Define data requirements, gather sources, and validate quality before analysis.
npx skills add https://github.com/bobby-andris/Allied-FeedOps --skill data-first
Please help me install this Agent Skill. Skill: data-first Source: https://github.com/bobby-andris/Allied-FeedOps/tree/main/.claude/skills/data-first Command: npx skills add https://github.com/bobby-andris/Allied-FeedOps --skill data-first
Gather and verify real data before any analysis or planning to prevent fabricated examples and assumptions. If data is unavailable, explicitly state assumptions and obtain user approval before proceeding.
Outline data requirements and initiate a verified data collection workflow before analysis.
High-intent search queries and answers about installing and using this skill.
To verify data quality before data analytics, you must define data requirements including sources, schema, and filters. You then gather real results, validate them across sources, and document any gaps before proceeding with analysis.
The best way to define data requirements for research planning is to explicitly identify necessary sources, expected volume, and filters. This ensures you collect factual data and prevent fabricated examples before analysis begins.
When real data sources are unavailable for market analysis, you must explicitly state your assumptions and obtain user approval. You then proceed using clearly labeled assumptions rather than fabricated examples.
No, automated data gathering for performance reviews requires validation. You must validate data quality across all sources, confirm data access, and document any gaps to ensure the review is based on verified data.
Data collection fails during audits when data access is unconfirmed or schema requirements are undefined. Successful audits require defining data requirements upfront, validating quality across sources, and documenting any existing gaps.
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