Individual skill
data-quality-checker
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.
skills/data-quality-checker/SKILL.md
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- Last checked
- Nov 9, 2026
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- Repository last updated
- Nov 12, 2026
- License
- MIT
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- engineering Derived category
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SKILL.mdcore · 5,942 bytesSKILL.mdcore · 5,942 bytesSKILL.mdcore · 5,942 bytesSKILL.mdcore · 5,942 bytesreferences/common_data_errors.mddoc · 6,843 bytesreferences/instrument_notation_standard.mddoc · 4,079 bytesrequirements.txtdoc · 70 bytesscripts/check_data_quality.pyruns · 26,385 bytesscripts/tests/conftest.pyruns · 304 bytesscripts/tests/test_check_data_quality.pyruns · 25,693 bytes
Read excerpt
## 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