Agent skill

Record Browser Gif

by singula-ai in singula-ai/alego

Record browser or Web UI interaction demos as optimized GIFs using the available browser-control workflow, optional Playwright Videos for higher capture frame rates, and deterministic encoding, then…

MITAuto-check: notesDevelopment

Install Record Browser Gif

skills CLI
$ npx skills add singula-ai/alego --skill record-browser-gif -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install singula-ai/alego record-browser-gif --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/singula-ai/alego.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/record-browser-gif .claude/skills/record-browser-gif && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
record-browser-gif
GitHub stars
109
Used in
1 other repo
Token cost
~4.1k tokens
SKILL.md length
2,138 words
Files
3 (incl. scripts)
Skills in repo
20
Repo updated
First seen
Licence
MIT

At a glance

Record browser or Web UI interaction demos as optimized GIFs using the available browser-control workflow, optional Playwright Videos for higher capture frame rates, and deterministic encoding, then…

  • Works in 4 steps: Require a clean worktree, record its… → Boot one server per port from that tree… → Treat one storyboard as one evidence… → …
  • Generate a GIF that demonstrates a browser workflow
  • SKILL.md covers Every GUI pull request…, Keep recording separate from…, Stage the application and Record the flow, plus 3 more sections
  • Runs Python scripts from its folder; calls git, gh and python3; reaches github.com

What it does

Record Browser Gif is an agent skill from singula-ai/alego. Record browser or Web UI interaction demos as optimized GIFs using the available browser-control workflow, optional Playwright Videos for higher capture frame rates, and deterministic encoding, then attach the GIF to a pull request with gh --attach, falling back to a dedicated assets branch where attach cannot apply. Use when asked to make, record, or generate a GIF that demonstrates a browser workflow, and for every pull request that changes product-user-visible GUI behavior, which MUST include a GIF recorded…

Its SKILL.md is about 4.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including scripts (for example `scripts/encode_gif.py` and `scripts/test_encode_gif.py`).

It sits in Development, covering Pull requests, Browser automation and Frontend development. It works with Playwright. The repository describes itself as: Build AI Agents like playing LEGOs. Everything is a Plugin. The licence is MIT.

When your agent uses it

  • Generate a GIF that demonstrates a browser workflow
  • For every pull request that changes product-user-visible GUI behavior
  • Which MUST include a GIF recorded from the pull requests real server and model flow

Example prompts

  • “/record-browser-gif”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Require a clean worktree, record its exact commit with git rev-parse HEAD, then build that recorded tree — here, pnpm run build && pnpm…
  2. Boot one server per port from that tree with fresh scratch ALEGO_HOME, ALEGO_AGENTS_HOME, workspace, and session state. Give the browser a…
  3. Treat one storyboard as one evidence run: every published frame comes from that server and those state roots, workspace, session, and…
  4. When switching between pull requests, stop the old server by PID or an exact match on its command line. A broad pkill -f pattern can match…

What it can do on your machine

Read from SKILL.md and the folder at commit a79fe9a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • git
    • gh
    • python3
    • pnpm

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com

    Also links to:

    • playwright.dev

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Record Browser Gif loads about 4.1k tokens when it runs. Until then it costs about 147 tokens; SKILL.md has 2,138 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~147
When it runs · the whole SKILL.md, loaded when a task matches
~4.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:30
    ot affect the evidence. Source the root `.env` for the API key through the application's normal path; never echo the key

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from singula-ai/alego at commit a79fe9a, republished under its MIT licence (© singula-ai). 2,138 words, ~4,130 tokens.

Download SKILL.mdSave it as .claude/skills/record-browser-gif/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
record-browser-gif
description
Record browser or Web UI interaction demos as optimized GIFs using the available browser-control workflow, optional Playwright Videos for higher capture frame rates, and deterministic encoding, then attach the GIF to a pull request with `gh --attach`, falling back to a dedicated assets branch where attach cannot apply. Use when asked to make, record, or generate a GIF that demonstrates a browser workflow, and for every pull request that changes product-user-visible GUI behavior, which MUST include a GIF recorded from the pull request's real server and model flow.

