Content
78%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is highly actionable with executable examples and a well-structured progressive-disclosure layout pointing to real reference files. The main improvement area is conciseness, as some explanatory prose and workflow redundancy could be tightened.
Suggestions
Tighten the Overview and 'Common Workflows' sections; the four named workflows overlap significantly with the numbered core workflow and could be condensed or merged.
Remove or relocate definitions Claude already knows (e.g., clinical trial phase meanings, what association scores are) unless they carry skill-specific interpretation.
Add a short error-handling checkpoint (e.g., how to react when execute_query raises or returns empty results) to round out workflow clarity for API-failure cases.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient with code blocks and field lists, but contains explanatory prose (overview, clinical-phase definitions, best-practice narratives) and overlap between the core workflow and 'Common Workflows' that could be trimmed. | 3 / 5 |
Actionability | Provides copy-paste-ready Python code with real function signatures, realistic arguments, and example return structures covering the common cases across all entity types. | 5 / 5 |
Workflow Clarity | A clear numbered core workflow plus four detailed scenario workflows give strong sequencing; minor gap is the lack of explicit error-recovery checkpoints for API failures, though the skill is read-only so no destructive-validation cap applies. | 4 / 5 |
Progressive Disclosure | SKILL.md serves as a concise overview with well-signaled, one-level-deep references to real files (api_reference.md, evidence_types.md, target_annotations.md) and a bundled script, making navigation easy. | 5 / 5 |
Total | 17 / 20 Passed |