Content
80%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 concise, well-structured skill body that gives concrete artifacts and schemas without padding. Its main weakness is the lack of explicit validation/checkpoint steps in the experimental workflow, which is important for batch statistical operations.
Suggestions
Add an explicit validation step in the workflow (e.g., 'Verify every claim_id in metrics.json maps to a formulated claim before interpreting results').
Include a short feedback loop: on failed runs, count them, diagnose, and re-run rather than silently dropping them.
Show one concrete runnable example (seed/folds/repetitions) so the artifact layout is immediately executable.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean and efficient — assumes Claude's competence with no padding explaining what experiments or statistics are; every section (plan, artifacts, schema, rules) earns its place. | 5 / 5 |
Actionability | Concrete guidance via an explicit artifact file layout and executable JSON schemas for metrics and claim verdicts, but it stops short of showing how to actually run an experiment, leaving a minor gap between structure and execution. | 4 / 5 |
Workflow Clarity | A rough sequence exists (plan → artifacts → schema → rules) but there are no explicit validation checkpoints or feedback loops for batch/statistical operations, which caps workflow clarity at 3 per the rubric. | 3 / 5 |
Progressive Disclosure | Well-organized into clearly labeled sections with no bundle files present and no need for external references; the content is self-contained and easy to navigate. | 5 / 5 |
Total | 17 / 20 Passed |