Content
90%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 concise, highly actionable, and well-structured with executable Python and CLI examples plus a validation checkpoint. Main gaps are the missing explicit error-recovery feedback loop and an external reference that cannot be verified within the skill bundle.
Suggestions
Add an explicit validate→fix→retry feedback loop in the Procedure (e.g., 'If validation finds residual leakage, adjust the policy/columns and re-run before releasing').
Either bundle the synthetic fixture inside the skill (e.g., assets/datasets_walkthrough.py) or confirm the ../../examples path resolves so the Repository example reference is reliable.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean and well-organized with no over-explanation of concepts Claude already knows; the procedure, code, and safety checks each earn their place. The CSV setup boilerplate is justified as it makes the example self-contained and runnable. | 5 / 5 |
Actionability | Provides fully executable, copy-paste-ready Python and CLI examples covering the common cases, with explicit parameter values (policy, lang, text_columns) and install guidance. | 5 / 5 |
Workflow Clarity | A clear six-step sequence includes an explicit validation checkpoint (step 6: validate recall and residual leakage before release) and a safety checklist, so the destructive/batch cap of 3 does not apply; however an explicit validate→fix→retry feedback loop is not spelled out, leaving a minor gap. | 4 / 5 |
Progressive Disclosure | Body is well-sectioned (Procedure, Runnable example, Safety checks, Repository example) with a clearly signaled one-level-deep reference, but the referenced ../../examples/datasets_walkthrough.py is external to the bundle and unverifiable, a minor organization gap. | 4 / 5 |
Total | 18 / 20 Passed |