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Auto Loom

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MikeFishbeinAtherial
auto-loom

Record a narrated product demo video without a screen recorder — an agent drives the browser while ElevenLabs narrates, perfectly synced, out to a polished MP4 with captions. Core engine + developer mode (you have the codebase, app on localhost). Use for client walkthrough videos, narrated demos, "video notes". Triggers - "video note", "record a walkthrough", "narrated demo", "client update video", "auto loom".

Overview

PublisherMikeFishbeinAtherial
Repositoryinfinite-headcount
Skill nameauto-loom
Stars
50
Forks
5
Bundled files
18
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.

  • 18 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Auto Loom 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/MikeFishbeinAtherial/infinite-headcount/tree/main/auto-loom
  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/MikeFishbeinAtherial/infinite-headcount.git /tmp/infinite-headcount
mkdir -p .claude/skills
cp -r /tmp/infinite-headcount/auto-loom .claude/skills/auto-loom
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Auto Loom 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 Auto Loom 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 Auto Loom 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.

Auto-Loom

Turn any web app into a narrated, self-driving MP4. The page scrolls, highlights elements, and navigates between routes while an AI voice narrates — timed to the millisecond. No manual screen recording, no fumbled Loom takes. Edit one line of narration and re-render in minutes.

How it works: ElevenLabs generates one audio clip per storyboard beat. ffprobe measures each clip's exact duration. Playwright drives the browser for exactly that long per beat while recording the window. ffmpeg muxes the final MP4 with a caption track. Audio duration is ground truth — visuals are slaved to it, so sync is exact.

This folder is the whole engine. Sibling skills are thin entry points over these same scripts:

  • auto-loom-proof — Software Factory proof video: performs a build's acceptance criteria on camera, one criterion per beat

This SKILL.md covers the engine plus developer mode: you have the codebase, the app runs on localhost, and you can add stable selectors to the JSX.


Setup (one-time, ~3 minutes)

bash
cd <this-skill-folder>
bash scripts/setup.sh        # installs Playwright + Chromium, creates .env.local
# edit .env.local → add your ElevenLabs API key
node scripts/doctor.mjs      # verifies node, ffmpeg, playwright, key, voice

No ElevenLabs account yet? Sign up with the maintainer's referral link: https://try.elevenlabs.io/atherial.

doctor.mjs prints the exact fix for anything missing. Requirements: Node 18+, ffmpeg (brew install ffmpeg), an ElevenLabs API key. Voice options: references/voices.md.

Smoke test (30 seconds, records example.com):

bash
node scripts/run.mjs --beats examples/smoke-beats.json

Step 0 — Interview the user first

Do not start building. Ask:

1. What URL or app are we recording? Is it running locally?
2. Who is the recipient — name and context?
3. What's the story? What changed, or what are we showing for the first time?
4. Which specific features, metrics, or sections should we highlight?
5. What's the tone? (client update, internal demo, prospect showcase)
6. Does the app require login? (see references/auth.md — never ask for passwords in chat)
7. Target length — 30s, 60s, or 75s?

In a hurry: ask 1–3 and infer the rest from the app.

Step 1 — Write the storyboard (beats.json)

Write the narration before touching any code and get it approved verbatim. Script rules and the beat framing that works: references/tts-rules.md.

json
{
  "meta": {
    "title": "Acme dashboard walkthrough",
    "slug": "acme-walkthrough",
    "cta": "Send me your feedback and we move to the next phase.",
    "targetLengthSec": 70
  },
  "baseUrl": "http://localhost:3000",
  "viewport": { "width": 1920, "height": 1080 },
  "beats": [
    {
      "id": 1,
      "page": "/dashboard/insights",
      "text": "Hey Jim. We shipped some updates to your A-I visibility tracker.",
      "actions": [
        { "fn": "waitFor",    "sel": "[data-vn=kpi-row]" },
        { "fn": "scrollToEl", "sel": "[data-vn=kpi-row]", "opts": { "instant": true } },
        { "fn": "hl",         "sel": "[data-vn=kpi-row]" }
      ]
    }
  ]
}

Full schema: schema/beats.schema.json. Action table and selector guidance: references/actions.md. Login options (scripted steps or saved session): references/auth.md.

