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Found 126 Skills
Guides exploration of $autocapture events captured by posthog-js to understand user interactions, find CSS selectors (especially data-attr attributes), evaluate selector uniqueness, query matching clicks ad-hoc, and create actions. Use when the user asks about autocapture data, wants to find what users are clicking, needs to build actions from click events, asks about elements_chain, wants to build a trend or funnel filtered by clicks or other autocapture interactions, asks which properties autocapture sends, or asks how to filter $autocapture events. Only applies to projects using posthog-js autocapture.
Audit the health of a PostHog project's data warehouse — find every broken or degraded pipeline item across sources, sync schemas, materialized views, batch exports, and transformations. Use when the user asks "what's broken in my warehouse?", "give me a health check", "audit my data pipeline", "why are some dashboards stale?", or wants a one-shot triage summary before deciding where to spend time. Produces a prioritized report of issues grouped by severity and type, with recommended next steps.
Set up an LLM-judge evaluation that extracts canonical use cases for a PostHog feature at scale and streams the results to a Slack channel as a live feed. Use when someone wants to understand how users are actually using a specific AI/LLM-powered feature in production — what they're investigating, what questions they're trying to answer, and what patterns surface — without manually reading hundreds of traces. Assumes the feature emits `$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).
Manage PostHog subscriptions — scheduled email, Slack, or webhook deliveries of insight or dashboard snapshots. Use when the user wants to subscribe to an insight or dashboard, check existing subscriptions, change delivery frequency, add or remove recipients, or stop receiving updates.
Guidelines to create/update a new mode for PostHog AI agent. Modes are a way to limit what tools, prompts, and prompt injections are applied and under what conditions. Achieve better results using your plan mode.
Django migration patterns and safety workflow for PostHog. Use when creating, adjusting, or reviewing Django/Postgres migrations, including non-blocking index/constraint changes, multi-phase schema changes, data backfills, migration conflict rebasing, and product model moves that require SeparateDatabaseAndState.
Add PostHog product analytics events to track user behavior. Use after implementing new features or reviewing PRs to ensure meaningful user actions are captured. Also handles initial PostHog SDK setup if not yet installed.
Research and qualify onboarding team referral leads for PostHog. Use this skill when a TAE receives a lead from the onboarding team and needs a full research brief before deciding how to engage. Triggers on 'research this onboarding lead', 'onboarding team referred [company]', 'look into [company] from onboarding', 'qualify this onboarding referral', 'what do we know about [company] from onboarding', or any request to research a company that came through the onboarding pipeline. Also trigger when a TAE pastes a company name and mentions it's from the onboarding team, or says something like 'onboarding sent me [company]', 'got a handoff for [company]', or '[name] from onboarding sent me [company]'. This skill does deep research and qualification, then drafts outreach when the recommendation is to engage.
Discover and use shared team skills stored in PostHog. Use when the user asks to list, browse, load, or manage "shared skills", "team skills", or references the "skills store" / "skill store".
Focused Signals scout for PostHog projects using session replay. Watches two promises the replay product makes: that sessions are actually being recorded (capture integrity — recording volume vanishing while site traffic doesn't), and that the friction evidence inside recordings gets seen (rage-click / dead-click clusters concentrating on a page or element, error-after-interaction cohorts, recurring replay vision themes nobody aggregates). Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet.
Diagnose why a PostHog endpoint is slow or expensive and propose a concrete fix — bump the cache TTL, enable materialisation, restructure variables, or rewrite the query. Use when the user says "this endpoint is slow", "my endpoint times out", "we're hitting the cost cap on this one", or asks "should I materialise this?". Focuses on a single named endpoint, not a project-wide audit.
Focused Signals scout for PostHog projects moving data through pipelines. Watches the three delivery surfaces — CDP destinations and transformations (hog functions), batch exports, and hog flows (workflows/messaging) — for contradictions between configured state and actual delivery: functions the watcher quietly degraded or disabled, failure rates stepping above a pipeline's own baseline, batch export runs failing or stalling (a growing data gap), and active flows failing for the people they trigger on. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.