Content
80%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 is lean and well-structured with a concrete decision tree, but the failed-experiment recovery workflow would benefit from explicit validation checkpoints to fully sequence the risky recovery decisions.
Suggestions
Add an explicit validation checkpoint in Failed Experiment Recovery (e.g., 'Confirm the failure is an assumption violation, not a measurement error, before redesigning') with a validate-then-proceed decision rule.
Include one short worked example walking the decision tree from a research question to a chosen design, to lift actionability toward a 5.
Make the decision rule for continuing, revising, or stopping concrete (e.g., thresholds or explicit stopping criteria) so the recovery loop is executable.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean body with no padding or re-explanation of known concepts (e.g., it does not define DiD); assumes Claude's competence and every line earns its place, matching the score-5 anchor. | 5 / 5 |
Actionability | Provides concrete, executable decision rules mapping structure to method (e.g., 'With multiple controls: Synthetic Control') and method to assumption; minor gap is the absence of a worked example, and it is instruction-only so no code is expected. | 4 / 5 |
Workflow Clarity | The decision framework is a clear sequenced Step 1-2-3 tree, but the Failed Experiment Recovery workflow lists steps without explicit validation checkpoints or a validate-fix-retry feedback loop, so checkpoints remain implicit. | 3 / 5 |
Progressive Disclosure | Under 50 lines, no bundle files present, and content is organized into clearly headed sections — meeting the rubric's simple-skill exception for a top score. | 5 / 5 |
Total | 17 / 20 Passed |