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Control Ui E2e

OrganizationPopular
openclaw
control-ui-e2e

Use when designing, testing, fixing, or extending the OpenClaw Control UI GUI, including UI stress-test galleries with feedback inputs, Vitest + Playwright end-to-end checks, mocked Gateway flows, screenshots/videos, or agent-verifiable browser proof.

Overview

Publisheropenclaw
Repositoryopenclaw
Skill namecontrol-ui-e2e
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391K
Forks
82.2K
Bundled files
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  • 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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by openclaw on GitHub. Read the source before you install it.

Installation

Install the Control Ui E2e 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/openclaw/openclaw/tree/main/.agents/skills/control-ui-e2e
  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/openclaw/openclaw.git /tmp/openclaw
mkdir -p .claude/skills
cp -r /tmp/openclaw/.agents/skills/control-ui-e2e .claude/skills/control-ui-e2e
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Control Ui E2e 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 Control Ui E2e 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 Control Ui E2e 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.

Control UI E2E

Use this for Control UI design feedback and real browser flows with deterministic Gateway data.

UI Stress Test

For substantial UI changes, build a local HTML stress-test gallery early so the user can compare meaningful states and give feedback against concrete examples. Use it when changing layouts, interactions, or components with multiple states; small copy or icon edits can skip it when a gallery adds no useful comparison.

  1. Derive examples from the affected components and their data contracts. Cover the relevant normal, loading, empty, error, unavailable, permission, selected, and expanded states, plus long text or dense content where they stress the layout. Show mutually exclusive states separately; label proposed states that the current implementation does not support.
  2. Build one browser-openable HTML overview with stable example IDs, short state labels, and enough context to understand each example. Prefer real components and deterministic mock fixtures. Label static or approximate renderings and link to the running UI for interactions they cannot reproduce. Keep generated galleries in task-owned artifact storage rather than committing them by default.
  3. Give every example a labeled feedback input. Persist feedback locally across refreshes using a gallery-specific storage key and stable example IDs. Include a Copy feedback action that exports the example IDs, state labels, and comments as Markdown or plain text for the user to return to the conversation.
  4. Open the gallery in the available preview browser and share its URL or file path. Keep the same gallery and example IDs during iteration, preserving existing comments as examples change. Apply the user's feedback to both the gallery and the implementation so they remain comparable.
  5. Before requesting feedback, inspect the rendered examples at relevant viewport sizes and verify that feedback survives a refresh and exports with the correct example labels. Report any unsupported states or preview limitations.

The gallery supports design review; it does not replace focused behavior tests or inspected before/after proof from the running UI. Preserve final proof using the fresh capture directories described below, separately from the evolving gallery.

Test Shape

  • Use ui/src/**/*.e2e.test.ts for full GUI flows.
  • Use ui/src/test-helpers/control-ui-e2e.ts to start the Vite Control UI and install a mocked Gateway WebSocket.
  • Keep scenarios deterministic. Do not use live provider keys, real channel credentials, or a real Gateway unless the user explicitly asks for live proof.
  • Prefer existing .browser.test.ts or unit tests for narrow rendering logic; use this E2E lane when the proof should cover routing, app boot, Gateway handshake, requests, and visible UI behavior together.

Commands

  • Target one E2E test in a Codex worktree:
bash
node scripts/run-vitest.mjs run --config test/vitest/vitest.ui-e2e.config.ts --configLoader runner ui/src/e2e/chat-flow.messaging.e2e.test.ts
  • Run the whole local lane in a normal checkout:
bash
pnpm test:ui:e2e

Use an existing ready dependency installation or a prepared normal checkout; do not reconcile a shared install while other jobs use it. Follow $openclaw-testing: trusted development proof may run locally, and remote proof needs a browser/platform, clean-environment, or source-isolation reason.

Visual Proof Default

For appearance changes, capture inspected before/after visual evidence. For other behavior, use the clearest appropriate boundary proof; a video and a screenshot set are not mandatory when assertions already demonstrate the change.

