Data analytics skills for PMs: SQL query generation, cohort analysis, A/B test analysis, funnel analysis, event tracking planning, metric definition, and North Star metric definition.
9 on GitHubby Tarun Narangv1.2.0MITupdated 1 month agoproduct-management
/
Plugin pm-data-analytics from marketplace pm-skills in tarunccet/pm-skills. Commands read from the repo's manifest — 1 month ago.
What it installs
Skills (7)
ab-test-analysis
Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant.
cohort-analysis
Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.
event-tracking-plan
Design an analytics instrumentation plan — define key events, properties, naming conventions, and produce a tracking spec document that engineers can implement. Use when setting up analytics for a new product, redesigning a tracking schema, or onboarding engineers to instrumentation work.
funnel-analysis
Analyze a conversion funnel — identify drop-off points, calculate stage-by-stage conversion rates, generate leakage hypotheses, and recommend improvement experiments. Use when diagnosing funnel performance, prioritizing optimization work, or designing experiments to improve conversion.
metric-definition
Define and distinguish operational, vanity, and actionable metrics. Write complete metric specs with name, definition, data source, calculation formula, owner, and review cadence. Use when formalizing metrics, building a metrics dictionary, or aligning a team on how metrics are measured.
product-metrics
Define a complete product metrics framework: North Star metric selection, input metrics, dashboard design, alert thresholds, and — for AI products — a four-layer AI metrics stack (model quality, operational, product-level, business). Use when setting up metrics for a new product, choosing a North St
sql-queries
Generate SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation. Use when writing SQL, building data reports, exploring databases, or translating business questions into queries.
Slash commands (7)
/pm-data-analytics:analyze-cohorts
Perform cohort analysis on user data — retention curves, feature adoption, and engagement trends
/pm-data-analytics:analyze-test
Analyze A/B test results — statistical significance, sample size validation, and ship/extend/stop recommendations
/pm-data-analytics:data-driven-discovery
Run a full data-driven product discovery cycle — from defining metrics through funnel analysis and experiments to actionable insights
/pm-data-analytics:design-funnel
Analyze a conversion funnel — identify drop-off points, calculate conversion rates at each stage, generate leakage hypotheses, and recommend improvement experiments
/pm-data-analytics:north-star
Define your North Star Metric and supporting input metrics — classify the business game and validate against best practices
/pm-data-analytics:plan-tracking
Design an analytics event tracking plan — define events, properties, naming conventions, and produce an engineer-ready tracking spec
/pm-data-analytics:write-query
Generate SQL queries from natural language — supports BigQuery, PostgreSQL, MySQL, and more
Interactive Socratic learning modules for PMs: guided exercises, simulations, and quiz checkpoints for discovery, strategy, metrics, prioritization, user research, stakeholder management, AI product management, and vibe coding.
Product strategy skills for PMs: vision, strategy canvas, value propositions, lean canvas, business model canvas, SWOT, PESTLE, Ansoff Matrix, Porter's Five Forces, monetization, pivot decisions, and devil's advocate stress-testing.
AI product management skills: model evaluation, responsible AI, AI metrics, prompt engineering, AI feature specs, build-vs-buy decisions, AI user research, data strategy, incident response, and competitive analysis.
Vibe coding skills for PM-builders: write AI coding specs, plan prototypes, make technical architecture decisions, review AI-generated code, run deployment checklists, debug with AI assistance, and understand codebases in PM-friendly terms.
Connect Claude Code directly to your CockroachDB clusters for hands-on database work — explore schemas, write optimized SQL, debug queries, and manage distributed database clusters. This plugin provides 14 tools across two active MCP backends (self-hosted MCP Toolbox and managed CockroachDB Cloud MCP Server), three specialized agents (DBA, Developer, Operator), 32 skills across 6 operational domains, and built-in safety hooks.
CockroachDB plugin for Claude Code — explore schemas, write optimized SQL, debug queries, and manage distributed database clusters directly from your AI coding agent.
Execution and product management skills: PRDs, OKRs, roadmaps, sprints, pre-mortems, stakeholder maps, user stories, prioritization frameworks, live Q&A and polls, and more.
Product, legal, and business specialists - product strategy, licensing, project management, UX research
VoltAgent/awesome-claude-code-subagents 26,513
Maintain pm-data-analytics?
This page updates itself from your repo. Add the badge to your README, or get it Featured to pin it above the organic results with a followed link to your site.