Data Quality Report Skill

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:

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

To run specific checks only:

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):

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:

references/instrument_notation_standard.md -- Standard ticker notation, digit-count hints, and naming conventions for each instrument class
references/common_data_errors.md -- Catalog of frequently observed errors including FRED data delays, ETF/futures scale confusion, holiday oversights, allocation total pitfalls, and unit confusion patterns

Use these references to explain findings and suggest corrections.

Step 4: Review Findings

Examine each finding in the output:

ERROR -- High confidence issues (e.g., date-weekday mismatches verified by calendar computation). Strongly recommend correction.
WARNING -- Likely issues that need human judgment (e.g., price scale anomalies, notation inconsistencies, allocation sums off by more than 0.5%).
INFO -- Informational notes (e.g., mixed bp/% usage that may be intentional).

Step 5: Generate Quality Report

The script produces two output files:

JSON report (data_quality_YYYY-MM-DD_HHMMSS.json): Machine-readable list of findings with severity, category, message, line number, and context.
Markdown report (data_quality_YYYY-MM-DD_HHMMSS.md): Human-readable report grouped by severity level.

Present the findings to the user with explanations referencing the knowledge base. Suggest specific corrections for each issue.

Output Format

JSON Finding Structure

{
  "severity": "WARNING",
  "category": "price_scale",
  "message": "GLD: $2,800 has 4 digits (expected 2-3 digits)",
  "line_number": 5,
  "context": "GLD: $2,800"
}

Markdown Report Structure

# Data Quality Report
**Source:** path/to/document.md
**Generated:** 2026-02-28 14:30:00
**Total findings:** 3

## ERROR (1)
- **[dates]** (line 12): Date-weekday mismatch: January 1, 2026 (Monday) -- actual weekday is Thursday

## WARNING (2)
- **[price_scale]** (line 5): GLD: $2,800 has 4 digits (expected 2-3 digits)
  > `GLD: $2,800`
- **[allocations]**: Allocation total: 110.0% (expected ~100%)

Resources

scripts/check_data_quality.py -- Main validation script
references/instrument_notation_standard.md -- Notation and price scale reference
references/common_data_errors.md -- Common error patterns and prevention

Key Principles

Advisory mode: All findings are warnings for human review. The script always exits with code 0 on successful execution, even when findings are present. Exit code 1 is reserved for script failures (file not found, parse errors).
Section-aware allocation checking: Only percentages within allocation sections (identified by headings like "配分", "Allocation", or table columns like "ウェイト", "目安比率") are checked. Random percentages in body text (probability, RSI, YoY growth) are ignored.
Bilingual support: Handles both English and Japanese date formats, weekday names, and section headings. Full-width characters (％, 〜, en-dash) are normalized before processing.
Year inference: For dates without an explicit year, the checker infers the year using (in priority order): the --as-of option, a YYYY pattern found in the document title/metadata, or the current year with a 6-month cross-year heuristic.
Digit-count heuristic: Price scale validation uses digit counts (number of digits before the decimal point) rather than absolute price ranges. This approach is resilient to price changes over time while still catching ETF/futures confusion errors.

Agent Skills: Data Quality Report

Install this agent skill to your local

Skill Files