Data Quality Checker
Validate data quality in market analysis documents and blog articles before publication.
Install
npx skills add tradermonty/claude-trading-skills --skill data-quality-checker -g -a claude-code -y- Repository
- tradermonty/claude-trading-skills3.5k
- Category
- Writing
- Updated
- 3d ago
- License
- MIT
- Context cost
- Lightabout 1,373 tokens
- Runs scripts
- Yes, review before use
What it does
Validate data quality in market analysis documents and blog articles before publication. Use when checking for price scale inconsistencies (ETF vs futures), instrument notation errors, date/day-of-week mismatches, allocation total errors, and unit mismatches. Supports English and Japanese content. Advisory mode -- flags issues as warnings for human review, not as blockers.
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:
python3 skills/data-quality-checker/scripts/check_data_quality.py \
--file path/to/document.md \
--output-dir reports/
To run specific checks only:
python3 skills/data-quality-checker/scripts/check_data_quality.py \
--file path/to/document.md \
--checks price_scale,dates,allocations
To provide a reference date for year inference (useful for documents without explicit year in dates):
python3 skills/data-quality-checker/scripts/check_data_quality.py \
--file path/to/document.md \
--as-of 2026-02-28
Step 3: Load Reference Standards
Read the relevant reference documents to contextualize findings:
