Content
77%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is highly actionable with an executable worked example and a clear, well-sequenced two-step workflow backed by error-recovery guidance. Its main weaknesses are mild verbosity in motivational bullets and a fully monolithic structure with no progressive disclosure into reference files.
Suggestions
Trim the 'This provides:' benefits bullet list and the 'Problematic Approach' example, since the recommended example already demonstrates the value concretely.
Move the full correlation_analysis.py worked example into a references/ file (e.g. EXAMPLES.md) and link to it from a brief inline skeleton, improving progressive disclosure for this >50-line skill.
Add an explicit validation checkpoint between the two steps, e.g. 'After writing, verify the file exists with run_shell ls -la before executing,' to make the workflow's quality gate explicit.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | It avoids explaining basic concepts and stays mostly efficient, but the benefits bullet list and the redundant 'Problematic Approach' example add padding that could be tightened. | 2 / 3 |
Actionability | Provides a complete, executable Python example with imports and try/except plus copy-paste-ready write_file and run_shell commands, making the guidance concrete and directly usable. | 3 / 3 |
Workflow Clarity | The two-step sequence is unambiguous, and an explicit error-recovery feedback loop (inspect errors, modify the persisted script, re-run) plus escalation verification commands give clear checkpoints for a simple skill. | 3 / 3 |
Progressive Disclosure | Sections are well-organized, but the skill is entirely monolithic with no reference files, and the lengthy worked example could be split out for a >50-line skill rather than kept fully inline. | 2 / 3 |
Total | 10 / 12 Passed |