Validate financial market analysis markdown for data quality issues.
npx skills add https://github.com/dnkrow/skill --skill data-quality-checker-dnkrow
Please help me install this Agent Skill. Skill: data-quality-checker Source: https://github.com/dnkrow/skill/tree/main/claude-global/data-quality-checker Command: npx skills add https://github.com/dnkrow/skill --skill data-quality-checker-dnkrow
This Skill helps you prevent costly publishing mistakes by validating financial market documents for common data quality problems before they go live.
Run the data-quality checker against your draft by asking: "Check the markdown file for market data quality issues and write a report to reports/ using all checks."
High-intent search queries and answers about installing and using this skill.
You can validate financial market analysis markdown by running an advisory data quality checker that reviews price scale consistency, instrument notation, date/weekday correctness, allocation totals, and unit usage. The tool outputs both JSON and Markdown advisory reports.
Yes, this markdown validator supports bilingual English and Japanese financial reports. It recognizes common weekday formats and allocation section headings in both languages to reduce false misses during your pre-publication review.
Price scale consistency checking validates ETF versus futures digit magnitudes and ratios in your financial markdown. It flags advisory warnings when price scales mismatch, helping you catch market data errors before publication.
Date verification checks detect weekday mismatches and infer correct years in your market analysis markdown. The validator scans your document for date and weekday inconsistencies and generates advisory warnings for your review.
No, the data quality checker treats all findings as advisory warnings rather than hard blockers. It outputs reports in JSON and Markdown formats so you can confidently correct allocation total and range issues without automated publication blocking.
The best way to automate pre-flight review for weekly strategy blogs is running a deterministic Python CLI that applies five validation categories to your markdown. It produces advisory reports covering price scales, dates, allocations, and units for confident publishing.
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