Content
50%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is reasonably organized with clear tables and parameters, but it duplicates reference content inline without linking to the existing bundle files, lacks executable code for the engine, and presents no sequenced workflow with validation. Signaling the references and adding concrete examples would lift most dimensions.
Suggestions
Replace the inline Fibonacci/wave-structure exposition with signaled links to 波浪结构.md and Fibonacci浪间关系.md (e.g. '**Wave structures**: See [波浪结构.md](references/波浪结构.md)') so the overview stays lean.
Add a small executable code snippet (e.g. how to call the engine on a DataFrame) so the skill is copy-paste ready rather than descriptive.
Lay out the detection/matching process as an explicit numbered workflow with a validation checkpoint (e.g. verify Fibonacci tolerances before emitting a signal).
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient tables and bullets, but the body restates Elliott Wave theory, the impulse/corrective structure, and Fibonacci ratios that duplicate content already in the reference files, so it could be tightened. | 2 / 3 |
Actionability | Provides parameter tables and signal conventions but no executable code for the actual detection/matching engine, only a dependency install line; guidance is concrete in places but incomplete. | 2 / 3 |
Workflow Clarity | Signal logic is presented as a lookup table rather than a sequenced detection process, with no validation checkpoints for the fragile wave-counting operation, so sequence is present but checkpoints are missing. | 2 / 3 |
Progressive Disclosure | Reference files exist (波浪结构.md, Fibonacci浪间关系.md) for the very structures and ratios described inline, but the body never links to or signals them, leaving content that should be separate inline and references not clearly signaled. | 2 / 3 |
Total | 8 / 12 Passed |