Content
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A concise, well-organized single-purpose skill with executable code, but it is weakened by placeholder/assumed values and a lack of validation checkpoints for a batch data workflow.
Suggestions
Replace the assumed 'auto_converted_data.parquet' path with a derived/validated path and add an existence check before read_parquet.
Add a verification step after export (e.g., confirm the output file exists and is non-empty) to create a validate-fix-retry feedback loop.
Make placeholder column names clearly template-only and show how to detect them from the dataframe rather than hardcoding example values.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean — three numbered steps with mostly purposeful code comments and a brief parent-workflow note — and avoids explaining concepts Claude already knows, matching the lean-and-efficient anchor. | 3 / 3 |
Actionability | The Python is executable rather than pseudocode, but it relies on placeholder column names ('目标列名', 'TARGET_VALUE') and a hardcoded, assumed parquet_path ('auto_converted_data.parquet'), leaving key details incomplete. | 2 / 3 |
Workflow Clarity | Steps are clearly sequenced (read → filter/summarize → export), but this batch data operation has no validation or verification checkpoints (e.g., confirming the parquet file exists, verifying export success), capping workflow clarity at 2. | 2 / 3 |
Progressive Disclosure | The skill is under 50 lines, single-purpose, with no external bundle files and well-organized Step1/Step2/Step3 sections, so progressive disclosure scores 3 per the simple-skills note. | 3 / 3 |
Total | 10 / 12 Passed |