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Agent skill

Scientific Pkg Gget

by affaan-m in affaan-m/ECC

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

MITAuto-check passedResearch & Science

Install Scientific Pkg Gget

skills CLI
$ npx skills add affaan-m/ECC --skill scientific-pkg-gget -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install affaan-m/ECC scientific-pkg-gget --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/scientific-pkg-gget .claude/skills/scientific-pkg-gget && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
scientific-pkg-gget
GitHub stars
277k
Used in
1 other repo
Token cost
~1.3k tokens
SKILL.md length
471 words
Files
1
Skills in repo
683
Repo updated
First seen
Licence
MIT

At a glance

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

  • Works in 5 steps: Identify the species, assembly, gene ID… → Check the current module documentation… → Run a small query first. → …
  • A task needs quick bioinformatics lookup across genomic reference databases with the gget CLI
  • SKILL.md covers When to Use, Installation, Basic Patterns and Common Modules, plus 4 more sections
  • Calls python and uv

What it does

Scientific Pkg Gget is an agent skill from affaan-m/ECC. gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs. Use when a task needs quick bioinformatics lookup across genomic reference databases with the gget CLI or Python package.

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Bioinformatics. It works with Python and Ensembl. The repository describes itself as: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. The licence is MIT.

When your agent uses it

  • A task needs quick bioinformatics lookup across genomic reference databases with the gget CLI
  • Tasks that involve Bioinformatics

Example prompts

  • “/scientific-pkg-gget”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Identify the species, assembly, gene ID type, and database needed.
  2. Check the current module documentation for arguments.
  3. Run a small query first.
  4. Save output with an explicit filename and date.
  5. Record module name, version, arguments, and database assumptions.

What it can do on your machine

Read from SKILL.md and the folder at commit 2d515e4. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python
    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • pachterlab.github.io
    • github.com
    • doi.org

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Scientific Pkg Gget loads about 1.3k tokens when it runs. Until then it costs about 78 tokens; SKILL.md has 471 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~78
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from affaan-m/ECC at commit 2d515e4, republished under its MIT licence (© affaan-m). 471 words, ~1,273 tokens.

Download SKILL.mdSave it as .claude/skills/scientific-pkg-gget/SKILL.md (or your agent's skills folder).
name
scientific-pkg-gget
description
gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs. Use when a task needs quick bioinformatics lookup across genomic reference databases with the gget CLI or Python package.
metadata.origin
community

gget

Use this skill when a task needs quick bioinformatics lookup across genomic reference databases with the gget CLI or Python package.

When to Use

  • Finding Ensembl IDs, gene metadata, transcript details, or sequences.
  • Running quick BLAST or BLAT lookups without building a full local pipeline.
  • Fetching reference genome links and annotations from Ensembl.
  • Querying protein structure, pathway, cancer, expression, or disease-association modules through a single interface.
  • Creating a reproducible first-pass evidence log before moving to heavier tools such as Biopython, Snakemake, Nextflow, BLAST+, or database-specific clients.

Use a dedicated workflow instead of gget when the task requires regulated clinical interpretation, high-throughput production pipelines, or fine-grained control over database versions and local indexes.

Installation

Use a clean Python environment.

bash
python -m venv .venv
. .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install --upgrade gget
gget --help

If uv is available:

bash
uv venv
. .venv/bin/activate
uv pip install gget

Before relying on an older environment, upgrade gget and re-check the module docs. The upstream databases queried by gget change over time.

Basic Patterns

CLI shape:

bash
gget <module> [arguments] [options]

Python shape:

python
import gget

result = gget.search(["BRCA1"], species="human")
print(result)

Common workflow:

  1. Identify the species, assembly, gene ID type, and database needed.
  2. Check the current module documentation for arguments.
  3. Run a small query first.
  4. Save output with an explicit filename and date.
  5. Record module name, version, arguments, and database assumptions.

Common Modules

Use current upstream docs for exact arguments. These modules are common first choices:

  • gget search: find Ensembl IDs from search terms.
  • gget info: retrieve metadata for Ensembl, UniProt, or related IDs.
  • gget seq: fetch nucleotide or amino-acid sequences.
  • gget ref: retrieve reference genome download links.
  • gget blast: run a quick BLAST query.
  • gget blat: locate a sequence against supported genome assemblies.
  • gget muscle: run multiple sequence alignment.
  • gget diamond: run local sequence alignment against reference sequences.
  • gget alphafold and gget pdb: inspect protein-structure references.
  • gget enrichr, gget opentargets, gget archs4, gget bgee, gget cbio, and gget cosmic: explore enrichment, target, expression, cancer, and disease association data.