  • Keep the Vitest E2E assertions deterministic; do not commit generated screenshots or videos.
  • After or alongside the focused E2E test, run the mocked Control UI app when available, for example pnpm dev:ui:mock -- --port <port>.
  • Drive Chromium with Playwright against the local mock URL. Capture the states needed to demonstrate the change, using screenshots or a short video.
  • Use browser.newContext({ recordVideo: { dir, size }, viewport }), page.screenshot({ path }), and close the context before reporting the video path.
  • The session-host command-state proof uses viewport-only captures, verified with Playwright 1.62.1 and Chrome 151.0.7922.34 (Linux real Gateway; macOS arm64 synthetic reproduction). Other recording owners have not been migrated or certified by this fix; verify their required screenshot content and finalized video separately. See the verified capture path and upstream limitation.
  • Allocate retained proof with createControlUiE2eArtifactDir(scope, parentDir?) from ui/src/test-helpers/control-ui-e2e-artifacts.ts. Each call atomically creates a fresh directory and logs its actual path. An explicit parent wins, then the trimmed existing OPENCLAW_UI_E2E_ARTIFACT_DIR, then the repository's .artifacts/control-ui-e2e parent. Existing custom output controls select parents; do not add or rewrite env vars to enable capture.
  • Allocate during the test/scenario or beforeEach, once per attempt; standalone scripts allocate once per invocation. Pass the owner explicitly to shared capture helpers. Keep the original gates, feature/stage names, viewports, waits, and recording options. Use distinct filenames for distinct stages and keep screenshots, reports, and video together.
  • Retain successful and failed evidence. Report actual allocated paths, including relocated filename overrides. Manually delete only exact owned directories after review; never clear shared parents before a replay. Disposable build/media fixtures and owned temporary raw video may keep their cleanup. New synthetic captures do not recover overwritten evidence.
  • Timeout diagnostics use fresh children beneath their existing diagnostic parent. Mantis retains every capture attempt under an invocation-owned directory and refuses to overwrite reports. Real-Gateway suites, chat-outbox-*, and chat-attachment-read-lifecycle remain separate owners; coordinate before claiming replay-safe retention there.
  • Treat recording as validation, not only demo capture. If the recorder fails or shows surprising behavior, stop, fix the behavior, add or update a regression test, then rerecord.
  • If visual proof is blocked, state the exact blocker and still report the textual E2E evidence.

Mock Pattern

Start the app server, install the mock before page.goto, then assert both Gateway traffic and visible UI:

ts
const server = await startControlUiE2eServer();
const page = await context.newPage();
const gateway = await installMockGateway(page, {
  historyMessages: [{ role: "assistant", content: [{ type: "text", text: "Ready." }] }],
});

await page.goto(`${server.baseUrl}chat`);
await page.locator(".agent-chat__composer-combobox textarea").fill("hello");
await page.getByRole("button", { name: "Send message" }).click();

const request = await gateway.waitForRequest("chat.send");
await gateway.emitChatFinal({ runId: String(request.params.idempotencyKey), text: "Done." });
await page.getByText("Done.").waitFor();

Extend installMockGateway with typed scenario options or method responses when a new flow needs more Gateway surface.

Run Inspector evidence

Use withControlUiRunInspector from ui/src/test-helpers/control-ui-run-inspector.ts for collection. It owns a separate page, closes it on success or failure, and leaves the caller's Chat page and unsent draft in place. Use preparePage for a mock Gateway or the campaign's existing per-tab authentication setup; a shared browser context does not copy another tab's session-storage token. The helper uses RunInspectorSelector and activityRunInspectorSelectorHref from the rendered component's model, including a selected receipt's decision cursor.

The rendered panel's data-run-id and data-execution-id identify the returned present identity, not just the requested URL. The selected detail's data-receipt-selector-id is DecisionReceiptDisplayV1.selectorId. Missing or ambiguous identity and missing selected receipts must not be treated as matches. These decision selectors are separate from the optional terminal transcript key agent.wait.terminalReceipt.assistantTranscriptIdempotencyKey; never manufacture that key from a DOM selector or history row, or claim its absence is repaired by Inspector evidence. For sidebar run state, the current accessible label is Active run, not Running.

Standalone Recording

For narrated captions, eased target zooms, or fast-forwarded pauses, use the proof-video dev skill. Its standalone template records raw evidence plus a cue sidecar and renders a captioned MP4 with system ffmpeg; keep the raw capture and attach the inspected polished video.

When recording an already-running mocked Control UI URL, use a temporary Playwright script or playwright test spec and keep the recording flow focused:

  • Open the mock URL, interact through stable data-* selectors or user-facing role selectors, and wait on asserted states instead of relying on fixed sleeps.
  • Assert both visible UI state and mocked Gateway traffic for request-driven flows. For example, verify the expected count/row is visible and that sessions.list was called with the expected search, offset, and limit.
  • Use short sleeps only after assertions to make the captured video readable.
  • Store the generated video in the invocation's fresh allocated directory; do not commit it or remove older captures.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Control Ui E2e AI skill do?

Use when designing, testing, fixing, or extending the OpenClaw Control UI GUI, including UI stress-test galleries with feedback inputs, Vitest + Playwright end-to-end checks, mocked Gateway flows, screenshots/videos, or agent-verifiable browser proof.

Why use Control Ui E2e on TypingMind?

Because you install it once and use it with any model. Control Ui E2e 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 Control Ui E2e in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/openclaw/openclaw/tree/main/.agents/skills/control-ui-e2e. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Control Ui E2e?

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 Control Ui E2e?

As many as you like. As long as a model supports skills, you can use Control Ui E2e with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Control Ui E2e AI skill free?

It is published on GitHub by openclaw. Check the repository for licensing terms. 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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