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.

2.86x

Quality

70%

Does it follow best practices?

Impact

63%

2.86x

Average score across 3 eval scenarios

Securityby

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The risk profile of this skill

Fix and improve this skill with Tessl

tessl review fix ./skills/bio/scikit-bio/SKILL.md

Quality

Content

72%Scale 1-3

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 comprehensive capabilities. The main weaknesses are moderate verbosity in introductory sections and missing validation checkpoints in multi-step workflows. The progressive disclosure is handled well with clear references to external documentation.

Suggestions

Condense the 'When to Use This Skill' section - Claude can infer appropriate use cases from the capability descriptions

Add explicit validation/verification steps to the 'Common Workflows' section (e.g., 'Verify alignment quality before building tree', 'Check distance matrix for NaN values')

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 multi-step workflows but lacks explicit validation checkpoints or error recovery steps. For operations like phylogenetic tree construction or diversity analysis, there are no verification steps to catch errors before proceeding.	2 / 3
Progressive Disclosure	Well-structured with clear sections, appropriate depth in the main file, and explicit references to 'references/api_reference.md' for detailed API documentation. Navigation is clear with numbered capability sections and a logical flow from overview to specific capabilities.	3 / 3
	Total	10 / 12 Passed

Description

67%Scale 1-3

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. However, it lacks explicit trigger guidance ('Use when...') and could benefit from including more natural language terms that non-expert users might employ when seeking microbiome analysis help.

Suggestions

Add a 'Use when...' clause with trigger scenarios like 'Use when analyzing microbiome data, working with DNA/RNA sequences, or performing ecological diversity analysis'

Include additional natural language terms users might say: 'DNA sequences', 'metagenomics', '16S rRNA analysis', 'bioinformatics', 'microbial communities'

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 capability listing, but lacks an explicit 'Use when...' clause or equivalent trigger guidance to indicate when Claude should select this skill.	2 / 3
Trigger Term Quality	Includes domain-specific terms like 'microbiome analysis', 'FASTA', 'phylogenetic trees', 'UniFrac' that experts would use, but missing common variations users might say like 'DNA sequences', 'metagenomics', '16S rRNA', or 'bioinformatics'.	2 / 3
Distinctiveness Conflict Risk	Highly specialized domain (microbiome/biological data analysis) with distinct technical terminology like UniFrac, PCoA, PERMANOVA, and FASTA/Newick that would not overlap with general data analysis or other skills.	3 / 3
	Total	10 / 12 Passed

Validation

90%

Warnings & errors only

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: wu-yc/LabClaw
Path: skills/bio/scikit-bio/SKILL.md
Commit: df37802

Reviewed: 5 days ago

Table of Contents

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