Content
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The content is concise, highly actionable, and well-structured with a real referenced bundle script, but its workflows omit validation/verification steps for batch and report-generation operations.
Suggestions
Add a validation/verification step to the batch workflow, e.g., checking the output CSV/report for expected columns or empty results before declaring success.
Include a brief feedback loop for report generation (e.g., 'Verify report.json contains liabilities, causes, and fixes for each input molecule').
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and assumes competence — no basic-concept tutorials, no library explanations — and every section (overview, when-to-use, workflows, script table) earns its place. | 3 / 3 |
Actionability | Provides fully executable commands with real SMILES examples, explicit flags, output paths, and a complete install command — copy-paste ready with concrete outputs documented. | 3 / 3 |
Workflow Clarity | Three named workflows are clearly sequenced with explicit commands, but batch/report operations lack validation or verification checkpoints, capping workflow clarity at 2 per the rubric. | 2 / 3 |
Progressive Disclosure | A clean overview organized into well-defined sections with a single one-level-deep bundle reference (scripts/reason_admet.py) that exists and is clearly signaled — appropriate structure for a skill of this size. | 3 / 3 |
Total | 11 / 12 Passed |