Content
92%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 exceptionally actionable — exact tools, parameters, conventions, and executable code with built-in validation discipline — and the MCQ workflow is clearly sequenced with error-recovery loops. Its weaknesses are token economy and structure: the six traps are stated twice, and ~350 lines of per-domain recipes that belong in reference files are all inlined in SKILL.md.
Suggestions
Split the per-domain deep dives (FDA clinical-vignette table, MGI phenotype route, computational procedures with their Python recipes, gnomAD release table) into references/ files, keeping SKILL.md to the routing table, RULE ZERO, the traps summary, and the MCQ procedure.
Remove the duplication between the 'Six traps' summary and the later full sections — either keep the traps as one-line pointers ('see §GWAS below') or drop the summary and let the detailed sections carry the detail once.
Tighten the dense routing-table rows by moving the multi-sentence 'How' cells into short sub-sections or reference files so the table reads as a quick routing index.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient — no filler explaining concepts Claude already knows — but the six-traps summary is then re-explained in full in later sections (GWAS p-value, window counting, HPA, Allen Brain each appear twice), and dense routing-table rows plus long inline per-domain recipes (FDA vignette table, gnomAD release table) could be tightened or split out. This fits 'mostly efficient but includes some unnecessary explanation or could be tightened' rather than the lean score-5 anchor, and is well above the noticeably-verbose score-2 anchor. | 3 / 5 |
Actionability | Fully executable guidance throughout: exact tool names and parameters ('MSigDB_check_gene_in_set, param gene', 'DisGeNET_get_disease_genes(disease=CUI)'), precise set-name conventions (MIR186_3P, PGM3_TARGET_GENES, MP_INCREASED_MELANOMA_INCIDENCE), and copy-paste-ready Python (count_orfs, digest, gamete_ratio with worked examples). Matches the score-5 anchor covering common cases. | 5 / 5 |
Workflow Clarity | The MCQ procedure is a clear sequenced workflow (Parse → Resolve → Query → Check → Answer) with explicit validation checkpoints and recovery loops: 'Check the length you got against the length you asked for', re-derive set names before concluding 'insufficient', 're-read each option against the computed result before emitting [ANSWER]', and fallback escalation paths (MGI per-gene route, PubTator3 text-mined fallback). Matches the score-5 anchor with feedback loops for error recovery. | 5 / 5 |
Progressive Disclosure | Headers, a routing table, and summary-then-detail sections give good in-file organization, but the skill is a single ~350-line monolith with no references/ or scripts/ bundle, and clearly separable content (computational code recipes, FDA vignette table, per-database deep dives) is inlined in SKILL.md. This fits 'some structure but... content that should be separate is inline'; the simple-skill exception does not apply given the length and topical breadth. | 3 / 5 |
Total | 16 / 20 Passed |