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Demo video

Skill alirezarezvani/claude-skills/engineering/demo-video/skills/demo-video

Use when the user asks to create a demo video, product walkthrough, feature showcase, animated presentation, marketing video, or GIF from screenshots or scene descriptions. Orchestrates playwright, ffmpeg, and edge-tts MCPs to produce polished video content.From its SKILL.md

Install
npx -y skills add alirezarezvani/claude-skills --skill demo-video

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SKILL.md

5.1 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it

Demo Video

You are a video producer. Not a slideshow maker. Every frame has a job. Every second earns the next.

Overview

Create polished demo videos by orchestrating browser rendering, text-to-speech, and video compositing. Think like a video producer — story arc, pacing, emotion, visual hierarchy. Turns screenshots and scene descriptions into shareable product demos.

When to Use This Skill

  • User asks to create a demo video, product walkthrough, or feature showcase
  • User wants an animated presentation, marketing video, or product teaser
  • User wants to turn screenshots or UI captures into a polished video or GIF
  • User says "make a video", "create a demo", "record a demo", "promo video"

Core Workflow

1. Choose a rendering mode

Before starting, verify available tools:

  • playwright MCP available? — needed for automated screenshots. Fallback: ask user to screenshot the HTML files manually.
  • edge-tts available? — needed for narration audio. Fallback: output narration text files for user to record or use any TTS tool.
  • ffmpeg available? — needed for compositing. Fallback: output individual scene images + audio files with manual ffmpeg commands the user can run.

If none are available, produce HTML scene files + scenes.json manifest + narration scripts. The user can composite manually or use any video editor.

ModeHowWhen
MCP OrchestrationHTML → playwright screenshots → edge-tts audio → ffmpeg compositeUse when playwright + edge-tts + ffmpeg MCPs are all connected
ManualWrite HTML scene files, provide ffmpeg commands for user to runUse when MCPs are not available

2. Pick a story structure

The Classic Demo (30-60s): Hook (3s) -> Problem (5s) -> Magic Moment (5s) -> Proof (15s) -> Social Proof (4s) -> Invite (4s)

The Problem-Solution (20-40s): Before (6s) -> After (6s) -> How (10s) -> CTA (4s)

The 15-Second Teaser: Hook (2s) -> Demo (8s) -> Logo (3s) -> Tagline (2s)

3. Design scenes

If no screenshots are provided:

  • For CLI/terminal tools: generate HTML scenes with terminal-style dark background, monospace font, and animated typing effect
  • For conceptual demos: use text-heavy scenes with the color language and typography system
  • Ask the user for screenshots only if the product is visual and descriptions are insufficient

Every scene has exactly ONE primary focus:

  • Title scenes: product name
  • Problem scenes: the pain (red, chaotic)
  • Solution scenes: the result (green, spacious)
  • Feature scenes: the highlighted screenshot region
  • End scenes: URL / CTA button

4. Write narration

  • One idea per scene. If you need "and" you need two scenes.
  • Lead with the verb. "Organize your tabs" not "Tab organization is provided."
  • No jargon. "Your tabs organize themselves" not "AI-powered tab categorization."
  • Use contrast. "24 tabs. One click. 5 groups."

Output Artifacts

For each video, produce these files in a demo-output/ directory:

  1. scenes/ — one HTML file per scene (1920x1080 viewport)
  2. narration/ — one .txt file per scene (for edge-tts input)
  3. scenes.json — manifest listing scenes in order with durations and narration text
  4. build.sh — shell script that runs the full pipeline:
    • playwright screenshot each HTML scene → frames/
    • edge-tts each narration file → audio/
    • ffmpeg concat with crossfade transitions → output.mp4

If MCPs are unavailable, still produce items 1-3. Include the ffmpeg commands in build.sh for the user to run manually.

Scene Design System

See references/scene-design-system.md for the full design system: color language, animation timing, typography, HTML layout, voice options, and pacing guide.

Quality Checklist

  • Video has audio stream
  • Resolution is 1920x1080
  • No black frames between scenes
  • First 3 seconds grab attention
  • Every scene has one focus point
  • End card has URL and CTA

Anti-Patterns

Anti-patternFix
Slideshow pacing — every scene same duration, no rhythmVary durations: hooks 3s, proof 8s, CTA 4s
Wall of text on screenMove info to narration, simplify visuals
Generic narration — "This feature lets you..."Use specific numbers and concrete verbs
No story arc — just listing featuresUse problem -> solution -> proof structure
Raw screenshotsAlways add rounded corners, shadows, dark background
Using ease or linear animationsUse spring curve: cubic-bezier(0.16, 1, 0.3, 1)

Cross-References

  • Related: engineering/browser-automation — for playwright-based browser workflows
  • See also: framecraft — open-source scene rendering pipeline

What ships with it: 1 file

1.6 KB alongside SKILL.md

references/

Gives 0 of the 12 instructions most product growth skills give in ~1.2k tokens

Counted across 694 of the 879 authors here whose files we hold, read 2026-09-06

  • Check for product marketing context firstin 49 of 694, across 20 files
  • Validate the why before building featuresin 18 of 694, across 4 files
  • Respond to every comment in real-timein 17 of 694, across 6 files
  • Structure launch marketing across three channel typesin 16 of 694, across 4 files
  • Recruit early users one-on-onein 13 of 694, across 2 files
  • Ask one question at a timein 13 of 694
  • Rank features using ICE scoringin 12 of 694, across 3 files
  • Identify primary conversion goalin 11 of 694, across 3 files
  • Identify traffic contextin 11 of 694, across 3 files
  • Evaluate headline effectivenessin 11 of 694, across 3 files
  • Check visual hierarchy and scannabilityin 11 of 694, across 3 files
  • Run product diagnosticsin 11 of 694, across 3 files

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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