Revyl Codex Dev Loop logo

Revyl Codex Dev Loop

Organization
RevylAI
revyl-codex-dev-loop

Run, preview, debug, and verify mobile app changes on Revyl cloud devices from Codex, with screenshots and semantic validation.

Overview

PublisherRevylAI
Repositoryrevyl-cli
Skill namerevyl-codex-dev-loop
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 Codex Dev Loop 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/skills/revyl-codex-dev-loop
  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/skills/revyl-codex-dev-loop .claude/skills/revyl-codex-dev-loop
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Revyl Codex Dev Loop 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 Codex Dev Loop 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 Codex Dev Loop 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 Codex Dev Loop

Use this skill for an authorized CLI development loop in a shell-capable host. This workflow does not start an MCP server or install hooks. Installation never changes an app's source or .revyl/config.yaml; later source/configuration work is allowed only when the task explicitly requires it and the user authorizes it.

For a request to browse Atlas or view an existing device, prefer the connected Revyl workspace tools when available; do not start a development loop just to open a view. Source builds and rebuilds stay on this pinned CLI workflow. If the host has no shell, explain that limitation instead of running shell commands through another tool or starting a replacement device.

Launcher and setup

Resolve the launcher from this skill's own absolute directory before changing to the app directory. On POSIX use ../../scripts/launch-revyl; on Windows use ../../scripts/launch-revyl.cmd. Call that absolute launcher for every Revyl command; do not use revyl from PATH, PLUGIN_ROOT, or another installed CLI.

sh
SKILL_DIR="<absolute directory containing this SKILL.md>"
LAUNCHER="$(cd "$SKILL_DIR/../../scripts" && pwd)/launch-revyl"
"$LAUNCHER" --version
"$LAUNCHER" auth status
bat
REM Windows cmd.exe
set "SKILL_DIR=<absolute directory containing this SKILL.md>"
set "LAUNCHER=%SKILL_DIR%\..\..\scripts\launch-revyl.cmd"
"%LAUNCHER%" --version
"%LAUNCHER%" auth status

If authentication is needed, use "$LAUNCHER" auth login. An existing environment-provided secret may authenticate the launcher without being copied or named in an argument. Never run credential-provisioning or secret-bridge commands, print credentials, or paste them into chat. Post the normal login approval URL as a clickable link and wait for approval; do not claim that a browser opened.

Authorization and selection

Before any build, remote session, cloud device, or paid operation, require authorization naming the app, environment, and paid operation. Resolve the exact profile and platform from the repository, task, and user context without changing them; ask only when they remain ambiguous. Run from the app directory that owns .revyl/config.yaml, not the monorepo root. Let dev create its new context, capture the returned context and session IDs, and clean up only those resources when the work ends or fails.

For native or rebuild-first apps, use the detached remote loop with explicit selection and no browser opening:

sh
"$LAUNCHER" dev --profile <exact-profile> --platform <ios-or-android> \
  --remote --detach --json --no-open

Return the handshake's viewer_url immediately without opening it. A viewer or --wait-ready only means the session is available, not that the current build succeeded. Wait for the build to reach its terminal success state before claiming a build result.

When open_revyl_workspace is available, it can display the same returned session using view="device" and session_id=<returned-session-id>. Never call MCP start_device_session for a session the CLI already created. Browser and CLI authentication are separate; inaccessible sessions require the user to connect the matching account, not a new session or credential copied into chat.

Use short-lived commands in separate calls. Target the returned context with --context for status and rebuild, and pass it positionally to stop:

sh
"$LAUNCHER" dev status --context <returned-context>
"$LAUNCHER" dev rebuild --context <returned-context> --wait --timeout 600 --json
"$LAUNCHER" dev stop <returned-context> --json

Target every device command with -s <returned-session-id>. Open each captured screenshot before describing it. A false or missing semantic verdict is a failed validation, but cleanup still runs for the returned context/session. Bound status polling to the configured build timeout. If that limit expires, report a timeout and stop the owned loop instead of polling indefinitely.

Detailed CLI mechanics

Resolve this skill's ../../references/revyl-cli-dev-loop.md to an absolute path and read it for framework behavior, evidence, and troubleshooting. This skill overrides that reference whenever they conflict:

  • Do not use its browser-opening, URL-opening, foreground, persistent-shell, PATH, or installed-CLI examples. Use --detach --json --no-open and the absolute launcher above.
  • Do not start a local app source/configuration change, a device, build, or paid action without the named authorization above.
  • Do not install, enable, configure, or invoke revyl-cli-auth-bypass. That skill is not shipped with this plugin, and authentication bypass is never an automatic fallback.
  • Use exact resolved profile and platform values, and clean up only the context and session created for the authorized request.

Report an unavailable authorization or ambiguous target instead of guessing.

Frequently asked questions

What does the Revyl Codex Dev Loop AI skill do?

Run, preview, debug, and verify mobile app changes on Revyl cloud devices from Codex, with screenshots and semantic validation.

Why use Revyl Codex Dev Loop on TypingMind?

Because you install it once and use it with any model. Revyl Codex Dev Loop 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 Codex Dev Loop in TypingMind?

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

Which AI models can use Revyl Codex Dev Loop?

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 Codex Dev Loop?

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

Is the Revyl Codex Dev Loop 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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