Record Browser GIF

Produce a short, truthful UI demonstration as a local GIF, and — only when the task includes attaching it to a pull request — publish it through the attach workflow at the end of this skill. The available browser-control workflow remains preferred. Use Playwright Videos when that workflow supports continuous capture at higher frame rates; use the bundled encoder for trimming, playback speed, final hold, dimensions, and size.

The evidence-chain decision owns why one storyboard comes from one isolated run and why publication revalidates both the artifact and the demonstrated pull-request head.

Every GUI pull request includes a GIF

A pull request that changes product-user-visible GUI behavior MUST include a demonstration GIF recorded with this skill and embedded in the pull request body via the attach workflow.

The recording itself is part of the evidence: use a real server booted from that pull request's branch tree, a real API key, and real model rounds. Never substitute fixture queries, mock transports, synthetic event injection, or test-only hooks unless the user explicitly asked for a fixture recording. Next to the embed, state the exact demonstrated commit SHA, the tree and origin that served it, any mode flags or browser-state exceptions, and whether a real model round ran, so reviewers know exactly what the recording proves.

Keep recording separate from publication

  • Recording produces local video or screenshots and one .gif artifact only; it never mutates remote state.
  • Publication — attaching the GIF to a pull request body with gh --attach, or pushing it to an assets branch and embedding its URL where attach cannot apply — is the separate final step, performed only when the task includes attaching the GIF to a pull request. It never touches the pull request's own branch.
  • Preserve the requested recording conditions. A real-server or real-API demo must not use fixture queries, mock transports, synthetic event injection, or test-only hooks. If credentials or the server are unavailable, report that limitation instead of substituting a fixture.
  • Never read or expose credential values. Use the application's normal configuration path and a benign demonstration prompt.

Stage the application

A GIF for a specific pull request demonstrates that pull request's tree, so stage per pull request:

  1. Require a clean worktree, record its exact commit with git rev-parse HEAD, then build that recorded tree — here, pnpm run build && pnpm run build:web. A GIF recorded against another commit's build misattributes the evidence.
  2. Boot one server per port from that tree with fresh scratch ALEGO_HOME, ALEGO_AGENTS_HOME, workspace, and session state. Give the browser a fresh isolated context or profile as well; if the browser workflow cannot create one, clear that origin's cookies and site storage before navigation so persisted client state cannot affect the evidence. Source the root .env for the API key through the application's normal path; never echo the key.
  3. Treat one storyboard as one evidence run: every published frame comes from that server and those state roots, workspace, session, and model-backed scenario run. If capture automation fails, discard its frames and rerun from fresh roots; never splice frames from separate runs.
  4. When switching between pull requests, stop the old server by PID or an exact match on its command line. A broad pkill -f pattern can match and kill the shell that launched it — including your own.

Record the flow

Follow the available browser-control workflow's setup, interaction, and cleanup instructions. When it exposes recordVideo, enable video on the same controlled context to capture more intermediate frames. Otherwise use screenshot capture within that workflow; video availability does not determine which browser-control workflow to use. Existing user browser state remains an explicit isolation exception.

Only when browser control is unavailable, use the repository-declared Playwright dependency in an isolated headless browser and state that fallback in the capture notes. In this repository it resolves from apps/web/package.json; do not install another driver or open the user's browser.

Before recording, identify the origin, built or development server, transport, and any mode overrides. When a production default opens a native surface that automation cannot drive, select an official browser-operable production backend through normal application configuration and disclose the override.

Store the script, raw video, timing notes, QA frames, and GIF under the repository's gitignored .playwright-mcp/ directory. Create the run directory first.

Capture video

Match viewport and recordVideo.size explicitly: Playwright otherwise scales the video down to fit 800×800, which can make UI text unreadable.

