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Posthog Onboarding

PostHogOfficialPublished by PostHog

  • Skill
  • Workflow
  • Data and AI
  • MIT
  • No issues found

From the author

Help existing PostHog customers improve their PostHog instance. Triggers on "help [customer] improve their PostHog setup", "audit [company]'s PostHog instance", "create tracking plan for [company]", "design data schema for [customer]", or requests to improve analytics coverage, fix instrumentation gaps, expand PostHog usage, or build better insights for customers already using PostHog. Use when working with a customer who already has PostHog installed.
— the description by PostHog

From the skill’s files

Taken from the skill’s own files (its description, section headings and stated usage). Not an AutoGineer summary, and not written by the author as one.

Help existing PostHog customers improve their PostHog instance. Triggers on "help [customer] improve their PostHog setup", "audit [company]'s PostHog instance", "create tracking plan for [company]", "design data schema for [customer]", or requests to improve analytics coverage, fix instrumentation gaps, expand PostHog usage, or build better insights for customers already using PostHog. Use when working with a customer who already has PostHog installed. Help existing PostHog customers get more value from their instance. This skill is for Customer Success, not sales — the customer already has PostHog installed, something isn't working well, and your job is to diagnose the gaps and give them a clear path forward.

What it does

  • Workflow
  • Step 1: Research
  • Step 2: Current State
  • Step 3: Customer Profile
  • Step 4: Ideal Schema
  • Step 5: Insights Plan

When to use it

  • Triggers on "help [customer] improve their PostHog setup", "audit [company]'s PostHog instance", "create tracking plan for [company]", "design data schema for [customer]", or requests to improve…
  • Use when working with a customer who already has PostHog installed.

Advantages

The skill’s files do not state this.

Usage examples

The skill’s files do not state this.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/posthog/posthog-onboarding/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip posthog-onboarding.zip -d .agents/skills

Documentation by PostHog

PostHog CS Improvement Skill

Help existing PostHog customers get more value from their instance. This skill is for Customer Success, not sales — the customer already has PostHog installed, something isn't working well, and your job is to diagnose the gaps and give them a clear path forward.

Core assumption: Their current implementation is probably underinstrumented, inconsistently named, or not being used by the right people. Start from that baseline.

Workflow

  1. Research – Understand what the company builds

  2. Current State – Ask the customer to describe their setup and pain points

  3. Customer Profile – Synthesize into a structured profile

  4. Ideal Schema – Design best-in-class events and properties for their business type

  5. Insights Plan – Prescribe insights by problem + maturity (see references/insights-plan.md)

  6. Improvement Plan – Prioritized, actionable recommendations

  7. Output – Customer-facing improvement plan

Writing Style: Follow references/writing-style.md for all outputs. Conversational and direct — not corporate, not salesy.

Step 1: Research

When triggered with a company name or domain, research:

  • What they build (product/service)

  • Their customers (B2B/B2C, market)

  • Business model (pricing, signup flow)

  • Tech signals (job postings, docs, stack)

Determine business type: B2B SaaS, B2C SaaS, E-commerce, Marketplace, Developer Tools, Fintech, Healthcare, Content/Media

See references/business-types.md for events and metrics by type.

Step 2: Current State

The goal here is to understand what's broken, missing, or ignored — not to validate what's working. Ask questions that surface problems.

Ask 2–3 questions max per message. Skip anything already known from research or context.

Must-Have
ContextWhyQuestion
Primary pain pointDrives everything"What's the #1 thing PostHog isn't giving you right now?"
Key user journeysCore of any tracking plan"What are the 2–3 most important flows in your product?"
Current eventsIdentifies gaps"What events are you tracking today? What do you wish you were tracking?"
Who uses PostHogShapes recommendations"Which teams actually open PostHog — and which ones should but don't?"
Products in useFinds expansion areas"Which PostHog products are you actively using vs. ignoring?"
Should-Have
  • How long they've had PostHog

  • Whether engineers are engaged or if CS/PM are blocked waiting for instrumentation

  • Any previous attempts to fix the problem that didn't stick

  • Compliance constraints

Step 3: Customer Profile

## Customer Profile: [Company]

### Overview
- **Business Type:** [type]
- **What They Build:** [1–2 sentences]
- **Their Customers:** [B2B/B2C, who]
- **Revenue Model:** [subscription/usage/etc]
- **Stage:** [Seed/A/B, employee count]

### PostHog Setup
- **Time Using PostHog:** [months/years]
- **Products Active:** [Analytics, Replay, Flags, Experiments, Surveys, etc]
- **Analytics Maturity:** [Beginner/Intermediate/Advanced]
- **Who Uses PostHog:** [roles]
- **Who Doesn't (But Should):** [roles being left out]

