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 PKU-YuanGroup/OpenAI4S --skill bio-database-access-ncbi-datasets-cligit clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4SWrote 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/pku-yuangroup/openai4s/bio-database-access-ncbi-datasets-cli)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-database-access-ncbi-datasets-cli"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-database-access-ncbi-datasets-cli/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/pku-yuangroup/openai4s/bio-database-access-ncbi-datasets-cli"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-database-access-ncbi-datasets-cli.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 | $0.00126 | $0.03756 |
| Opus 5 | $0.00063 | $0.01878 |
| Sonnet 5 | $0.00025 | $0.00751 |
| Haiku 4.5 | $0.00013 | $0.00376 |
Grade A, and why
bio-ncbi-datasets-cli scanned grade A 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -O https://ftp.ncbi.nlm.nih.gov/pub/datasets/command-line/v2/linux-amd64/datasets Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
out = subprocess.run(cmd, capture_output=True, text=True, check=True) This is a copy
95% identical to bio-ncbi-datasets-cli — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 319 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: NCBI Datasets CLI 16.0+ (2024), dataformat 16.0+
Before using code patterns, verify installed versions match. If versions differ:
- CLI:
datasets --version,dataformat --version - Subcommand help:
datasets <subcommand> --help
If a subcommand or flag is unrecognized, run datasets --help and adapt. The CLI is under active development; major releases (v15 -> v16) added subcommands and renamed flags.
NCBI Datasets CLI
"Pull genome / gene / ortholog data from NCBI in 2026" -> The Datasets v2 CLI (launched 2023) is the official, supported bulk endpoint for genome and gene-centric data. It replaces the prior best-practice of scraping assembly_summary.txt + parallel FTP + manual checksum verification. For genome-scale data, it is strictly better than E-utilities (EFetch).
The CLI is not the right answer for everything. PubMed, SRA reads, and custom Entrez queries still belong to E-utilities. The defection rule: if the question is about genome assemblies, gene records, or pre-computed orthologs, use Datasets; otherwise stay with E-utilities.
- CLI:
datasets download genome accession GCF_... - CLI:
datasets summary gene symbol BRCA1 --taxon human - Python:
subprocesswrapper; Python clientncbi-datasets-pylib(experimental as of 2024)
Installation
# conda
conda install -c conda-forge ncbi-datasets-cli
# Or direct download (Linux, macOS, Windows binaries)
curl -O https://ftp.ncbi.nlm.nih.gov/pub/datasets/command-line/v2/linux-amd64/datasets
datasets --version # 16.0+ expected
dataformat --version # bundled companion tool
What's in scope (use Datasets) vs out of scope (use E-utilities or other tools)
| Question | Datasets | Use instead |
|---|---|---|
| Genome assembly download | yes | — |
| All reference genomes for a taxon | yes | — |
| Gene record metadata (multi-species) | yes | — |
| Ortholog data for a gene | yes (datasets summary gene ... --ortholog) |
OrthoDB / Compara for tree-aware orthology |
| Virus data (assemblies, metadata) | yes (datasets download virus) |
— |
| Annotation files (GFF3, GTF) for a genome | yes | — |
| Protein records (curated, with cross-refs) | partial | UniProt REST for richer annotation |
| PubMed | no | entrez-search / entrez-fetch |
| SRA reads | no | sra-data |
| BLAST | no | blast-searches / local-blast |
| Custom Entrez queries | no | entrez-search |
| Pre-computed alignments (Compara) | no | ensembl-rest |
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 319 lines · 126 tokens per session scan A 41a9c1553400
bio-ncbi-datasets-cli is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 126 tokens to every session and 3,756 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 2 findings (makes network calls, runs shell commands). It is 95% identical to bio-ncbi-datasets-cli, differing in 12 lines, and is treated as a copy.
Other skills, from other repositories
boltz-structure-prediction
Boltz-1 / Boltz-2 structure prediction for proteins, complexes, and ligand-aware validation. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC…
imaging-data-commons
Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.
flow-cytometry-analysis
Complete flow cytometry analysis pipeline. FCS file handling, compensation, manual/automated gating, immunophenotyping, CFSE proliferation analysis, cell cycle analysis (Dean-Jett-Fox), and apoptosis assays. Extends flowio with analytical workflows. For raw FCS parsing only use flowio.
scientific-critical-thinking
Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review…
cellxgene-census
Query the CELLxGENE Census (61M+ cells) programmatically. Use when you need expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. Best for population-scale queries, reference atlas comparisons. For analyzing your own data use scanpy or scvi-tools.
glycobiology
Glycosylation site prediction and glycobiology analysis. N-glycosylation motif finding, O-glycosylation hotspot prediction, glycan structure resources. Lightweight, pure Python. For protein function queries use uniprot-database; for structure analysis use alphafold-database.