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.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill scikit-bioOverall
score
80%
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
83%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a technically strong description with excellent specificity and domain-appropriate trigger terms that bioinformaticians would naturally use. The main weakness is the lack of an explicit 'Use when...' clause, which means Claude must infer when to select this skill rather than having clear trigger guidance. Adding explicit trigger conditions would elevate this from good to excellent.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when the user mentions microbiome data, 16S rRNA, metagenomics, OTU tables, or phylogenetic analysis.'
Consider adding common user phrasings like '16S analysis', 'OTU', 'ASV', 'metagenomics' to improve trigger term coverage for the broader microbiome community.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, and specific file formats (FASTA/Newick I/O). | 3 / 3 |
Completeness | Clearly answers 'what does this do' with comprehensive capabilities, but lacks an explicit 'Use when...' clause or equivalent trigger guidance. The 'when' is only implied through the domain mention 'for microbiome analysis'. | 2 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'microbiome analysis', 'sequence analysis', 'phylogenetic trees', 'FASTA', 'Newick', 'diversity metrics', 'UniFrac', 'PCoA', 'PERMANOVA' - these are terms bioinformaticians naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche with domain-specific terminology (UniFrac, PCoA, PERMANOVA, Newick) that would not conflict with general data analysis or other bioinformatics skills. Clear microbiome/phylogenetic focus. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
73%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured, highly actionable skill with excellent code examples covering scikit-bio's extensive capabilities. The main weaknesses are moderate verbosity in introductory sections and lack of explicit validation/error-handling steps in workflows. The progressive disclosure is handled well with clear references to external documentation.
Suggestions
Condense the 'When to Use This Skill' section into a brief bullet list or remove it entirely since the capability sections already make usage clear
Add validation checkpoints to the 'Common Workflows' section (e.g., 'Verify alignment quality before tree construction', 'Check p-value significance before interpreting PERMANOVA results')
Remove explanatory phrases like 'Reduce high-dimensional biological data to visualizable lower-dimensional spaces' that explain concepts Claude already understands
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is comprehensive but includes some unnecessary explanations Claude would know (e.g., 'Reduce high-dimensional biological data to visualizable lower-dimensional spaces'). The 'When to Use This Skill' section is verbose and could be condensed. However, code examples are appropriately lean. | 2 / 3 |
Actionability | Excellent executable code examples throughout all 10 capability sections. Code is copy-paste ready with proper imports, realistic variable names, and complete patterns. Each section provides concrete, working Python code rather than pseudocode. | 3 / 3 |
Workflow Clarity | The 'Common Workflows' section lists 4 workflows but only as high-level sequences without validation checkpoints or error handling. For operations like file I/O and statistical testing, explicit validation steps (e.g., checking p-values, verifying file integrity) are missing. | 2 / 3 |
Progressive Disclosure | Well-structured with clear sections, appropriate use of headers, and explicit references to external documentation ('references/api_reference.md'). The skill provides a comprehensive overview while pointing to detailed API reference for advanced usage. Navigation is straightforward. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
88%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 14 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
description_trigger_hint | Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...') | Warning |
metadata_version | 'metadata.version' is missing | Warning |
Total | 14 / 16 Passed | |
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.