go-kegg-enrichment
Performs GO (Gene Ontology) and KEGG pathway enrichment analysis on gene lists. Trigger when: - User provides a list of genes (symbols or IDs) and asks for enrichment analysis - User mentions "GO enrichment", "KEGG enrichment", "pathway analysis" - User wants to understand biological functions of gene sets - User provides differentially expressed genes (DEGs) and asks for interpretation - Input: gene list (file or inline), organism (human/mouse/rat), background gene set (optional) - Output: enriched terms, statistics, visualizations (barplot, dotplot, enrichment map)
82
Quality
82%
Does it follow best practices?
Impact
72%
1.00xAverage score across 3 eval scenarios
Passed
No known issues
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 clearly defines a specialized bioinformatics capability. It provides comprehensive trigger terms that match natural user language in this domain, explicitly states both what the skill does and when to use it, and occupies a distinct niche that won't conflict with other skills. The structured format with explicit input/output specifications further enhances clarity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'GO (Gene Ontology) and KEGG pathway enrichment analysis', specifies inputs (gene list, organism, background gene set) and outputs (enriched terms, statistics, visualizations including barplot, dotplot, enrichment map). | 3 / 3 |
Completeness | Clearly answers both what (performs GO and KEGG enrichment analysis with specific inputs/outputs) AND when (explicit 'Trigger when' section with multiple specific scenarios including user mentions of GO/KEGG enrichment, pathway analysis, and DEG interpretation). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'GO enrichment', 'KEGG enrichment', 'pathway analysis', 'gene list', 'differentially expressed genes', 'DEGs', 'biological functions', 'gene symbols', 'gene IDs'. These are exactly what bioinformatics users would naturally mention. | 3 / 3 |
Distinctiveness Conflict Risk | Highly specialized bioinformatics domain with distinct triggers like 'GO enrichment', 'KEGG', 'pathway analysis', 'DEGs'. Very unlikely to conflict with general-purpose skills; clear niche in gene/pathway analysis. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill provides strong actionable guidance with executable commands and clear parameter documentation, but suffers from significant bloat with boilerplate sections (security checklists, lifecycle status, evaluation criteria) that don't help Claude perform the task. The workflow lacks validation checkpoints for a complex multi-step bioinformatics pipeline where errors are common (ID mapping failures, API timeouts).
Suggestions
Remove boilerplate sections (Risk Assessment, Security Checklist, Evaluation Criteria, Lifecycle Status) that don't provide actionable guidance for performing enrichment analysis
Add explicit validation steps to the workflow: verify gene ID mapping success rate, check for KEGG API connectivity, validate output file generation before interpretation
Include error handling guidance: what to do when gene symbols don't map, how to handle KEGG rate limits, fallback options when specific databases are unavailable
Move the organism table and dependency installation to a separate SETUP.md file, keeping SKILL.md focused on the analysis workflow
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill contains significant padding including boilerplate sections (Risk Assessment, Security Checklist, Evaluation Criteria, Lifecycle Status) that add little value. The Features list and organism table are useful but could be more compact. Some sections like 'Technical Difficulty' explanation are unnecessary. | 2 / 3 |
Actionability | Provides fully executable commands with clear parameter tables, concrete input/output examples, and copy-paste ready code for both Python CLI usage and R package installation. The input format examples and output directory structure are specific and actionable. | 3 / 3 |
Workflow Clarity | The 'Example Workflow' section lists 4 steps but lacks validation checkpoints. No explicit verification steps between running analysis and interpreting results. Missing feedback loops for handling errors (e.g., what if gene IDs don't map, what if KEGG API fails). | 2 / 3 |
Progressive Disclosure | References to 'references/' directory for documentation is good, but the main file is bloated with sections that should be separate (Risk Assessment, Security Checklist, Evaluation Criteria). The core usage information is buried among boilerplate content. | 2 / 3 |
Total | 9 / 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 |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- aipoch/medical-research-skills
- Commit
ca9aaa4
- Reviewed
Table of Contents
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