Content
36%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 content is well-sectioned and concise but lacks executable guidance and a real validated workflow, and it points to a non-existent reference file.
Suggestions
Replace the generic Instructions with concrete, copy-paste-ready examples (e.g. a vectorized backtest snippet or a VaR computation) using pandas/numpy/scipy.
Turn the Approach principles into a sequenced workflow with explicit validation checkpoints (e.g. validate data, run backtest, check out-of-sample, report metrics).
Either create `resources/implementation-playbook.md` (and place it under a real bundle directory) or remove the dangling reference so navigation is sound.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly lean with concise lists, but the generic Instructions boilerplate ("Apply relevant best practices and validate outcomes", "Provide actionable steps and verification") adds little quant-specific value and could be tightened. | 3 / 5 |
Actionability | Guidance is high-level ("Clarify goals, constraints, and required inputs", "Use pandas, numpy, and scipy") with no executable code, commands, or concrete step detail, leaving only minimal concrete direction. | 2 / 5 |
Workflow Clarity | The numbered Approach lists principles ("Data quality first", "Out-of-sample testing") rather than a sequenced executable workflow, and validation is only implied with no explicit checkpoints. | 2 / 5 |
Progressive Disclosure | Sections are reasonably organized, but the body references `resources/implementation-playbook.md` which does not exist in any bundle directory, making the reference broken rather than clearly navigable. | 3 / 5 |
Total | 10 / 20 Passed |