Content
85%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 executable code, a well-sequenced recovery workflow that includes verification, and clean section organization. The only notable weakness is mild redundancy where the complete example and supplementary sections restate content already covered.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly efficient with no basic-concept padding, but the 'Complete Example' largely re-illustrates the procedure already shown in Steps 2-3, and 'Why This Works' and 'Common Use Cases' add somewhat redundant length, fitting 'mostly efficient but could be tightened'. | 2 / 3 |
Actionability | It provides fully executable, copy-paste-ready guidance: concrete write_file(...) calls with real Python, run_shell(command="python3 script_name.py"), a complete pandas/openpyxl example, and troubleshooting commands like 'pip3 install pandas openpyxl', matching 'fully executable code/commands; copy-paste ready'. | 3 / 3 |
Workflow Clarity | A clear 4-step sequence is given with an explicit validation checkpoint ('Step 4: Verify Output', reinforced by 'ls -la pnl_report.xlsx' in the example) plus a Troubleshooting feedback loop for error recovery, matching 'clear sequence with explicit validation steps; feedback loops for error recovery'. | 3 / 3 |
Progressive Disclosure | No bundle files exist and none are needed; the skill is organized into clear, well-labeled sections (When to Use, Recovery Procedure, Complete Example, Best Practices, Troubleshooting), so per the simple-skills note it scores 3 on well-organized sections alone. | 3 / 3 |
Total | 11 / 12 Passed |