Content
90%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.
An efficient, actionable skill body with concrete tooling and a clear phased workflow. Main gap is the absence of explicit validation checkpoints in the main pipeline and no in-bundle progressive file structure.
Suggestions
Add an explicit validation checkpoint between phases (e.g., 'After Phase 1, confirm each hit has ≥1 orthogonal data source before scoring') to strengthen the workflow's feedback loop.
Insert a brief 'verify output' step in Phase 6 (e.g., sanity-check tier distribution, flag any T1 hit lacking clinical evidence) to close the batch-processing loop.
If the cross-skill depmap script is load-bearing, consider noting availability/error handling inline so failure does not stall the workflow.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean and token-efficient: compact tool signatures, a tight scoring table, and domain-specific guiding principles rather than explanations of concepts Claude already knows; every section earns its place. | 5 / 5 |
Actionability | Provides copy-paste-ready bash commands (e.g. 'python depmap_gene_dependency.py gene KRAS --lineage Lung --top 20') and exact tool-call syntax with parameter names and delimiters, covering the common cases. | 5 / 5 |
Workflow Clarity | A clear six-phase sequence with per-phase fallbacks (gnomAD overloaded retry, gene-not-in-DepMap fallback) and an explicit validate-through-orthogonal-evidence philosophy, but lacks explicit step-level validation checkpoints in the main flow. | 4 / 5 |
Progressive Disclosure | Well-organized into cohesive sections with no content that clearly belongs in a separate bundle file; the single cross-skill script reference is clearly signaled by full path, though no own-bundle reference files exist to demonstrate deeper structuring. | 4 / 5 |
Total | 18 / 20 Passed |