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Agent skill

Scikit Bio

by aipoch in aipoch/medical-research-skills

A Python bioinformatics toolkit for sequence, phylogeny, and microbiome/community-ecology analysis; use it when you need to compute diversity/ordination/statistics from biological data and standard…

MITAuto-check passedData & Analytics

Install Scikit Bio

skills CLI
$ npx skills add aipoch/medical-research-skills --skill scikit-bio -a claude-code

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

GitHub CLI
$ gh skill install aipoch/medical-research-skills scikit-bio --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/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'scientific-skills/Data Analysis/scikit-bio' .claude/skills/scikit-bio && 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
scikit-bio
GitHub stars
2k
Token cost
~1.4k tokens
SKILL.md length
328 words
Files
3 (incl. references)
Skills in repo
567
Repo updated
First seen
Licence
MIT

At a glance

A Python bioinformatics toolkit for sequence, phylogeny, and microbiome/community-ecology analysis; use it when you need to compute diversity/ordination/statistics from biological data and standard…

  • You need to compute diversity/ordination/statistics from biological data and standard formats (FASTA/FASTQ/Newick/BIOM)
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Tasks that involve Bioinformatics

What it does

Scikit Bio is an agent skill from aipoch/medical-research-skills. A Python bioinformatics toolkit for sequence, phylogeny, and microbiome/community-ecology analysis; use it when you need to compute diversity/ordination/statistics from biological data and standard formats (FASTA/FASTQ/Newick/BIOM).

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/api_reference.md` and `scikit-bio_audit_result_v1.json`).

It sits in Data & Analytics, covering Bioinformatics and Statistics. It works with Python. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • You need to compute diversity/ordination/statistics from biological data and standard formats (FASTA/FASTQ/Newick/BIOM)
  • Tasks that involve Bioinformatics
  • Tasks that involve Statistics

Example prompts

  • “/scikit-bio”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. 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

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

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

  • Network

    No URLs in SKILL.md.

    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

Scikit Bio loads about 1.4k tokens when it runs, and up to ~6.2k if it reads all its reference files. Until then it costs about 61 tokens; SKILL.md has 328 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~61
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.2k

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 aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 328 words, ~1,365 tokens.

Download SKILL.mdSave it as .claude/skills/scikit-bio/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
scikit-bio
description
A Python bioinformatics toolkit for sequence, phylogeny, and microbiome/community-ecology analysis; use it when you need to compute diversity/ordination/statistics from biological data and standard formats (FASTA/FASTQ/Newick/BIOM).
license
MIT
author
AIPOCH

Source: https://github.com/aipoch/medical-research-skills

When to Use

  • You need to parse, validate, and manipulate biological sequences (DNA/RNA/protein) and their metadata.
  • You are running microbiome/community-ecology workflows (alpha/beta diversity, UniFrac, ordination, PERMANOVA).
  • You need to build, transform, or compare phylogenetic trees (Newick I/O, pruning/rerooting, patristic distances).
  • You want to compute and work with distance matrices and downstream multivariate analyses (PCoA, Mantel, ANOSIM).
  • You need to read/write common bioinformatics formats (FASTA/FASTQ, Newick, BIOM) and convert between them.

Key Features

  • Sequence objects: DNA, RNA, Protein, and generic Sequence with validation, slicing, motif search, reverse complement, transcription/translation, and metadata handling.
  • Alignment utilities: pairwise local alignment (SSW-based) and multiple sequence alignment containers (TabularMSA) with consensus support.
  • Phylogenetics: TreeNode manipulation, tree construction from distance matrices (e.g., Neighbor Joining), and tree distance/metrics.
  • Diversity: alpha diversity (e.g., Shannon, Faith’s PD) and beta diversity (e.g., Bray-Curtis, UniFrac) returning Series/DistanceMatrix.
  • Ordination & stats: PCoA and ecological hypothesis tests (PERMANOVA, ANOSIM, Mantel) operating on distance matrices.
  • I/O ecosystem: FASTA/FASTQ and Newick reading/writing; BIOM table support via Table.

Dependencies

  • scikit-bio>=0.6.0
  • numpy>=1.23
  • pandas>=1.5

Example Usage

python
# pip install scikit-bio numpy pandas

import numpy as np
import pandas as pd

import skbio
from skbio import DNA, TreeNode
from skbio.diversity import alpha_diversity, beta_diversity
from skbio.stats.ordination import pcoa
from skbio.stats.distance import permanova

# ----------------------------
# 1) Sequence manipulation
# ----------------------------
seq = DNA("ACGTACGTNN--ACGT", metadata={"id": "seq1"})
seq_clean = seq.degap()
rc = seq_clean.reverse_complement()
motif_hits = seq_clean.find_with_regex("ACG[TA]")

print("Original:", str(seq))
print("Degapped:", str(seq_clean))
print("Reverse complement:", str(rc))
print("Motif hits:", list(motif_hits))

# ----------------------------
# 2) Microbiome-style counts
# ----------------------------
# rows = samples, cols = features/OTUs/ASVs
counts = np.array([
    [10,  0,  3,  1],
    [ 0,  8,  2,  0],
    [ 5,  1,  0,  4],
], dtype=int)

sample_ids = ["S1", "S2", "S3"]
feature_ids = ["F1", "F2", "F3", "F4"]

# Alpha diversity (Shannon)
shannon = alpha_diversity("shannon", counts, ids=sample_ids)
print("\nAlpha diversity (Shannon):")
print(shannon)

# Beta diversity (Bray-Curtis) -> DistanceMatrix
dm = beta_diversity("braycurtis", counts, ids=sample_ids)
print("\nBeta diversity (Bray-Curtis) distance matrix:")
print(dm)

