data-quality-checker

1## Overview
2 
3Detect common data quality issues in market analysis documents before
4publication. The checker validates five categories: price scale consistency,
5instrument notation, date/weekday accuracy, allocation totals, and unit usage.
6All findings are advisory -- they flag potential issues for human review rather
7than blocking publication.
8 
9## When to Use
10 
11- Before publishing a weekly strategy blog or market analysis report
12- After generating automated market summaries
13- When reviewing translated documents (English/Japanese) for data accuracy
14- When combining data from multiple sources (FRED, FMP, FINVIZ) into one report
15- As a pre-flight check for any document containing financial data
16 
17## Prerequisites
18 
19- Python 3.9+
20- No external API keys required
21- No third-party Python packages required (uses only standard library)
22 
23## Workflow
24 
25### Step 1: Receive Input Document
26 
27Accept the target markdown file path and optional parameters:
28- `--file`: Path to the markdown document to validate (required)
29- `--checks`: Comma-separated list of checks to run (optional; default: all)
30- `--as-of`: Reference date for year inference in YYYY-MM-DD format (optional)
31- `--output-dir`: Directory for report output (optional; default: `reports/`)
32 
33### Step 2: Execute Validation Script
34 
35Run the data quality checker script:
36 
37```bash
38python3 skills/data-quality-checker/scripts/check_data_quality.py \
39  --file path/to/document.md \
40  --output-dir reports/
41```
42 
43To run specific checks only:
44 
45```bash
46…