data

# Data Skill Two modes. Match the request to a section. | Signal | Mode | |--------|------| | Analyze data, CSV, metrics, A/B test, trend, KPI, funnel, distribution | A. Data Analysis | | Enrich references, generate references, decompose skill, improve depth | B. Reference Enrichment | --- ## A. Data Analysis Every analysis starts with the decision it supports, works backward to evidence required, then touches the data. Analysis without a decision is arithmetic. ### Phase 1: FRAME Establish what decision this analysis supports. 1. Identify the decision, decision-maker, options, and default action if no analysis is done. 2. If the user cannot articulate a decision, ask: "What will you do differently based on this analysis?" If exploratory, switch to Exploratory Mode (apply rigor gates, make no causal claims). 3. Define evidence requirements: what evidence favors each option, minimum threshold for changing the default, deal-breakers. 4. Save `analysis-frame.md`. **Gate**: Decision identified, options enumerated, evidence requirements saved. ### Phase 2: DEFINE Lock metric definitions before loading data. Defining after seeing data enables cherry-picking. For each metric: name, exact formula (numerator/denominator), population (included/excluded), time window, segments. For comparisons: define groups and verify fairness. Save `metric-definitions.md`. Definitions are locked once Phase 3 starts. If data reveals a definition is unworkable, return here, update, and docu...