Content
80%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The content is strong and token-efficient: concrete executable code, actionable reference tables, and no generic padding. The main weaknesses are the lack of explicit workflow validation checkpoints and a broken bundle reference (example_signal_engine.py is cited but missing).
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and code-driven with brief, domain-relevant rationale (e.g. 'Without vol-adjustment, crypto eats the entire risk budget') and no padding with concepts Claude already knows, matching 'lean and efficient; every token earns its place'. | 3 / 3 |
Actionability | Provides real, syntactically valid Python (the _vol_adjust function is fully self-contained) plus concrete parameter and market-detection tables and a copy-paste config.json example, clearly above the pseudocode level of score 2. | 3 / 3 |
Workflow Clarity | A loose sequence is implied by the numbered Key Concepts and the generate() code, but there are no explicit validation checkpoints and the multi-step process (group -> signal -> vol-adjust) is only implicitly sequenced, fitting 'steps listed but checkpoints missing or implicit'. | 2 / 3 |
Progressive Disclosure | The body is well-sectioned rather than a monolithic wall, but its single external reference, example_signal_engine.py in the Supporting Files section, does not exist on disk, so the disclosure is broken and content that could be split remains inline. | 2 / 3 |
Total | 10 / 12 Passed |