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mixpanelyst

Query and analyze Mixpanel product analytics data using Python and the mixpanel_headless library.

5.3k|765|Updated Mar 4, 2026
Search Tags:#Python#Pandas#retention#cohort analysis#funnel analysis#Mixpanel#product analytics
One-click install
npx skills add https://github.com/openai/plugins --skill mixpanelyst
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mixpanelyst
Source: https://github.com/openai/plugins/tree/main/plugins/mixpanel-headless/skills/mixpanelyst
Command: npx skills add https://github.com/openai/plugins --skill mixpanelyst

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires mixpanel_headless, pandas, and includes scripts (resource) components.

What problem does it solve?

Analyzing Mixpanel product analytics data requires knowing exact event names, property schemas, and API parameters, and guessing them causes silent empty results or TypeErrors. This Skill provides a structured workflow for discovering the data schema and API surface before writing queries, so funnel, retention, flow, and segmentation analyses return correct results.

Core Features & Use Cases

  • Five Query Engines: Run insights queries, funnel conversion analysis, retention curves, user flow/path analysis, and user profile queries through the mixpanel_headless Workspace API, with results returned as pandas DataFrames.
  • Schema and API Discovery: List real event names, properties, and property values before querying, and use the bundled help.py script to look up exact method signatures, type constructors, and enum values.
  • Exploratory Analysis Workflow: Follow a systematic five-step process to orient on the event schema, classify properties, scan for significant segments, deep-dive on findings, and clean messy string properties with custom property formulas.
  • Use Case: A product analyst asks why checkout conversion dropped. The Skill discovers the real event names, builds a funnel with an appropriate conversion window, compares segments across platforms, and quantifies the drop-off step with median time-to-convert.

Quick Start

Ask the assistant to analyze Mixpanel data, for example: show the signup-to-purchase funnel conversion rate by platform over the last 90 days.

Frequently Asked Questions about mixpanelyst

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I query Mixpanel data with Python?▼

Use the mixpanel_headless library by creating a Workspace object and calling ws.query() with an event name and time range. Results return as pandas DataFrames via the .df property, and you can apply filters, group-bys, and math aggregations like total, unique, or average.

How do I build a funnel analysis in Mixpanel programmatically?▼

Call ws.query_funnel() with a list of at least two step event names and a conversion window matched to your user journey. The result includes per-step conversion ratios and average time-to-convert, and you can segment with group_by or filter with where clauses.

Why does my Mixpanel query return empty results?▼

Empty results usually come from guessing event or property names that do not exist in the project. Always run ws.events() and ws.properties(event) first to discover the real schema, and verify filter values with ws.property_values() before querying.

How do I find the correct API parameters for mixpanel_headless methods?▼

Run the bundled help.py script with the method or type name, such as python3 scripts/help.py Workspace.query_funnel, to see exact signatures, parameter names, and enum values. This prevents TypeErrors caused by guessing parameter names.

Can I analyze retention and user cohorts with mixpanel_headless?▼

Yes, ws.query_retention() computes retention curves given a born event and return event, with configurable retention units, alignment, and bucket sizes. You can also break results down by saved cohorts or properties using group_by parameters.

What are the limitations of Mixpanel custom property formulas?▼

The regex engine is case-insensitive by default, does not support \d or \w shorthand classes, and curly-brace quantifiers like {4} conflict with formula syntax. Use explicit character classes like [0-9] and repeated classes instead of quantifiers.

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