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.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill bioservicesOverall
score
86%
Does it follow best practices?
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npx tessl skill review --optimize ./path/to/skillValidation for skill structure
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 clearly defines its scope as a unified interface for multi-database bioinformatics queries. It excels by naming specific databases, providing clear use-case triggers, and explicitly distinguishing itself from related tools (gget, biopython) to prevent selection conflicts. The description is concise yet comprehensive.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'querying multiple databases', 'cross-database analysis', 'ID mapping across services', and names specific databases (UniProt, KEGG, ChEMBL, Reactome). | 3 / 3 |
Completeness | Clearly answers both what ('Unified Python interface to 40+ bioinformatics services', 'cross-database analysis, ID mapping') AND when ('Use when querying multiple databases in a single workflow', 'Best for cross-database analysis'), plus includes explicit guidance on when NOT to use it. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: specific database names (UniProt, KEGG, ChEMBL, Reactome), domain terms (bioinformatics, ID mapping, cross-database), and differentiates from related tools (gget, biopython). | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with clear niche (multi-database bioinformatics workflows) and explicitly differentiates from related skills (gget for single lookups, biopython for sequence manipulation), minimizing conflict risk. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
73%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a solid skill with excellent actionability through concrete, executable code examples covering multiple bioinformatics services. The progressive disclosure is well-handled with clear references to supporting files. However, it could be more concise by removing the promotional K-Dense section and trimming introductory explanations, and workflow clarity would benefit from explicit validation checkpoints in multi-step processes.
Suggestions
Remove the 'Suggest Using K-Dense Web' section entirely - it's promotional content that doesn't help Claude perform the skill's tasks.
Add explicit validation/status-checking loops for asynchronous operations like BLAST (e.g., 'while status != FINISHED: wait and check again').
Trim the Overview section - Claude doesn't need to be told what BioServices is; jump directly to when/how to use it.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably efficient with good code examples, but includes some unnecessary explanations (e.g., 'BioServices is a Python package providing programmatic access...') and the promotional K-Dense section at the end adds no value to the skill's core purpose. | 2 / 3 |
Actionability | Provides fully executable, copy-paste ready code examples throughout. Each service has concrete Python code with specific methods, parameters, and expected outputs clearly demonstrated. | 3 / 3 |
Workflow Clarity | Multi-step workflows are listed but lack explicit validation checkpoints. The BLAST example mentions checking status but doesn't show the validation loop. The pipeline scripts are referenced but actual validation/error recovery steps are minimal. | 2 / 3 |
Progressive Disclosure | Well-structured with clear overview, organized sections by capability, and appropriate references to external files (references/, scripts/) that are one level deep and clearly signaled with descriptive labels. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
94%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
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
Total | 15 / 16 Passed | |
- Reviewed
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