PostHog Analytics Expert

Transform PostHog data into actionable product insights. This skill combines product analytics expertise with the PostHog MCP server to help discover patterns, surface opportunities, and build a data-informed product strategy.

Product Context Management

Before diving into analysis, establish product context. Store discovered knowledge in

.claude/product-context.md

for persistence across sessions.

First Session: Discovery

Check for existing context: Read
```
.claude/product-context.md
```
if it exists
Interview the user (if context is missing or incomplete):
- What does the product do? Who are the users?
- What are the key user actions/conversions?
- What business metrics matter most?
Explore PostHog data:
- ```
event-definitions-list
```
  - Discover tracked events
- ```
properties-list
```
  - Understand available properties
- ```
insights-get-all
```
  - See existing insights
- ```
dashboards-get-all
```
  - Review current dashboards
Save context: Write discovered knowledge to
```
.claude/product-context.md
```

Context File Structure

markdown

# Product Context

## Product Overview
[What the product does, target users]

## Key Events
| Event | Meaning | Importance |
|-------|---------|------------|
| $pageview | Page visit | Navigation tracking |
| signup_completed | User registered | Core conversion |
| [custom events discovered] | | |

## Important Properties
- user_tier: free/pro/enterprise
- [other key properties]

## Key Metrics
- Primary: [e.g., Weekly Active Users, Conversion Rate]
- Secondary: [e.g., Feature Adoption, Retention]

## Funnels
- Activation: signup → onboarding_complete → first_value_action
- [other key funnels]

## Last Updated: [date]

Core Capabilities

1. Proactive Insight Discovery

When asked to "find insights" or "what's interesting", run this discovery workflow:

1. Trends Analysis
   - query-run: Total events over 30 days (spot volume changes)
   - query-run: DAU/WAU/MAU trends (engagement health)
   - query-run: Key conversion events over time

2. Funnel Health
   - query-run: Core activation funnel
   - query-run: Conversion funnel (trial → paid if SaaS)
   - Look for: Drop-off points, conversion changes

3. Retention Check
   - query-run: Cohort retention (week-over-week)
   - Look for: Retention curve shape, changes over time

4. Feature Adoption
   - query-run: Feature usage by user segment
   - Look for: Underused features, power user patterns

5. Error Impact
   - list-errors: Top errors by occurrence
   - error-details: Impact on user journeys

Insight Presentation Format:

## [Insight Title]
**Finding**: [One sentence summary]
**Evidence**: [Specific numbers/data]
**Impact**: [Why this matters]
**Recommended Action**: [What to do about it]

2. Answering Analytics Questions

Map common questions to PostHog queries:

Question Pattern	Approach
"How many users..."	`query-run` with TrendsQuery, `math: "dau"` or `"total"`
"What % convert..."	`query-run` with FunnelsQuery
"Where do users drop off..."	FunnelsQuery → analyze step-by-step conversion
"Which feature is most used..."	TrendsQuery with breakdown by feature/event
"How is X changing over time..."	TrendsQuery with `interval: "day"` or `"week"`
"Who are our power users..."	TrendsQuery with breakdown by user property
"What's causing errors..."	`list-errors` → `error-details` for top issues

3. Dashboard Creation

When building dashboards, follow this structure:

Executive Dashboard (high-level health):

Active users (DAU/WAU/MAU)
Core conversion rate
Retention (week 1, week 4)
Revenue metrics (if applicable)

Product Dashboard (feature-level):

Feature adoption rates
Feature engagement depth
User journey completion
Error rates by feature

Growth Dashboard (acquisition/activation):

Signup funnel
Activation funnel
Traffic sources (if tracked)
Onboarding completion

Workflow:

```
dashboard-create
```
with descriptive name
Build insights with
```
query-run
```
→
```
insight-create-from-query
```
Add to dashboard with
```
add-insight-to-dashboard
```
Organize with
```
dashboard-reorder-tiles
```

4. Experiment Design

When setting up A/B tests:

Clarify hypothesis: What change, expected impact, and why
Find existing flags:
```
feature-flag-get-all
```
(reuse if appropriate)
Choose metrics: Use
```
event-definitions-list
```
to find trackable events
Set up experiment:
```
experiment-create
```
with:
- Clear name and description
- Primary metric (what you're optimizing)
- Secondary metrics (guardrails)
- Appropriate sample size (MDE guidance)

See references/experiments.md for detailed experiment patterns.

5. Cohort & Segment Analysis

For understanding user segments:

1. Define cohort criteria (user properties, behaviors)
2. Compare cohorts on key metrics:
   - query-run with breakdownFilter by cohort property
   - Conversion rates per segment
   - Retention per segment
3. Identify highest-value segments
4. Recommend targeting strategies

Query Patterns

TrendsQuery (counts over time)

json

{
  "kind": "InsightVizNode",
  "source": {
    "kind": "TrendsQuery",
    "dateRange": {"date_from": "-30d"},
    "interval": "day",
    "series": [{
      "kind": "EventsNode",
      "event": "event_name",
      "custom_name": "Display Name",
      "math": "total"
    }]
  }
}

Math options:

total

dau

weekly_active

monthly_active

unique_session

avg

sum

min

max

FunnelsQuery (conversion analysis)

json

{
  "kind": "InsightVizNode",
  "source": {
    "kind": "FunnelsQuery",
    "dateRange": {"date_from": "-30d"},
    "series": [
      {"kind": "EventsNode", "event": "step_1", "custom_name": "Step 1"},
      {"kind": "EventsNode", "event": "step_2", "custom_name": "Step 2"},
      {"kind": "EventsNode", "event": "step_3", "custom_name": "Step 3"}
    ],
    "funnelsFilter": {
      "funnelWindowInterval": 7,
      "funnelWindowIntervalUnit": "day"
    }
  }
}

Breakdown Analysis

Add to any query:

json

"breakdownFilter": {
  "breakdown": "property_name",
  "breakdown_type": "event"  // or "person"
}

SaaS Metrics Framework

For SaaS products, prioritize these metrics:

Metric	Query Approach	Why It Matters
Activation Rate	Funnel: signup → key_action	Validates onboarding
DAU/MAU Ratio	Trends: DAU ÷ MAU	Engagement stickiness
Feature Adoption	Trends: feature_used by user	Product-market fit signals
Retention (D7, D30)	Cohort retention query	Long-term value predictor
Conversion (Trial→Paid)	Funnel: trial_start → subscription	Revenue health
Expansion Revenue	Trends: upgrade events	Growth efficiency
Churn Indicators	Declining usage patterns	Early warning system

Resources

references/experiments.md - Detailed experiment design patterns
references/saas-playbook.md - SaaS-specific analytics strategies

posthog-analytics

NPX Install

Tags

SKILL.md Content

PostHog Analytics Expert

Product Context Management

First Session: Discovery

Context File Structure

Core Capabilities

1. Proactive Insight Discovery

2. Answering Analytics Questions

3. Dashboard Creation

4. Experiment Design

5. Cohort & Segment Analysis

Query Patterns

TrendsQuery (counts over time)

FunnelsQuery (conversion analysis)

Breakdown Analysis

SaaS Metrics Framework

Resources