scikit-bio
Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.
npx skills add K-Dense-AI/scientific-agent-skills --skill scikit-bio --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.
What it does
A skill for biological data analysis in Python, covering sequence handling, alignment, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA, CA, CCA, RDA), PERMANOVA, ANOSIM, Mantel tests, and file I/O across 19+ formats. It also supports BIOM tables and table interoperability with optional plotting libraries.
How it works
The skill exposes operations across multiple domains:
- Sequence Manipulation: read/write FASTA/FASTQ/GenBank/EMBL, slicing, reverse complement, transcription, translation, motif search, and quality-aware handling from FASTQ.
- Sequence Alignment: uses pair_align_nucl and pair_align_prot with configurable scoring, gap penalties, and path-based results; supports TabularMSA construction and alignment reading.
- Phylogenetic Trees: build/read trees from Newick, construct from distance matrices with nj/upgma/gme/bme, prune/ reroot/ lca, compute cophenet, compare_rfd.
- Diversity Analysis: alpha and beta diversity computations, including phylogenetic metrics with tree and taxa mapping; supports rarefaction and various input table types.
- Ordination Methods: pcoa and related ordination analyses with plotting, handling of OrdinationResults and associated metadata.
- Statistical Testing: PERMANOVA, ANOSIM, PERMDISP, Mantel, Bioenv, and differential abundance tests in skbio.stats.composition; requires raw counts for some tests.
- File I/O and Formats: read/write 19+ formats with automatic detection; generators for large files; format conversion helpers via skbio.io.
- Distance Matrices: DistanceMatrix and PairwiseMatrix construction, indexing, and use in downstream analyses.
- Biological Tables: BIOM I/O, dispatch system allowing table-like inputs (BIOM Table, pandas/polars DataFrame, NumPy array, AnnData) and data augmentation techniques.
When to use it
Use when working with biological sequences, alignments, phylogenetic trees, microbiome diversity analyses, ordination, and ecological statistics, especially with microbiome-derived data and protein embeddings.
What it can touch
- Reads and writes via Read/Write operations
- File system interactions for I/O and formats via Bash-capable workflows
- Data structures: DNA/RNA/Protein, TabularMSA, DistanceMatrix, Table, OrdinationResults, and various skbio.* modules
Caveats
- Requires Python 3.10+ and scikit-bio 0.7+; NumPy 2.0+; optional plotting and BIOM-related formats may need extra packages
- Some tests expect raw counts for differential abundance analyses; results depend on input format compatibility and tree availability
# 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 comp
- Overview
- When to Use This Skill
- Core Capabilities
- 1. Sequence Manipulation
- 2. Sequence Alignment
- 3. Phylogenetic Trees
- 4. Diversity Analysis
- 5. Ordination Methods
- 6. Statistical Testing
- 7. File I/O and Format Conversion
- 8. Distance Matrices
- 9. Biological Tables
- 10. Protein Embeddings
- Best Practices
uv pip install scikit-bio
What does the scikit-bio skill do?
Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill scikit-bio --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 K-Dense-AI/scientific-agent-skills, a repository with 32,619 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.