data-quality-checker

Original：🇺🇸 English

Translated

3 scripts

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.

2installs

Sourcetradermonty/claude-trading-skills

Added on2026-03-08

NPX Install

npx skill4agent add tradermonty/claude-trading-skills data-quality-checker

SKILL.md Content

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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:

```
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

json

{
  "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

markdown

# 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.

data-quality-checker

NPX Install

Tags

SKILL.md Content

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