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Synthetic Screen Recording

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calesthio
synthetic-screen-recording

Synthetic terminal-style screen recording guidance for Remotion `TerminalScene`.

Overview

Publishercalesthio
RepositoryOpenMontage
Skill namesynthetic-screen-recording
Stars
67K
Forks
8.5K
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 calesthio on GitHub. Read the source before you install it.

Installation

Install the Synthetic Screen 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.
    https://github.com/calesthio/OpenMontage/tree/main/.agents/skills/synthetic-screen-recording
  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/calesthio/OpenMontage.git /tmp/OpenMontage
mkdir -p .claude/skills
cp -r /tmp/OpenMontage/.agents/skills/synthetic-screen-recording .claude/skills/synthetic-screen-recording
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Synthetic Screen 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 Synthetic Screen 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 Synthetic Screen 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.

Synthetic Screen Recording (Remotion TerminalScene)

Decision this skill answers: When the user wants a screen-recording-looking demo of a terminal, CLI tool, or coding workflow — do I capture the real desktop (OS screen recording via screen_recorder, Windows-MCP, Cap, or Playwright), or do I synthesize it in Remotion with the TerminalScene component?

Heuristic: If the agent can author the exact command/output sequence in advance, synthesize. Only capture live when the real behavior is unpredictable, needs a real app UI, or the user explicitly asked for a real recording.

Why this exists

v3 of the OpenMontage showcase tried to use Windows-MCP + screen_recorder to drive a Git-Bash window for the install walkthrough. It stalled on window positioning, focus races, and taskbar privacy concerns. We pivoted to pure Remotion rendering — a React component named TerminalScene that draws a fake terminal and types commands character-by-character. The output is visually indistinguishable from a real screen recording (same traffic-light window chrome, blinking cursor, scrolling output) but deterministic, privacy-safe, pixel-perfect at 1080p, and pace-controllable to the frame.

That component + pattern is the capability this skill makes discoverable.

When to use synthetic (TerminalScene)

YES, synthesize when:

  • The demo is a terminal / CLI / coding session where commands and outputs are predictable
  • The user wants a polished tutorial feel (clean typography, floating pills, cursor blink)
  • Install walkthroughs, setup demos, API key config, make targets, git clone flows
  • You need tight sync with narration — every command must land on a specific beat
  • You want the result reproducible (re-render gets identical pixels)
  • The user's actual desktop has private apps/windows visible you'd otherwise have to crop

NO, capture a real screen when:

  • The demo is a real app UI that can't be faked (Figma, Photoshop, a web app with live state, a browser flow)
  • The user explicitly asked for a recording of their actual screen
  • The behavior depends on timing you can't script (streaming LLM output, real network latency)
  • There's a visual quirk (a cursor effect, a plugin pop-up) that only appears in the live environment

For a browser demo → playwright-recording skill, not this one. For a real desktop → screen_recorder tool or Cap via cap_recorder.

The component — TerminalScene

Located at: remotion-composer/src/components/TerminalScene.tsx Exported from: remotion-composer/src/components/index.ts Wired in dispatch: remotion-composer/src/Explainer.tsx (if (cut.type === "terminal_scene"))

Props:

ts
interface TerminalSceneProps {
  title?: string;           // shown in the window title bar
  steps: TerminalStep[];    // the timeline
  prompt?: string;          // "$", ">", etc.
  accentColor?: string;     // pill + prompt glow
  backgroundColor?: string;
}

Step kinds:

ts
{ kind: "cmd",   text: string, typeSpeed?: number, holdSeconds?: number }
{ kind: "out",   text: string, holdSeconds?: number }
{ kind: "pause", seconds: number }
{ kind: "pill",  text: string, color?: string, durationSeconds?: number }
  • cmd — prints the prompt, types the text character-by-character (typeSpeed is seconds per character, default 0.035), then holds for holdSeconds (default 0.3)
  • out — a line of program output, reveals instantly with a short fade-in
  • pause — dead time. Terminal holds on last visible state. USE THIS TO SYNC WITH NARRATION.
  • pill — non-blocking floating badge (top-right). Spring-in, hold, spring-out. Does NOT advance the cursor — the next step runs in parallel.

Authoring pattern

Author a new scene by adding a cut to build_composition.py (or your equivalent props builder):

python
install_steps = [
    {"kind": "pause", "seconds": 7.0},                 # wait for intro narration
    {"kind": "cmd", "text": "git clone https://github.com/calesthio/OpenMontage.git",
     "typeSpeed": 0.045, "holdSeconds": 0.3},
    {"kind": "out", "text": "Cloning into 'OpenMontage'..."},
    {"kind": "out", "text": "remote: Enumerating objects: 2847, done."},
    {"kind": "pill", "text": "repo cloned", "color": "#34D399", "durationSeconds": 2.6},
    {"kind": "pause", "seconds": 3.8},                 # bridge to next narration cue
    # ...
]

cuts.append({
    "id": "install-terminal",
    "type": "terminal_scene",
    "terminalTitle": "bash — OpenMontage setup",
    "prompt": "$",
    "accentColor": "#22D3EE",
    "steps": install_steps,
    "in_seconds": 50.0,
    "out_seconds": 110.0,
})

THE RULE: pace with narration, never ahead

The #1 failure mode: steps run continuously and burn through all content in the first 40% of the scene, leaving the terminal frozen for the remaining 60%. This is what killed the v3 first pass — the capability menu rendered at t=80s but narration didn't announce it until t=92s.

