bio-ncbi-datasets-cli
Download genome assemblies, gene records, and ortholog data from NCBI using the modern Datasets v2 CLI (replaces assembly_summary.txt scraping and many EFetch workflows). Use when bulk-pulling genome assemblies, gene metadata across species, ortholog sets, or BLAST databases; when E-utilities are too slow for genome-scale work; or when automatic checksum verification, parallel download, and clean accession-driven retrieval are required. Encodes the JSON-lines output format, dataformat conversion, --dehydrated for cloud workflows, and when Datasets is/isn't the right tool.
npx skills add BioTender-max/awesome-bio-agent-skills --skill ncbi-datasets-cli --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## 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 summa
- Version Compatibility
- Installation
- What's in scope (use Datasets) vs out of scope (use E-utilities or other tools)
- Subcommand taxonomy
- Key parameters (download)
- JSON-lines output + dataformat
- When to use --dehydrated for cloud workflows
- Checksum verification (automatic)
- Code patterns
- Download a single reference genome
- Bulk download all reference bacterial genomes
- Gene metadata across species
- Find orthologs for a gene
- Filter assemblies by quality and date
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 datasets summary genome taxon "Escherichia coli" --reference --as-json-lines \ datasets download genome accession GCF_000001405.40 \ unzip -q human_grch38.zip -d human_grch38/ ls -lh human_grch38/ncbi_dataset/data/GCF_000001405.40/
What does the bio-ncbi-datasets-cli skill do?
Download genome assemblies, gene records, and ortholog data from NCBI using the modern Datasets v2 CLI (replaces assembly_summary.txt scraping and many EFetch workflows). Use when bulk-pulling genome assemblies, gene metadata across species, ortholog sets, or BLAST databases; when E-utilities are too slow for genome-scale work; or when automatic checksum verification, parallel download, and clean accession-driven retrieval are required. Encodes the JSON-lines output format, dataformat conversion, --dehydrated for cloud workflows, and when Datasets is/isn't the right tool.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill ncbi-datasets-cli --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.