AI AGENTS

Loom dev walkthrough to GitHub docs PR

Converts a published Loom engineering walkthrough into a Markdown SOP and opens a pull request adding it to the repo's docs folder for code-review-style approval.

CategoryAI Agents
Enginepaperclip
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew Loom video publishedLoomLoom
  • ActionFetch Loom transcriptLoomLoom
  • ActionConvert transcript to Markdown SOPHugging FaceHugging Face
  • ActionCommit doc to new branchGitHubGitHub
  • OutputOpen GitHub pull request for reviewGitHubGitHub

What it does

Takes an engineer's Loom walkthrough of a setup, deploy, or debugging process and turns it into a Markdown doc, then opens a GitHub pull request so the SOP gets reviewed and versioned like code.

When to use it

Use it for engineering teams that keep docs-as-code in the repo. When someone records how a service works or how to run a migration, this drafts the written version and routes it through your normal PR review instead of letting tribal knowledge stay in a video.

How it works

  1. 1A new Loom video in the engineering workspace triggers the flow.
  2. 2The agent fetches the recording transcript.
  3. 3A Hugging Face model converts the transcript into a clean Markdown SOP with code blocks, headings, and an embedded Loom reference.
  4. 4The agent creates a new branch and commits the file under the repo's docs directory.
  5. 5It opens a GitHub pull request titled with the SOP name, body noting it is an AI draft from a Loom recording pending human edits.
  6. 6Reviewers approve, edit, or reject through the standard PR flow.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect LoomVideo transcripts, libraries.
  2. 2
    Connect Hugging FaceModels, datasets, spaces — the open-source hub.
  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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