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This skill provides a comprehensive, multi-step procedure for analyzing user session recordings. It enables an agent to move beyond simple metadata retrieval by correlating session recordings with person profiles, event timelines, and specific error tracking issues. The workflow is designed to be executed in parallel where possible, allowing the agent to construct a complete narrative of a user's session—including who they are, what actions they took, and any technical issues they encountered—while also offering optional AI-powered summarization via Replay Vision.
What it does
- Retrieves session recording metadata and associated user profile information.
- Queries event timelines to reconstruct the user's journey and actions.
- Identifies and links specific error tracking issues to the session.
- Synthesizes findings into a coherent narrative covering user context, actions, and problems.
- Checks for existing AI-generated session summaries or triggers new ones.
- Provides a fallback mechanism to recover session IDs if events are missing from the recording ID.
When to use it
- When a user asks for an investigation into a specific session or provides a session ID.
- When you need to correlate user behavior with technical errors or exceptions.
- When you need to provide a summary of a user's journey across multiple pages.
- When you need to determine if a session's errors are linked to known, tracked issues.
Advantages
- Reduces manual effort by automating the retrieval of disparate data points.
- Provides a holistic view of user sessions rather than isolated data fragments.
- Includes built-in logic for handling missing event data or anonymous users.
- Offers a structured approach to integrating AI-driven session analysis.
Usage examples
- “What happened in session 12345?”
- The agent retrieves the recording metadata, identifies the user, lists the sequence of page views and errors, and reports any linked error tracking issues.
- “Can you summarize the user's experience in this recording?”
- The agent checks for an existing AI summary or performs an inline scan to provide a concise overview of the session's intent, outcome, and friction points.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/posthog/investigating-replay/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip investigating-replay.zip -d .agents/skills
Documentation by PostHog
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
| Tool | Purpose |
|---|---|
posthog:session-recording-get | Recording metadata (duration, counts, status) |
posthog:persons-retrieve | Person profile (properties, distinct IDs) |
posthog:execute-sql | Query events, errors, and page views in session |
posthog:query-error-tracking-issues-list | Find error tracking issues linked to the session |
posthog:vision-observations-list | Check for an existing Replay Vision AI summary |
posthog:vision-observations-get | Read one observation in full (scanner_result) |
posthog:vision-scanners-inline-scan | Generate an AI summary of the session (slow, optional) |
posthog:vision-scanners-list | Find saved summarizer scanners (scanner_type=summarizer) |
posthog:vision-scanners-scan-session | Run a saved summarizer scanner on the session (slow) |
Workflow
Step 1 — Get recording metadata and person profile
Start with the recording to get metadata and the person's distinct ID:
posthog:session-recording-get
{
"id": "<session_id>"
}The recording id and the event $session_id are the same value. It selects the
recording here and the same-session events in Step 2. The response includes
distinct_id, person, start_time, end_time, duration, interaction counts,
console error counts, and viewing status. Use the distinct_id to fetch
the full person profile:
posthog:persons-retrieve
{
"id": "<person_uuid_from_recording>"
}Step 2 — Query same-session events
Use the recording id from Step 1 as the $session_id value. Get the timeline
of what the user did during the session:
posthog:execute-sql
SELECT
timestamp,
event,
properties.$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 $session_id = '<session_id>'
ORDER BY timestamp ASC
LIMIT 200For sessions with many events, focus on the most informative ones:
posthog:execute-sql
SELECT
timestamp,
event,
properties.$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 100No rows? Recover the event session ID
The recording id is the session ID. No rows means the session's events were
ingested without it. Find candidates from the person's events in the recording
window, padded by 100 seconds like the replay events query. person_id covers
all of the person's distinct IDs:
posthog:execute-sql
SELECT
properties.$session_id AS session_id,
count() AS event_count,
min(timestamp) AS first_seen,
max(timestamp) AS last_seen
FROM events
WHERE person_id = '<person_uuid>'
AND timestamp >= toDateTime('<start_time>') - INTERVAL 100 SECOND
AND timestamp <= toDateTime('<end_time>') + INTERVAL 100 SECOND
AND properties.$session_id IS NOT NULL
GROUP BY session_id
ORDER BY event_count DESC
LIMIT 10Continue only when one session ID clearly matches. Use it for the Step 2 and
Step 3 queries only. The replay URL and all Replay Vision calls take the
recording id.