### Current State
- **What They Think Is Working:** [their perception]
- **Primary Pain Point:** [the #1 problem, in their words]
- **Likely Gaps:** [based on what they told you — missing events, underused products, no dashboards, wrong people]

### Technical Context
- **Stack:** [web/mobile/backend, frameworks]
- **User Volume:** [DAU/MAU or events/month if known]
- **Compliance:** [requirements if any]

### Their Product
- **Key Journeys:** [1] [2] [3]
- **Activation Metric:** [what success looks like for their users]

Step 4: Ideal Schema

Don't try to audit what they have — you don't have visibility into their data. Instead, design the best-in-class schema for their business type and key journeys, then use it as a benchmark. The customer can map their current tracking against it.

See references/data-schema.md for full property lists, group schemas, and AARRR event templates. See references/business-types.md for events and metrics specific to their business type.

What to design

Person properties — what you'd want to know about every user (role, plan, signup source, activation status, etc.)

Group properties — for B2B: org name, plan, seat count, MRR, health score

Core events — cover the full AARRR journey for their product type:

  • Acquisition: how users arrive and sign up

  • Activation: the moment they first get value

  • Retention: the actions that predict they'll come back

  • Referral: sharing, inviting, expanding

  • Revenue: upgrade, purchase, renewal

Key properties on each event — include context that makes the event useful: source, method, plan, is_first_time, duration_seconds, etc.

Naming conventions
  • snake_case for everything

  • is_ prefix for booleans

  • _count suffix for numbers

  • _at suffix for timestamps

  • Be specific: subscription_upgraded not upgrade

Present this as: "Here's what great looks like for a company like yours — let's figure out how close you are."

Step 5: Insights Plan

Prescribe insights based on their pain point and maturity. See references/insights-plan.md for full detail.

Maturity Levels

Beginner: No analytics or just GA, no custom events, no dashboards Intermediate: Some events, knows funnels/cohorts, has used Mixpanel or Amplitude Advanced: Mature tracking, warehouse integration, running experiments

By Problem
ProblemBeginnerIntermediateAdvanced
ChurnRetention chart, replays of churned usersBehavioral cohorts, correlation analysisLTV analysis, churn prediction experiments
Trial conversionSignup funnel, replaysBreakdown by source/planExit surveys, A/B test CTAs
OnboardingStep funnel, replaysPaths, device breakdownA/B test flows, in-product surveys
Feature adoptionUsage trends, stickinessFeature retention, power usersCorrelation with retention
PMF12-week retention, WAU/MAUPower user cohortsSean Ellis survey

Always suggest 2–3 capabilities they're probably not using:

  • Session Replay with filters (most underused)

  • Correlation analysis

  • Feature flags for safe rollouts

  • In-product surveys

  • User paths

  • Group analytics for B2B accounts

Step 6: Improvement Plan

Prioritize by what unblocks the most value fastest. Frame as three phases:

Fix first (Days 1–7): Broken or missing events that are blocking any meaningful analysis. Naming fixes. Enabling products they already have access to.

Strengthen (Week 2–3): Add tracking for key journeys identified in Step 2. Build 2–3 core dashboards tied directly to their pain point. Get the right people in PostHog.

Expand (Week 3+): Add PostHog products they're not using (Flags, Experiments, Surveys). Deepen with group analytics or warehouse if relevant.

SDK reference (for instrumentation fixes)
StackSDKNotes
React/Next.jsposthog-js + ProviderUse PostHogProvider
Vue/Nuxtposthog-jsManual init
React Nativeposthog-react-nativeHandles persistence
Flutterposthog-flutterDart
iOSposthog-iosSwift/ObjC
Androidposthog-androidKotlin/Java
Node.jsposthog-nodeServer-side
Pythonposthog-pythonDjango/Flask/FastAPI
Watch-outs
  • No engineering bandwidth to implement fixes — flag this early, don't build a plan that requires it

  • Pain point is vague — keep asking until it's specific and measurable

  • Tracking exists but no one looks at it — this is an insights gap, not a data gap; solve differently

  • The champion isn't actually using PostHog — whoever you're talking to needs to be a real user

Step 7: Output — Customer Improvement Plan

One document, shared with the customer. Conversational and direct — written like a PostHog doc, not a sales deck.

# PostHog improvement plan – [Company]

## What we're trying to fix
[One or two sentences. Name the problem specifically — not "improve analytics" but "understand why users churn in week 2" or "know which features drive retention."]

## Where you are now
[Honest, kind assessment. What's working, what isn't, and what's missing. Don't sugarcoat but don't be harsh. 3–5 sentences.]

