Revyl Workspace logo

Revyl Workspace

Organization
RevylAI
revyl-workspace

Open Revyl Atlas maps and existing device viewers inside a compatible host, or manage an explicitly authorized device session through connected Revyl workspace tools. Use revyl-cloud-app for cloud builds and exploration, or the bundled CI-proof skill for existing artifacts.

Overview

PublisherRevylAI
Repositoryrevyl-cli
Skill namerevyl-workspace
Stars
522
Forks
25
Bundled files
Instructions only
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 RevylAI on GitHub. Read the source before you install it.

Installation

Install the Revyl Workspace 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/RevylAI/revyl-cli/tree/main/plugins/revyl-openai/skills/revyl-workspace
  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/RevylAI/revyl-cli.git /tmp/revyl-cli
mkdir -p .claude/skills
cp -r /tmp/revyl-cli/plugins/revyl-openai/skills/revyl-workspace .claude/skills/revyl-workspace
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Revyl Workspace 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 Revyl Workspace 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 Revyl Workspace 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.

Revyl Workspace

Use the connected Revyl tools for workspace data and device sessions. This workflow does not need a shell. Check the tools actually available in the host; never invent a tool or silently fall back to a shell command. If the connection is absent or expired, ask the user to connect Revyl through the host's normal authentication flow. Never request API keys, passwords, or login codes in chat.

Choose one execution path

  • For Atlas or a viewer, use this workflow. Opening a view is not authorization to provision a device, build an app, or run a test.
  • For an existing test, use the connected test tools when available; no shell is needed to run a saved test against a known build.
  • For a new app, standalone remote build, new persisted test, exploration, or other CLI operation, use revyl-cloud-app in a shell-capable cloud workspace.
  • For a continuous development loop in an existing repository, use the bundled revyl-codex-dev-loop skill in a shell-capable host.
  • For exact-SHA proof of an existing CI artifact, use the bundled revyl-codex-proof-ci skill and its bounded artifact lookup.

The CLI and MCP can display the same server-issued session ID, but one workflow must own its creation and cleanup. Never call start_device_session to recreate a CLI-owned session just to embed its viewer. Account mismatches require the correct account connection, not a replacement session or a copied credential.

Open Atlas or an existing device

Call open_revyl_workspace with view="atlas" and a known authorized app_id. When no app is selected, use empty arguments to open the Atlas app picker. Do not guess an identifier or use a URL in an identifier field.

For a device, reuse a session ID from the current workflow or resolve an exact session with list_device_sessions. Call open_revyl_workspace with view="device" and session_id. With no session selected, the device view opens the session list; it does not start a new device. If several sessions match, ask which one instead of choosing arbitrarily.

The tool links a client-rendered UI resource. A returned URL is not proof that the iframe loaded, and the absence of an image in the text response is not proof that embedding failed. Report the confirmed tool outcome without claiming to see the live UI. Use a normal viewer link as the fallback when the host cannot render the component. Never publish or share the session merely to make embedding work. In ChatGPT, present the returned chatgpt_url as an Open in ChatGPT link. Clicking it navigates the persistent panel to the selected view; another tool call alone need not update an already-open panel. Do not invent this link when it is absent. Keep workspace_url as the ordinary browser fallback. Say that the view is ready to open, not that it is already visible or playing, unless separate evidence confirms that outcome.

Start a device only when requested

Require an authorized app, environment, platform, and device operation. Resolve the target from the user's request and available read-only tools; ask when it is ambiguous. Supply one artifact selector to start_device_session: a known build_version_id for a pinned artifact, or app_id for the expressly selected app's latest build. Use no_open=true so the client owns viewer navigation and set a bounded idle_timeout, normally 900 seconds.

Record the returned session ID and index. Open that same session's workspace when needed; do not invoke startup again to retry UI rendering. If startup times out, inspect list_device_sessions before considering another start. Never automatically retry an ambiguous provisioning result.

When the request includes a later test against the same build, resolve the latest compatible build once with the available build-list tool and retain its build version ID before startup. Pin both the session and the test to that ID; do not resolve "latest" again between operations.

Pass the server-issued session_id to every session-scoped tool that declares it. For an older tool accepting only session_index, first confirm that the index still maps to the intended ID. Use the tool's declared arguments for screenshots, interactions, and validation. Inspect returned screenshots before describing them, and treat missing or false validation results as unverified or failed.

For a bounded app walkthrough, use screenshots and the declared interaction tools on that same session. Respect the authorized action/time budget and avoid destructive or externally visible actions unless separately authorized. Separate observed failures, usability judgments, and measured timings.

To run an explicitly requested existing test, resolve its exact name or ID. Use manage_tests(action="run", params={test_name, build_version_id}) in the core/full profiles, or run_test when that tool is exposed. Inspect the returned status and poll the declared status tool within a bound when necessary; a queued execution is not a passing test. A test execution owns its own device lifecycle and does not reuse the manual session. Do not create or change a test as a fallback when the named test is missing.

When the authorized work ends, stop only the owned session with stop_device_session(session_id=...); for an older index-only tool, recheck the ID-to-index mapping first. Never set all=true. A successful request to stop is not the same as a released device: check the terminal result and the active-session list. Use bounded polling, and report pending cleanup without claiming that it completed.

Do not create, modify, or publish tests, builds, workflows, shares, or reports unless the user requested that particular action. Keep signed media URLs, launch values, credentials, and customer content out of public artifacts.

Frequently asked questions

What does the Revyl Workspace AI skill do?

Open Revyl Atlas maps and existing device viewers inside a compatible host, or manage an explicitly authorized device session through connected Revyl workspace tools. Use revyl-cloud-app for cloud builds and exploration, or the bundled CI-proof skill for existing artifacts.

Why use Revyl Workspace on TypingMind?

Because you install it once and use it with any model. Revyl Workspace 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 Revyl Workspace in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/RevylAI/revyl-cli/tree/main/plugins/revyl-openai/skills/revyl-workspace. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Revyl Workspace?

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 Revyl Workspace?

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

Is the Revyl Workspace AI skill free?

It is published on GitHub by RevylAI. 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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