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data-quality-checker

Validate market analysis documents for data quality issues and emit JSON and Markdown reports.

Updated Apr 17, 2026
Search Tags:#markdown#validation#finance#data-quality#market-analysis#date-parsing#digit-count
One-click install
npx skills add https://github.com/pasie15/claude-trading-skills-marketplace --skill data-quality-checker-pasie15
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-quality-checker
Source: https://github.com/pasie15/claude-trading-skills-marketplace/tree/main/plugins/trading-dividend-investing/skills/data-quality-checker
Command: npx skills add https://github.com/pasie15/claude-trading-skills-marketplace --skill data-quality-checker-pasie15

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill detects and flags common data quality issues in market analysis documents and blog posts so authors can fix scale, notation, date, allocation, and unit errors before publication.

Core Features & Use Cases

  • Price scale validation: Uses digit-count heuristics to spot ETF vs futures or index scale mismatches (e.g., GLD vs GC, SPY vs SPX).
  • Notation and unit checks: Verifies ticker/instrument notation, basis points vs percent, dollar vs cent ambiguity, and per-contract vs per-unit statements.
  • Date and allocation validation: Confirms weekday/date consistency (bilingual support for English/Japanese), infers missing years with a reference date option, and performs section-aware allocation totaling with rounding and range checks.
  • Outputs for review: Produces machine-readable JSON and human-readable Markdown reports with severity levels (ERROR, WARNING, INFO) for advisory human review.

Quick Start

Run the data-quality-checker on a market report markdown to generate JSON and Markdown reports that list potential issues for human review.

Frequently Asked Questions about data-quality-checker

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I validate market analysis markdown documents for price scale and notation errors?▼

Date validation for financial reports checks weekday and date consistency across bilingual English and Japanese text, infers missing years using an optional reference date, and flags inconsistencies in translated market summaries for human review.

Can I check allocation totals and rounding in market summary sections automatically?▼

Yes, you can automatically check allocation totals in market summaries by running section-aware validation that confirms allocations add up correctly, applies rounding rules, and verifies range constraints to catch errors before publication.

What is the best way to generate data quality reports for market analysis blog posts?▼

The best way to generate data quality reports for market analysis blogs is to run a validation process that outputs both machine-readable JSON and human-readable Markdown reports, categorizing detected issues by severity levels like ERROR, WARNING, and INFO for advisory review.

Does automated data quality validation work with translated Japanese market reports?▼

Automated data quality validation supports translated Japanese market reports by performing bilingual date inference, checking instrument notation, and validating price scales to ensure accuracy across both English and Japanese financial documents.

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