## The ideal setup for a company like yours
[Brief description of what best-in-class looks like for their business type — reference the ideal schema from Step 4. Frame as "here's where you want to get to."]

## What we recommend

### Fix first
[2–3 specific, immediately actionable fixes. Name the event or property. Explain what it unlocks in one sentence.]

### Events to add
[Key missing events from the ideal schema. For each: event name, why it matters, one key property to include.]

### Insights to build
[3–5 specific insights tied to their pain point. Name the insight type, what question it answers.]

### Products to start using
[Any PostHog products they have access to but aren't using. One sentence on why each is relevant to their problem.]

## Your action plan

### Week 1
**Your team:**
- [ ] [Specific action + who owns it]

**We'll handle:**
- [ ] [What PostHog CS will do]

**End of week 1 goal:** [What "done" looks like]

### Week 2–3
[Next phase actions, same format]

## How we'll know it's working
| What | Target | How to check |
|------|--------|--------------|
| [Metric] | [Specific number or outcome] | [Where to look in PostHog] |

Reference Files

  • references/data-schema.md – Person/group/event properties with stakeholder value

  • references/insights-plan.md – Insights by problem and maturity

  • references/business-types.md – Events and metrics by business type

  • references/writing-style.md – PostHog writing guidelines

Files

references/insights-plan.md

Insights Plan Reference

Prescribe specific insights based on customer's stated problem and analytics maturity.

Analytics Maturity Levels

Beginner
  • No analytics or just Google Analytics

  • No custom event tracking beyond pageviews

  • No dashboards or regular reporting

  • Team unfamiliar with product analytics concepts

Intermediate
  • Has used Mixpanel, Amplitude, or similar

  • Some custom event tracking in place

  • Basic dashboards exist

  • Knows what funnels/cohorts are

Advanced
  • Mature tracking plan with consistent naming

  • Data warehouse integration

  • Uses cohorts, experiments, feature flags

  • Has dedicated analytics/data person


Problem: "Understand why users churn"

MaturityInsight TypeSpecific InsightWhat It ShowsPostHog Feature
BeginnerRetentionWeek-over-week retention by signup cohortBasic retention patternsRetention insight
BeginnerReplayRecordings filtered to churned usersWhat they did before leavingSession Replay
BeginnerTrendActive users over time (7d, 30d)Is retention improving?Trends
IntermediateFunnelActivation funnel with conversion timeWhere users drop offFunnels
IntermediateCohort"Did X" vs "Didn't do X" cohortsFeatures correlated with retentionCohorts
IntermediateRetentionRetention by initial referrer/campaignWhich channels bring retained usersRetention
AdvancedCorrelationFunnel correlation analysisStatistical churn predictorsFunnel correlation
AdvancedSQLLTV by signup sourceWhich sources most valuableSQL insights
AdvancedExperimentTest onboarding variantsProve changes improve retentionExperiments
Quick Wins (< 30 min total)
  1. Build retention chart (5 min)

  2. Watch 10 replays of churned users (20 min)

  3. Create "Active" vs "Churned" cohorts (5 min)


Problem: "Improve trial-to-paid conversion"

MaturityInsight TypeSpecific InsightWhat It ShowsPostHog Feature
BeginnerFunnelSignup → Activation → ConversionWhere trials failFunnels
BeginnerTrendDaily trial starts vs conversionsConversion rate over timeTrends
BeginnerReplayTrial users who didn't convertWhat blocked themSession Replay
IntermediateFunnelOnboarding steps funnelWhich steps cause drop-offFunnels
IntermediateBreakdownConversion by signup sourceWhich channels convert bestTrends + Breakdown
IntermediateRetentionTrial engagement by dayWhen engagement dropsRetention
AdvancedCorrelationTrial behaviors → conversionWhat predicts conversionCorrelation
AdvancedExperimentTest pricing page variantsOptimize conversionExperiments
AdvancedSurveyExit survey for non-convertersWhy they didn't convertSurveys
Quick Wins
  1. Build trial funnel: Signup → [Activation] → Payment (10 min)

  2. Add breakdown by signup_source (5 min)

  3. Watch 5 replays of trials that dropped at activation (15 min)


Problem: "Improve onboarding completion"

MaturityInsight TypeSpecific InsightWhat It ShowsPostHog Feature
BeginnerFunnelOnboarding steps (Step 1 → 2 → 3 → Done)Where users drop offFunnels
BeginnerReplayUsers who didn't complete onboardingUX friction pointsSession Replay
BeginnerTrendOnboarding completion rate over timeIs it improving?Trends
IntermediateBreakdownCompletion by device typeMobile vs desktop differencesFunnels + Breakdown
IntermediatePathPaths from signup to first featureActual vs designed flowUser Paths
IntermediateCohort"Completed onboarding" vs "Skipped"Behavioral differencesCohorts
AdvancedExperimentTest onboarding flow variantsA/B test improvementsExperiments
AdvancedFlagProgressive onboarding rolloutGradual improvementFeature Flags
AdvancedSQLTime-to-complete by cohortTrend over timeSQL
Quick Wins
  1. Build step-by-step onboarding funnel (15 min)

