By PostHog
Manage PostHog from your coding tool: create and monitor experiments, feature flags, and error tracking; investigate session replays and AI observability traces; set up alerts, dashboards, and data warehouse imports; automate workflows with signals and scouts.
Manually send a Claude Code session log to PostHog LLM Analytics
Set up PostHog LLM Analytics to capture Claude Code sessions
Check if Claude Code sessions are being sent to PostHog LLM Analytics
Create PostHog error tracking suppression rules to drop high-volume, low-value errors at ingestion. Use when the user asks "stop capturing this error", "drop browser extension errors", "ignore ResizeObserver loops", "suppress bot-driven errors", or wants to reduce ingestion cost from noisy unactionable errors. Identifies suppression candidates, scopes the filter tightly, decides between full suppression and sampling, and confirms the rule before creating it. Suppressed errors are dropped permanently — this skill defaults to caution.
Triage PostHog error tracking issues during a daily or on-call review. Use when the user asks "what's broken?", "what new errors do we have?", "show me top errors today", "what should I look at this morning", or wants a prioritized list of active issues to work on. Surfaces new and high-impact issues, ranks by users affected and recency, points at linked replays, and proposes next actions (investigate, assign, suppress, merge).
Inspects PostHog Visual Review (VR) runs that gate PR merges with screenshot regression checks. Use when the user mentions "visual review", "VR", "snapshot diff", "screenshot test", "storybook regression", "playwright snapshot", asks why a PR is blocked or what changed visually, wants to triage the VR backlog, decide whether a snapshot diff is real vs flaky, or check whether a story has been changing across runs. Also invoke when a PR has a failing `visual-review` status check, when a PR comment mentions "Visual review", or when the user is on a branch with an open VR run.
Change the sync configuration of an existing data warehouse schema — switch sync_type, pick a different incremental_field, set primary_key_columns, choose cdc_table_mode, or change sync_frequency. Use when the user asks "switch my orders table from full refresh to incremental", "this table is syncing too slowly / too frequently", "I need to pick a different incremental column", "set up CDC for this Postgres table", or when diagnosis of a failing sync pointed to an incremental-field or PK misconfiguration.
Best practices for agents managing PostHog skills via the MCP `skill-*` tools — how to discover, read, create, update, and refactor skills efficiently, especially large skills with many bundled files. Use whenever you are about to call any `skill-*` tool, asked to author or edit a shared skill, or troubleshoot why a skill write was rejected. Pairs with `skills-store` (which covers the raw tool surface) by adding the decision-tree, efficiency, and pitfall guidance.
External network access
Connects to servers outside your machine
Uses power tools
Uses Bash, Write, or Edit tools
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Official PostHog plugin for AI clients. Access PostHog products directly from your AI coding tool.
Install the plugin:
claude plugin install posthog
Authenticate via OAuth:
# Just enter Claude Code anywhere
claude
# Then, use the /mcp command within Claude, select plugin:posthog:posthog, and press Enter
/mcp
Then follow the browser prompts to log into PostHog.
(Optional) Send Claude Code sessions to PostHog LLM Analytics.
Add to ~/.claude/settings.json (global) or .claude/settings.local.json (per-project):
{
"env": {
"POSTHOG_LLMA_CC_ENABLED": "true",
"POSTHOG_API_KEY": "phc_...",
"POSTHOG_HOST": "https://eu.i.posthog.com"
}
}
Both POSTHOG_LLMA_CC_ENABLED=true and POSTHOG_API_KEY are required. Sessions are sent when Claude Code exits. Set POSTHOG_LLMA_PRIVACY_MODE=true to redact prompt/output content. Add custom properties to all events with POSTHOG_LLMA_CUSTOM_PROPERTIES (JSON string, e.g. '{"ai_product": "my-app"}').
Install from the Cursor Marketplace or add manually in Cursor Settings > Plugins.
Add the marketplace:
codex plugin marketplace add PostHog/ai-plugin
Install the plugin from inside Codex:
codex
# Then run /plugins, select PostHog, and install
/plugins
gemini extensions install https://github.com/PostHog/ai-plugin
Install the plugin:
grok plugin install PostHog/ai-plugin --trust
Authenticate via OAuth:
On first use of a PostHog tool, Grok prompts you to authorize in your browser. Log into PostHog to connect.