Do not assume every module supports every Python version or dependency set. Some optional scientific dependencies have narrower version support than the core package.

Show full SKILL.md (143 more words)Show less

Quick Examples

Find genes:

bash
gget search -s human brca1 dna repair -o brca1-search.json

Fetch gene metadata:

bash
gget info ENSG00000012048 -o brca1-info.json

Fetch a sequence:

bash
gget seq ENSG00000012048 -o brca1-seq.fa

Run a small BLAST query:

bash
gget blast "MEEPQSDPSVEPPLSQETFSDLWKLLPEN" -l 10 -o blast-results.json

Python example:

python
import gget

genes = gget.search(["BRCA1", "DNA repair"], species="human")
info = gget.info(["ENSG00000012048"])
sequence = gget.seq("ENSG00000012048")

Reproducibility Log

For scientific outputs, include enough metadata to replay the query.

markdown
| Date | gget version | Module | Query | Species/assembly | Output | Notes |
| --- | --- | --- | --- | --- | --- | --- |
| 2026-05-11 | `gget --version` | search | `BRCA1 DNA repair` | human | `brca1-search.json` | Docs checked before run |

Also record:

  • Python version and environment manager.
  • Any optional dependency installed through gget setup.
  • Database-specific identifiers returned by the query.
  • Whether output is JSON, CSV, FASTA, or a DataFrame export.
  • Any failures that were resolved by upgrading gget.

Review Checklist

  • Did you upgrade or verify the installed gget version?
  • Did you check the current upstream module docs before using arguments?
  • Is the species or assembly explicit?
  • Are identifiers preserved exactly, including Ensembl/UniProt prefixes?
  • Is the result labeled as database output rather than clinical interpretation?
  • Is the query reproducible from the saved command or Python snippet?
  • Are optional dependencies installed in an isolated environment?

References

© affaan-m, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/scientific-pkg-gget of affaan-m/ECC.

Open the folder on GitHubat commit 2d515e4

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in affaan-m/ECC, which our catalogue first saw on October 7, 2026.

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Works with

Categories

Questions about Scientific Pkg Gget

What does Scientific Pkg Gget do?

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs. Scientific Pkg Gget is an agent skill from affaan-m/ECC. gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

When should I use Scientific Pkg Gget?

Scientific Pkg Gget fits situations like: A task needs quick bioinformatics lookup across genomic reference databases with the gget CLI; tasks that involve Bioinformatics.

How do I install Scientific Pkg Gget in Claude Code?

Run `npx skills add affaan-m/ECC --skill scientific-pkg-gget -a claude-code`. Or copy the skill folder (skills/scientific-pkg-gget in affaan-m/ECC) into .claude/skills/scientific-pkg-gget in your project. Claude Code loads it when a task matches its description.

How do I install Scientific Pkg Gget in Codex?

Run `npx skills add affaan-m/ECC --skill scientific-pkg-gget -a codex`. Or copy the skill folder (skills/scientific-pkg-gget in affaan-m/ECC) into .agents/skills/scientific-pkg-gget in your project. Codex loads it when a task matches its description.

Can I use Scientific Pkg Gget in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add affaan-m/ECC --skill scientific-pkg-gget -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scientific-pkg-gget, .gemini/skills/scientific-pkg-gget, .github/skills/scientific-pkg-gget and .opencode/skills/scientific-pkg-gget in your project.

What does Scientific Pkg Gget need to run?

Going by SKILL.md and its folder, Scientific Pkg Gget needs the command-line tools its instructions call (python and uv). Our summary lists: Python 3.

Does Scientific Pkg Gget access the network?

SKILL.md names 3 domains. As links in the text: pachterlab.github.io, github.com and doi.org. This is read from the text; nothing was executed.

Is Scientific Pkg Gget safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Scientific Pkg Gget use?

Scientific Pkg Gget is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Scientific Pkg Gget use?

About 1.3k tokens (SKILL.md is roughly 5.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Scientific Pkg Gget?

Skills that share tags, products or a category with Scientific Pkg Gget: Gget (davila7/claude-code-templates, 33k stars), Tooluniverse Epigenomics (wu-yc/LabClaw, 1.1k stars), Gget (aipoch/medical-research-skills, 1.9k stars) and Gget Genomic Databases (jaechang-hits/SciAgent-Skills, 374 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Scientific Pkg Gget?

affaan-m (a GitHub user) maintains it in affaan-m/ECC, which has 276,673 GitHub stars. The repository holds 683 skills in this directory. The repository was last updated on October 11, 2026.

Source: affaan-m/ECC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.