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Finding Replay For Issue

Organization
PostHog
finding-replay-for-issue

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.

Overview

PublisherPostHog
Repositoryskills
Skill namefinding-replay-for-issue
Stars
77
Forks
8
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by PostHog on GitHub. Read the source before you install it.

Installation

Install the Finding Replay For Issue AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/PostHog/skills/tree/main/skills/omnibus/finding-replay-for-issue
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/PostHog/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/omnibus/finding-replay-for-issue .claude/skills/finding-replay-for-issue
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Finding Replay For Issue in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Finding Replay For Issue on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Finding Replay For Issue is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

json
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:

sql
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:

json
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:

json
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:

    json
    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:

    json
    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):

    json
    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

Frequently asked questions

What does the Finding Replay For Issue AI 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.

Why use Finding Replay For Issue on TypingMind?

Because you install it once and use it with any model. Finding Replay For Issue is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Finding Replay For Issue in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/PostHog/skills/tree/main/skills/omnibus/finding-replay-for-issue. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Finding Replay For Issue?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Finding Replay For Issue?

As many as you like. As long as a model supports skills, you can use Finding Replay For Issue with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Finding Replay For Issue AI skill free?

Yes. It is published on GitHub by PostHog under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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