Content
75%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 highly actionable, well-sequenced operational skill whose commands and input contracts are copy-paste ready. Its weaknesses are token efficiency and file placement: substantial detail (output schema, report template) is inlined rather than pushed to the already-present reference files, some content is repeated, and a few documented bundle files do not match the actual bundle.
Suggestions
Move the full JSON output example and the Key Output Fields table into references/theme_detection_methodology.md, keeping only the handful of fields needed to interpret results inline in SKILL.md.
Replace the inlined Step 6 report template with a pointer to assets/report_template.md (it duplicates that file nearly verbatim), and state execution-time and direction-display mapping details once instead of two to three times.
Reconcile the scripts listing with the actual bundle: finviz_industry_scanner.py and requirements.txt do not exist (the actual data client is finviz_performance_client.py), and theme_classifier.py lives in scripts/calculators/ rather than scripts/.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly operational and avoids explaining concepts Claude already knows, but it is noticeably padded in several places: a ~70-line inlined JSON output example, a full report template inlined in Step 6 despite assets/report_template.md existing, execution times repeated three times (Step 2, the mode-differences table, and the footer), direction-display mapping stated twice, and a "3-Dimensional Scoring Model" header that then lists five numbered items. Not a 2 because the core workflow text is dense and information-bearing rather than fluff. | 3 / 5 |
Actionability | Guidance is fully executable throughout: copy-paste bash invocations for every mode (public, Elite, FMP, custom limits, scan-hits), an exact uv fallback command, precise scan-hit and narrative-score input contracts with field names and thresholds, a concrete WebSearch query pattern, and an explicit confidence combination matrix. Common cases are covered with ready-to-run commands. | 5 / 5 |
Workflow Clarity | Six clearly sequenced steps (verify environment → run script → parse output → narrative confirmation → analysis → report) with an environment check up front and explicit decision rules for confidence elevation. Not a 5 because there is no explicit error-recovery checkpoint after script execution (e.g., what to do if the run fails or data_quality flags problems) beyond the uv setup workaround; the operation is read-only rather than destructive, so no cap applies. | 4 / 5 |
Progressive Disclosure | Structure is good: clear sections, a Resources directory documenting the bundle, and one-level-deep references to references/cross_sector_themes.md, thematic_etf_catalog.md, theme_detection_methodology.md, and finviz_industry_codes.md — all verified present. Not a 5 because the full JSON output schema and Key Output Fields table are inlined in SKILL.md when the existing theme_detection_methodology.md is the natural home, the Step 6 template duplicates assets/report_template.md, and the scripts listing references files that do not exist in the bundle (finviz_industry_scanner.py, requirements.txt) while placing theme_classifier.py in the wrong directory. | 4 / 5 |
Total | 16 / 20 Passed |