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Demo Video

Community
alinaqi
demo-video

Record captioned product walkthrough videos of any web app with Playwright + ffmpeg — proof videos for stakeholders, feature demos, bug-fix evidence

Overview

Publisheralinaqi
Repositorymaggy
Skill namedemo-video
Stars
707
Forks
57
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 alinaqi on GitHub. Read the source before you install it.

Installation

Install the Demo Video 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/alinaqi/maggy/tree/main/skills/demo-video
  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/alinaqi/maggy.git /tmp/maggy
mkdir -p .claude/skills
cp -r /tmp/maggy/skills/demo-video .claude/skills/demo-video
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Demo Video 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 Demo Video 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 Demo Video 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.

Demo Video Skill — visual validation for web apps

Record a captioned walkthrough video of a running web app, verify it frame-by-frame, convert to mp4, and deliver it. Battle-tested on real client-feedback proof videos.

This is the harness's default visual validation for web projects: a demo video walks a real user flow end-to-end and doubles as a passing E2E test, so "it works" is proven visually, not claimed. It complements [[visual-validation]] (autonomous screenshot-regression checks) and [[playwright-testing]] (the E2E suite): screenshots catch pixel regressions, the suite proves behavior, and a demo video proves the whole user-facing flow the way a person would see it.

The method

  1. Write a temporary Playwright spec (e2e/zz-<name>-video.spec.ts or scratchpad) that walks the real flows as one continuous test. One test = one video.
  2. Record via Playwright's built-in video, never external screen capture:
    ts
    test.use({ video: { mode: 'on', size: { width: 1280, height: 800 } }, viewport: { width: 1280, height: 800 } })
    test.setTimeout(240000)
  3. Narrate with an injected caption bar — the video must explain itself without audio. Call before each chapter; the pause gives viewers time to read:
    ts
    async function caption(page: Page, text: string) {
      await page.evaluate((t) => {
        let bar = document.getElementById('demo-caption')
        if (!bar) {
          bar = document.createElement('div')
          bar.id = 'demo-caption'
          bar.style.cssText =
            'position:fixed;left:0;right:0;bottom:0;z-index:99999;background:#142b1e;color:#f7f5f0;' +
            'font:600 15px/1.4 Inter,system-ui,sans-serif;padding:12px 20px;letter-spacing:0.01em'
          document.body.appendChild(bar)
        }
        bar.textContent = t
      }, text)
      await page.waitForTimeout(2200)
    }
    The caption bar is re-injected automatically because caption() re-creates it after each page.goto (the element does not survive navigation — call caption AFTER navigating).
  4. Structure as chapters: caption → act → assert → caption the outcome. Keep real assertions in the spec so the video doubles as a passing test — a video of a broken flow must fail loudly, never ship silently.
  5. Reuse the project's e2e helpers (logins, seeded data, fixtures) instead of hand-rolling flows. Prefer fake/deterministic AI modes so runs are repeatable.

Recording gotchas (learned the hard way)

  • Scroll-reveal animations: elements animated in by IntersectionObserver stay invisible in captures unless you actually scroll. Walk the page (scrollIntoViewIfNeeded or step-scroll) before asserting/capturing.
  • OTP/async steps: after submitting a form that triggers an email/side effect, wait for the next UI state (await expect(page.locator('#code')).toBeVisible()) before reading outboxes/fixtures — racing the write is the #1 flake.
  • Selector traps: "first link" often matches a New/Create button; exclude it (a[href^="/x/"]:not([href$="/new"])).
  • Demonstrating error states: drive the app's real error surface (e.g. navigate to the URL the failing action redirects to) rather than mocking pages that don't exist.

Produce and verify

bash
rm -rf test-results/zz-<name>*          # stale videos have the same filename
npx playwright test e2e/zz-<name>-video.spec.ts
find test-results -name "video.webm"    # the recording

# Convert to mp4 (universal playback, ~2-3x smaller)
ffmpeg -y -i "<video.webm>" -c:v libx264 -pix_fmt yuv420p -movflags +faststart out.mp4

# ALWAYS spot-check frames before delivering — extract a few and LOOK at them
ffmpeg -y -i out.mp4 -vf "select='eq(n\,60)+eq(n\,300)+eq(n\,600)'" -vsync vfr frame_%d.png

Read the extracted frames with the Read tool and confirm captions render and each chapter shows what it claims. Never deliver an unviewed video.

Deliver and clean up

  • Copy the mp4 to your stakeholder-updates archive (e.g. ~/Documents/updates/<project>/, the house archive for stakeholder updates), and send it to the user (SendUserFile or equivalent).
  • Naming is mandatory and identifying: YYYY-MM-DD-NN-<project>-<what-it-proves>.mp4 — the date it was recorded, then a two-digit index so several videos on the same day stay ordered and every video is uniquely identifiable at a glance. Allocate the next free index with a no-clobber loop — never a count (wc -l), which reuses an index if an earlier file was deleted and silently overwrites an existing mp4:
    bash
    DIR=~/Documents/updates/<project>; DATE=$(date +%F); mkdir -p "$DIR"
    NN=1
    while printf -v F '%s/%s-%02d-<project>-<what-it-proves>.mp4' "$DIR" "$DATE" "$NN" && [ -e "$F" ]; do
      NN=$((NN+1))
    done
    cp out.mp4 "$F"
    Never video.mp4, never a name without its date and index.
  • Delete the temporary spec — it is a capture script, not a test; it must not join the suite or CI.

Prerequisites

  • Playwright installed in the project (@playwright/test) with a working dev/preview server.
  • ffmpeg on PATH for the webm→mp4 conversion and frame extraction.

Frequently asked questions

What does the Demo Video AI skill do?

Record captioned product walkthrough videos of any web app with Playwright + ffmpeg — proof videos for stakeholders, feature demos, bug-fix evidence

Why use Demo Video on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/alinaqi/maggy/tree/main/skills/demo-video. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Demo Video?

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 Demo Video?

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

Is the Demo Video AI skill free?

Yes. It is published on GitHub by alinaqi 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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