posthog/ai-plugin

investigating-replay

Investigates a session recording by gathering metadata, person profile, same-session events, and linked error tracking issues in one pass.

Ver código fuente

Cómo usar

  1. Copia el comando y revisa el paquete antes de instalarlo en tu entorno de agente.
  2. Consulta las herramientas, credenciales y entornos necesarios en el documento original.
  3. Describe las entradas y el resultado deseado; contrasta la salida con las fuentes.
npx skills add https://github.com/posthog/ai-plugin --skill investigating-replay

Antes de empezar

  • La instalación no incluye suscripción al modelo, cuota API ni permisos sobre tus archivos.
  • Las estrellas e instalaciones reflejan actividad, no una prueba independiente de calidad o seguridad.
Documento original del Skill

Contenido del repositorio de origen con títulos, ejemplos, código, tablas, enlaces e imágenes preservados.

Investigating a session recording

When a user asks "what happened in this session?" or provides a recording/session ID to investigate, gather all relevant context in parallel rather than making them ask for each piece.

Available tools

ToolPurpose
posthog:session-recording-getRecording metadata (duration, counts, status)
posthog:persons-retrievePerson profile (properties, distinct IDs)
posthog:execute-sqlQuery events, errors, and page views in session
posthog:query-error-tracking-issues-listFind error tracking issues linked to the session
posthog:vision-observations-listCheck for an existing Replay Vision AI summary
posthog:vision-scanners-listFind summarizer scanners (scanner_type=summarizer)
posthog:vision-scanners-scan-sessionRun a summarizer scanner on the session (slow, optional)
posthog:vision-scanners-createCreate a temporary summarizer scanner (ask first)
posthog:vision-scanners-deleteDelete a temporary scanner after summarizing

Workflow

Step 1 — Get recording metadata and person profile

Start with the recording to get metadata and the person's distinct ID:

json
posthog:session-recording-get
{
  "id": "<recording_id>"
}

The response includes distinct_id, person, duration, interaction counts, console error counts, and viewing status. Use the distinct_id to fetch the full person profile:

json
posthog:persons-retrieve
{
  "id": "<person_uuid_from_recording>"
}

Step 2 — Query same-session events

Get the timeline of what the user did during the session:

sql
posthog:execute-sql
SELECT
    timestamp,
    event,
    properties.$current_url AS url,
    properties.$browser AS browser,
    properties.$os AS os,
    properties.$device_type AS device_type,
    properties.$screen_width AS screen_width
FROM events
WHERE $session_id = '<session_id>'
ORDER BY timestamp ASC
LIMIT 200

For sessions with many events, focus on the most informative ones:

sql
posthog:execute-sql
SELECT
    timestamp,
    event,
    properties.$current_url AS url,
    if(event = '$exception', properties.$exception_values[1], null) AS exception_message,
    if(event = '$exception', properties.$exception_types[1], null) AS exception_type
FROM events
WHERE $session_id = '<session_id>'
    AND event IN ('$pageview', '$pageleave', '$autocapture', '$exception', '$rageclick')
ORDER BY timestamp ASC
LIMIT 100

Step 3 — Check for linked error tracking issues

If the recording has console errors or exceptions, find related error tracking issues:

sql
posthog:execute-sql
SELECT DISTINCT
    properties.$exception_fingerprint AS fingerprint,
    properties.$exception_types[1] AS type,
    properties.$exception_values[1] AS message,
    count() AS occurrences
FROM events
WHERE $session_id = '<session_id>'
    AND event = '$exception'
GROUP BY fingerprint, type, message
ORDER BY occurrences DESC
LIMIT 10

If fingerprints are found, search for the corresponding error tracking issues to provide links and status:

json
posthog:query-error-tracking-issues-list
{
  "searchQuery": "<exception_type or message>"
}

Step 4 — Synthesize the investigation

Present the findings as a coherent narrative:

  1. Who — person properties (name, email, country, plan, etc.)
  2. What — sequence of pages visited and key actions taken
  3. Problems — exceptions, console errors, rage clicks, and their frequency
  4. Related issues — linked error tracking issues with their status (active/resolved)
  5. Context — session duration, device/browser, activity score

Optional: AI summary via Replay Vision

If the user wants a deeper analysis without reading through events manually, offer a Replay Vision summary. Follow "check-then-scan" — don't scan blindly, a scanner can only observe a given session once.

  1. Check for an existing summary. A scheduled scanner may already have one:
json
   posthog:vision-observations-list
   {
     "session_id": "<session_id>"
   }

Look for an observation where scanner_snapshot.scanner_type is summarizer and status is succeeded. If found, read scanner_result.model_output (title, summary, intent, outcome, friction_points, keywords) — done, no new scan needed.