Configure video through the chosen browser-control workflow. The standalone Playwright fallback uses:

js
const { chromium } = createRequire(join(repo, 'apps/web/package.json'))('playwright')
const browser = await chromium.launch()
const size = { width: 1440, height: 900 }
const context = await browser.newContext({
  viewport: size,
  recordVideo: { dir: join(runDir, 'videos'), size },
})
try {
  const page = await context.newPage()
  const video = page.video()
  // Navigate and exercise the real application here.
  await context.close()
  await video.saveAs(join(runDir, 'demo.webm'))
} finally {
  await context.close()
  await browser.close()
}

Import createRequire from node:module and join from node:path; set repo and a fresh runDir to absolute paths in the recording script. Retain the page's video handle before closing it. Await context.close() before video.saveAs() or encoding; closing only the browser does not guarantee the video's flush. Each page has its own video: choose the demonstrated page explicitly and do not concatenate unrelated pages or runs. Failed runs are diagnostic only.

Choose a short story with three to six meaningful states. Wait for unique semantic locators before acting; use exact: true for accessible-name equality and exact-text completion predicates that cannot match a prompt echo. Fixed waits may provide a reading hold after the state is verified, but never establish readiness. When capturing video, preserve animations and scrolling.

When demonstrating a tool call, rejection, or recovery, open its detail or trajectory so the video shows the tool identity, status or stable error code, and downstream result. If a transient running state matters, prompt for a slow foreground operation and observe its concrete DOM marker; continuous video captures its intermediate frames. Give the model a short final sentinel to anchor completion. Stop an unnecessarily long real-API run after the demonstrated state is visible.

Capture no secrets, personal data, unrelated tabs, or notifications. Browser video contains page content, not browser chrome; avoid rendering credential-bearing URLs in the application. Review the whole selected interval, including intermediate states. Keep one viewport throughout.

Encode the GIF

Require python3, ffmpeg, and ffprobe. If a media binary is missing, report the dependency instead of installing software without authorization. Export GIF_SKILL_DIR on its own line before using it; an inline assignment cannot affect argument expansion in the same command.

sh
export GIF_SKILL_DIR=/absolute/path/to/this/skill
python3 "$GIF_SKILL_DIR/scripts/encode_gif.py" \
  /absolute/path/to/demo.webm \
  /absolute/path/to/demo.gif \
  --start 2 --end 32 --speed 2 --final-hold 3 \
  --fps 10 --max-width 1200 --colors 128

--start and --end select one continuous source interval in seconds. Defaults retain the full video at 1× speed and add a two-second final hold. --speed changes playback speed; disclose it and the selected interval beside the GIF so the demo cannot imply measured response latency. Use observed video times, not guessed wall-clock offsets, and preserve the complete cause and outcome of the demonstrated behavior. The final hold repeats the last selected frame. --fps sets the encoded GIF frame rate; increasing it cannot recover motion that the source recording did not capture. Keep the original WebM for QA; do not splice separate runs or synthesize missing states.

The encoder probes WebM container duration, applies trim and speed before palette conversion, and checks encoded duration, animation, width, and byte size. It refuses an empty or out-of-range interval, a selection shorter than two output frames, mode-inappropriate flags, and accidental overwrite. Reduce --max-width, then --colors or --fps for a large artifact; preserve readable text. Use --force only after resolving the exact output path.

Screenshot capture

When continuous video is unavailable or the user requests a storyboard, follow the available browser-control workflow. Capture three to six verified states from one isolated run with the browser's screenshot API. Save returned image bytes directly under one run directory as 00-initial.png, 01-typed.png, and so on; use identical dimensions and crop. For a transient state, poll its DOM marker and capture within the same browser-script call.

sh
python3 "$GIF_SKILL_DIR/scripts/encode_gif.py" \
  /absolute/path/to/frames /absolute/path/to/demo.gif \
  --durations 1.5,1.5,1.5,3.5 --fps 10 --max-width 1200 --colors 128

One duration applies to every screenshot; otherwise supply one positive duration per frame and hold the settled state longest. Directory input rejects fewer than two frames and mismatched dimensions or duration counts. Video timing flags apply only to video files; --durations and --pattern apply only to screenshot directories.

