Content
62%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-structured, clearly sequenced analysis workflow with a useful quality checklist and well-signaled reference files. It is held back by verbosity and section-to-section redundancy, vague 'select appropriate tools' directions lacking concrete tooling, and broken references to a nonexistent examples/ directory.
Suggestions
Collapse the 'Common Mistakes and Pitfalls' and 'Best Practices Summary' sections into a single reference (e.g. fold into references/common-pitfalls.md) to remove the duplicated ✅/❌ lists and tighten the body.
Replace vague directives like 'Select appropriate tools for data loading' and 'Use appropriate visualization tools' with named libraries/commands or concrete code snippets so the guidance is executable.
Either create the referenced examples/ directory with example-analysis-report.md and example-results-section.md, or remove those references from the body to avoid broken navigation.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly domain-specific and organized, but it explains concepts Claude already knows (basic statistics such as Mean/Standard Deviation/Confidence Interval, common chart types) and carries substantial redundancy — the 'Common Mistakes and Pitfalls' and 'Best Practices Summary' sections repeat the same cherry-picking, statistical-significance, colorblind-palette, and error-bar points, and the closing 'Workflow Overview' restates the opening pipeline. | 2 / 3 |
Actionability | It provides concrete guidance such as a Results-section structure template, validation checks, and a quality checklist, but key directions are vague ('Select appropriate tools for data loading', 'Use appropriate visualization tools to generate publication-quality figures') with no specific libraries, commands, or executable code, so it is not fully copy-paste ready. | 2 / 3 |
Workflow Clarity | The six-step pipeline (Data Loading → Validation → Statistical Analysis → Visualization → Writing → Quality Check) is clearly sequenced and ends in an explicit Quality Check checklist with concrete verification items, matching the clear-sequence-with-checklist anchor. | 3 / 3 |
Progressive Disclosure | The four references/ guides are real files and clearly signaled at one level deep ('See references/statistical-methods.md'), but the body also references an examples/ directory (example-analysis-report.md, example-results-section.md) that does not exist, and the duplicated pitfalls/best-practices content is inline material that should have been externalized. | 2 / 3 |
Total | 9 / 12 Passed |