DOI, then normalized title.
- Reflection loop: Evaluate coverage → identify gaps → generate follow-up queries.
Citation Rules
- ZERO hallucinated citations. Every reference must come from real search data.
- Unified article schema: source_type, title, authors, year, journal, url, doi, pmid, abstract.
- Numbered references [1], [2], [3] — built only from retrieved articles.
Best Practices
- Start broad, then narrow. First search finds the landscape; follow-up queries fill gaps.
- Cross-domain search. The breakthrough paper might be in an unexpected field.
- Check preprints AND published papers. Recent findings may only be on bioRxiv/arXiv.
- Verify high-impact claims. Use Semantic Scholar's citation count to identify landmark papers.
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.
- 8d ago First seen · 35 lines · 2 tokens per session scan A 1a8b68a95cc8
literature is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 2 tokens to every session and 362 once invoked, about $0.0000 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 2026-09-03.
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