Step 3 — Check for linked error tracking issues
If the recording has console errors or exceptions, find related error tracking issues:
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 10If fingerprints are found, search for the corresponding error tracking issues to provide links and status:
posthog:query-error-tracking-issues-list
{
"searchQuery": "<exception_type or message>"
}Step 4 — Synthesize the investigation
Present the findings as a coherent narrative:
Who — person properties (name, email, country, plan, etc.)
What — sequence of pages visited and key actions taken
Problems — exceptions, console errors, rage clicks, and their frequency
Related issues — linked error tracking issues with their status (active/resolved)
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.
Check for an existing summary. A scheduled scanner may already have one:
posthog:vision-observations-list { "session_id": "<session_id>" }The rows come back narrowed to
id,session_id,status,summary_lineandscanner_id. Look for one whosestatusissucceeded, then read it in full withvision-observations-getfor thatid: itsscanner_snapshot.scanner_typetells you whether it is asummarizer, andscanner_result.model_outputcarriestitle,summary,intent,outcome,friction_pointsandkeywords. If you find one, you are done — no new scan needed.Generate one with an inline scan of the built-in summary. It runs the same scan as the player's Summarize button, so a summary either of you started is shared, while a prompt of your own mints a separate scanner and a separate summary.
posthog:vision-scanners-inline-scan { "session_ids": ["<session_id>"], "built_in": "summarize_recording" }Leave
modelout so the server default applies. Warn the user this is async and takes several minutes (rasterize + LLM). Nothing is scheduled and there is nothing to clean up: the scanner an inline scan mints never sweeps on its own. A 400 here usually means the organization has not approved AI data processing yet.Read
results[0].scan_outcomebefore polling.startedmeans pollvision-observations-list(step 1) until the new observation reachessucceeded.already_scannedmeans a terminal observation already exists — read it via step 1, and if its status isfailedorineligiblesay so rather than polling. A nullscan_idorskipped_quotameans nothing ran: report the quota, do not poll.
The project already has a summarizer scanner
Use an inline scan for a one-off summary even then. Only reach for a saved scanner when the user wants that scanner's own prompt rather than the built-in one:
posthog:vision-scanners-list
{
"scanner_type": "summarizer"
}Show the user the scanners (name + prompt), ask which to use, then run it against
the session and poll vision-observations-list until the observation reaches
succeeded:
posthog:vision-scanners-scan-session
{
"id": "<scanner_id>",
"session_id": "<session_id>"
}Never create a scanner to answer a single question — vision-scanners-create
leaves a scheduled sweep behind that the inline scan does not.
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_urlfrom the recording tells you where the user's journey began — use this to frame the narrative.If
personis 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 placefinding-replay-for-issue— start from an error tracking issue and find its linked recordingsdiagnosing-missing-recordings— when a recording that should exist doesn'tcreating-replay-vision-scanners— automate this kind of watching as a scheduled scanner
Files
- ATTRIBUTION.mdAdded by AutoGineer859 B
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md9.3 KiB
ATTRIBUTION.md
Attribution
This folder is an unmodified copy of the Agent Skill "investigating-replay", distributed by AutoGineer (https://autogineer.com/agent-skills).
Commit: a5d30e775d092e16e198f7b582f1f66dac2cee45
Published by: PostHog (https://github.com/PostHog)
Publisher: PostHog (https://github.com/PostHog)
Licence: MIT License (https://github.com/PostHog/skills/blob/a5d30e775d092e16e198f7b582f1f66dac2cee45/LICENSE)
Licence evidence: repository licence LICENSE
Retrieved: 2026-11-07
The skill's own files are unmodified copies. AutoGineer is not affiliated with or endorsed by the author or the publisher.
Files added by AutoGineer
ATTRIBUTION.md: this file, written by AutoGineer
LICENSE: copied from LICENSE
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Security
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Attribution and licence
- Published by
- PostHog
- Publisher
- PostHog (Official)
- Source
- PostHog/skills · skills/omnibus/investigating-replay · commit a5d30e7
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by PostHog.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download investigating-replay.zip