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.

1.17x

Quality

75%

Does it follow best practices?

Impact

96%

1.17x

Average score across 3 eval scenarios

Securityby

Advisory

Suggest reviewing before use

Optimize this skill with Tessl

npx tessl skill review --optimize ./scientific-skills/biopython/SKILL.md

Quality

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, clearly states both what the skill does and when to use it, and even proactively differentiates itself from related skills. The explicit cross-referencing to gget and bioservices is a particularly strong feature that minimizes conflict risk.

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 explicit 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, phylogenetics, sequence manipulation. These are all terms a bioinformatics user would naturally use.	3 / 3
Distinctiveness Conflict Risk	Highly distinctive with clear niche in molecular biology/Biopython. The description explicitly differentiates 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, but it is severely bloated with unnecessary explanations, redundant sections, and content Claude already knows. The progressive disclosure structure is well-conceived (referencing 7 topic-specific files) but undermined by the SKILL.md itself containing too much inline content and the referenced files not existing in the bundle. Workflow clarity is adequate but lacks validation checkpoints for network-dependent operations.

Suggestions

Cut the 'When to Use This Skill' section entirely and trim 'Core Capabilities' to just the numbered list without descriptions — Claude knows what sequence handling and alignment analysis are.

Move 'Common Patterns', 'Troubleshooting', and 'Best Practices' into reference files to reduce the main SKILL.md to a lean overview with quick examples and navigation links.

Remove the 'Summary' section which repeats the workflow guidelines, and remove generic best practices like 'Keep Biopython updated' and 'Test with small datasets.'

Add explicit validation/retry steps to the NCBI Entrez and BLAST workflow patterns (e.g., check HTTP status, implement retry with backoff, verify downloaded record is non-empty).

Dimension	Reasoning	Score
Conciseness	Extremely verbose. The 'When to Use This Skill' section is a 12-item bullet list of things Claude already knows. The 'Core Capabilities' section restates what the reference sections already cover. 'Best Practices' lists 10 generic items like 'Keep Biopython updated' and 'Test with small datasets.' The 'Overview' explains what Biopython is, which Claude already knows. The 'Summary' repeats the workflow guidelines. Massive token waste throughout.	1 / 3
Actionability	The skill provides numerous fully executable, copy-paste-ready code examples across all major modules (SeqIO, Entrez, BLAST, PDB, Phylo, alignments). Common patterns section gives complete working pipelines. Troubleshooting section provides specific error messages with concrete solutions.	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 potentially destructive or error-prone operations like batch NCBI downloads. The BLAST+fetch pattern lacks error handling or rate-limiting verification steps. Network operations lack retry/validation feedback loops.	2 / 3
Progressive Disclosure	The skill references seven separate reference files in a references/ directory, which is good structure. However, no bundle files are provided, so these references don't actually exist. Additionally, the SKILL.md itself is monolithic — it includes extensive code examples, common patterns, best practices, troubleshooting, and a quick reference section that could all be in separate files. The main file tries to be both an overview and a comprehensive guide.	2 / 3
	Total	8 / 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: K-Dense-AI/claude-scientific-skills
Commit: cbcae7b

Reviewed: 1 day ago

Table of Contents

Discovery Implementation Validation

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