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 and highly actionable with complete, executable code and a clear three-step sequence. The main gap is the absence of explicit validation/verification checkpoints between steps.
Suggestions
Add an explicit verification step after Step1 (e.g. assert counts.sum() > 0 / print the summary table) and after chart generation (confirm the PNG file exists), to create a validate→fix feedback loop.
Note required runtime dependencies (pandas, matplotlib) and the CJK font requirement once at the top instead of repeating the font rcParams block in two functions.
Consider a short 'Inputs/Outputs' summary so the expected DataFrame shape and produced file paths are clear before reading the code.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean: short prose intros per step and functional code, with no padding about what pandas/matplotlib are, so every token earns its place. | 3 / 3 |
Actionability | Each step provides fully executable, complete Python functions with real imports, parameters, return values, and high-DPI save logic — copy-paste ready. | 3 / 3 |
Workflow Clarity | Steps are clearly sequenced (statistics → basic chart → comprehensive report) and Step1 raises ValueError for a missing column, but there are no explicit validation checkpoints or feedback loops for verifying outputs. | 2 / 3 |
Progressive Disclosure | A single cohesive SKILL.md organized into three clearly headed steps with no nested or deep references, appropriate for a self-contained visualization skill. | 3 / 3 |
Total | 11 / 12 Passed |