Agent skill · AI & Agents

protein-phylogeny

Protein (gene) phylogeny inference pipeline: generates a .qmd analysis script that performs alignment, optional trimming, and tree building. Use when building phylogenetic trees from protein sequences, aligning protein families, running IQ-TREE or MAFFT for phylogenetics, or when the user says "gene tree" or "protein tree." Covers single domains, whole proteins, and multi-domain proteins across deep evolutionary distances (sponges, animals, eukaryotes). Do NOT load for nucleotide-only phylogenies, species trees from concatenated matrices, or tree visualization (use tree-formatting skill for th

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill protein-phylogeny --agent claude-code

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

Facts
Files in the skill folder: 2
SKILL.md size: 13 KB
Bundled scripts: none
Path: skills/ai-ml/protein-phylogeny/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

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

From the SKILL.md

# Protein Phylogeny Inference Pipeline for building protein phylogenies across deep evolutionary distances. Designed for single domains, whole single-domain proteins, and multi-domain proteins, from ~10 to several thousand sequences, spanning sponges to all eukaryotes. **If the user says "gene tree" they may mean protein sequences.** Confirm if ambiguous, or inspect the input sequences (amino acid alphabet vs nucleotide). --- ## Overview: Two-Phase Workflow This skill generates a **reproducible .qmd analysis script** (Python) rather than running commands directly. The workflow is: 1. **Discuss** — Resolve all pipeline decisions with the user (input sequences, algorithm, model tier, trimming, etc.) 2. **Generate** — Write a complete `.qmd` script encoding all decisions as configuration variables, following quarto-docs and script-organization skill conventions 3. **Render** — User renders with `quarto render` (or Claude renders it), producing all outputs in `outs/<subdirectory>/XX_script_name/` One script per major deliverable (one protein family or domain tree). All pipeline steps go in one `.qmd` unless there is a strong reason to split. --- ## Phase 1: Discussion Resolve these que

What's inside
Steps it walks through
  1. Overview: Two-Phase Workflow
  2. Phase 1: Discussion
  3. 1. Input validation
  4. 2. Minimum length cutoff
  5. 3. CD-HIT redundancy reduction (optional, off by default)
  6. 4. MAFFT algorithm selection
  7. 5. Trimming (optional, off by default)
  8. 6. IQ-TREE tier selection
  9. 7. Branch support
  10. 8. No automated outlier removal
  11. Phase 2: Script Generation
  12. Naming and placement
  13. .qmd structure
  14. Key patterns
Ships with 1 file
  • metadata.json
Commands it runs
source ~/miniconda3/etc/profile.d/conda.sh && conda activate <env>
quarto render scripts/<subdirectory>/XX_name.qmd --output-dir outs/<subdirectory>/XX_name/
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About this skill
What does the protein-phylogeny skill do?

Protein (gene) phylogeny inference pipeline: generates a .qmd analysis script that performs alignment, optional trimming, and tree building. Use when building phylogenetic trees from protein sequences, aligning protein families, running IQ-TREE or MAFFT for phylogenetics, or when the user says "gene tree" or "protein tree." Covers single domains, whole proteins, and multi-domain proteins across deep evolutionary distances (sponges, animals, eukaryotes). Do NOT load for nucleotide-only phylogenies, species trees from concatenated matrices, or tree visualization (use tree-formatting skill for th

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill protein-phylogeny --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 majiayu000/claude-skill-registry, a repository with 534 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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