Data exploration & SQL assistant
Ask a question about your data and the analyst profiles it, writes SQL, verifies the results and the statistics, and drafts a sound analysis summary for you to approve.
Saves you ~2.2 h / run
How it works
- Trigger
- When you start the “Explore data” workflow.
- Job
- Profile, write SQL, draft, and hand off.
- Outcome
- Verified SQL, findings, and decision-ready analysis.
What it installs
Agents 3
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Data Analyst
Writes dialect-correct SQL, validates results, and drafts an analysis summary.
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Analysis Verifier
Sanity-checks query grain, magnitudes, and statistical reasoning before approval.
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Data exploration & SQL assistant Quality Reviewer
Checks primary evidence, domain controls, deliverable completeness, and communication quality, stopping the run when the work is wrong, unsupported, incomplete, or uncertain.
Teams 1
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Data Exploration Team
Pairs the analyst who writes the SQL with the verifier who checks the work.
Workflows 1
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Explore data
Profile, write SQL, independently verify results and statistics, draft, and hand off.
Documents 1
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Schema reference and metric definitions
Your editable data dictionary — tables, grain, keys, caveats, and agreed metric definitions.
Goals 1
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Questions answered with validated evidence
Business questions are answered with reconciled SQL and statistically honest analysis. Success looks like: Every question closes with dialect-correct, reconciled SQL, results sanity-checked for grain and magnitude, and an approved analysis summary that reports distributions honestly and flags its own statistical caveats.
Skills 3
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sql-queries
Write correct, performant, readable SQL across warehouse dialects — CTEs, window functions, cohort/funnel/dedup patterns, and dialect gotchas. Adapted from anthropics/knowledge-work-plugins/sql-queries.
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explore-data
Profile a dataset before analyzing it — structure, column classification, per-column stats, quality flags, and relationships. Adapted from anthropics/knowledge-work-plugins/explore-data.
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statistical-analysis
Apply sound statistics — distributions, trends, outliers, significance — and flag traps like correlation-vs-causation and Simpson's paradox. Adapted from anthropics/knowledge-work-plugins/statistical-analysis.
Folders 1
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Data Exploration
Requirements
- Connected product analytics — Queries run against your connected product analytics through PostHog. The analyst reads the schema, runs read-only queries, and validates results. Until you connect it, it works from attached exports and the schema document.
Setup guide
How to Automate SQL Data Exploration
A practical guide to answering business questions with profiled data, checked SQL, statistical caveats, and approval.
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