dashboard-expert
openbooklet.com/s/dashboard-expertopenbooklet.com/s/dashboard-expert@1.0.0GET /api/v1/skills/dashboard-expertFull CRUD and analysis for Mixpanel dashboards. Use when the user asks to build, create, analyze, read, understand, explain, modify, update, enhance, or manage dashboards, or asks about dashboard layout, text cards, or report arrangement. Covers dashboard analysis (read + understand existing), creation (new builds), modification (update existing), and explanation (data-driven annotation).
This skill should be used when the user asks about Mixpanel product analytics, event data, funnel analysis, retention curves, cohort analysis, segmentation queries, JQL, user behavior, conversion rates, churn, DAU/MAU, ARPU, revenue metrics, feature adoption, A/B test results, or any request to query, explore, visualize, or analyze Mixpanel data using Python.
Analyze Mixpanel analytics data using the mixpanel_data Python library or mp CLI. Use when working with Mixpanel event data, user profiles, funnels, retention, cohorts, segmentation queries, JQL scripts, or SQL analysis on local DuckDB. Triggers on mentions of Mixpanel, event analytics, funnel analysis, retention curves, user behavior tracking, JQL queries, filter expressions, 'fetch data from Mixpanel', 'query Mixpanel with SQL', 'run DuckDB queries on events', 'analyze user behavior', 'export Mixpanel data', 'mp command', or requests to work with analytics pipelines. Supports filter expressions for WHERE/ON clauses, JQL (JavaScript Query Language) for complex transformations, Python scripts with pandas integration, and CLI pipelines with jq/Unix tools.
This skill should be used when the user asks about Mixpanel product analytics, event data, funnel analysis, retention curves, cohort analysis, segmentation queries, JQL, user behavior, conversion rates, churn, DAU/MAU, ARPU, revenue metrics, feature adoption, A/B test results, user paths, flow analysis, or any request to query, explore, visualize, or analyze Mixpanel data using Python.
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
Generate a custom checklist for the current feature based on user requirements.
Auto-commit changes after a Spec Kit command completes
Create a feature branch with sequential or timestamp numbering
Initialize a Git repository with an initial commit
Detect Git remote URL for GitHub integration
Validate current branch follows feature branch naming conventions
Execute the implementation plan by processing and executing all tasks defined in tasks.md
Create or update the feature specification from a natural language feature description.
Convert existing tasks into actionable, dependency-ordered GitHub issues for the feature based on available design artifacts.
Auto-indexed from jaredmcfarland/mixpanel_data
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