Content
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is a lean, highly actionable skill with executable examples and clean one-level-deep references. The main gap is the absence of validation/verification checkpoints in the batch-filtering workflows.
Suggestions
Add a verification step to the workflow patterns, e.g. after writing filtered_compounds.csv, assert the row count and inspect a sample of flagged 'reasons'/'status' columns before declaring success.
Include a brief sanity-check (e.g. expected pass-rate range or a known-good control molecule) so mis-specified filters are caught early in batch runs.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean and efficient — direct executable code blocks with minimal conceptual padding; no explaining of basic library/programming concepts, and the version note and best-practices sections each earn their tokens. | 3 / 3 |
Actionability | Fully executable, copy-paste-ready code throughout: real imports, concrete function calls, runnable workflow patterns, and a CLI script invocation with exact flags. | 3 / 3 |
Workflow Clarity | Three clearly sequenced workflow patterns are present, but the batch-filtering operations lack explicit validation/verification checkpoints (e.g. confirming output non-empty or sanity-checking pass rates), which caps workflow clarity at 2. | 2 / 3 |
Progressive Disclosure | SKILL.md is an overview with well-signaled one-level-deep references to real, existing files (references/api_guide.md, references/rules_catalog.md, scripts/filter_molecules.py), each described in one line; content is appropriately split. | 3 / 3 |
Total | 11 / 12 Passed |