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 ncbi-entrezgit 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/ncbi-entrez)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/ncbi-entrez"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/ncbi-entrez/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/ncbi-entrez"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/ncbi-entrez.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00082 | $0.01639 |
| Opus 5 | $0.00041 | $0.00820 |
| Sonnet 5 | $0.00016 | $0.00328 |
| Haiku 4.5 | $0.00008 | $0.00164 |
Grade C, and why
ncbi-entrez scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
IDS=$(curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gene&term=insulin[Gene]+AND+human[Organism]&retmode=json" | python3 -c "import sys,json; print(','.join(json.load(sys.stdin)['esearchresult'][' 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NCBI Entrez E-utilities API
Access NCBI databases (Gene, SNP, ClinVar, Nucleotide, Protein, OMIM) through the Entrez Programming Utilities. Supports search, fetch, linking, and summary operations.
API Endpoints
Base: https://eutils.ncbi.nlm.nih.gov/entrez/eutils
Authentication & Rate Limits
Set the NCBI_API_KEY environment variable for higher throughput.
- With API key: 10 requests/second
- Without API key: 3 requests/second
Append &api_key=$NCBI_API_KEY to all requests when available.
esearch.fcgi -- Search a database and return IDs
# Search for TP53 gene in human
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gene&term=TP53[Gene]+AND+Homo+sapiens[Organism]&retmode=json"
# Search ClinVar for BRCA1 pathogenic variants
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=clinvar&term=BRCA1[gene]+AND+pathogenic[clinical_significance]&retmode=json&retmax=20"
# Search nucleotide database
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=nucleotide&term=SARS-CoV-2[Organism]+AND+complete+genome&retmode=json&retmax=5"
efetch.fcgi -- Retrieve full records by ID
# Fetch gene record for TP53 (Gene ID: 7157)
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=gene&id=7157&retmode=xml"
# Fetch nucleotide sequence in FASTA format
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=nucleotide&id=NM_000546.6&rettype=fasta&retmode=text"
# Fetch SNP record
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=snp&id=rs1042522&retmode=json"
# Fetch ClinVar record in XML
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=clinvar&id=37653&rettype=clinvarset&retmode=xml"
esummary.fcgi -- Retrieve document summaries
# Get gene summary for TP53
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=gene&id=7157&retmode=json"
# Get summaries for multiple SNPs
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=snp&id=rs1042522,rs28897696&retmode=json"
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 · 120 lines · 82 tokens per session scan C 97052dbe6a7c
ncbi-entrez is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 82 tokens to every session and 1,639 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, 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.