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.
npx skills add BaggaT236/AI-Trading-Skills --skill data-quality-checker --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## 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 - Node.js 18+ - No external API keys required - Run scripts with `npx tsx` (or compile TS to JS first) ## 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 (option
- Overview
- When to Use
- Prerequisites
- Workflow
- Step 1: Receive Input Document
- Step 2: Execute Validation Script
- Step 3: Load Reference Standards
- Step 4: Review Findings
- Step 5: Generate Quality Report
- Output Format
- JSON Finding Structure
- Markdown Report Structure
- Resources
- Key Principles
npx tsx skills/data-quality-checker/scripts/check_data_quality.ts \
What does the data-quality-checker skill do?
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.
How do I install it?
Run `npx skills add BaggaT236/AI-Trading-Skills --skill data-quality-checker --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BaggaT236/AI-Trading-Skills, a repository with 118 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.