biopython
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
82
75%
Does it follow best practices?
Impact
96%
1.17xAverage score across 3 eval scenarios
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/biopython/SKILL.mdQuality
Discovery
100%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 an excellent skill description that covers specific capabilities, includes abundant natural trigger terms from the bioinformatics domain, and explicitly addresses both what the skill does and when to use it. The inclusion of differentiation guidance against related skills (gget, bioservices) is a notable strength that reduces conflict risk. The description is concise yet comprehensive.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, programmatic NCBI/PubMed access, batch processing, custom bioinformatics pipelines, BLAST automation. | 3 / 3 |
Completeness | Clearly answers both 'what' (sequence manipulation, file parsing, phylogenetics, NCBI/PubMed access) and 'when' ('Use for...', 'Best for batch processing, custom bioinformatics pipelines, BLAST automation'). Also provides differentiation guidance ('For quick lookups use gget; for multi-service integration use bioservices'). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: FASTA, GenBank, PDB, NCBI, PubMed, Bio.Entrez, BLAST, bioinformatics, molecular biology, sequence manipulation, phylogenetics. These are all terms a bioinformatics user would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with clear niche in molecular biology/Biopython. Explicitly differentiates itself from related skills (gget for quick lookups, bioservices for multi-service integration), which directly reduces conflict risk. | 3 / 3 |
Total | 12 / 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 is highly actionable with excellent executable code examples covering all major Biopython modules, and it uses a good reference file structure. However, it is extremely verbose—easily 2-3x longer than necessary—with redundant sections (When to Use, Core Capabilities, Summary all overlap), generic advice Claude already knows, and inline content that should live in the reference files. Trimming the overview to quick examples + reference pointers would dramatically improve token efficiency.
Suggestions
Remove the 'When to Use This Skill', 'Core Capabilities', and 'Summary' sections entirely—they repeat information already conveyed by the section headers and reference pointers.
Move 'Common Patterns', 'Troubleshooting', and 'Best Practices' into a reference file (e.g., references/patterns.md) and keep only a one-line pointer in the main skill.
Cut generic best practices Claude already knows (e.g., 'test with small datasets', 'handle errors gracefully', 'document analysis parameters') to reduce noise.
Add explicit validation steps to workflows involving NCBI access (e.g., verify response status, check record count before processing) to improve workflow clarity.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose. The 'When to Use This Skill' section is a 12-item bullet list that Claude doesn't need. 'Core Capabilities' repeats what the later sections cover. The 'Overview' explains what Biopython is (Claude knows this). The 'Best Practices' section contains generic advice like 'test with small datasets' and 'keep Biopython updated.' The 'Summary' section restates the workflow guidelines. Massive redundancy throughout. | 1 / 3 |
Actionability | The skill provides numerous executable, copy-paste-ready code examples across all major modules. Each section includes concrete Python code with proper imports, and the common patterns section shows realistic multi-step pipelines. Code is complete and runnable. | 3 / 3 |
Workflow Clarity | The 'General Workflow Guidelines' section provides a reasonable sequence (identify module → read reference → extract patterns → combine), but there are no validation checkpoints. For operations involving NCBI network access and file parsing (which can fail), there's no explicit validate-then-proceed loop. The error handling section is a flat troubleshooting list rather than integrated into workflows. | 2 / 3 |
Progressive Disclosure | Good use of references/ directory with clear pointers to 7 reference files. However, the main SKILL.md is far too long with extensive inline content that duplicates what the reference files presumably contain. The quick examples for each section are useful, but the Common Patterns, Best Practices, Troubleshooting, and Quick Reference sections bloat the overview significantly. | 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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