Content
86%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.
A well-structured, highly actionable skill body: executable commands for every input mode, a clearly sequenced workflow with manual-validation checkpoints, and clean one-level-deep reference organization. The main gaps are minor token redundancy with the description and references, and the absence of error-recovery guidance for API or empty-result failures.
Suggestions
Trim the 'When to Use' bullets and the Step 2 setup-family/scoring lists, which restate the frontmatter description and references/scoring_system.md — replace with a one-line pointer to the scoring reference.
Add brief feedback-loop guidance in Step 2 or the prerequisites for failure paths: what to do on FMP API errors, stale --use-quote-latest data, or zero survivors (e.g., retry without quote override, widen --max-symbols, or record as no-setup day).
State the expected file format for --prices-json (a minimal example object) so Mode C is fully executable without opening the script.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is largely efficient — setup definitions, command blocks, and per-candidate output fields are all non-obvious domain content, with no explanation of concepts Claude already knows. Not 5: the 'When to Use' bullets partially restate the frontmatter description, and the Step 2 setup-family/scoring lists duplicate what references/scoring_system.md already carries, so a few tokens could be trimmed. | 4 / 5 |
Actionability | All three input modes plus the quote-override variant are given as complete, copy-paste bash commands with flags ('--fmp-universe --max-symbols 300 --market-gate allowed --output-dir reports/'), and Steps 3–4 specify exact output fields and rating-based downstream routing. Not 4: there are no gaps — commands are executable as written and cover the common cases. | 5 / 5 |
Workflow Clarity | A clear four-step sequence (choose input mode → run screening → review output → route survivors) with explicit review checkpoints such as 'validate chart manually, check earnings/news risk' and reject-reason review for this batch operation. Not 5: there are no feedback loops for failure paths (e.g., what to do on API errors, empty candidate sets, or stale quote data); not 3: validation checkpoints for the batch output are present, just not error-recovery loops. | 4 / 5 |
Progressive Disclosure | The body is a lean overview pointing to three clearly signaled, one-level-deep reference files (verified to exist and to contain no nested .md references), a script, and tests, each with a one-line purpose description in the Resources section. Not 4: there are no organization gaps — all detail is appropriately pushed to the referenced files. | 5 / 5 |
Total | 18 / 20 Passed |