Content
72%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 highly actionable with complete, executable code and concrete schemas/templates, and is largely token-efficient; its main gaps are the absence of explicit validation checkpoints in the batch workflow and a monolithic structure with no external reference files despite the length.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is code- and table-dense with little padding of concepts Claude already knows, but contains minor redundancy (the 'Signal Convention' section restates the combine_signals formula and the alpha default is stated in both the docstring and prose), keeping it just below the lean 5. | 4 / 5 |
Actionability | It provides fully executable, copy-paste-ready code (compute_event_signal and combine_signals with imports), a concrete CSV schema with example rows, and a ready-to-use LLM scoring prompt template that cover the common cases, matching the 'fully executable; copy-paste ready' anchor. | 5 / 5 |
Workflow Clarity | The 4-step workflow (read_url → LLM score → write event CSV → signal_engine aggregation) is clearly sequenced, but this is a batch backtest operation with no explicit validation/verification checkpoints in the steps themselves (look-ahead and range checks appear only in the pitfalls prose), so per the feedback-loop cap workflow_clarity cannot exceed 3. | 3 / 5 |
Progressive Disclosure | The body is well-sectioned with clear headers, but at ~176 lines everything (full code implementations, the prompt template) is inlined with no external reference files, and the referenced signal_engine.py is not an actual bundle file, fitting 'some structure but content that should be separate is inline; references not clearly signaled'. | 3 / 5 |
Total | 15 / 20 Passed |