Content
67%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 well-organized, actionable for a database skill with no public API, and uses a clean one-level reference structure. It would benefit from trimming generic explanatory prose and adding explicit verification steps to the workflows.
Suggestions
Remove or condense generic explanatory prose (e.g. the HMDB overview paragraph and Best Practices repetition) that Claude already knows, to improve token efficiency.
Add explicit verification/validation checkpoints to the research workflows (e.g. 'Verify candidate by cross-checking molecular weight and MS-MS fragmentation before confirming identification').
Tighten the Reference Documentation section by linking the data-fields reference inline where the 130+ data fields are first mentioned.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient and well-structured, but includes unnecessary explanation Claude likely already knows (e.g. the generic HMDB overview) and repetitive Best Practices sections that could be tightened. | 3 / 5 |
Actionability | Provides concrete guidance — specific URLs, example HMDB IDs, an install command for hmdbQuery, contact emails, and download formats — with only minor gaps since no public API exists to demonstrate. | 4 / 5 |
Workflow Clarity | Numbered multi-step workflows for metabolite ID, biomarker discovery, pathway analysis, and DB integration are clearly sequenced, though validation checkpoints are mostly implicit rather than explicit. | 4 / 5 |
Progressive Disclosure | Good structure with a single clearly-signaled one-level-deep reference (references/hmdb_data_fields.md) and well-organized sections; minor organization gaps keep it just below a 5. | 4 / 5 |
Total | 15 / 20 Passed |