  2. Add breakdown by device_type (5 min)

  3. Watch 5 replays of drop-offs at biggest friction point (10 min)


Problem: "Understand feature adoption"

MaturityInsight TypeSpecific InsightWhat It ShowsPostHog Feature
BeginnerTrendFeature usage over time (each feature)What's used mostTrends
BeginnerStickinessDays/week users use each featureFeature engagement depthStickiness
BeginnerReplayUsers discovering featuresHow they find and use featuresSession Replay
IntermediateRetentionFeature-specific retentionWhich features drive retentionRetention
IntermediatePathPaths to feature discoveryHow users find featuresUser Paths
IntermediateBreakdownUsage by plan tierPremium vs free behaviorTrends + Breakdown
AdvancedCorrelationFeature usage → retentionWhich features matter mostCorrelation
AdvancedExperimentTest feature discovery flowsImprove adoptionExperiments
AdvancedSurveyFeature satisfaction surveysQualitative feedbackSurveys
Quick Wins
  1. Create feature usage dashboard with all major features (20 min)

  2. Build stickiness chart for top 3 features (10 min)

  3. Compare retention: users who used Feature X vs didn't (10 min)


Problem: "Measure product-market fit"

MaturityInsight TypeSpecific InsightWhat It ShowsPostHog Feature
BeginnerRetention12-week unbounded retentionClassic PMF metricRetention
BeginnerTrendWAU/MAU ratioEngagement intensityTrends (formula)
BeginnerSurveyNPS surveyCustomer satisfactionSurveys
IntermediateCohortPower users (top 10% by usage)Who loves the productCohorts
IntermediateStickinessDays active per week distributionUsage patternsStickiness
IntermediateRetentionRetention by referral sourceOrganic vs paid qualityRetention
AdvancedSurvey"Very disappointed" % (Sean Ellis)PMF survey metricSurveys + SQL
AdvancedCorrelationPower user behaviors vs churnedWhat differentiates successCorrelation
AdvancedLifecycleNew/Returning/Resurrecting/DormantUser health distributionLifecycle
Quick Wins
  1. Build 12-week retention chart (10 min)

  2. Calculate WAU/MAU ratio in trends (10 min)

  3. Create "Power User" cohort (top 10% by events) (10 min)


Problem: "Replace [competitor tool]"

MaturityInsight TypeSpecific InsightWhat It ShowsPostHog Feature
AllDashboardReplicate their top 3 dashboardsParity demonstrationDashboards
AllTrendCompare event counts to old toolData accuracyTrends
AllReplayWatch recordings (new capability)Added valueSession Replay
AllFunnelRecreate their key funnelWorkflow parityFunnels
IntermediateCohortRecreate their key segmentsCohort parityCohorts
AdvancedSQLComplex queries from old toolQuery capabilitySQL
Quick Wins
  1. List their top 3 dashboards, recreate first one (30 min)

  2. Compare one key metric between tools (15 min)

  3. Show session replay as net-new capability (10 min)


Bonus Capabilities

Always recommend 2-3 based on use case:

CapabilityWhen to RecommendWhat It UnlocksPitch
Session Replay + FiltersAny UX problemWatch exactly the sessions where something went wrong"Filter replays to churned users and see what happened"
Correlation AnalysisChurn/conversionStatistically identify success predictors"Find behaviors that predict conversion without guessing"
Feature FlagsDeployment riskSafe rollouts, instant rollback"Ship to 10% of users, measure, then expand"
ExperimentsA/B testing needsStatistical significance, auto analysis"Run real experiments with automatic winner detection"
SurveysQualitative gapsIn-product surveys triggered by events"Ask users why right after they do something"
User PathsNavigation confusionVisualize actual user flows"See real paths users take vs designed ones"
Lifecycle AnalysisRetention concernsNew/Returning/Dormant breakdown"See if acquiring faster than churning"
Group AnalyticsB2B multi-userAccount-level metrics"Track companies, not just users"
Data WarehouseExternal dataCombine PostHog with revenue/CRM"Join Stripe data with product behavior"
HogQL (SQL)Power usersArbitrary event queries"Query your data however you want"

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python, Needs Node.js

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Attribution and licence

Published by
PostHog
Publisher
PostHog (Official)
Source
PostHog/skills · skills/team/customer-success/posthog-onboarding · commit a5d30e7
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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