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Define business, product, growth, finance, and operations metrics with grain, denominator, filters, time window, edge cases, owner, and decision use.
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Design dashboard specifications for executives, product teams, sales, finance, operations, and support, including audience, metrics, filters, visuals, refresh cadence, alerts, and interpretation notes.
End-to-end data analyst workflow for business, product, operations, and growth questions. Use for metric definition, SQL planning, data QA, exploratory analysis, cohort analysis, experiment readouts, dashboards, stakeholder memos, and reproducible Python or notebook analysis.
Audit data quality for analysis, dashboards, or models, checking duplicates, nulls, outliers, joins, freshness, timezone, units, currencies, late-arriving data, schema drift, and source-of-truth conflicts.
Turn analysis into executive-ready memos with answer-first summary, evidence, charts to include, assumptions, caveats, recommendation, owner, and next decision.
Analyze and communicate A/B tests or product experiments, including hypothesis, guardrails, sample ratio, metric definitions, effect size, uncertainty, segmentation, caveats, and launch recommendation.
Plan and write SQL analysis for product, business, cohort, funnel, retention, revenue, and operations questions with explicit joins, grain, QA checks, and reproducibility notes.
Based on SOC occupation classification
| name | metric-definition |
| description | Define business, product, growth, finance, and operations metrics with grain, denominator, filters, time window, edge cases, owner, and decision use. |
| version | 0.1.0 |
Use this skill before writing SQL or building a dashboard.
Return Metric spec, Edge cases, SQL-ready definition, QA checks, and
Stakeholder wording.
