
Data Quality Frameworks
Write data validation checks with Great Expectations, dbt tests and data contracts
- Repository
- wshobson/agents
- Stars
- 42,095
- Licence
- MIT
- Files
- 2 files
What it does
When installed, the agent knows how to set up data quality checks for data pipelines. It can suggest and write Great Expectations expectation suites (rules such as 'this column is never empty' or 'this ID is unique'), dbt test suites, and data contracts that define what a dataset should look like between teams. It also covers wiring these checks into CI/CD and failing a pipeline when data is bad.
Great for
- Data engineers who want consistent validation rules across pipelines
- Analytics teams adding dbt tests or Great Expectations to an existing warehouse
- Teams agreeing on data contracts between producers and consumers
Skip it if
- Non-technical users who only need a one-off spreadsheet check, since the skill assumes a Python and data pipeline setup
- Projects not using Great Expectations or dbt, where the setup steps would not apply without adaptation (the patterns are general but the code is specific)
Try asking
- “Write a Great Expectations suite that checks order_id is not null and unique for my orders table”
- “Set up dbt tests for my customers model and explain which columns need them”
- “Draft a data contract for the events table that producers and consumers can review”
- “Add a data quality step to our CI pipeline that fails the build when checks fail”
Install in 3 steps
- 1
Download the skill
The ZIP contains the 2 original files, the MIT licence and an attribution note.
data-quality-frameworks.zip - 2
Add it to your agent
Claude Code (all projects)
mkdir -p ~/.claude/skills && unzip data-quality-frameworks.zip -d ~/.claude/skills/Claude Code / Agent SDK (this project only)
mkdir -p .claude/skills && unzip data-quality-frameworks.zip -d .claude/skills/Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.
- 3
Ask for the task
No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.
Get better results
- Point the agent at your actual table and column names so the generated expectations match your schema
- Review generated checks for thresholds before enabling them in production, since the skill itself warns against hardcoded thresholds
- Ask the agent to run the checks against a sample dataset first to confirm they behave as expected
What's inside
- references/details.md
- SKILL.md
Redistributed unmodified from wshobson/agents under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.