Content
71%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 concise and well-structured for a small single-purpose skill, with a clear scoring-to-signal rule. Its main gap is the lack of any executable code or usage example for what is described as a pandas implementation.
Suggestions
Add a minimal executable usage example showing how to invoke the engine on OHLC data (import, function call, and reading the returned signal).
Fix the double-candle count inconsistency: the header says 'Double-Candle Patterns (5)' but the table lists 6 rows.
Collapse the redundant 'Signal Convention' into 'Signal Logic' to remove the duplicated long/short/aside mapping.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean, using compact tables and minimal prose without explaining what candlesticks are, but the 'Purpose' section restates the description and 'Signal Convention' partly duplicates 'Signal Logic', so a few tokens could be trimmed. | 4 / 5 |
Actionability | Concrete elements exist (pip install command, parameter defaults, explicit +1/-1 scoring rule), but for a 'pure pandas vectorized implementation' engine there is no executable usage code, import, or function-call example, leaving the guidance incomplete. | 3 / 5 |
Workflow Clarity | The decision rule is unambiguous and clearly sequenced (patterns score +1/-1, sum the total, then long/short/stand-aside), which suits this single-purpose skill, though there are no explicit validation checkpoints. | 4 / 5 |
Progressive Disclosure | At well under 50 lines with no external references needed, the content is cleanly organized into well-labeled sections (Purpose, Signal Logic, Parameters, Dependencies, Signal Convention), satisfying the simple-skill exception. | 5 / 5 |
Total | 16 / 20 Passed |