# ----------------------------
# 3) Ordination (PCoA)
# ----------------------------
ord_res = pcoa(dm)
print("\nPCoA sample coordinates (first 2 axes):")
print(ord_res.samples[["PC1", "PC2"]])

# ----------------------------
# 4) PERMANOVA on the distance matrix
# ----------------------------
grouping = pd.Series(["A", "A", "B"], index=sample_ids)
perma = permanova(dm, grouping=grouping, permutations=99)
print("\nPERMANOVA result:")
print(perma)

# ----------------------------
# 5) Tree I/O (Newick) + basic manipulation
# ----------------------------
newick = "((F1:0.1,F2:0.2):0.3,(F3:0.2,F4:0.4):0.1);"
tree = TreeNode.read([newick])
subtree = tree.shear(["F1", "F2", "F3"])
print("\nSheared tree (tips F1,F2,F3):")
print(subtree.ascii_art())

Implementation Details

  • Sequence model

    • Use DNA/RNA/Protein for alphabet-aware validation and biological operations (e.g., reverse_complement, transcribe, translate).
    • Use Sequence when you need a generic container without strict alphabet constraints.
    • FASTQ quality scores (when read via scikit-bio I/O) are stored as positional metadata.

  • Diversity computations

    • alpha_diversity(metric, counts, ids=...) returns a per-sample vector (typically a pandas Series).
    • beta_diversity(metric, counts, ids=...) returns a DistanceMatrix suitable for ordination and hypothesis tests.
    • Count inputs should be non-negative integers representing abundances (not relative frequencies). Phylogenetic metrics (e.g., Faith’s PD, UniFrac) additionally require a tree and feature/OTU IDs.

  • Distance matrices

    • DistanceMatrix enforces symmetry and a zero diagonal; IDs are used for consistent alignment with metadata and group labels.
    • Many downstream methods (PCoA, PERMANOVA, ANOSIM, Mantel) operate directly on DistanceMatrix.

  • Ordination

    • pcoa(dm) performs eigen-decomposition on a transformed distance matrix and returns OrdinationResults containing eigenvalues and sample coordinates.

  • Permutation-based statistics

    • permanova(dm, grouping, permutations=N) estimates significance by permuting group labels; increase permutations (e.g., 999+) for more stable p-values in real analyses.

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

Files

SKILL.md and 2 other files (references) in scientific-skills/Data Analysis/scikit-bio of aipoch/medical-research-skills.

  • SKILL.md
  • references/api_reference.md
  • scikit-bio_audit_result_v1.json

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Scikit Bio next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Scikit Bio compared with similar skills
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Scikit Bio this skillaipoch/medical-research-skills2k—~1.4kAutomated safety check: PassMIT
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PyDESeq2 Differential Expressiondavila7/claude-code-templates32k12 repos~4kAutomated safety check: PassMIT
Bio Population Genetics Scikit Allel AnalysisGPTomics/bioSkills1.2k1 repos~5kAutomated safety check: PassMIT
Tooluniverse Epigenomicswu-yc/LabClaw1.1k2 repos~14kAutomated safety check: PassNone
Bio Population Genetics Linkage DisequilibriumGPTomics/bioSkills1.2k1 repos~4.7kAutomated safety check: PassMIT

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Works with

Categories

Questions about Scikit Bio

What does Scikit Bio do?

A Python bioinformatics toolkit for sequence, phylogeny, and microbiome/community-ecology analysis; use it when you need to compute diversity/ordination/statistics from biological data and standard…. Scikit Bio is an agent skill from aipoch/medical-research-skills. A Python bioinformatics toolkit for sequence, phylogeny, and microbiome/community-ecology analysis; use it when you need to compute diversity/ordination/statistics from biological data and standard formats (FASTA/FASTQ/Newick/BIOM).

When should I use Scikit Bio?

Scikit Bio fits situations like: you need to compute diversity/ordination/statistics from biological data and standard formats (FASTA/FASTQ/Newick/BIOM); tasks that involve Bioinformatics; tasks that involve Statistics.

How do I install Scikit Bio in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill scikit-bio -a claude-code`. Or copy the skill folder (scientific-skills/Data Analysis/scikit-bio in aipoch/medical-research-skills) into .claude/skills/scikit-bio in your project. Claude Code loads it when a task matches its description.

How do I install Scikit Bio in Codex?

Run `npx skills add aipoch/medical-research-skills --skill scikit-bio -a codex`. Or copy the skill folder (scientific-skills/Data Analysis/scikit-bio in aipoch/medical-research-skills) into .agents/skills/scikit-bio in your project. Codex loads it when a task matches its description.

Can I use Scikit Bio 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 aipoch/medical-research-skills --skill scikit-bio -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scikit-bio, .gemini/skills/scikit-bio, .github/skills/scikit-bio and .opencode/skills/scikit-bio in your project.

What does Scikit Bio need to run?

SKILL.md names no scripts, command-line tools or credentials: Scikit Bio is instructions for the agent only. Our summary lists: Python 3.

Does Scikit Bio access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Scikit Bio 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 Scikit Bio use?

Scikit Bio is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Scikit Bio use?

About 1.4k tokens (SKILL.md is roughly 5.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.9k tokens, read only when the agent opens those files.

What are the alternatives to Scikit Bio?

Skills that share tags, products or a category with Scikit Bio: Bio Metagenomics Visualization (GPTomics/bioSkills, 1.2k stars), PyDESeq2 Differential Expression (davila7/claude-code-templates, 32k stars), Bio Population Genetics Scikit Allel Analysis (GPTomics/bioSkills, 1.2k stars) and Tooluniverse Epigenomics (wu-yc/LabClaw, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Scikit Bio?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,974 GitHub stars. The repository holds 567 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.