Agent skill · data analytics · tradermonty
data-quality-checker
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.
Why this skill is useful
Adds a validation script for detecting specific data quality issues in financial documents that the AI wouldn't reliably generate on its own.
What it needs
Requires python installed locally. About 3k tokens when loaded. Last updated 2026-08-06. 2,581 stars on the source repository.
What this skill does
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: To run specific checks only: To provide a reference date for year inference (useful for documents without explicit year in dates): Step 3: Load Reference Standards Read the relevant reference documents to contextualize findings: references/instrumentnotationstandard.md -- Standard ticker notation, digit-count hints, and naming conventions for each instrument class references/commondataerrors.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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills tradermonty/data-quality-checker