data-quality-checks
Design the data quality checks for a table or pipeline across the standard dimensions. Use when asked to add data quality tests, define DQ checks, catch bad data before it hits dashboards, or set up monitoring for a dataset. Produces a checks plan across completeness, validity, uniqueness, freshness, consistency, and accuracy — each with the rule, severity, and where it runs (dbt test / Great Expectations / SQL assertion).
npx skills add mohitagw15856/pm-claude-skills --skill data-quality-checks --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Data Quality Checks Skill Bad data quietly poisons dashboards and models until someone notices the number is wrong. The fix is checks that fail loudly *before* that — across the standard DQ dimensions. This skill designs them for a specific table/pipeline: the exact rule per dimension, its severity (block vs. warn), and where it runs (dbt test, Great Expectations, or a SQL assertion), so quality is enforced, not hoped for. ## Required Inputs Ask for these only if they aren't already provided: - **The table/pipeline** and what it represents (grain, key columns). - **The columns that matter** — keys, required fields, enums, ranges, dates. - **Freshness expectation** — how current the data must be. - **Tooling** — dbt tests, Great Expectations, Soda, or raw SQL assertions. ## Output Format ### Data Quality Checks: `[table]` Checks organised by dimension — each with the **rule**, **severity** (🔴 block the pipeline / 🟡 warn), and **where it runs**: | Dimension | Check | Rule | Severity | Implement as | |---|---|---|---|---| | **Completeness** | required fields non-null | `not_null` on [cols] | 🔴 | dbt test | | **Uniqueness** | grain key unique | `unique` on [key] | 🔴 | dbt test |
- Required Inputs
- Output Format
- Data Quality Checks: [table]
- Quality Checks
- Anti-Patterns
- Based On
What does the data-quality-checks skill do?
Design the data quality checks for a table or pipeline across the standard dimensions. Use when asked to add data quality tests, define DQ checks, catch bad data before it hits dashboards, or set up monitoring for a dataset. Produces a checks plan across completeness, validity, uniqueness, freshness, consistency, and accuracy — each with the rule, severity, and where it runs (dbt test / Great Expectations / SQL assertion).
How do I install it?
Run `npx skills add mohitagw15856/pm-claude-skills --skill data-quality-checks --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mohitagw15856/pm-claude-skills, a repository with 1,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.