Browser Video Proof logo

Browser Video Proof

Community
ahmadrosid
browser-video-proof

Produce H.264 MP4 browser video proof for a UI fix, upload it to GitHub user-attachments, and put the URL in a PR description. Use for scroll/ layout/interaction proofs (e.g. /automations expanded-run scrolling), recorded E2E demos, or when a reviewer needs visual evidence on a PR.

Overview

Publisherahmadrosid
Repositorynakama
Skill namebrowser-video-proof
Stars
492
Forks
91
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 ahmadrosid on GitHub. Read the source before you install it.

Installation

Install the Browser Video Proof 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/ahmadrosid/nakama/tree/main/.agents/skills/browser-video-proof
  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/ahmadrosid/nakama.git /tmp/nakama
mkdir -p .claude/skills
cp -r /tmp/nakama/.agents/skills/browser-video-proof .claude/skills/browser-video-proof
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Browser Video Proof 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 Proof 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 Proof 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 proof for PRs

Record a short, playable browser video that proves a UI fix, attach it to the PR description, and verify the live body. Do not change application code for the recording unless the user explicitly asks.

Reference run: PR #723 (/automations expanded long-run scroll).

Prerequisites

  • gh authenticated with push access to the target repo
  • Bun + repo deps installed in the worktree (bun install)
  • Google Chrome (or Chromium) on the host
  • ffmpeg on PATH (for webm → H.264 MP4)
  • Playwright in an isolated dir under /tmp (do not add it to the repo):
bash
mkdir -p /tmp/<run>/playwright
cd /tmp/<run>/playwright
npm i playwright
npx playwright install ffmpeg

Process

1. Isolate with a git worktree

Keep the main checkout clean.

bash
cd "$(git rev-parse --show-toplevel)"
git fetch origin main
git worktree add -b <branch> .worktrees/<slug> origin/main   # or attach to the fix branch
cd .worktrees/<slug>

Work only inside that worktree for servers and recording.

2. Inspect fixtures and UI first

Before inventing data:

  1. Read the page under test (labels, expand controls, scroll containers).
  2. Check existing test fixtures / factories for the same feature.
  3. Prefer seeding via the public HTTP API. Fall back to a one-off SQLite insert in an isolated NAKAMA_CONFIG_DIR only when no API can create the needed row (e.g. a completed automation run with huge output).

3. Seed deterministic tall content

For scroll / overflow proofs, the fixture must be taller than the pane.

  • Put a unique top marker near the start: SCROLL_PROOF_MARKER_TOP
  • Put a unique bottom marker at the end: SCROLL_PROOF_MARKER_BOTTOM
  • Use enough body text that scrollHeight - clientHeight is large (thousands of px)

Example shape for run output:

markdown
# SCROLL_PROOF_MARKER_TOP — long automation run output

## Section 1
...

## Section 80
...

# SCROLL_PROOF_MARKER_BOTTOM — end of tall content

Never seed into the operator's real ~/.nakama data. Always use a fresh NAKAMA_CONFIG_DIR under /tmp/<run>/.

4. Start isolated local servers

Pick free ports. Do not steal the default stack if something already listens on 4310 / 3000 / 3003.

bash
RUN=/tmp/<run>
API_PORT=4330
WEB_PORT=3014
mkdir -p "$RUN/nakama-data" "$RUN/logs"

# Keep processes alive after the shell exits (plain nohup ... & dies here)
setsid nohup env NAKAMA_CONFIG_DIR="$RUN/nakama-data" NAKAMA_PORT=$API_PORT \
  bun run apps/server/src/index.ts \
  >"$RUN/logs/server.log" 2>&1 < /dev/null &

setsid nohup env VITE_API_URL="http://127.0.0.1:${API_PORT}" \
  bun run --cwd apps/web dev -- --host 127.0.0.1 --port $WEB_PORT \
  >"$RUN/logs/web.log" 2>&1 < /dev/null &

Wait until curl -sf http://127.0.0.1:$API_PORT/health succeeds and the web origin responds. Fresh setup: POST /v1/auth/setup, then create provider / profile / automation (or reuse fixtures) with Cookie + X-CSRF-Token + X-Org-Id.

5. Record with Playwright (H.264 MP4)

Scaffold the recorder under /tmp/<run>/ only. Never commit it.

js
const browser = await chromium.launch({
  channel: "chrome", // if Chrome is installed
  headless: false,
  args: ["--disable-gpu"],
});
const context = await browser.newContext({
  viewport: { width: 1440, height: 900 },
  recordVideo: { dir: rawDir, size: { width: 1440, height: 900 } },
});

Rules:

  1. Drive the real UI path under test (role/text locators).
  2. Make motion visible — mouse wheel or stepped scroll, not a single invisible jump only.
  3. Assert scroll moved (scrollTop delta) and that the bottom marker becomes reachable.
  4. Capture page.video()?.path() before context.close().
  5. Convert: ffmpeg -y -i in.webm -c:v libx264 -pix_fmt yuv420p out.mp4
  6. Write results.json + before/after screenshots under artifacts/e2e-<id>/.

