Content
70%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The content is highly actionable with concrete commands and output schemas, and progressive disclosure is well handled with real one-level references. It loses points on conciseness (redundant inline report examples) and workflow clarity (no validation checkpoints for the batch fetch/analyze/generate pipeline).
Suggestions
Add a validation checkpoint between analysis and HTML generation — e.g. verify reports/downtrend_analysis_*.json is non-empty and well-formed before running generate_histogram_html.py.
Trim the inline JSON and markdown report examples to a minimal schema sketch, or move the full schema into a reference file to reduce token overhead.
Include brief error-recovery guidance for FMP API failures (rate limits, missing symbols) in the workflow steps.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient with executable command blocks and a focused structure, but it includes inline JSON and markdown report examples that largely restate the summary statistics already described, and the 'Key Principles' section restates concepts Claude already knows about percentiles and segmentation. | 2 / 3 |
Actionability | Provides concrete, copy-paste-ready bash commands with arguments, names the exact output files, and shows the precise JSON/markdown output schema Claude should expect — fully executable guidance. | 3 / 3 |
Workflow Clarity | Steps are clearly sequenced (fetch → analyze → generate → review), but this batch data-fetching operation has no validation checkpoints: no confirmation that the JSON analysis output exists or is non-empty before generating the HTML, and no error-recovery guidance for API failures. | 2 / 3 |
Progressive Disclosure | SKILL.md is an overview with well-signaled one-level-deep references — the Resources section points to real bundle files (scripts/analyze_downtrends.py, scripts/generate_histogram_html.py, references/downtrend_methodology.md), all verified to exist, and detail is appropriately split rather than nested. | 3 / 3 |
Total | 10 / 12 Passed |