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.
72
66%
Does it follow best practices?
Impact
73%
2.21xAverage score across 6 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/scikit-bio/SKILL.mdQuality
Discovery
82%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 description excels at specificity and distinctiveness, listing numerous concrete bioinformatics capabilities with domain-specific terminology that clearly carves out a unique niche. Its main weakness is the absence of an explicit 'Use when...' clause, which means Claude must infer when to select this skill rather than having clear trigger guidance. The description is concise and well-structured but would benefit from adding explicit selection criteria.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about microbiome analysis, biological sequence processing, phylogenetic analysis, or diversity calculations.'
Consider adding common user-facing variations like '16S rRNA', 'metagenomics', 'OTU tables', or 'amplicon sequencing' to broaden trigger term coverage.
| 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 format I/O (FASTA/Newick). | 3 / 3 |
Completeness | Clearly answers 'what does this do' with specific capabilities, but lacks an explicit 'Use when...' clause or equivalent trigger guidance. The domain context ('microbiome analysis') is stated but when to select this skill is only implied, not explicitly stated. | 2 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'microbiome analysis', 'sequence analysis', 'phylogenetic trees', 'FASTA', 'Newick', 'UniFrac', 'PCoA', 'PERMANOVA', 'alpha/beta diversity'. These are terms bioinformaticians naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with domain-specific terminology like UniFrac, PCoA, PERMANOVA, FASTA/Newick, and microbiome analysis. Very unlikely to conflict with other skills given the specialized biological/bioinformatics niche. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
50%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill provides highly actionable, executable code examples across a comprehensive range of scikit-bio capabilities, which is its primary strength. However, it is far too verbose for a SKILL.md—it reads more like full library documentation than a concise skill file, with unnecessary explanatory text and sections that should be offloaded to reference files. The workflow descriptions lack validation checkpoints and error recovery steps that would be important for multi-step bioinformatics analyses.
Suggestions
Reduce the SKILL.md to a concise overview (~50-80 lines) with quick-start examples for the 2-3 most common workflows, and move the 10 detailed capability sections into separate reference files (e.g., references/sequences.md, references/diversity.md).
Remove the 'When to Use This Skill' section entirely—Claude can infer applicability from the content itself.
Add explicit validation/verification steps to the Common Workflows section (e.g., 'Verify distance matrix is valid before ordination', 'Check alignment quality scores before tree construction').
Trim 'Important notes' subsections to only include non-obvious gotchas that Claude wouldn't already know (e.g., remove 'use generators for large files', keep 'counts must be integers not relative frequencies').
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose at ~300+ lines. The 'When to Use This Skill' section is entirely unnecessary—Claude can infer applicability from the content. Sections like 'Overview' explain what scikit-bio is (Claude already knows). Many 'Important notes' repeat common knowledge (e.g., 'use generators for large files'). The 10 numbered capability sections each contain explanatory prose that could be cut significantly. | 1 / 3 |
Actionability | The code examples throughout are concrete, executable, and copy-paste ready. Each section provides real import paths, function calls with actual parameters, and realistic usage patterns. The examples cover both simple and complex use cases with proper Python syntax. | 3 / 3 |
Workflow Clarity | The 'Common Workflows' section at the end lists four multi-step workflows but only as brief arrow-separated summaries without validation checkpoints or error recovery steps. For operations like phylogenetic tree construction or diversity analysis where intermediate validation matters, there are no explicit verification steps or feedback loops. | 2 / 3 |
Progressive Disclosure | There is a reference to 'references/api_reference.md' at the end, which is good, but the main file itself is a monolithic wall of content with 10 major sections that could benefit from being split into separate reference files. The SKILL.md should be a concise overview pointing to detailed capability docs rather than containing all the detail inline. | 2 / 3 |
Total | 8 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
Total | 10 / 11 Passed | |
- Repository
- K-Dense-AI/claude-scientific-skills
- Commit
25e1c0f
- Reviewed
Table of Contents
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