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product-analytics Deep integration with product analytics platforms for metrics, funnels, retention, and experimentation. Query Amplitude/Mixpanel/Heap data, generate retention curves, calculate conversion metrics, and build dashboard configurations.
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name product-analytics description Deep integration with product analytics platforms for metrics, funnels, retention, and experimentation. Query Amplitude/Mixpanel/Heap data, generate retention curves, calculate conversion metrics, and build dashboard configurations. allowed-tools Read, Grep, Write, Bash, Edit, Glob graph {"domains":["domain:software-engineering"],"specializations":["specialization:product-management"],"skillAreas":["skill-area:product-analytics","skill-area:a-b-testing"],"roles":["role:product-manager","role:product-analyst"],"workflows":["workflow:product-discovery","workflow:competitive-analysis"]}
Product Analytics Skill
Query and analyze product analytics data for metrics definition, funnel analysis, retention curves, and experiment tracking.
Overview
This skill provides comprehensive capabilities for working with product analytics platforms. It enables data-driven product decisions through metric queries, funnel analysis, cohort retention tracking, and dashboard generation.
Capabilities
Analytics Platform Integration
Query Amplitude, Mixpanel, Heap, GA4 data
Execute custom event queries
Pull predefined report data
Sync metric definitions
Funnel Analysis
Define and calculate conversion funnels
Identify drop-off points and friction
Segment funnels by user attributes
Compare funnel performance over time
Retention Analysis
Generate retention curves and matrices
Calculate cohort retention rates
Analyze retention by user segment
Identify retention drivers and predictors
Metric Definition
Define North Star and supporting metrics
Create event tracking specifications
Document metric calculations
Build metric hierarchies (trees)
Dashboard Configuration
Generate dashboard layouts
Configure chart specifications
Define alert thresholds
Export dashboard configs
Prerequisites
Analytics Platform Access Supported Platforms:
- Amplitude (API key required)
- Mixpanel (service account)
- Heap (API access)
- Google Analytics 4 (BigQuery export)
- Posthog (API key)
Configuration {
"platform" : "amplitude" ,
"credentials" : {
"api_key" : "${AMPLITUDE_API_KEY}" ,
"secret_key" : "${AMPLITUDE_SECRET_KEY}"
} ,
"project_id" : "123456" ,
"timezone" : "America/Los_Angeles"
}
Usage Patterns
Funnel Analysis Query ## Funnel Definition
### Funnel: Signup to First Value
**Steps** :
1. Page View: /signup
2. Event: signup_started
3. Event: signup_ completed
4. Event: first_action_ completed
**Filters** :
- Platform: web
- Date range: last 30 days
- New users only
**Segmentation** :
- By traffic source
- By device type
Funnel Query Example (Amplitude-style)
funnel_config = {
"events" : [
{"event_type" : "signup_started" },
{"event_type" : "signup_completed" },
{"event_type" : "onboarding_completed" },
{"event_type" : "first_value_action" }
],
"filters" : {
"platform" : ["web" , "ios" , "android" ],
"date_range" : {
"start" : "2026-01-01" ,
"end" : "2026-01-24"
}
},
"conversion_window" : "7 days" ,
"group_by" : ["platform" , "utm_source" ]
}
funnel_results = {
"overall" : {
"step_1" : {"users" : 10000 , "rate" : 1.0 },
"step_2" : {"users" : 6500 , "rate" : 0.65 },
"step_3" : {"users" : 4200 , "rate" : 0.65 },
"step_4" : {"users" : 2100 , "rate" : 0.50 }
},
"overall_conversion" : 0.21 ,
"segments" : {
"web" : {"conversion" : 0.18 },
"ios" : {"conversion" : 0.25 },
"android" : {"conversion" : 0.19 }
}
}
Retention Analysis ## Retention Query
### Cohort Definition
- **Cohort by** : signup_date (weekly)
- **Retention event** : any_ active_event
- **Time periods** : Day 1, 7, 14, 30, 60, 90
### Output: Retention Matrix
| Cohort Week | Users | D1 | D7 | D14 | D30 | D60 | D90 |
|-------------|-------|-----|-----|-----|-----|-----|-----|
| Jan 1-7 | 1000 | 45% | 30% | 25% | 20% | 15% | 12% |
| Jan 8-14 | 1200 | 48% | 32% | 27% | 22% | - | - |
| Jan 15-21 | 1100 | 46% | 31% | - | - | - | - |
Retention Query Example
retention_config = {
"cohort_definition" : {
"event" : "signup_completed" ,
"grouping" : "week"
},
"retention_event" : {
"event_type" : "any_active" ,
"conditions" : ["page_view" , "feature_used" , "content_created" ]
},
"periods" : [1 , 7 , 14 , 30 , 60 , 90 ],
"date_range" : {
"start" : "2025-10-01" ,
"end" : "2026-01-24"
},
"segments" : ["subscription_tier" , "signup_source" ]
}
retention_results = {
"retention_matrix" : [
{
"cohort" : "2025-W40" ,
"cohort_size" : 1000 ,
"retention" : {
"D1" : 0.45 ,
"D7" : 0.30 ,
"D14" : 0.25 ,
"D30" : 0.20 ,
"D60" : 0.15 ,
"D90" : 0.12
}
}
],
"averages" : {
"D1" : 0.46 ,
"D7" : 0.31 ,
"D14" : 0.26 ,
"D30" : 0.21 ,
"D60" : 0.16 ,
"D90" : 0.13
},
"trends" : {
"D30_trend" : "+2%" ,
"D7_trend" : "-1%"
