data-stack

Recommends which data skill to run next. Use when starting a new analysis session or unsure where to begin. Asks what you are trying to accomplish and recommends the right skill with reasoning. Trigger phrases: "where should I start?", "what skill should I use?", "advisor", "help me figure out what to run", "I don't know where to begin".

Period-over-period or cohort comparison. Use to understand what changed between two time periods, groups, or experiments. Breaks down the delta by key dimensions to find where the difference comes from. Trigger phrases: "last week vs prior week", "before vs after launch", "compare these two groups", "A/B results", "what changed?", "why is X different?".

Dataset discovery and schema mapping. Use when starting fresh with data, exploring what's available, or mapping a new data source. Opens an Upsolve thread, surfaces available tables and schemas, profiles row counts and key columns, and outputs a structured data map. Strongly recommended as the first step before any analysis. Trigger phrases: "what data do I have?", "show me my tables", "explore this dataset", "what's available?", "I'm new to this data".

Funnel drop-off analysis. Define conversion steps and get conversion rates, absolute drop-off counts, and segment breakdowns at each stage. Use for product analytics, marketing funnels, checkout flows, or any sequential user journey. Trigger phrases: "funnel analysis", "where are users dropping off?", "conversion rate", "show me the funnel", "why aren't users converting?".

Anomaly root cause analysis. Use when a metric looks wrong or surprising. Runs a structured 4-phase investigation over a single Upsolve thread: confirm → narrow → hypothesize → validate. Findings use numbered codes (1A CONFIRMED, 1B CANDIDATE, 2A RULED_OUT). Trigger phrases: "revenue dropped", "metric is spiking", "why did X change?", "something looks wrong", "root cause", "debug this metric".

KPI deep dive report. Given a metric name, produces a structured brief: current value, WoW and MoM trend, top contributing segments, and notable outliers. Outputs a shareable summary for Slack, docs, or stakeholder updates. Trigger phrases: "summarize this metric", "metric brief", "what's happening with X", "give me a KPI summary", "how is [metric] doing?", "weekly metric update".

Data pipeline health check. Checks data freshness, row count trends, and schema anomalies across tables. Outputs a health dashboard with OK/WARN/FAIL per table. Use for daily pipeline monitoring, SLA checks, or debugging stale data. Trigger phrases: "check pipeline health", "is my data fresh?", "data SLA", "pipeline monitoring", "are my tables up to date?", "something seems stale".

Data quality audit for a table or dataset. Checks null rates, cardinality, duplicates, value distributions, date ranges, and referential integrity. Outputs a quality scorecard with OK/WARN/FAIL ratings per dimension. Run before building models, pipelines, or reports on top of a dataset. Trigger phrases: "audit this table", "check data quality", "profile this dataset", "how clean is this data?", "is this table safe to use?".

Check and verify the Upsolve MCP connection and data-stack prerequisites. Use when first setting up, troubleshooting a connection issue, or verifying the environment is ready for data analysis. Trigger phrases: "setup", "check connection", "is Upsolve connected?", "troubleshoot", "verify setup", "MCP not working".

Upgrade data-stack to the latest version. Detects global vs vendored install, runs the upgrade, and shows what's new.