MCPInstall

finding-replay-for-issue

by posthog Official

Finds the most informative session recording linked to an error tracking issue. Use when a user has an error tracking issue ID and wants to watch a replay showing what the user was doing when the error occurred. Ranks linked sessions by recency, activity score, and journey completeness, then summarizes the pre-error context. Replaces blind session picking from potentially hundreds of linked recordings.

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203
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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/posthog/ai-plugin --skill finding-replay-for-issue
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads finding-replay-for-issue automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Finding the best replay for an error tracking issue

When a user says "show me a replay for this error" or "find a recording for issue X", the goal isn't just any linked session — it's the one that best shows what led to the error. Popular issues can have hundreds of linked sessions, and most are crash-only fragments or duplicate occurrences. This skill picks the most useful one.

Available tools

ToolPurpose
posthog:query-error-tracking-issueGet issue details (fingerprint, status, volume)
posthog:execute-sqlQuery exception events to find linked sessions
posthog:query-session-recordings-listFetch recording metadata for candidate sessions
posthog:session-recording-getGet full details for the selected recording
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 recording (optional, slow)

Workflow

Step 1 — Get the issue details

Fetch the error tracking issue to understand what you're looking for:

posthog:query-error-tracking-issue
{
  "issueId": "<issue_id>"
}

Note the issue's fingerprint, name, and description — you'll need the fingerprint to find linked sessions.

Step 2 — Find sessions with this error

Query exception events to get session IDs where this error occurred. Order by recency and include basic context:

posthog:execute-sql
SELECT
    $session_id AS session_id,
    count() AS occurrences,
    min(timestamp) AS first_seen,
    max(timestamp) AS last_seen,
    any(properties.$current_url) AS url
FROM events
WHERE event = '$exception'
    AND properties.$exception_fingerprint = '<fingerprint>'
    AND $session_id IS NOT NULL
    AND timestamp > now() - INTERVAL 30 DAY
GROUP BY session_id
ORDER BY last_seen DESC
LIMIT 20

This gives you up to 20 candidate sessions. More candidates means better selection.

Step 3 — Rank the candidates

Fetch recording metadata for the candidate sessions to rank them:

posthog:query-session-recordings-list
{
  "session_ids": ["<id1>", "<id2>", "<id3>", ...],
  "date_from": "-30d"
}

Pick the best recording by filtering out bad candidates, then ranking what's left:

Filter out:

  • Sessions under 10 seconds (crash-only fragments, no pre-error context)
  • Sessions over 1 hour (too much data to load, error is a needle in a haystack)

Rank by:

  1. Sweet-spot duration — 2-15 minutes is ideal. Long enough to show the user's journey before the error, short enough to be practical to watch or summarize.
  2. Active time ratio — compare active_seconds to recording_duration. A 20-minute recording with 10 seconds of activity is mostly idle tabs — the user walked away. Prefer sessions where active_seconds / recording_duration is above 0.3 (30%).
  3. Activity score — higher activity_score means the user was actively interacting, not idle. More interesting to watch.
  4. Recency — more recent sessions reflect current app behavior.

Step 4 — Present the finding

Fetch full details for the selected recording:

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

Present to the user:

  • The recording with a link to watch it
  • Why this one — briefly explain the selection ("longest session with the error, user was browsing 3 pages before hitting it")
  • Pre-error context — what pages the user visited and key actions before the exception, derived from the events query in step 2 (the url and first_seen columns)
  • Error frequency — how many times the error occurred in this session

Optional: AI summary via Replay Vision

If the user wants a narrative summary without watching, use Replay Vision — "check-then-scan", since a scanner can only observe a given session once.

  1. Check for an existing summary on the selected recording:

    posthog:vision-observations-list
    {
      "session_id": "<best_recording_id>"
    }
    

    If an observation has scanner_snapshot.scanner_type summarizer and status succeeded, read scanner_result.model_output (title, summary, intent, outcome, friction_points, keywords) — done.

  2. Find a summarizer scanner if none exists:

    posthog:vision-scanners-list
    {
      "scanner_type": "summarizer"
    }
    

    One → use it. More than one → ask the user which (show name + prompt). None → offer to create one via the creating-replay-vision-scanners skill.

  3. Scan the recording with the chosen scanner (async, several minutes):

    posthog:vision-scanners-scan-session
    {
      "id": "<scanner_id>",
      "session_id": "<best_recording_id>"
    }
    
  4. Retrieve by polling vision-observations-list until succeeded.

Tips

  • If all candidate sessions are very short (<10 seconds), the error likely crashes the page immediately. Note this — it's useful context even without a long replay.
  • When the issue has very few linked sessions (<3), skip the ranking and just present what's available with a note about the small sample.
  • If $session_id is null on many exception events, session replay may not be enabled for the affected users. Mention this as a possible gap.
  • Replay Vision has no per-call focus parameter — a summarizer scanner's focus comes from its own prompt. For error-focused summaries, prefer (or create) a summarizer scanner whose prompt targets error/exception context rather than the whole session.

Related skills

  • investigating-error-issue — the quantitative side: volume, breakdowns, and stack traces for the same issue
  • investigating-replay — deep-dive one of the linked sessions
  • diagnosing-missing-recordings — when exception events have no $session_id or the linked recordings are gone

FAQ

What does the finding-replay-for-issue skill do?

Finds the most informative session recording linked to an error tracking issue. Use when a user has an error tracking issue ID and wants to watch a replay showing what the user was doing when the error occurred. Ranks linked sessions by recency, activity score, and journey completeness, then summarizes the pre-error context. Replaces blind session picking from potentially hundreds of linked recordings.

How do I install finding-replay-for-issue?

Run npx skills add https://github.com/posthog/ai-plugin --skill finding-replay-for-issue in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/finding-replay-for-issue/ (personal) or .claude/skills/finding-replay-for-issue/ (project).

Which AI agents can use finding-replay-for-issue?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is finding-replay-for-issue safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains finding-replay-for-issue?

It is published by posthog in posthog/ai-plugin, and it is an official skill from the maker of the technology it covers. It has 203 recorded installs.

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