Show full SKILL.md (835 more words)Show less

Verify the artifact

  1. Read the encoder's JSON summary and confirm the output path, source interval and speed (or screenshot count), encoded frame count, dimensions, duration, and byte size.
  2. Visually read the encoded GIF itself, not only the source frames. Confirm that the transition is legible, the last state is held long enough, and no sensitive content appears. If the viewer renders only the first frame, decode representative frames from the encoded GIF with ffmpeg and inspect those; the pre-encode screenshots do not prove the encoded order, palette, or final hold.
  3. Run git status --short and confirm raw video, QA frames, and the artifact landed only under ignored paths.
  4. Return the absolute GIF path, render it when the client supports local media, and state whether the recording used a real API, fixture, or another transport. When the task does not include attaching the GIF to a pull request, stop here.

Encoder maintenance: run python3 -m unittest discover -s "$GIF_SKILL_DIR/scripts" -p 'test_*.py' -v with the media prerequisites installed. These local media tests do not run in repository CI.

Publish the GIF

Perform this step only when the task includes attaching the GIF to a pull request.

Never commit a GIF to the pull request's own branch or any branch that merges into a long-lived branch: binary media committed there bloats the repository history for every future clone. Prefer gh --attach, which uploads the GIF to GitHub and rewrites the body reference in one command, so no branch carries the media.

Attach with gh

gh --attach requires gh v2.99.0 or later (gh --version), a repository on github.com — GitHub Enterprise Server is not supported — write access to the repository, and a GIF at or below 10 MB. Confirm the verified artifact fits that limit; when it does not, shrink it with --max-width, then --colors or --fps, before attaching.

Write the GIF into the body file as an ordinary local-path reference, using the same path passed to --attach; gh rewrites the reference in place to the uploaded URL, keeping its position and alt text:

markdown
![<alt text>](<path/to/demo.gif>)

The demonstrated pull request is normally the publication target. A tooling pull request may instead embed a clearly labeled example from another pull request; name that source PR and compare its live head in every check below. Never attribute the example to the tooling branch.

Immediately before attaching, re-read the demonstrated pull request's live head — for a new demonstrated pull request, the pushed branch tip — and compare it with the commit recorded next to the GIF. Stop and re-record when it moved. Then attach:

sh
gh pr create --body-file <body.md> --attach <path/to/demo.gif>     # new pull request
gh pr edit <pr> --body-file <body.md> --attach <path/to/demo.gif>  # existing pull request

--attach is repeatable but refuses the same file twice. A GIF the body does not reference is appended at the end, where alt text set on the flag (--attach '<path>#<alt text>') applies; a rewritten reference keeps the body's alt text. After attaching, re-read the demonstrated live head and require it to remain at that recorded commit. Re-read the live body and confirm the reference now points at the uploaded URL, render the body through GitHub's Markdown API and confirm the expected <img>, and fetch the uploaded URL once to confirm 200 and image/gif.

Fall back to an assets branch

Use the assets-branch workflow only when gh --attach cannot apply: the GIF still exceeds 10 MB, gh is older than v2.99.0, or the repository is not on github.com. GIFs then live on a dedicated orphan assets branch — a branch with no parent commit and nothing but media — and one assets branch serves a whole pull request series (named <series>-assets; list existing ones with git ls-remote --heads origin '*assets*').

Before either workflow below pushes, verify that the assets branch contains media only and that the staged GIF's checksum matches the verified local artifact.

For an existing assets branch, work in a shallow single-branch scratch clone so the publication cannot touch your working tree:

sh
git clone --branch <assets-branch> --single-branch --depth 1 <repo-url> /tmp/assets-checkout
cp /absolute/path/to/demo.gif /tmp/assets-checkout/<name>.gif
cd /tmp/assets-checkout
git add <name>.gif
git commit -m "assets: <what it shows> gif (#<pr>)"
git push origin <assets-branch>

For a new series, make a fresh shallow scratch clone (git clone --depth 1 <repo-url> /tmp/assets-checkout), create the orphan branch with git switch --orphan <assets-branch>, then add the GIF, commit, and push the same way.

After pushing, use authenticated GitHub API or raw requests to confirm the remote path, byte size, checksum, 200 response, and image/gif content type. An anonymous 404 does not disprove a private-repository asset; authenticate the verification instead. This proves the repository-member review path, not public availability.

