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 academic-literature-searchgit 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/academic-literature-search)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/academic-literature-search"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/academic-literature-search/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/academic-literature-search"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/academic-literature-search.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 | $0.00004 | $0.04381 |
| Opus 5 | $0.00002 | $0.02191 |
| Sonnet 5 | $0.00001 | $0.00876 |
| Haiku 4.5 | $0.00000 | $0.00438 |
Grade A, and why
academic-literature-search scanned grade A with 1 finding 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 13d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Uses stdlib only (`urllib.request` + `xml.etree.ElementTree`), no external dependencies: Copies of this mod
2 near-identical copies found in the catalogue:
- academic-literature-search — 97% identical, 835 lines differ
- academic-literature-search — 97% identical, 835 lines differ
How it starts
The opening of the file, as written. The whole thing — 421 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Academic Literature Search — 学术文献检索与引用管理
Use this skill when the user asks to search for academic papers, retrieve literature, generate citations, format references, or any task involving PubMed, bioRxiv, arXiv, or academic reference management. Trigger keywords: "搜文献", "检索", "找论文", "参考文献", "引用", "citation", "search papers", "PubMed", "bioRxiv", "arXiv", "GB/T 7714", "PMID", "DOI", "批量引用".
Core Principles
- MCP first, Python second: PubMed operations → MCP tools (zero code). Python only for arXiv, GB/T 7714 formatting, and citation post-processing.
- Code-driven citations: Citation formatting, validation, deduplication, renumbering — ALL via Python code. NEVER fabricate PMIDs, DOIs, author names, or journal names.
- GB/T 7714-2015 sequential numbering:
[1][2][3]in-text, references numbered by order of first appearance. - Journal name consistency: Use full journal names throughout (NOT ISO abbreviations). E.g.,
Nature MedicinenotNat Med. If MCP returns abbreviated names, expand them; if expansion is uncertain, use the name as returned.
Tool Routing Decision Table
| 操作 | 用什么 | 为什么 |
|---|---|---|
| PubMed 关键词搜索 | MCP pubmed_search_articles |
原生日期/类型过滤/排序，Agent 零代码 |
| PMID 批量获取详情 | MCP pubmed_fetch_contents |
4种详情级别，一次200个，含 MeSH |
| 相似论文发现 | MCP pubmed_article_connections (similar) |
直接调用，返回结构化数据 |
| 被引论文发现 | MCP pubmed_article_connections (citedin) |
同上 |
| 论文参考文献（它引了谁） | MCP pubmed_article_connections (references) |
MCP 独有，ELink 不支持 |
| RIS/BibTeX 导出 | MCP pubmed_article_connections (citation_formats) |
内置格式化 |
| bioRxiv/medRxiv 搜索 | MCP pubmed_search_articles + journal filter |
queryTerm 加 biorxiv[journal] |
| arXiv 搜索 | Python | MCP 不覆盖 arXiv |
| GB/T 7714-2015 格式化 | Python | MCP 无国标格式 |
| 引用后处理/去重/编号 | Python | MCP 不覆盖 |
MCP Operations (PubMed — 主力)
1. 关键词搜索
Tool: pubmed_search_articles
Parameters:
queryTerm: "large language model bioinformatics"
maxResults: 20
sortBy: "relevance" ← 或 "pub_date"
fetchBriefSummaries: 10 ← 返回前10篇摘要
dateRange: ← 可选
minDate: "2022"
maxDate: "2026"
dateType: "pdat"
filterByPublicationTypes: ["Review"] ← 可选
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.
- 13d ago First seen · 421 lines · 4 tokens per session scan A 71c34d958bc4
academic-literature-search is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 4 tokens to every session and 4,381 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
biopython
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use…
scanpy
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use…
structure-prediction
Protein structure prediction from sequence. ESMFold-based, single GPU, no MSA needed. Predicts 3D structures with pLDDT confidence scores for drug discovery targets.
biomcp
Search and retrieve biomedical data - genes, variants, clinical trials, diagnostic tests, articles, drugs, diseases, pathways, proteins, adverse events, pharmacogenomics, and phenotype-disease matching. Use for gene function, variant pathogenicity, trials, diagnostics, drug safety, pathway context, disease workups…
biomcp-research
Do biomedical literature and variant research with the BioMCP CLI, and file what you learn about the tool itself as issues in the biomcp repo.
biological-expert
Expert-level biology, biotechnology, genetics, bioinformatics, and computational biology. Use when the user mentions biology, biotechnology, genetics, bioinformatics, or genomics, or when the task involves Molecular Biology, Genomics & Bioinformatics, Systems Biology, or Data Analysis.