By pendo-io
Bring Pendo analytics into Claude Code with skills for account health, feature adoption, session replays, and feedback analysis
Prepare for a customer call by synthesizing engagement, sentiment, and feedback from Pendo analytics. Use this skill whenever someone asks about account health, wants to prepare for a customer call, needs an account health report, asks about engagement or usage for a specific customer, or wants a summary of how an account is doing. Also trigger when users mention preparing for a QBR, checking on a customer, or reviewing an account before a meeting — even if they don't say "account health" explicitly.
Analyze feature adoption rates, identify power users vs laggards, and track adoption trends using Pendo analytics. Use this skill whenever someone asks about feature adoption, feature usage, who's using a feature, adoption rates, feature rollout progress, or wants to understand how a specific feature is performing. Also trigger when users mention tracking a feature launch, finding champions or power users of a feature, identifying accounts that haven't adopted a feature, comparing adoption across segments, or analyzing usage trends over time — even if they don't say "adoption" explicitly. If someone asks "who's using X" or "how is feature Y doing", this is the right skill.
Analyze customer feedback using Pendo's feedback tools — cluster themes, extract insights, and surface churn/frustration risks. Use whenever the user asks about feedback trends, top complaints, feature requests, Voice of the Customer, churn risks, or what customers are saying about a topic or account. Triggers on phrases like "feedback report", "what are customers asking for", "any red flags in sentiment", or "what's the feedback looking like". Requires Pendo connector.
Find and surface relevant session replays for debugging, UX research, and understanding user behavior. Use this skill whenever someone asks about session replays, wants to watch how users interact with their product, needs to debug a user issue by seeing what happened, or wants to find sessions with frustration signals like rage clicks or errors. Also trigger when users mention watching recordings, finding replays for a specific visitor or page, investigating UX problems, or looking for sessions with high activity — even if they don't say "session replay" explicitly.
External network access
Connects to servers outside your machine
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Pendo analytics for Claude Code: account health, feature adoption, session replays, feedback analysis, and agent analytics setup.
This marketplace contains two plugins:
| Plugin | Description |
|---|---|
pendo-analytics | Pendo analytics skills for account health, feature adoption, session replays, and feedback analysis |
setup-agent-analytics | Detect AI agents in your codebase and instrument them with Pendo agent analytics |
Add the marketplace: Inside Claude Code, run:
/plugin marketplace add pendo-io/claude-pendo-plugin
Install plugins:
Run /plugin, navigate to the Marketplaces section, select the pendo-plugins marketplace, and install the plugins you want.
Authenticate the Pendo MCP server:
Run the /mcp command inside Claude Code and follow the authentication flow.
Run a skill:
pendo-analytics:
/pendo-analytics:account-health <account-name>
/pendo-analytics:feature-adoption <feature-name>
/pendo-analytics:feedback-analysis
/pendo-analytics:session-replay
setup-agent-analytics:
/setup-agent-analytics:setup-agent-analytics [agent-id-1] [agent-id-2] ...
Clone the repo:
git clone https://github.com/pendo-io/claude-pendo-plugin.git
Start Claude Code with the plugin:
claude --plugin-dir /path/to/claude-pendo-plugin
Authenticate the Pendo MCP server:
Run the /mcp command inside Claude Code and follow the authentication flow.
Run a skill:
pendo-analytics:
/pendo-analytics:account-health <account-name>
/pendo-analytics:feature-adoption <feature-name>
/pendo-analytics:feedback-analysis
/pendo-analytics:session-replay
setup-agent-analytics:
/setup-agent-analytics:setup-agent-analytics [agent-id-1] [agent-id-2] ...
| Skill | Description |
|---|---|
account-health | Prepare for a customer call by synthesizing engagement, sentiment, and feedback from Pendo analytics |
feature-adoption | Analyze feature adoption rates, identify power users vs laggards, and track adoption trends |
feedback-analysis | Deep analysis of customer feedback - discover themes, extract insights, and identify risks |
session-replay | Find and surface relevant session replays for debugging, UX research, and understanding user behavior |
| Skill | Description |
|---|---|
setup-agent-analytics | Detect AI agents in a codebase and instrument them with Pendo trackAgent() calls or the server-side Conversations API |
| Tool | Purpose |
|---|---|
activityQuery | Engagement metrics and activity data |
productEngagementScore | PES calculations |
searchEntities | Find accounts, pages, features |
accountQuery | Account metadata |
accountMetadataSchema | Account metadata schema |
visitorQuery | Visitor metadata |
sessionReplayList | Find session recordings |
generate_feedback_topics | Cluster feedback into themes |
get_feedback_insights | Extract key insights |
get_feedback_items | Raw feedback data |
guideMetrics | Guide performance metrics |
segmentList | Available segments |
list_all_applications | List Pendo applications |
MIT
npx claudepluginhub pendo-io/claude-pendo-plugin --plugin pendo-analyticsDetect AI agents in your codebase and instrument them with Pendo agent analytics
Access PostHog analytics, feature flags, experiments, error tracking, and insights directly from your AI coding tool. Optionally capture Claude Code sessions to PostHog LLM Analytics.
Claude Code skill pack for PostHog (24 skills)
OTel + Hooks analytics for Claude Code. Run /init-claudalytics to configure telemetry and hooks capture for this project.
AI agent skills that make SaaS products data-ready for product analytics — from codebase scan to tracking plan to working instrumentation code.
Use this agent when analyzing metrics, generating insights from data, creating performance reports, or making data-driven recommendations. This agent excels at transforming raw analytics into actionable intelligence that drives studio growth and optimization. Examples:\n\n<example>\nContext: Monthly performance review needed
Use this agent when you need to analyze user feedback from multiple sources, identify patterns in user complaints or requests, synthesize insights from reviews, or prioritize feature development based on user input. This agent excels at turning raw feedback into actionable product insights. Examples:\n\n<example>\nContext: Weekly review of user feedback