}
}
Metric Definition Specification ## Metric Specification Template
### Metric: Weekly Active Users (WAU)
**Definition** : Unique users who performed at least one qualifying action in a 7-day period.
**Calculation** :
```sql
SELECT COUNT(DISTINCT user_id)
FROM events
WHERE event_type IN ('page_view', 'feature_used', 'content_created')
AND event_timestamp >= CURRENT_DATE - INTERVAL '7 days'
page_view (any page)
feature_used
content_created
content_shared
Bot traffic (user_agent filter)
Internal users (email domain filter)
By platform (web, ios, android)
By subscription tier
By signup cohort
Warning: >5% week-over-week decline
Critical: >10% week-over-week decline
### Event Tracking Specification
```json
{
"event_name": "feature_used",
"description": "User interacted with a product feature",
"category": "engagement",
"properties": {
"feature_name": {
"type": "string",
"required": true,
"description": "Name of the feature used",
"examples": ["search", "export", "share"]
},
"feature_version": {
"type": "string",
"required": false,
"description": "Version of the feature"
},
"action": {
"type": "string",
"required": true,
"enum": ["click", "view", "complete", "cancel"]
},
"duration_ms": {
"type": "integer",
"required": false,
"description": "Time spent on feature"
}
},
"user_properties": {
"subscription_tier": "string",
"signup_date": "date"
}
}
Integration with Babysitter SDK
Task Definition Example const analyticsQueryTask = defineTask ({
name : 'analytics-query' ,
description : 'Query product analytics data' ,
inputs : {
queryType : { type : 'string' , required : true },
config : { type : 'object' , required : true },
platform : { type : 'string' , default : 'amplitude' },
dateRange : { type : 'object' , required : true }
},
outputs : {
results : { type : 'object' },
visualizations : { type : 'array' },
insights : { type : 'array' }
},
async run (inputs, taskCtx ) {
return {
kind : 'skill' ,
title : `Run ${inputs.queryType} analysis` ,
skill : {
name : 'product-analytics' ,
context : {
operation : inputs.queryType ,
config : inputs.config ,
platform : inputs.platform ,
dateRange : inputs.dateRange
}
},
io : {
inputJsonPath : `tasks/${taskCtx.effectId} /input.json` ,
outputJsonPath : `tasks/${taskCtx.effectId} /result.json`
}
};
}
});
Dashboard Configuration
Dashboard Specification {
"dashboard_name" : "Product Health Dashboard" ,
"refresh_interval" : "1h" ,
"layout" : {
"columns" : 3 ,
"rows" : 4
} ,
"widgets" : [
{
"id" : "wau_trend" ,
"type" : "line_chart" ,
"position" : { "row" : 1 , "col" : 1 , "width" : 2 } ,
"metric" : "weekly_active_users" ,
"time_range" : "90d" ,
"comparison" : "previous_period"
} ,
{
"id" : "retention_heatmap" ,
"type" : "heatmap" ,
"position" : { "row" : 1 , "col" : 3 , "width" : 1 } ,
"metric" : "cohort_retention" ,
"periods" : [ 1 , 7 , 30 ]
} ,
{
"id" : "funnel_chart" ,
"type" : "funnel" ,
"position" : { "row" : 2 , "col" : 1 , "width" : 3 } ,
"funnel_id" : "signup_to_activation" ,
"segments" : [ "platform" ]
}
] ,
"alerts" : [
{
"metric" : "weekly_active_users" ,
"condition" : "decrease_percent > 5" ,
"severity" : "warning" ,
"notification" : "slack"
}
]
}
Output Formats
Funnel Analysis Report # Funnel Analysis Report: Signup to First Value
## Overview
- **Period** : January 1-24, 2026
- **Total Users** : 10,000
- **Overall Conversion** : 21%
## Step-by-Step Analysis
| Step | Event | Users | Conv Rate | Drop-off |
|------|-------|-------|-----------|----------|
| 1 | signup_started | 10,000 | 100% | - |
| 2 | signup_ completed | 6,500 | 65% | 35% |
| 3 | onboarding_completed | 4,200 | 65% | 35% |
| 4 | first_ value_action | 2,100 | 50% | 50% |
## Key Insights
1. **Biggest Drop-off** : Step 4 (onboarding to first value) - 50% drop
2. **Best Performing Segment** : iOS users (25% overall conversion)
3. **Opportunity** : Mobile onboarding flow optimization
## Recommendations
1. Simplify first value action guidance
2. Add progress indicators in onboarding
3. Implement re-engagement for drop-offs at step 3
Best Practices
Define Metrics Clearly : Document calculation logic and edge casesUse Consistent Time Zones : Align all queries to single timezoneSegment Everything : Always analyze by key user segmentsValidate Data Quality : Check for tracking gaps and anomaliesVersion Event Schemas : Track changes to event definitionsSet Appropriate Alerts : Avoid alert fatigue with meaningful thresholds
References 