Content
76%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 efficient and information-dense, naming precise APIs and per-experiment exceptions without padding. Its main weaknesses are implicit/conditional validation in the workflow and a References section whose target files are absent from the bundle.
Suggestions
Add explicit validation checkpoints to the workflow (e.g., 'After fitting, verify posterior diagnostics and pre-period counterfactual fit before reporting effects') and a feedback loop for when checks fail.
Provide at least one minimal copy-paste example (e.g., instantiating and fitting a LinearRegression experiment with sample_kwargs/priors) to lift actionability to fully executable.
Ensure the referenced reference/*.md files exist in the bundle, or note that they are provided externally, so the References navigation is not broken.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and assumes Claude's competence — it never explains what causal inference, PyMC, or the experiment classes are, and every section earns its place with specific APIs and exceptions. | 5 / 5 |
Actionability | Names concrete, executable APIs (summary(), effect_summary(), cp.Pipeline(...).run(), specific model and fitter classes) and gives concrete alternatives for stub methods, but provides no full copy-paste code blocks. | 4 / 5 |
Workflow Clarity | A clear six-step sequence is present, but validation checkpoints are implicit and conditional ('only where the chosen experiment supports them', 'when robustness matters') rather than explicit validate-fix-retry loops. | 3 / 5 |
Progressive Disclosure | The body is well-sectioned and points to a clearly signaled one-level-deep References list, but the referenced reference/*.md files do not exist in the bundle, creating a navigation gap that keeps it just below a clean 5. | 4 / 5 |
Total | 16 / 20 Passed |