Content
80%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 strong, command-driven skill body: fully executable invocations with complete parameter documentation and excellent progressive disclosure into real, one-level reference files. The main gap is the absence of validation/error-recovery steps for a rate-limit-prone batch pipeline, which caps workflow clarity; minor duplication of the frontmatter in the intro and When to Use sections costs some token efficiency.
Suggestions
Add a validation checkpoint after the screening run: verify the timestamped output files (vcp_screener_*.json/.md) were created and contain candidates before presenting analysis, with a fix path if the run failed.
Add error-recovery guidance for FMP rate limits (e.g., free tier's 250 calls/day vs. the pipeline's ~201 calls; what to do on a 429 or partial run — retry, reduce universe, or resume from the last completed ticker).
Trim the duplicated content between the frontmatter description, the intro line, and the When to Use section to reclaim context tokens.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and tool-specific (commands, flag tables, notes on API costs and dedup) with no explanation of concepts Claude already knows — VCP theory is correctly deferred to references. However, the intro line ("Screen S&P 500 stocks for Mark Minervini's VCP, identifying Stage 2 uptrend stocks with contracting volatility...") and the "When to Use" bullets largely duplicate the frontmatter description, so a few sections could be trimmed — the anchor-4 "minor instances that could be trimmed" rather than anchor-5's "every token earns its place". | 4 / 5 |
Actionability | Every mode has copy-paste-ready bash invocations with concrete flags: default screening, custom universe, --full-sp500, --strict, three historical-mode variants, and advanced tuning, supplemented by parameter tables giving defaults and valid ranges (e.g. "--min-contractions | 2 | 2-4"). This covers all common cases exactly as the anchor-5 example does. | 5 / 5 |
Workflow Clarity | The four-step sequence (run → review results → present analysis → actionable guidance) is clear and the execution-state/rating decision tables are excellent, but there are no validation or error-recovery checkpoints. This is a batch operation (~101+100 API calls per the 3-Phase Pipeline against a 250-call/day free tier, so quota exhaustion mid-run is a realistic failure), and the rubric caps workflow_clarity at 3 for batch operations missing validate/fix/retry loops — e.g., verifying the timestamped output files were created before presenting, or handling FMP rate-limit errors. | 3 / 5 |
Progressive Disclosure | The body is a proper overview: methodology, scoring thresholds, and API endpoints live in the three referenced files (all present in the bundle, one level deep), signaled both in Step 2 and in a Resources section. The inline content (commands, flag tables) is operational and belongs in SKILL.md, so the split is appropriate and navigation is easy — matching the anchor-5 example structure. | 5 / 5 |
Total | 17 / 20 Passed |