agentleFS
alinaqialinaqi/claude-bootstrap/skills/demo-video/SKILL.md

demo-video

Skill708 starsChanged 5 months agoMITView on GitHub

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

  • Deletes or force-pushes

27 other projects use a version of this, 1 of them edited.

Adopted by

Most recently taken up by

Used with

Projects with it also use karpathy-coder, ai-act-readiness and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • csiddhant796-blipSkill

    karpathy-coder

    Use when writing, reviewing, or committing code to enforce Karpathy's 4 coding principles — surface assumptions before coding, keep it simple, make surgical changes, define verifiable goals. Triggers on "review my diff", "check complexity", "am I overcomplicating this", "karpathy check", "before I commit", or any code quality concern where the LLM might be overcoding.

    Also in 4 projects that use this

  • Hussain-ElfalahSkill

    ai-act-readiness

    Also in 3 projects that use this

  • Hussain-ElfalahSkill

    c-level-agents

    Also in 3 projects that use this

  • csiddhant796-blipSkill

    self-eval

    Honestly evaluate AI work quality using a two-axis scoring system. Use after completing a task, code review, or work session to get an unbiased assessment. Detects score inflation, forces devil's advocate reasoning, and persists scores across sessions.

    Also in 4 projects that use this

  • +8 more from Hussain-Elfalah

The file

We split this skill into the rules it gives an agent

4 rules. 0 are common, 2 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 24 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Call before each chapter; the pause gives viewers time to read:

    line 31
  2. Very specific:

    OTP/async steps: after submitting a form that triggers an email/side effect, wait for the next UI state (await …

    line 62
  3. Distinctive:

    Read the extracted frames with the Read tool and confirm captions render and each chapter shows what it claims.

    line 84
  4. Very specific:

    Never video.mp4, never a name without its date and index.

    line 105

“Common” is measured: kept by at least 6 in 10 of the 24 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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:
    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:
    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

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:
    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.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.