Content
72%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 content is concise, actionable, and well-structured for a simple single-purpose skill, with strong progressive disclosure. The main gap is workflow clarity: it reads as a recipe collection rather than a sequenced process with validation checkpoints.
Suggestions
Complete the JSON example with executable analysis code instead of the "# Process and analyze..." placeholder.
Reframe the "Common Tasks" as a short sequenced workflow (load -> summarize -> visualize) and add a validation checkpoint such as printing null counts/dtypes before proceeding on large datasets.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean with copy-paste-ready code and no concept over-explanation; minor padding in the library bullet descriptions (e.g. "DataFrames, CSV/JSON/Excel processing") keeps it just below a 5. | 4 / 5 |
Actionability | Three concrete, executable code blocks cover the common cases, but the JSON example ends with a placeholder comment ("# Process and analyze...") rather than a complete working snippet. | 4 / 5 |
Workflow Clarity | Tasks are listed as independent recipes under "Common Tasks" with no explicit sequence or validation checkpoints, fitting the anchor for steps-present-but-checkpoints-missing. | 3 / 5 |
Progressive Disclosure | Under 50 lines, single-purpose, no external bundle files, and well-organized into Common Libraries, Common Tasks, and Guidelines sections, qualifying for the simple-skill exception. | 5 / 5 |
Total | 16 / 20 Passed |