WebM alone is not enough for GitHub playback. Ship H.264 + yuv420p.

6. Validate the video before claiming anything

bash
ffprobe -v error -show_entries stream=codec_name,width,height \
  -show_entries format=duration -of json out.mp4
# Extract early / late frames and look at them
ffmpeg -y -ss 00:00:02 -i out.mp4 -vframes 1 frame-early.png
ffmpeg -y -ss 00:00:08 -i out.mp4 -vframes 1 frame-late.png

Pass only if:

  • codec is h264
  • duration is long enough to see the interaction
  • early frame shows the start state (e.g. top marker / expanded pane)
  • late frame shows scroll progress (bottom marker or advanced sections)
  • results.json reports a real scrollTop delta (or equivalent proof metric)

7. Upload to GitHub user-attachments

Undocumented but token-auth works for images/video when you have push access:

bash
TOKEN=$(gh auth token)
REPO_ID=$(gh api repos/<owner>/<repo> --jq .id)
MP4=artifacts/e2e-<id>/<name>.mp4

UPLOAD_JSON=$(curl -sS -X POST \
  "https://uploads.github.com/user-attachments/assets?name=$(basename "$MP4")&repository_id=${REPO_ID}" \
  -H "Authorization: Bearer ${TOKEN}" \
  -H "Content-Type: video/mp4" \
  -H "Accept: application/vnd.github+json" \
  --data-binary @"${MP4}")

VIDEO_URL=$(printf '%s' "$UPLOAD_JSON" | bun -e 'console.log(JSON.parse(await Bun.stdin.text()).url)')
echo "$VIDEO_URL"  # https://github.com/user-attachments/assets/<uuid>

Save the URL. Confirm a HEAD/GET on the asset returns a video content type.

8. Update the PR description

Add a short Browser proof section with the steps shown and the bare https://github.com/user-attachments/assets/... URL on its own line (GitHub embeds the player). Prefer writing the body via a file:

bash
gh api repos/<owner>/<repo>/pulls/<n> --method PATCH --input body.json
# or: gh pr edit <n> --body-file /tmp/pr-body.md

Follow .agents/skills/adhd-pr-description/SKILL.md for the rest of the body.

9. Verify the live PR body

bash
gh api repos/<owner>/<repo>/pulls/<n> --jq .body | rg 'user-attachments/assets/<uuid>|Browser proof'

Do not report success until this check passes.

Example: automations expanded-run scroll

  1. Worktree on the fix branch (or docs-only branch for this skill).
  2. Isolated API 4330 + web 3014, fresh NAKAMA_CONFIG_DIR.
  3. POST /v1/auth/setup → provider → create automation Long output digester.
  4. Insert/complete one run whose output is the long markdown with SCROLL_PROOF_MARKER_TOP / SCROLL_PROOF_MARKER_BOTTOM.
  5. Playwright: open /automations → select the automation → Expand run → wheel-scroll the detail pane until the bottom marker is in view.
  6. Convert to H.264 MP4, inspect frames, upload, patch PR body, verify.

Cleanup

  1. Kill the isolated API/web listeners on the ports you chose.
  2. Leave /tmp/<run>/ and profile artifacts/e2e-<id>/ as local evidence (do not commit videos or recorder scripts).
  3. Remove the worktree when the branch is merged or abandoned: git worktree remove .worktrees/<slug>

Honesty rules

  1. Never claim browser proof without watching frames or ffprobe + screenshots.
  2. Never paste a user-attachments URL you did not upload and re-fetch from the live PR body.
  3. Never treat a green unit test as a substitute for the video when the ask was visual proof.
  4. If scroll did not move, markers are wrong, or the MP4 is empty/corrupt — say it failed. Do not soften.
  5. Do not modify application code “to make the video easier” unless the user asked for a product fix.
  6. Do not touch the operator’s real Nakama config dir.

Frequently asked questions

What does the Browser Video Proof AI skill do?

Produce H.264 MP4 browser video proof for a UI fix, upload it to GitHub user-attachments, and put the URL in a PR description. Use for scroll/ layout/interaction proofs (e.g. /automations expanded-run scrolling), recorded E2E demos, or when a reviewer needs visual evidence on a PR.

Why use Browser Video Proof on TypingMind?

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

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

Which AI models can use Browser Video Proof?

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 Proof?

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

Is the Browser Video Proof AI skill free?

Yes. It is published on GitHub by ahmadrosid 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