Content
60%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 body presents a clear, well-sequenced five-step cohort analysis workflow with useful examples and good organization. Its main weaknesses are verbosity in overlapping sections and a lack of executable code or templates despite mentioning script generation.
Suggestions
Trim redundant sections: 'Key Capabilities' and 'Output Format' restate the Step 1-5 content — consolidate or cut to improve conciseness.
Add at least one concrete, copy-paste-ready Python snippet (e.g., a pandas retention-rate pivot) instead of only describing that scripts will be generated.
Add an explicit validation feedback loop in Step 1 (e.g., 'If validation fails: report issues, request corrected data, do not proceed') to strengthen workflow clarity.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient bullet-listed steps with no padding of concepts Claude already knows, but sections like 'Key Capabilities' and 'Output Format' restate the step content and could be tightened. | 3 / 5 |
Actionability | Provides concrete guidance on data formats, metrics to compute, and chart types, but describes outputs ('Generate Python analysis scripts using pandas and numpy if requested') rather than supplying executable code, leaving key implementation details missing. | 3 / 5 |
Workflow Clarity | Clear five-step sequence with explicit data validation in Step 1, but lacks explicit error-recovery feedback loops between steps; the analysis is read-only so the destructive/batch cap does not apply. | 4 / 5 |
Progressive Disclosure | Well-organized single-file structure with clear section headers and appropriately placed external 'Further Reading' links, though no one-level-deep bundle references exist and some content could be split out. | 4 / 5 |
Total | 14 / 20 Passed |