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Found 92 Skills
PostHog error tracking for Nuxt
PostHog integration for Next.js Pages Router applications
Investigate LLM analytics clusters — understand usage patterns in AI/LLM traffic, compare cluster behavior, compute cost/latency metrics, and drill into individual traces within clusters.
PostHog integration for Ruby on Rails applications
Add PostHog LLM analytics to trace AI model usage. Use after implementing LLM features or reviewing PRs to ensure all generations are captured with token counts, latency, and costs. Also handles initial PostHog SDK setup if not yet installed.
PostHog logs for Other Languages
PostHog error tracking for Node.js
Research, qualify, and suggest outreach for PostHog big fish product-led leads — large companies (500+ or 1000+ employees) using PostHog on free tier without a payment method. Use this skill when a TAE needs to work a big fish alert from Salesforce. Triggers on 'work this big fish lead', 'research this product-led lead', 'big fish alert', '500+ employees no payment method', '1000+ employees no payment method', or any request involving a large-company product-led lead that needs research, qualification, and an outreach recommendation. Also trigger when a TAE pastes Salesforce lead details with matching criteria like 'Big fish alert' or '500+ employees, no payment method'.
Diagnose why a product metric changed (dropped, spiked, or plateaued) by orchestrating breakdowns, actors, paths, lifecycle, retention, and annotations queries. Use when the user reports an anomaly, asks "why did X change?", or needs root-cause analysis for a trend, funnel, retention, stickiness, or lifecycle metric.
Guide the user through connecting a new data warehouse source — Postgres, MySQL, Stripe, Hubspot, MongoDB, Salesforce, BigQuery, Snowflake, and so on. Use when the user wants to "connect Stripe", "import data from Postgres", "add a new data source", "sync my warehouse tables", or wants to pick sync methods for each table. Walks through source-type discovery, credential validation, table discovery, per-table sync_type selection, and the final create call. Also covers picking a good prefix and what to do right after creation.
PostHog feature flags for API applications
Set up an LLM-judge evaluation that extracts canonical use cases for a PostHog feature at scale and streams the results to a Slack channel as a live feed. Use when someone wants to understand how users are actually using a specific AI/LLM-powered feature in production — what they're investigating, what questions they're trying to answer, and what patterns surface — without manually reading hundreds of traces. Assumes the feature emits `$ai_generation` and `$ai_evaluation` events with `$session_id` linkage to the trigger user's recording (the standard setup post the session-summary linkage PRs).