Content
50%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is well-structured and domain-rich with concrete query strings and report templates, but it teaches concepts Claude already knows, lacks runnable code, and is monolithic with no progressive disclosure or bundle references. It is competent but improvable across all dimensions.
Suggestions
Trim the conceptual exposition in Step 3 (DAG confounders/mediators/colliders, Mendelian randomization basics) to terse reminders, since Claude already knows these, and keep only the skill-specific application guidance.
Add at least one copy-paste runnable Python example showing the retrieve-then-analyze pattern (e.g., calling find_tools, loading results into pandas, and computing an annotation table) to satisfy the 'COMPUTE, DON'T DESCRIBE' directive.
Add explicit workflow checkpoints for the multi-database querying (e.g., 'if a source returns no hits, retry with broader terms before dropping it') and consider moving the evidence-grading and report-template tables into a single reference file to enable progressive disclosure.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient (mapping tables, concrete find_tools queries, report templates) but spends substantial prose explaining causal-inference concepts Claude already knows (DAG confounders/mediators/colliders, Mendelian randomization logic), so it is not fully lean; not a 1 because the tables and tool queries earn their place. | 2 / 3 |
Actionability | Provides concrete tool-discovery query strings (e.g. `find_tools("gene enrichment pathway analysis")`) and reusable table/report templates, but no copy-paste executable code despite the "COMPUTE, DON'T DESCRIBE" directive; not a 3 because there are no runnable examples, and not a 1 because guidance is specific rather than abstract. | 2 / 3 |
Workflow Clarity | Steps are clearly sequenced (Step 1 through Step 5) and Step 4 adds a falsification mindset, but there are no explicit workflow-execution validation checkpoints or error-recovery feedback loops for the batch multi-database querying; not a 1 because the sequence is explicit, but the missing checkpoints cap it at 2 per the batch-operations guideline. | 2 / 3 |
Progressive Disclosure | Content is well-sectioned into headers/tables, but it is a monolithic ~130-line inline body with no bundle files or one-level-deep references, and the evidence-grading/causal-reasoning material arguably could be split out; the under-50-line simple-skill exception does not apply, so it does not reach a 3. | 2 / 3 |
Total | 8 / 12 Passed |