Content
61%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, actionable skill body with concrete commands, defined output schemas, and a properly organized bundle. Weaknesses are the absence of validation checkpoints in the batch workflow, duplicated JSON/Markdown report examples, and a slightly padded Key Principles section.
Suggestions
Add an explicit validation checkpoint between analysis and visualization (e.g., verify the JSON report exists and total_downtrends > 0 before running generate_histogram_html.py), with error-recovery guidance for API failures.
Consolidate the JSON and Markdown report examples — the Markdown example largely restates the JSON schema — or move one to references/downtrend_methodology.md.
Trim the 'Key Principles' section, whose points are already implicit in the workflow and output format.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly focused on non-obvious specifics (commands, flags, output filenames), but the full JSON report example (~45 lines) and the Markdown report example (~33 lines) duplicate each other in conveying the output format, and the 'Key Principles' section restates what the workflow already shows. This is more than minor trimmable over-explanation, but not severely padded. | 3 / 5 |
Actionability | Both workflow steps provide copy-paste-ready bash commands with concrete flags ('--sector Technology', '--lookback-years 5') and exact output filename patterns. Minor gaps: the hardcoded 'skills/downtrend-duration-analyzer/scripts/...' path assumes a specific install location, and there is no guidance for handling an invalid or missing FMP API key. | 4 / 5 |
Workflow Clarity | The four steps are clearly sequenced (fetch/analyze, auto-analysis, generate HTML, review insights), but there are no validation checkpoints — nothing verifies the JSON report exists or contains downtrends before generating the HTML, nor any error-recovery guidance. Per the guidelines, a batch operation (fetching data for a universe of stocks) without validation steps is capped at 3. | 3 / 5 |
Progressive Disclosure | The SKILL.md is a well-organized overview with a Resources section that clearly annotates each one-level-deep bundle file ('Peak/trough detection algorithms and market cap tier definitions'), and all referenced files (analyze_downtrends.py, generate_histogram_html.py, downtrend_methodology.md) exist. The dual inlined report examples could partially move to a reference, a minor organization gap that keeps it below a 5. | 4 / 5 |
Total | 14 / 20 Passed |