Content
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill body is lean, actionable, and well-structured with real executable commands and properly disclosed one-level references. Its only gap is the absence of an explicit validation/verification checkpoint in the batch screening workflow.
Suggestions
Add a brief verification step to Step 3 (e.g., confirm both JSON and Markdown reports were written and re-run or flag missing-symbol errors) to close the workflow validation gap for batch operations.
State explicitly how to interpret a partial/failed API-call run when --max-api-calls is hit, so the operator knows whether to rerun or escalate to manual review.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and assumes competence: it skips generic explanations of candlestick or screening concepts and every line advances the workflow with commands or setup details. | 3 / 3 |
Actionability | Three complete, copy-paste-ready command modes with real, verified flags (--fmp-universe, --use-quote-latest, --max-api-calls, --market-gate, --prices-json, --profiles-json) plus a concrete output-field checklist for review. | 3 / 3 |
Workflow Clarity | The four-step sequence is clear, but the batch screening workflow has no explicit validation/verification checkpoint (e.g., confirm report was written and re-run on data failure); per the rubric, missing validation in batch operations caps this dimension at 2. | 2 / 3 |
Progressive Disclosure | A concise overview body points to three clearly signaled, one-level-deep references (methodology, scoring, near-close operations), all of which are real files, with detail appropriately split out rather than inlined. | 3 / 3 |
Total | 11 / 12 Passed |