Immediately before editing the pull-request body, re-read the demonstrated pull request's live head and compare it with the commit recorded next to the GIF. Stop and re-record when it moved. After the edit, re-read the demonstrated live head and require it to remain at that recorded commit. Separately, render the body through GitHub's Markdown API and confirm that the expected <img> is present.

Embed the GIF in the pull request body with the raw blob URL; the ?raw=true suffix is required, because the plain blob URL renders GitHub's file page instead of the image:

markdown
![<alt text>](https://github.com/<owner>/<repo>/blob/<assets-branch>/<name>.gif?raw=true)

Never delete or rewrite an assets branch, and never force-push it: merged pull request bodies reference its URLs forever. Append new commits only.

© singula-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (scripts) in .agents/skills/record-browser-gif of singula-ai/alego.

  • SKILL.md
  • scripts/encode_gif.py
  • scripts/test_encode_gif.py

Open the folder on GitHubat commit a79fe9a

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in singula-ai/alego, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Record Browser Gif next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Playwrightmagnus919/agent-skills115—~3.4kAutomated safety check: NotesMIT
Vitemagnus919/agent-skills115—~1.5kAutomated safety check: NotesMIT
Code Reviewnteract/semiotic2.7k—~1.5kAutomated safety check: PassApache-2.0

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Works with

Questions about Record Browser Gif

What does Record Browser Gif do?

Record browser or Web UI interaction demos as optimized GIFs using the available browser-control workflow, optional Playwright Videos for higher capture frame rates, and deterministic encoding, then…. Record Browser Gif is an agent skill from singula-ai/alego. Record browser or Web UI interaction demos as optimized GIFs using the available browser-control workflow, optional Playwright Videos for higher capture frame rates, and deterministic encoding, then attach the GIF to a pull request with gh --attach, falling back to a dedicated assets branch where attach cannot apply.

When should I use Record Browser Gif?

Record Browser Gif fits situations like: generate a GIF that demonstrates a browser workflow; for every pull request that changes product-user-visible GUI behavior; which MUST include a GIF recorded from the pull requests real server and model flow.

How do I install Record Browser Gif in Claude Code?

Run `npx skills add singula-ai/alego --skill record-browser-gif -a claude-code`. Or copy the skill folder (.agents/skills/record-browser-gif in singula-ai/alego) into .claude/skills/record-browser-gif in your project. Claude Code loads it when a task matches its description.

How do I install Record Browser Gif in Codex?

Run `npx skills add singula-ai/alego --skill record-browser-gif -a codex`. Or copy the skill folder (.agents/skills/record-browser-gif in singula-ai/alego) into .agents/skills/record-browser-gif in your project. Codex loads it when a task matches its description.

Can I use Record Browser Gif in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add singula-ai/alego --skill record-browser-gif -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/record-browser-gif, .gemini/skills/record-browser-gif, .github/skills/record-browser-gif and .opencode/skills/record-browser-gif in your project.

What does Record Browser Gif need to run?

Going by SKILL.md and its folder, Record Browser Gif needs Python for the scripts in its folder and the command-line tools its instructions call (git, gh, python3 and pnpm). Our summary lists: Python 3.

Does Record Browser Gif access the network?

SKILL.md names 2 domains. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. As links in the text: playwright.dev. This is read from the text; nothing was executed.

Is Record Browser Gif safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Record Browser Gif use?

Record Browser Gif is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Record Browser Gif use?

About 4.1k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Record Browser Gif?

Skills that share tags, products or a category with Record Browser Gif: Sap Browser Automation (secondsky/sap-skills, 462 stars), Browser Use (QwenLM/qwen-code-examples, 143 stars), Playwright (magnus919/agent-skills, 115 stars) and Vite (magnus919/agent-skills, 115 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Record Browser Gif?

singula-ai (a GitHub organization) maintains it in singula-ai/alego, which has 109 GitHub stars. The repository holds 20 skills in this directory. The repository was last updated on September 28, 2026.

Source: singula-ai/alego on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.