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 well-structured, actionable skill body with a clear phased workflow, concrete tool calls, and useful grading/fallback tables. Adding explicit validation checkpoints and trimming minor redundancy would push it higher.
Suggestions
Add explicit validation/verification checkpoints between phases (e.g., confirm UniProt accession resolved before proceeding to Phase 1) to strengthen workflow_clarity.
Tighten redundant framing — "LOOK UP DON'T GUESS", "COMPUTE, DON'T DESCRIBE", and "KEY PRINCIPLES" overlap; consolidate to reduce token use.
Provide one complete end-to-end runnable Python example that chains ToolUniverse retrieval with pandas/scipy analysis to lift actionability toward copy-paste readiness.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean, table-driven body that assumes Claude's competence and avoids explaining basic PTM concepts; a few section headings and the brief "Domain Reasoning" could be trimmed but overall efficient. | 4 / 5 |
Actionability | Concrete tool invocations with parameters (e.g. `iPTMnet_get_ptm_sites(operation="get_ptm_sites", uniprot_id="P04637")`) and a parameter reference table give mostly executable guidance, with minor gaps in full end-to-end runnable examples. | 4 / 5 |
Workflow Clarity | Clear Phase 0–5 sequence with an evidence-grading scheme and fallbacks, but lacks explicit validate-then-proceed feedback loops; this is analysis (not destructive/batch), so the destructive cap does not apply. | 4 / 5 |
Progressive Disclosure | Single self-contained file with well-organized sections and no nested external references; no bundle files exist, so it scores on structure alone, which is good but not the exemplar of one-level-deep external references. | 4 / 5 |
Total | 16 / 20 Passed |