Check it before spending anything:

bash
node scripts/validate.mjs --beats <file>       # structure + TTS + pacing lint
node scripts/run.mjs --beats <file> --script-only   # print the narration and stop

The validator catches unknown actions, missing selectors, TTS problems (em dashes, bare initialisms, run-on sentences), and pacing that won't fit the target length.

--script-only is the money-saving review gate: it prints every beat's narration with per-beat timing estimates and exits before any ElevenLabs call or recording. Get the script approved here, then re-run without the flag to spend credits.

Step 2 — Instrument the page (developer mode)

Playwright needs stable selectors. With codebase access, add data-vn attributes to the component JSX:

tsx
<div data-vn="kpi-row" className="grid grid-cols-4 gap-4">
<Card data-vn="competitor-chart" ...>

Then reference them: { "fn": "hl", "sel": "[data-vn=kpi-row]" }.

Suppress interactive drilldowns while recording. Clickable cards with role="button" / tabIndex={0} can get auto-focused during highlights and open a drawer mid-video. Gate them on your demo-mode flag: {!isDemoMode() && <MetricBreakdownSheet ... />}.

If the app requires auth locally, either wire a demo mode (skip auth, fixture data that matches the narration numbers exactly) or use an auth path from references/auth.md.

Note on CSP: the recorder launches Chromium with bypassCSP: true so it can inject presenter.js into strict-CSP pages. If you point it at a production app with a live session, know that Content-Security-Policy is disabled inside that automation browser context.

Step 3 — One command

bash
node scripts/run.mjs --beats <file>

That runs: validate → audio (cached by text hash) → preflight (headless pass resolving every selector before anything is recorded) → record → captions → mux → verify. Output lands in demo-video-out/<slug>/:

  • <slug>-demo.mp4 — final video (H.264/AAC, soft caption track)
  • <slug>-demo-contact-sheet.png — sampled frames for eyeballing
  • qa/beat-NN.png — exact screenshot at each beat's hold point
  • captions.srt, audio/, video/recording.webm

All flags: --script-only (print the narration script and exit before any ElevenLabs call or recording — the review gate from Step 1), --preflight-only (check selectors, spend nothing), --skip-audio (visual-only changes — audio is reused), --skip-preflight (skip the headless selector check — only when preflight just passed and nothing changed), --burn-captions (hardcode subtitles into pixels for platforms that strip tracks), --no-captions, --out <dir>, --env <envfile>.

Step 4 — Verify before sending

run.mjs checks duration (MP4 = sum of audio within 0.5s), audio level (silent-track detection), and drops QA screenshots. You still eyeball:

  • Contact sheet + qa/beat-NN.png: each frame shows what its beat narrates — no login pages, error states, or surprise modals.
  • First beat names the recipient; last beat delivers the CTA verbatim.

Iterating

  • Narration edit → re-run; only the changed beat's audio regenerates.
  • Visual/pacing edit → --skip-audio.
  • Selector broke → --preflight-only shows exactly which beat and selector.

Known gotchas

  • Hot reload wipes the injected presenter. Already handled — presenter re-injects before every beat. But avoid editing the app while recording.
  • Port drift. yarn dev takes 3001 if 3000 is busy — update baseUrl.
  • Free vs paid voices. Cloned/library voices throw payment_required on free tier. doctor.mjs catches this before render time.
  • i18n redirects. /en/dashboard → /dashboard redirects are followed correctly; same-page beats don't re-navigate.
  • Delivery. The MP4 is self-contained — email, Slack, LinkedIn DM, Notion embed, or import into Descript for a personal intro on top.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Auto Loom AI skill do?

Record a narrated product demo video without a screen recorder — an agent drives the browser while ElevenLabs narrates, perfectly synced, out to a polished MP4 with captions. Core engine + developer mode (you have the codebase, app on localhost). Use for client walkthrough videos, narrated demos, "video notes". Triggers - "video note", "record a walkthrough", "narrated demo", "client update video", "auto loom".

Why use Auto Loom on TypingMind?

Because you install it once and use it with any model. Auto Loom 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 Auto Loom in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MikeFishbeinAtherial/infinite-headcount/tree/main/auto-loom. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Auto Loom?

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 Auto Loom?

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

Is the Auto Loom AI skill free?

Yes. It is published on GitHub by MikeFishbeinAtherial 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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