Content
87%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 body that defers detail to verified reference files and ships ready-to-run scripts. The main gap is the absence of explicit validation/inspection checkpoints in the batch multi-sample quantification workflow.
Suggestions
Add an explicit validation/inspection checkpoint to the multi-sample quantification recipe (e.g., run inspect_ms_data.py or check feature counts) between detection and consensus matrix export.
Insert a brief 'verify outputs' step after the align_link_quantify → consensus_to_matrix pipeline to confirm sample counts and feature linkage before downstream analysis.
Surface a one-line validation note in the script recipes for other batch operations (e.g., process_identifications FDR filtering) to confirm expected hit counts before export.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean and efficient throughout; assumes Claude's competence without explaining basic mass-spec concepts, and isolates version-sensitive details in a 'Key 3.5.0 API notes' section rather than scattering them. | 5 / 5 |
Actionability | Copy-paste-ready install/verify snippets, concrete script invocations with real flags, and executable API examples (FeatureMap.get_df, ConsensusMap DataFrames, Param management) cover the common cases. | 5 / 5 |
Workflow Clarity | Script recipes give a clear sequence (align_link_quantify → consensus_to_matrix) but the multi-sample quantification batch workflow lacks explicit validation checkpoints, capping workflow clarity at 3 per the batch-operations rule. | 3 / 5 |
Progressive Disclosure | SKILL.md is a concise overview with well-signaled, one-level-deep references to six real reference files and sixteen real scripts, with content appropriately split between overview and detail. | 5 / 5 |
Total | 18 / 20 Passed |