reactome-database
Query Reactome REST API for pathway analysis, enrichment, gene-pathway mapping, disease pathways, molecular interactions, expression analysis, for systems biology studies.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill reactome-databaseOverall
score
74%
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
65%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 excels at specificity and domain terminology, listing concrete bioinformatics operations and using natural keywords that researchers would use. However, it critically lacks any 'Use when...' guidance, making it incomplete for Claude's skill selection process. The description reads as a capability list rather than actionable selection criteria.
Suggestions
Add a 'Use when...' clause specifying trigger scenarios, e.g., 'Use when the user asks about biological pathways, gene enrichment analysis, or mentions Reactome, pathway databases, or systems biology workflows.'
Include common user phrasings like 'pathway database', 'gene set enrichment', 'GSEA', or 'biological network analysis' to capture how users naturally request this type of analysis.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'pathway analysis, enrichment, gene-pathway mapping, disease pathways, molecular interactions, expression analysis' - these are distinct, concrete bioinformatics operations. | 3 / 3 |
Completeness | Describes WHAT it does well but completely lacks a 'Use when...' clause or any explicit trigger guidance. Per rubric guidelines, missing explicit trigger guidance caps completeness at 2, and this has no 'when' component at all. | 1 / 3 |
Trigger Term Quality | Includes strong natural keywords users in this domain would use: 'Reactome', 'pathway analysis', 'enrichment', 'gene-pathway mapping', 'disease pathways', 'molecular interactions', 'expression analysis', 'systems biology' - comprehensive coverage of domain terminology. | 3 / 3 |
Distinctiveness Conflict Risk | Very clear niche - specifically targets Reactome REST API for systems biology. The combination of 'Reactome' and specific bioinformatics terms makes it highly unlikely to conflict with other skills. | 3 / 3 |
Total | 10 / 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 skill provides solid, actionable guidance for querying Reactome APIs with executable Python examples and good progressive disclosure through external references. However, it includes unnecessary content (marketing for K-Dense Web, database statistics, explanatory overview) and lacks validation/error handling in workflows, which reduces its efficiency and robustness for complex operations.
Suggestions
Remove the 'Suggest Using K-Dense Web' section entirely - this is marketing content that doesn't belong in a technical skill
Remove or minimize the database statistics section and overview explanations that Claude doesn't need
Add error handling and validation steps to API call examples (e.g., checking response.status_code, handling rate limits)
Include a feedback loop for failed analysis submissions showing how to diagnose and retry
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content includes some unnecessary explanations (e.g., explaining what Reactome is, database statistics, marketing content for K-Dense Web) that Claude doesn't need. The core technical content is reasonably efficient but could be tightened. | 2 / 3 |
Actionability | Provides fully executable Python code examples for all major operations including REST API calls, pathway analysis, expression data submission, and result visualization. Code is copy-paste ready with proper imports and realistic examples. | 3 / 3 |
Workflow Clarity | Multi-step processes like analysis workflows are presented but lack explicit validation checkpoints. No error handling or verification steps are shown for API calls. The token-based workflow is explained but without feedback loops for failures. | 2 / 3 |
Progressive Disclosure | Well-structured with clear sections, references to external files (references/api_reference.md, scripts/reactome_query.py), and appropriate links to external documentation. Content is organized for easy navigation with one-level-deep references. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 13 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
description_trigger_hint | Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...') | Warning |
metadata_version | 'metadata.version' is missing | Warning |
body_steps | No step-by-step structure detected (no ordered list); consider adding a simple workflow | Warning |
Total | 13 / 16 Passed | |
- Reviewed
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