# Data Quality Checker

Validate data quality in market analysis documents and blog articles before publication.

- Repository: https://github.com/tradermonty/claude-trading-skills (3536 stars)
- Category: Writing
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add tradermonty/claude-trading-skills --skill data-quality-checker -g -a claude-code -y`
Codex: `npx skills add tradermonty/claude-trading-skills --skill data-quality-checker -g -a codex -y`
Cursor: `npx skills add tradermonty/claude-trading-skills --skill data-quality-checker -g -a cursor -y`
Gemini CLI: `npx skills add tradermonty/claude-trading-skills --skill data-quality-checker -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## 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:

```bash
python3 skills/data-quality-checker/scripts/check_data_quality.py \
  --file path/to/document.md \
  --output-dir reports/
```

To run specific checks only:

```bash
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):

```bash
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:

The rest: https://github.com/tradermonty/claude-trading-skills/blob/HEAD/skills/data-quality-checker/SKILL.md