Content
75%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.
A strong, well-structured instruction skill: the three-phase workflow is clearly sequenced, the tool calls include exact parameter names and a dedicated gotcha list covering the exact failure modes (wrong param names, string-vs-array formats, broken tools) that Claude could not know. The main gaps are the absence of a worked end-to-end example and the absence of any validation step after retrieval phases.
Suggestions
Add one worked end-to-end mini-example (real disease, real gene) with concrete tool invocations and a small Python analysis snippet, so the 'Compute, don't describe' mandate has an executable template rather than placeholder calls like <disease>.
Add an explicit validation checkpoint between phases — e.g., after Step 1, verify the prioritized gene list is non-empty and each gene has an EFO/Ensembl ID before running enrichment; after Step 3, verify drug-gene interactions returned results.
Define the ranking procedure concretely: 'Rank pathways by enrichment FDR x number of GWAS genes x biological plausibility' and 'Genetic Evidence x Druggability x Pathway Centrality' currently have no scoring scale or tie-breaking rule, so convert them to an explicit numeric scoring formula.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense with non-obvious operational knowledge (parameter gotchas, broken-tool warnings, response-format quirks) and doesn't re-explain generic concepts. However, sections like the TCF7L2/Wnt worked example and the "Undruggable... does not mean permanently undruggable" paragraph spend tokens on biology exposition Claude could largely supply itself. This matches 'efficient; minor instances of over-explanation that could be trimmed' — not a 5, but well above the 'mostly efficient with some unnecessary explanation' midpoint. | 4 / 5 |
Actionability | Guidance is highly concrete: exact tool names with parameter signatures ("ReactomeAnalysis_pathway_enrichment(identifiers=\"P04637 P38398 ...\")\" — space-separated UniProt STRING, not an array"), exact param gotchas, and some real values (ENSG00000148737.11, species=9606, gtex_v8). What's missing for a 5 is a copy-paste-ready worked example — the calls use placeholders like <disease> and <gene>, and there is no complete end-to-end invocation or Python snippet showing the 'compute, don't describe' mandate in practice. This fits 'mostly executable guidance; concrete code or commands with minor gaps'. | 4 / 5 |
Workflow Clarity | The Three-Step Workflow (GWAS to causal gene → enrichment → drug target) is clearly sequenced with numbered steps, evidence tiers (High/Medium/Low), and cross-validation checkpoints (cross-check with OpenTargets, converge across Reactome/KEGG/STRING). This matches 'clear sequence with most checkpoints present; minor validation gaps' — the rankings are conceptual formulas ("enrichment FDR x number of GWAS genes x biological plausibility") rather than defined procedures, and there is no explicit verify-output step. Not a 3 because decision checkpoints (evidence tiers, convergence criteria, classification of candidates) are present and explicit. | 4 / 5 |
Progressive Disclosure | No bundle files exist (no references/, scripts/, or assets/), so the skill is a single self-contained document — no nested or dead references. Sections are well-organized with clear headers (When to Use, Tool Selection Guide by phase, Three-Step Workflow, Gotchas, Limitations, Related Skills). This fits 'good structure; most content is appropriately placed; minor organization gaps' — not a 5 because at ~157 lines the Tool Selection Guide and Key Parameter Gotchas sections are candidates for a separate reference file if the skill grows, keeping the core even leaner. | 4 / 5 |
Total | 16 / 20 Passed |