Content
67%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 skill with a clear three-step workflow, concrete report templates, and properly signaled one-level references that match the actual bundle. The main drag is token efficiency — several sections restate generic market knowledge Claude already has — and the script documentation undersells/mismatches what the CLI actually does.
Suggestions
Trim or move generic knowledge sections (Timezone Awareness, Customization Options, Data Source Priority, 'Maintain objective analysis' style Troubleshooting lines) into a reference file or delete them, keeping only the tailored guidance Claude could not infer.
Fix the script usage section to accurately describe `python scripts/market_utils.py` output (header, market status, trading hours, checklist) and list all public functions including get_market_status, generate_checklist, and calculate_trading_days_to_event, with a one-line import/call example.
Add an explicit validation checkpoint inside the core workflow (e.g., cross-check collected prices across two sources and confirm each market's open/closed status via market_utils.py before writing the report) instead of leaving verification guidance only in Troubleshooting.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Sections like "Timezone Awareness" (US markets: Evening to early morning), "Customization Options" (Day Traders/Swing Traders descriptions), and Troubleshooting lines like "Maintain objective analysis" restate knowledge Claude already has, matching anchor 3 ('mostly efficient but includes some unnecessary explanation or could be tightened'). Not anchor 2 because the core workflow, report template, and output example are dense and useful, and the padded sections are each short. | 3 / 5 |
Actionability | Concrete elements are present: exact data to collect ("S&P 500, NASDAQ, Dow, Nikkei 225..."), an executable command ("python scripts/market_utils.py"), a report-format template, and a full sample output. Minor gaps keep it at anchor 4: the script comment says "Generate report header" though the CLI actually prints header, market status, sessions, and a checklist; and no example shows how to call the listed Python functions or what search queries to use. Not anchor 5 because coverage of common cases is not copy-paste complete. | 4 / 5 |
Workflow Clarity | The core workflow is a clear sequence (Initial Data Collection → Market Environment Assessment → Report Structure), and checkpoints exist in "Verify with multiple sources", the Data Source Priority list, and the holiday/DST notes. Anchor 4 ('clear sequence with most checkpoints present; minor validation gaps') fits because these checks live in Troubleshooting rather than being integrated as explicit validation steps in the main flow (e.g., no 'confirm markets are open before quoting current values' step). | 4 / 5 |
Progressive Disclosure | The body is a genuine overview with well-signaled, one-level-deep references that exist in the bundle ("Reference when you need" → references/indicators.md; "Reference when analyzing" → references/analysis_patterns.md; scripts/market_utils.py documented). Anchor 4 rather than 5 because of minor organization gaps: market_utils.py exposes public functions (get_market_status, generate_checklist, calculate_trading_days_to_event) not listed in SKILL.md, and some inline material (Customization Options, Timezone Awareness) could live in a reference file. | 4 / 5 |
Total | 15 / 20 Passed |