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Browser Video Recording

CommunityPopular
MengTo
browser-video-recording

Create polished 60 fps 4:3 4K browser screen-recording style videos from Codex in-app browser captures, with browser-only crop, natural macOS cursor styling, deliberate click choreography, zoom-follow framing, ffprobe/thumbnail verification, and optional native recording compatibility checks. Use when the user asks to record or re-record browser actions, show cursor clicks and zooms, make Dribbble/UI inspiration or product demo recordings, or asks whether Codex, Playwright, or an MCP can produce a natural browser demo video.

Overview

PublisherMengTo
RepositorySkills
Skill namebrowser-video-recording
Stars
6.1K
Forks
717
Bundled files
6
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.

  • 6 bundled files

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

  • Open source

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

Installation

Install the Browser Video Recording 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.
  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/MengTo/Skills.git /tmp/Skills
mkdir -p .claude/skills
cp -r /tmp/Skills/agent-skills/codex/browser-video-recording .claude/skills/browser-video-recording
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Browser Video Recording 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 Browser Video Recording 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 Browser Video Recording 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.

Browser Video Recording

Dependency Model

Separate the workflow into three layers:

  1. Browser control and source frames: Use the Codex in-app browser through the Browser skill/MCP. Do not use Chrome when the user asks for Codex browser or the project says not to. The browser layer supplies real screenshots and real click/scroll/navigation states.
  2. Local video renderer: Use the bundled Python script to render screenshots into an MP4 with a natural macOS cursor, subtle click scale, and calm zoom-follow framing. This does not require a screen-recording MCP.
  3. Optional native recorder integration: Use a native recording MCP only when callable and explicitly useful for starting/stopping an actual app recording or opening a generated project. If unavailable, blocked, or unable to capture the Codex browser surface, fall back to the screenshot-to-video renderer and explain the fallback.

Self-Contained Execution

This skill is enough for another Codex instance to perform the Python rendering as long as the full skill folder is installed, including scripts/render_browser_demo.py. Codex should run the bundled script instead of recreating the renderer.

Required local tools:

  • python3
  • Python package Pillow
  • ffmpeg and ffprobe

Optional macOS cursor extraction uses swift and /usr/sbin/screencapture. If those are unavailable, provide a transparent PNG through cursor_asset and set cursor_hotspot in the config.

The Browser skill/MCP is still needed for fresh Codex in-app browser screenshots. If it is unavailable, use the best available browser automation screenshot source and state the fallback.

Optional Native Recorder Support

For native app recording, verify the MCP supports:

  • Use frameRate: 60.
  • Use resolutionRawValue: "4K".
  • Use ratioPresetRawValue: "4:3" for region recordings.
  • Use an explicit selectedRegionOnScreen when cropping to an embedded browser viewport.

For the local renderer, 4K 4:3 means 3840x2880. If a native recorder exposes a 4K enum, treat it as an app-side resolution preset and pair it with 4:3 region selection when recording through that MCP.

Capture Workflow

Use browser:control-in-app-browser first. After loading its docs, drive the in-app browser with the Node/browser API:

  • Set viewport to 1920x1440 for 4:3 4K output, then reset it before finishing.
  • Navigate to the requested page and perform actual clicks, backs, searches, and scrolls.
  • Save browser-only screenshots after each important state change. Use names like 01-results-top.png, 02-after-card-click.png, 03-back-results.png.
  • Record the viewport coordinates of each click/scroll target. The final cursor path should land on those points, pause, click, then move to the next meaningful point.

Avoid desktop coordinate automation unless the user explicitly needs system UI. Codex browser visibility may not expose a capturable macOS surface; screenshots from the browser API are the reliable source.

Motion Rules

Make the cursor feel purposeful:

  • Use sparse, mostly straight cursor paths.
  • Pause briefly on a target before clicking.
  • Do not wander over unrelated UI.
  • Let the page transition happen after the click, not before.
  • Use only subtle click feedback: scale dip/rebound, not decorative rings unless requested.
  • Keep zoom-follow calmer than the cursor. The camera should frame the clicked content, not chase every small mouse move.

Default visual target:

  • Real macOS pointer extracted from the compositor, or an existing transparent cursor asset.
  • Cursor scale 3.0, matching the large cursor style commonly used in edited demo recordings.
  • Click animation strength 15 for a subtle click scale dip.
  • Rotation strength up to 8deg, only from horizontal velocity.

Render Script

Use scripts/render_browser_demo.py for deterministic 60 fps 4:3 4K output:

bash
python3 /path/to/browser-video-recording/scripts/render_browser_demo.py \
  --config /tmp/browser-demo-config.json \
  --output /tmp/browser-demo-4x3-4k-60fps.mp4

Generate a starter config:

bash
python3 /path/to/browser-video-recording/scripts/render_browser_demo.py --write-template

The config supplies:

  • shots: map of state names to screenshot PNG paths.
  • scene_starts: [time, shotName, transition] entries.
  • cursor_keys: [time, x, y] viewport-coordinate entries.
  • camera_keys: [time, x, y, zoom] entries.
  • click_times: click timestamps.
  • duration, fps, output_size, source_size, cursor_scale.

Preferred defaults:

json
{
  "fps": 60,
  "output_size": [3840, 2880],
  "source_size": [1920, 1440]
}

If cursor_asset is omitted, the script briefly shows a solid-color overlay and uses screencapture to extract the real current macOS cursor into a transparent PNG. This is local rendering support, not an MCP dependency.

Verification

Always verify the delivered video:

bash
ffprobe -v error \
  -show_entries stream=codec_name,width,height,r_frame_rate \
  -show_entries format=duration,size \
  -of json /path/to/output.mp4

Extract at least one mid-video thumbnail and inspect it:

bash
ffmpeg -y -ss 8 -i /path/to/output.mp4 -frames:v 1 -update 1 /tmp/browser-demo-thumb.png

Confirm:

  • Resolution is the requested output, usually 3840x2880 for 4:3 4K.
  • Frame rate is 60/1 or equivalent.
  • Duration matches the request.
  • Cursor is visible, natural, and on-target.
  • The video is cropped to browser content, not the desktop.
  • The browser viewport override has been reset.

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 Browser Video Recording AI skill do?

Create polished 60 fps 4:3 4K browser screen-recording style videos from Codex in-app browser captures, with browser-only crop, natural macOS cursor styling, deliberate click choreography, zoom-follow framing, ffprobe/thumbnail verification, and optional native recording compatibility checks. Use when the user asks to record or re-record browser actions, show cursor clicks and zooms, make Dribbble/UI inspiration or product demo recordings, or asks whether Codex, Playwright, or an MCP can produce a natural browser demo video.

Why use Browser Video Recording on TypingMind?

Because you install it once and use it with any model. Browser Video Recording 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 Browser Video Recording in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MengTo/Skills/tree/main/agent-skills/codex/browser-video-recording. 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 Browser Video Recording?

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 Browser Video Recording?

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

Is the Browser Video Recording AI skill free?

Yes. It is published on GitHub by MengTo 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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