scikit-bio

scikit-bio is a skill for Claude Code, Codex from AndyZhuang/Opentest. It costs 54 tokens per session (3,589 once invoked), scanned A, a copy of scikit-bio, MIT.

A Python toolkit for biological data, including DNA, RNA, and protein sequences, evolutionary trees, microbial communities, and statistical analysis. It also reads and writes common biology file formats such as FASTA, FASTQ, GenBank, Newick, and BIOM.

In plain words
What is it for?
Use it to manipulate or align sequences, search for motifs, build or study phylogenetic trees, analyze microbiome diversity, perform ordination and community statistics, and work with biological data tables.
Why use it?
It provides one set of tools for handling biological data types and analyses that would otherwise require separate libraries and custom file-processing code.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to manipulate or align sequences, search for motifs, build or study phylogenetic trees, analyze microbiome diversity, perform ordination and community statistics, and work with biological data tables.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/andyzhuang/opentest/scikit-bio
Install

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.

Any agent
npx skills add AndyZhuang/Opentest --skill scikit-bio
Clone the repo
git clone --depth 1 https://github.com/AndyZhuang/Opentest

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for scikit-bio

README.md
[![agentmods](https://agentmods.dev/badge/skills/andyzhuang/opentest/scikit-bio/github.svg)](https://agentmods.dev/skills/andyzhuang/opentest/scikit-bio)
Your own site
<a href="https://agentmods.dev/skills/andyzhuang/opentest/scikit-bio"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/scikit-bio/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.

agentmods 80×15 button for scikit-bio

Your own site · 80×15
<a href="https://agentmods.dev/skills/andyzhuang/opentest/scikit-bio"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/scikit-bio.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,589 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 88% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00054 $0.03589
Opus 5 $0.00027 $0.01795
Sonnet 5 $0.00011 $0.00718
Haiku 4.5 $0.00005 $0.00359

Measured 8d ago against content hash 8a1dbf1fdd0b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

scikit-bio scanned grade A with 0 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 8d 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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

88% identical to scikit-bio — 154 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.

skills/labclaw/bio/scikit-bio/SKILL.md · 437 lines

How it starts

The opening of the file, as written. The whole thing — 437 lines — stays where its author put it; the contents beside it link to each section on GitHub.

scikit-bio

Overview

scikit-bio is a comprehensive Python library for working with biological data. Apply this skill for bioinformatics analyses spanning sequence manipulation, alignment, phylogenetics, microbial ecology, and multivariate statistics.

When to Use This Skill

This skill should be used when the user:

  • Works with biological sequences (DNA, RNA, protein)
  • Needs to read/write biological file formats (FASTA, FASTQ, GenBank, Newick, BIOM, etc.)
  • Performs sequence alignments or searches for motifs
  • Constructs or analyzes phylogenetic trees
  • Calculates diversity metrics (alpha/beta diversity, UniFrac distances)
  • Performs ordination analysis (PCoA, CCA, RDA)
  • Runs statistical tests on biological/ecological data (PERMANOVA, ANOSIM, Mantel)
  • Analyzes microbiome or community ecology data
  • Works with protein embeddings from language models
  • Needs to manipulate biological data tables

Core Capabilities

1. Sequence Manipulation

Work with biological sequences using specialized classes for DNA, RNA, and protein data.

Key operations:

  • Read/write sequences from FASTA, FASTQ, GenBank, EMBL formats
  • Sequence slicing, concatenation, and searching
  • Reverse complement, transcription (DNA→RNA), and translation (RNA→protein)
  • Find motifs and patterns using regex
  • Calculate distances (Hamming, k-mer based)
  • Handle sequence quality scores and metadata

Common patterns:

import skbio

# Read sequences from file
seq = skbio.DNA.read('input.fasta')

# Sequence operations
rc = seq.reverse_complement()
rna = seq.transcribe()
protein = rna.translate()

# Find motifs
motif_positions = seq.find_with_regex('ATG[ACGT]{3}')

# Check for properties
has_degens = seq.has_degenerates()
seq_no_gaps = seq.degap()

Important notes:

  • Use DNA, RNA, Protein classes for grammared sequences with validation
  • Use Sequence class for generic sequences without alphabet restrictions
  • Quality scores automatically loaded from FASTQ files into positional metadata
  • Metadata types: sequence-level (ID, description), positional (per-base), interval (regions/features)

Read the full file on GitHub · 437 lines

Changes

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.

  1. 8d ago First seen · 437 lines · 54 tokens per session scan A 8a1dbf1fdd0b

Subscribe to this mod's changes

scikit-bio is a skill published in the GitHub repository AndyZhuang/Opentest (22 stars, last pushed 6mo ago), licensed MIT. It adds 54 tokens to every session and 3,589 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to scikit-bio, differing in 154 lines, and is treated as a copy.

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