  1. Find a summarizer scanner if none exists yet:
json
   posthog:vision-scanners-list
   {
     "scanner_type": "summarizer"
   }
  • Exactly one → use it.
  • More than one → show the user the scanners (name + prompt) and ask which to use.
  • None → no summarizer scanner exists. See

No summarizer scanner? Run a temporary one below.

  1. Scan the session with the chosen scanner. Warn this is async and takes

several minutes (rasterize + LLM):

json
   posthog:vision-scanners-scan-session
   {
     "id": "<scanner_id>",
     "session_id": "<session_id>"
   }
  1. Retrieve the result by polling vision-observations-list (step 1) until

the new observation reaches succeeded.

No summarizer scanner? Run a temporary one

If the project has no summarizer scanner, you can still produce a one-off summary with a throwaway scanner — but ask the user's permission before creating anything.

  1. Ask permission to create a temporary summarizer scanner just to summarize

this one session.

  1. Create it disabled so it never sweeps on a schedule — a disabled scanner

only runs when you trigger it on demand, so it won't touch other sessions or burn quota in the background:

json
   posthog:vision-scanners-create
   {
     "name": "Temporary on-demand summary",
     "scanner_type": "summarizer",
     "scanner_config": {
       "prompt": "Summarize what the user was trying to do, whether they succeeded, and any friction they hit."
     },
     "query": { "kind": "RecordingsQuery" },
     "model": "gemini-3-flash-preview",
     "enabled": false
   }
  1. Scan this session on demand with the new scanner, then poll for the result:
json
   posthog:vision-scanners-scan-session
   {
     "id": "<new_scanner_id>",
     "session_id": "<session_id>"
   }

Poll vision-observations-list until the observation reaches succeeded and read scanner_result.model_output.

  1. Ask whether to keep or delete the scanner. Once you have the observation,

ask the user if they want to keep the temporary scanner or delete it with vision-scanners-delete. Deleting is safe: the summary you just read is also emitted as an event that persists after the scanner is gone, so cleaning up the temporary scanner does not lose the result.

Tips

  • Run steps 1-3 in parallel when possible — they're independent queries.
  • If the recording has very few events, the session was likely very short.

Note this rather than suggesting something is broken.

  • Console error count from the recording metadata is a good signal for whether

to dig into exceptions. If it's 0, skip step 3.

  • The start_url from the recording tells you where the user's journey began —

use this to frame the narrative.

  • If person is null on the recording, the user was anonymous.

Person properties won't be available, but events still are.

Related skills

  • `finding-sessions-to-watch` — choose which sessions are worth investigating in the first place
  • `finding-replay-for-issue` — start from an error tracking issue and find its linked recordings
  • `diagnosing-missing-recordings` — when a recording that should exist doesn't
  • `creating-replay-vision-scanners` — automate this kind of watching as a scheduled scanner

Relacionados por tarea

del mismo repositorio

Más Skills

Todos los Skills
posthog
Oficial

composing-grid-canvases

Compose PostHog grid canvases — widget grids (including the user's home canvas) built from reusable component canvases. Use when a task asks to add, fill, move, resize, or remove a widget on a grid or home canvas, to compose a whole canvas of widgets from one ask, to build a reusable widget/component, or when a placement id or grid canvas id is the target. Covers the component store search → configure → fork → build ladder, the component placement contract (size, configSchema), the placement lif

Instalaciones / 1h
1
GitHub Stars
96
Actualizado
31 oct
posthog
Oficial

consuming-endpoints-from-client-code

Wire a PostHog endpoint into a client app or SDK. Covers fetching the OpenAPI spec, generating a typed client with openapi-generator or @hey-api/openapi-ts, sending the right auth header, shaping the variables payload (HogQL codename vs insight breakdown property), handling rate-limit and materialised-endpoint error responses. Use when the user says "how do I call my endpoint", "generate a client for this", or "what auth header do I use".

Instalaciones / 1h
1
GitHub Stars
96
Actualizado
31 oct
posthog
Oficial

designing-email-templates

Author, save, and edit email templates in the PostHog workflows library — compose email design JSON with Liquid personalization and create and round-trip-edit templates over MCP. Use when asked to design, build, update, or fix an email template for workflows, broadcasts, or campaigns.

Instalaciones / 1h
1
GitHub Stars
96
Actualizado
31 oct
posthog
Oficial

exploring-llm-evaluations

Investigate AI observability evaluations — hog (deterministic code-based), llmjudge (LLM-prompt-based), and sentiment (user-message sentiment). Find existing evaluations, inspect their configuration, run them against specific generations, query individual results, and set up scheduled reports on an evaluation. Use when the user asks to debug why an evaluation is failing, surface common failure modes, compare results across filters, dry-run a Hog evaluator, prototype a new LLM-judge prompt, inspe

Instalaciones / 1h
1
GitHub Stars
96
Actualizado
31 oct