Do this instead:

  1. Know your narration cues — for each scene, write down the exact video-time each narration segment starts.
  2. Start with a pause that reaches the first narration cue before any command types.
  3. Time each command to land with its narration line — cmd should start typing the moment narration says its line, not before.
  4. Put pauses between command groups that bridge to the next narration cue.
  5. End with a closer hold — a pause long enough that the final state is readable after narration ends.

Sanity-check your steps before rendering — every minute of Remotion render is precious. Sum the step durations and verify they equal scene duration:

python
import math
def trace(steps, scene_start, fps=30):
    t = 0.0
    for s in steps:
        k = s["kind"]
        if k == "cmd":
            tf = math.ceil(len(s["text"]) * s.get("typeSpeed", 0.035) * fps)
            t += tf / fps + s.get("holdSeconds", 0.3)
        elif k == "out":
            t += max(2, math.ceil(0.08 * fps)) / fps + s.get("holdSeconds", 0.15)
        elif k == "pause":
            t += s["seconds"]
        # "pill" is non-blocking — does NOT advance cursor
        print(f"  {t + scene_start:6.2f}s  {k}: {s.get('text', '')[:40]}")
trace(install_steps, 50)

Look at the output column. Each narration cue's video-time must appear adjacent to the command/output it announces. If a command lands 10s before or after its cue, adjust pauses.

See lib/verify_scene_pacing.py for a reusable version of this script.

Design rules (inherited from the v3 retune)

  • Intro pause — every terminal scene opens with at least 2s of empty-terminal-with-blinking-cursor before anything types. The viewer needs to register the window.
  • Pill timing — a pill should fire at the exact moment its named event completes on screen (e.g., repo cloned immediately after the last Receiving objects line). Pills are your substitute for real-world UI notifications.
  • Command hold after typing — keep holdSeconds ≥ 0.3 on every cmd so viewers register the completed command before the first output scrolls in.
  • Output cadence — space holdSeconds on output lines between 0.4 and 1.0. Output that flies too fast feels like a bug; output that crawls feels boring.
  • Auto-scroll works — the terminal holds the most recent 18 lines. Don't worry about off-screen content.
  • Cursor blinks only on the latest command line while typing + a ~0.2s tail after typing completes.

ProviderChip (companion component)

The .agents/skills/synthetic-screen-recording pattern also owns ProviderChip — a rotating badge overlay that cycles through a list of provider names at a fixed cadence. Used in the v3 showcase to cycle through all 11 AI video-gen providers during the "generated motion" section.

python
overlays.append({
    "type": "provider_chip",
    "providers": ["Veo 3.1", "Seedance 2.0", "Kling 2.5", ...],
    "cycleSeconds": 2.5,
    "position": "bottom-right",
    "accentColor": "#22D3EE",
    "label": "generated with",
    "in_seconds": 195.0,
    "out_seconds": 222.5,
})

Wired in dispatch at: remotion-composer/src/Explainer.tsx overlay renderer (overlay.type === "provider_chip").

Adding new synthetic-UI components

The pattern generalizes. When you need to fake another UI surface (Claude Code chat bubbles, a Jira ticket view, a GitHub PR diff, a Slack message, a VS Code status bar):

  1. Copy TerminalScene.tsx as a template.
  2. Define a steps interface for the relevant timeline primitives.
  3. Render each step by interpolating frame against cumulative start/end times.
  4. Wire it into Explainer.tsx's SceneRenderer dispatch with a new cut.type.
  5. Add the type to the Cut interface in Explainer.tsx and to components/index.ts.
  6. Add a section to this skill documenting it.
  7. Update remotion-composer/SCENE_TYPES.md with the new cut type.

Related skills

  • .agents/skills/remotion — general Remotion authoring (hooks, springs, sequences)
  • .agents/skills/playwright-recording — real browser-flow capture for web apps
  • tools/capture/screen_recorder — ffmpeg-based desktop capture
  • tools/capture/cap_recorder — Cap.so polished desktop capture
  • skills/pipelines/screen-demo/asset-director.md — chooses between synthetic and real for a screen-demo project

Provenance

Introduced: OpenMontage showcase v3 render (2026-04-16). Original motivation: the v3 setup walkthrough section needed a 60-second install demo where every command aligned to Chirp 3 HD narration cues, and Windows-MCP-driven real capture was too flaky in practice. See projects/openmontage-showcase/build_composition.py for the reference implementation.

Frequently asked questions

What does the Synthetic Screen Recording AI skill do?

Synthetic terminal-style screen recording guidance for Remotion `TerminalScene`.

Why use Synthetic Screen Recording on TypingMind?

Because you install it once and use it with any model. Synthetic Screen 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 Synthetic Screen Recording in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/calesthio/OpenMontage/tree/main/.agents/skills/synthetic-screen-recording. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Synthetic Screen 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 Synthetic Screen Recording?

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

Is the Synthetic Screen Recording AI skill free?

Yes. It is published on GitHub by calesthio 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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