data-quality
Skill4 starsChanged 8 days agoMITView on GitHub
Use when auditing data quality, defining validation rules, measuring defects, prioritizing data issues, or recommending remediation.
13 other projects use a version of this.
Adopted by
Most recently taken up by
- SWEStash/swe-workflow-skillstheir own version, first seen Nov 8, 2026
- Happy-Technologies-LLC/happy-platform-skillstheir own version, first seen Nov 1, 2026
- newsc2/claude-skillstheir own version, first seen Oct 20, 2026
- nimadorostkar/Claude-Skills-collectiontheir own version, first seen Oct 13, 2026
- SkillMedev/data-engineering-pipelinetheir own version, first seen Oct 5, 2026
- priyanshijani/data-agentstheir own version, first seen Sep 30, 2026
Other versions13 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 13
- nahid-sparktales/agent-dispatcher★ 59same name
- Happy-Technologies-LLC/happy-platform-skills★ 41same name
- nimadorostkar/Claude-Skills-collection★ 33same name
- brainbytes-dev/everything-claude-trading★ 16same name
- khuynh22/agent-dev-team★ 9same name
- yeaight7/agent-powerups★ 6same name
- gmanch94/claude-code-aidlc-template★ 2same name
- SWEStash/swe-workflow-skills★ 1same name
- Abdox-menara/guard-skills★ 0same name
- IngAs8/ClaudeSkill★ 0same name
3 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository6 other files it gives its agents.
- site/AGENTS.mdAGENTS.md
- bi-expertSkill
- data-analyst-workflowSkill
- data-cleaningSkill
- feature-engineeringSkill
- sql-expertSkill
The file
We split this skill into the rules it gives an agent
6 rules. 0 are common, 6 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 11 edited versions we read keep these.
6
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 8
Create evidence-based data-quality assessments that make defects measurable, explain their impact, and identify practical next actions.
- Distinctive: line 12
Check for duplicate business keys, referential-integrity failures, missing critical values, invalid ranges or formats, inconsistent …
- Distinctive: line 19
Use a concise table with: rule/defect, definition and impact, validation logic, count and rate, severity, root-cause hypothesis, and …
- Distinctive: line 22
Use only supplied data, schema, and business rules.
- Distinctive: line 23
Avoid repeating personal or confidential values.
- Distinctive: line 25
Do not claim to execute checks or modify source data unless an execution tool is actually available.
“Common” is measured: kept by at least 6 in 10 of the 11 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Data Quality
Create evidence-based data-quality assessments that make defects measurable, explain their impact, and identify practical next actions.
Workflow
- Clarify the dataset's grain, critical fields, business definitions, expected ranges and relationships, and the decision or report at risk.
- Check for duplicate business keys, referential-integrity failures, missing critical values, invalid ranges or formats, inconsistent mappings, invalid dates, cross-field contradictions, and reconciliation differences.
- For each rule, state its definition, affected fields, and business impact. Provide validation logic in the requested tool or SQL dialect.
- Quantify affected rows and rates when data is available. State the denominator and treatment of nulls; never invent counts or results.
- Prioritize by business impact, scope, reporting risk, and fix feasibility. Clearly label root-cause explanations as hypotheses.
- Recommend a specific fix and owner/action. Separate safe normalization from decisions that require business approval.
Report format
Use a concise table with: rule/defect, definition and impact, validation logic, count and rate, severity, root-cause hypothesis, and recommended action. Omit unavailable measurements rather than estimating them.
Correctness and privacy
- Use only supplied data, schema, and business rules. Distinguish observed facts from assumptions and recommendations.
- Avoid repeating personal or confidential values; suggest masking sensitive fields before sharing.
- Do not solicit or process payment-card data subject to PCI DSS, protected health information, government identifiers, or authentication credentials and secrets. If supplied data contains these fields, ask the user to remove or mask them before analysis. Process other sensitive or special-category personal data only when strictly necessary for the task, legally adequate consent is confirmed, and the user was explicitly informed before providing it; otherwise ask for removal or masking.
- Do not claim to execute checks or modify source data unless an execution tool is actually available.
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