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bioservices

Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.

62

Quality

74%

Does it follow best practices?

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tessl review fix ./skills/bioservices/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

61%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is well-organized and actionable with strong, executable examples and excellent version-specific caveats, but it is padded with general-knowledge sections and lacks validation checkpoints for batch operations.

Suggestions

Trim or move the Output Format Handling, generic Error Handling, and Integration with Other Tools sections (or into a reference file) since they restate knowledge Claude already has.

Add explicit validation/verification steps to the batch and export workflows (e.g. confirm converted IDs exist in the target database before reporting success) to lift workflow clarity above the batch-operation cap of 3.

Ensure inline code examples define all referenced variables (e.g. protein_sequence in the BLAST example) so snippets are copy-paste runnable.

DimensionReasoningScore

Conciseness

Mostly efficient with genuinely valuable non-obvious version caveats, but sections like Output Format Handling, generic error handling, and Integration with Other Tools explain concepts Claude already knows and could be trimmed.

3 / 5

Actionability

Provides many concrete, executable code blocks with real method calls and runnable scripts, with only minor gaps such as inline examples referencing variables like protein_sequence that are not defined in the snippet.

4 / 5

Workflow Clarity

Multi-step workflows are sequenced and scripts are invokable, but batch operations (e.g. batch_id_converter.py) and pathway/export workflows lack explicit validation checkpoints or feedback loops, capping this dimension at 3 per the rubric.

3 / 5

Progressive Disclosure

Good structure with clearly signaled, one-level-deep references to real files (references/services_reference.md, workflow_patterns.md, identifier_mapping.md) and four real scripts, though the body still inlines a fair amount of API detail that could live in references.

4 / 5

Total

14

/

20

Passed

Description

87%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is well-constructed, clearly stating capability, explicit trigger conditions, and sharp disambiguation from adjacent skills. Minor room for improvement only in trigger-term breadth and action-verb variety.

DimensionReasoningScore

Specificity

Names the domain ('40+ bioinformatics services') and several concrete actions ('querying multiple databases', 'cross-database analysis', 'ID mapping'), but the set of distinct action verbs is slightly fewer than the comprehensive 5-anchor example.

4 / 5

Completeness

Explicitly answers both what ('Unified Python interface to 40+ bioinformatics services') and when ('Use when querying multiple databases...'), with concrete trigger phrases plus negative guidance to gget and biopython.

5 / 5

Trigger Term Quality

Includes natural trigger phrases ('querying multiple databases', 'cross-database analysis', 'ID mapping') plus concrete database names (UniProt, KEGG, ChEMBL, Reactome), but omits common synonyms a user might say such as 'bioinformatics'.

4 / 5

Distinctiveness Conflict Risk

Carves a clear niche (cross-database workflows) and explicitly disambiguates from gget and biopython, minimizing the chance of triggering for the wrong skill.

5 / 5

Total

18

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_field

'metadata' should map string keys to string values

Warning

Total

15

/

16

Passed

Repository
K-Dense-AI/scientific-agent-skills
Reviewed

Table of Contents

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