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 pubmed-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/pubmed-search)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/pubmed-search"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/pubmed-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/pubmed-search"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/pubmed-search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Supply Chain · line 28 Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.Fix: Avoid downloading and executing remote scripts. Use trusted packages from PyPI/npm. If remote fetch is required, verify checksums and use HTTPS.
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 | $0.00090 | $0.01116 |
| Opus 5 | $0.00045 | $0.00558 |
| Sonnet 5 | $0.00018 | $0.00223 |
| Haiku 4.5 | $0.00009 | $0.00112 |
Grade A, and why
pubmed-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 9d 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.
metadata: { "openclaw": { "emoji": "🏥", "requires": { "bins": ["curl"] } } } How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PubMed Search
Search PubMed/MEDLINE (36M+ citations) via NCBI E-utilities REST API.
API Endpoints
Base: https://eutils.ncbi.nlm.nih.gov/entrez/eutils/
esearch -- Search and get PMIDs
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=COVID-19+vaccine+efficacy&retmax=10&retmode=json"
Parameters: db=pubmed, term= (URL-encoded query), retmax= (default 20, max 10000),
retstart= (pagination), retmode=json, sort=relevance|pub_date,
mindate=/maxdate= (YYYY/MM/DD), datetype=pdat.
efetch -- Retrieve records by PMID
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=39142890,39088712&retmode=xml&rettype=abstract"
einfo -- Database metadata
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/einfo.fcgi?db=pubmed&retmode=json"
Query Syntax
Boolean: AND, OR, NOT, parentheses for grouping.
Field tags: [ti] title, [tiab] title/abstract, [au] author, [mesh] MeSH heading,
[majr] MeSH major topic, [pt] publication type, [dp] date, [la] language, [jour] journal.
MeSH terms: Standardized vocabulary with automatic explosion to narrower terms.
Use [mesh:noexp] for exact heading only. Qualifiers: /therapy, /diagnosis,
/epidemiology, /genetics, /prevention and control.
Example: "Breast Neoplasms"[mesh] AND "Drug Therapy"[mesh]
Rate Limiting
- Without API key: 3 requests/sec
- With
NCBI_API_KEY: 10 requests/sec (append&api_key=${NCBI_API_KEY}) - Register at: https://www.ncbi.nlm.nih.gov/account/settings/
Two-Step Search Pattern
PMIDS=$(curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=QUERY&retmax=5&retmode=json" \
| python3 -c "import sys,json; d=json.load(sys.stdin); print(','.join(d['esearchresult']['idlist']))")
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=${PMIDS}&retmode=xml&rettype=abstract"
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.
- 9d ago First seen · 85 lines · 90 tokens per session scan A 66034897d947
pubmed-search is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 90 tokens to every session and 1,116 once invoked, about $0.0005 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 2026-09-03.
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.