ENGINEERING

Loom Bug Video to GitHub Issue with Repro Steps

When a Loom recording is shared, transcribes it, extracts numbered reproduction steps and expected vs actual behavior, and opens a structured GitHub issue with the video embedded.

CategoryEngineering
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew Loom video shared in workspaceLoomLoom
  • ActionFetch Loom transcript and metadataLoomLoom
  • ActionExtract repro steps + expected/actual via LLMOpenAI
  • LogicRequire minimum fields (steps + actual behavior)
  • OutputCreate structured GitHub issue with embedGitHubGitHub

What it does

Turns a raw Loom bug-report video into a clean, engineer-ready GitHub issue. It pulls the transcript, uses an LLM to extract the exact reproduction steps, expected behavior, and observed behavior, then files an issue that follows your bug template with the Loom embedded for context.

When to use it

Use it when teammates, support, or beta users report bugs by recording a quick Loom instead of writing a ticket. It removes the manual step of an engineer watching the video and hand-writing the issue, and guarantees every report lands in the same structured format.

How it works

  1. 1A new Loom video in the watched workspace triggers the flow.
  2. 2The flow fetches the video transcript and metadata from Loom.
  3. 3An OpenAI step parses the transcript into numbered repro steps, expected vs actual behavior, and a one-line title, and flags missing details.
  4. 4A logic step checks the report has at least the minimum fields (steps + actual behavior) before proceeding.
  5. 5A GitHub action creates an issue using the bug template, with the Loom link embedded and labels applied.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect LoomVideo transcripts, libraries.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect GitHubRepos, issues, pull requests, actions.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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