Agent skill · Databases

bio-local-blast

Build local BLAST databases and run searches using NCBI BLAST+ command-line tools. Use when running >50 queries, building custom databases with -parse_seqids and -taxid, downloading prebuilt NCBI databases via update_blastdb.pl, choosing -task variants (megablast/dc-megablast/blastn/blastn-short), tuning soft/hard masking, scaling threads, or extracting hits with blastdbcmd. Encodes BLAST v5 vs v4 database format, taxonomy filtering, makeblastdb pitfalls.

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeships scriptsNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill local-blast --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 6
SKILL.md size: 16 KB
Bundled scripts: yes
Path: skills/bioskills/local-blast/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 135
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

## Version Compatibility Reference examples tested with: NCBI BLAST+ 2.15+ Before using code patterns, verify installed versions match. If versions differ: - CLI: `blastn -version` then `blastn -help` to confirm flags - CLI: `makeblastdb -help` to confirm database build options If a flag is unrecognized or behavior changes, introspect with `-help` and adapt the example to match the installed version rather than retrying. # Local BLAST **"Run BLAST locally for speed and control"** -> Build or download a BLAST+ database, run the appropriate program with carefully chosen `-task`, masking, and thread settings, parse tabular output. Local BLAST is the right tool when remote is rate-limited or when the database must be reproducible (frozen). The biggest mistakes are (a) using `nt`/`nr` without realizing they're >250 GB and grow weekly, (b) not building with `-parse_seqids` and then being unable to extract hit sequences with `blastdbcmd`, (c) using default `blastn` for cross-species when `dc-megablast` is correct, and (d) thinking `-num_threads 32` will scale -- past ~16 threads BLAST is I/O bound. - CLI: `makeblastdb`, `blastn`/`blastp`, `blastdbcmd`, `update_blastdb.pl` (NCBI BLAST+) -

What's inside
Steps it walks through
  1. Version Compatibility
  2. Installation
  3. Database format: v5 vs v4
  4. makeblastdb flag taxonomy
  5. -task taxonomy (the most-misused BLAST setting)
  6. Soft vs hard masking
  7. Thread scaling
  8. Output format reference (-outfmt)
  9. Prebuilt NCBI databases via updateblastdb.pl
  10. Code patterns
  11. Build and search a custom protein database
  12. Cross-species DNA with dc-megablast
  13. Short primer search
  14. Taxonomy-filtered search (BLAST v5 only)
Ships with 5 files
  • examples/blast_wrapper.py
  • examples/create_database.sh
  • examples/reciprocal_best.sh
  • examples/run_blast.sh
  • usage-guide.md
Commands it runs
conda (preferred)
conda install -c bioconda blast
macOS
brew install blast
Ubuntu
sudo apt install ncbi-blast+
Verify
blastn -version    # NCBI BLAST+ 2.15+ expected
update_blastdb.pl --showall pretty | head
makeblastdb -in reference.fasta -dbtype nucl \
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About this skill
What does the bio-local-blast skill do?

Build local BLAST databases and run searches using NCBI BLAST+ command-line tools. Use when running >50 queries, building custom databases with -parse_seqids and -taxid, downloading prebuilt NCBI databases via update_blastdb.pl, choosing -task variants (megablast/dc-megablast/blastn/blastn-short), tuning soft/hard masking, scaling threads, or extracting hits with blastdbcmd. Encodes BLAST v5 vs v4 database format, taxonomy filtering, makeblastdb pitfalls.

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill local-blast --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.

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