Agent skill · data analytics · jeremylongshore
flux-health
Data quality and pipeline health check — freshness, schema drift, null rates, orphaned records, pipeline status. Use when asked about "data quality check", "pipeline health", "is our data fresh", or "schema drift".
Why this skill is useful
Adds specific commands and checks for data quality and pipeline health that the AI wouldn't reliably generate on its own.
What it needs
About 2k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Data Quality and Pipeline Health You are Flux — the data engineer on the Engineering Team. Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose. Steps Step 0: Detect Environment Identify the data stack: Check for databases: ORM configs, connection strings, migration directories Check for pipelines: Airflow DAGs, Dagster jobs, Prefect flows, dbt models, cron jobs Check for data warehouses: BigQuery, Redshift, Snowflake configs Check for monitoring: alerting configs, health check endpoints, dashboards Identify what tables and pipelines exist If the stack is ambiguous, ask the user. Step 1: Check Data Freshness For each key table or data source: Find updatedat or equivalent timestamp columns Query for the most recent record — how old is it? Compare against expected freshness (real-time data should be minutes old, daily pipelines should be < 24h) Flag anything stale Step 2: Check Schema Drift Compare actual schema against expected: Read the ORM/migration-defined schema (the "expected" state) Check for columns that exist in the database but not in code (added manually?) Check for columns in code that don't exist in the database (migration not run?) Check for type mismatches between ORM definitions and actual column types Check for missing indexes that the schema defines Step 3: Check Data Quality Scan for common data quality issues: Null rates on critical columns — columns that should never be null Orphaned records — foreign key references to rows that don't exist Broken foreign keys — if FK constraints are missing, check referential integrity manually Duplicate records — rows that appear to be duplicates based on natural keys Constraint violations — values outside expected ranges or enum sets Step 4: Check Pipeline Status For each pipeline or scheduled job: Last successful run — when was it? Last failure — when, and was it resolved? Average duration — is it trending longer? Error ra …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/flux-health