Clone and install the plugin:
git clone https://github.com/PostHog/ai-plugin
claude --plugin-dir ./ai-plugin
Authenticate via OAuth:
/mcp
Then follow the browser prompts to log into PostHog.
This plugin provides access to 27+ PostHog tools across these categories:
The plugin also ships 30+ task-specific skills that your AI client loads on demand to follow PostHog best practices — covering HogQL query patterns, experiment creation and lifecycle, feature flags, data warehouse setup and troubleshooting, LLM analytics exploration, session replay diagnostics, and SDK instrumentation. Skills activate automatically when their description matches your request (e.g. "create an experiment", "why isn't my Stripe sync working?", "audit my feature flags"), so you generally don't need to invoke them by name.
> What feature flags do I have?
> Create a feature flag called new-onboarding for 50% of users
> Show me errors from the last 24 hours
> Which errors are affecting the most users?
> How many users signed up this week?
> What's the conversion rate for the checkout funnel?
> Show me all my experiments
> What are the results of the checkout-flow experiment?
> Create a new dashboard called Product Metrics
> Add the signup funnel insight to the Growth dashboard
> What are the responses to the NPS survey?
> Create a feedback survey for the checkout page
> What's my most triggered event?
> Show me the top 10 pages by pageviews
For self-hosted PostHog instances, set the POSTHOG_MCP_URL environment variable to point to your instance:
export POSTHOG_MCP_URL="https://mcp.your-posthog-instance.com/mcp"
MIT
npx claudepluginhub anthropics/claude-plugins-official --plugin posthogShare Claude Code sessions to GitHub repositories
Use Amplitude like an expert - instrument analytics, discover product opportunities, analyze charts, create dashboards, manage experiments, and understand users and accounts
Amplitude-powered analytics skills — analyze dashboards, charts, experiments, feedback, and account health with AI.
Claude Code skill pack for PostHog (24 skills)
AI agent skills that make SaaS products data-ready for product analytics — from codebase scan to tracking plan to working instrumentation code.
PROACTIVELY use this agent when experiments are started, modified, or when results need analysis. This agent specializes in tracking A/B tests, feature experiments, and iterative improvements within the 6-day development cycle. Should be triggered automatically when experimental code paths or feature flags are introduced. Examples:\n\n<example>\nContext: When implementing feature flags or A/B test variants\nuser: "Add a feature flag to test the new onboarding flow"\nassistant: "I've implemented the feature flag for the onboarding flow A/B test. Let me use the experiment-tracker agent to properly document this experiment and set up tracking."\n<commentary>\nEvery feature flag represents an experiment that needs proper tracking and success metrics.\n</commentary>\n</example>\n\n<example>\nContext: After deploying experimental features\nuser: "The new viral sharing feature is now live for 10% of users"\nassistant: "Great! The viral sharing feature is deployed. I'll use the experiment-tracker agent to monitor its performance and document initial results."\n<commentary>\nLive experiments need immediate tracking setup to capture valuable data from the start.\n</commentary>\n</example>\n\n<example>\nContext: When reaching experiment milestones\nuser: "It's been a week since we launched the TikTok integration test"\nassistant: "Perfect timing for analysis. Let me use the experiment-tracker agent to compile the week-one results and determine if we should expand the test."\n<commentary>\nRegular experiment checkpoints ensure data-driven decisions about feature rollouts.\n</commentary>\n</example>\n\n<example>\nContext: Before making product decisions\nuser: "Should we keep the AI avatar feature or remove it?"\nassistant: "Let me analyze the experiment data to inform this decision. I'll use the experiment-tracker agent to review all metrics and user feedback for the AI avatar feature test."\n<commentary>\nProduct decisions should be backed by experiment data, not gut feelings.\n</commentary>\n</example>
Data analytics skills for PMs: SQL query generation and cohort analysis. Analyze user data, generate queries, and identify retention patterns.