Validate data analyses for accuracy, bias, and aggregation logic.
npx skills add https://github.com/kiryteo/opencode-setup --skill data-validation-kiryteo
Please help me install this Agent Skill. Skill: data-validation Source: https://github.com/kiryteo/opencode-setup/tree/main/skills/data-validation Command: npx skills add https://github.com/kiryteo/opencode-setup --skill data-validation-kiryteo
QA analyses before sharing with stakeholders — ensure accuracy, detect bias, and verify aggregation logic for reproducibility and credibility.
Review the data-analysis results using the Pre-Delivery QA Checklist before sharing with stakeholders.
High-intent search queries and answers about installing and using this skill.
To run QA checks on data analysis results, apply a structured pre-delivery checklist that verifies data sources, completeness, null handling, and deduplication to ensure accuracy and reproducibility before sharing with stakeholders.
Survivorship bias detection in data validation identifies hidden skew from filtered-out populations in model outputs and reports. You need it during pre-delivery QA to verify aggregation logic and ensure data credibility.
To validate aggregation logic and denominators in reports, check calculation and presentation accuracy by verifying date alignment, confirming denominator values, and ensuring charts and tables accurately reflect the processed data.
Yes, you can create reproducible documentation for data quality checks by using standardized documentation templates that support audit trails and governance, ensuring your validation process and reporting remain fully traceable.
The best way to check data freshness and filter correctness is to systematically review data sources against a structured QA checklist, verifying query filters and data recency to guarantee pre-delivery analysis accuracy and reproducibility.
Data validation works for model outputs and reports, as well as pre-delivery analyses. It applies structured checklists to verify data quality, detect survivorship bias, and validate aggregation logic across all output formats.
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