data-quality-checkerlisted

## Overview Detect common data quality issues in market analysis documents before publication. The checker validates five categories: price scale consistency, instrument notation, date/weekday accuracy, allocation totals, and unit usage. All findings are advisory -- they flag potential issues for human review rather than blocking publication. ## When to Use - Before publishing a weekly strategy blog or market analysis report - After generating automated market summaries - When reviewing translated documents (English/Japanese) for data accuracy - When combining data from multiple sources (FRED, FMP, FINVIZ) into one report - As a pre-flight check for any document containing financial data ## Prerequisites - Python 3.9+ - No external API keys required - No third-party Python packages required (uses only standard library) ## Workflow ### Step 1: Receive Input Document Accept the target markdown file path and optional parameters: - `--file`: Path to the markdown document to validate (required) - `--checks`: Comma-separated list of checks to run (optional; default: all) - `--as-of`: Reference date for year inference in YYYY-MM-DD format (optional) - `--output-dir`: Directory for report output (optional; default: `reports/`) ### Step 2: Execute Validation Script Run the data quality checker script: ```bash python3 skills/data-quality-checker/scripts/check_data_quality.py \ --file path/to/document.md \ --output-dir reports/ ``` To run specific checks only: ```bash p