Parse, transform, and analyze phylogenetic trees with ETE Toolkit.
npx skills add https://github.com/shushuzn/Rairos --skill etetoolkit-shushuzn
Please help me install this Agent Skill. Skill: etetoolkit Source: https://github.com/shushuzn/Rairos/tree/main/skills/etetoolkit Command: npx skills add https://github.com/shushuzn/Rairos --skill etetoolkit-shushuzn
ETE Toolkit helps you manipulate phylogenetic trees, detect evolutionary events, compare topologies, integrate NCBI taxonomy, and generate publication-ready visualizations without manually stitching together multiple tools.
Use the etetoolkit skill to render a publication-quality visualization of a Newick tree by running: visualize the tree file "tree.nw" into "tree.pdf" with leaf names shown and branch support displayed.
High-intent search queries and answers about installing and using this skill.
To visualize a phylogenetic tree for publication, load your Newick file and render it to PNG, PDF, or SVG using TreeStyle and NodeStyle. This allows you to style nodes, display branch support, and add graphical faces like labels or charts for clear figure outputs.
To detect gene duplication and speciation events in a gene tree, you can link your tree to alignments and use phylogenetic event analysis. This process identifies evolutionary events, derives ortholog and paralog groups, and allows you to split or collapse expansions.
Yes, you can integrate NCBI taxonomy data to annotate a phylogenetic tree. The system fetches and caches taxonomy locally, translates names or taxids, retrieves lineages, and builds minimal topology trees connecting your taxa to annotate tree nodes accurately.
The best way to compare phylogenetic tree topologies is by computing Robinson–Foulds distances. This method directly measures topological differences between trees, allowing you to accurately evaluate structural discrepancies and variations in branching patterns.
To evaluate clustering dendrogram quality using silhouette scores, use ClusterTree with your matrix linkage data. This computes cluster quality metrics like the silhouette and Dunn index, directly quantifying the validity and separation of your hierarchical clustering results.
You do not need a pre-existing local database to fetch NCBI lineages, but optional local taxonomy caching is supported. The system fetches and caches taxonomy data locally during operations, enabling faster subsequent translations of names and taxids.
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