Preview logo

Preview

Community
tobihagemann
preview

Stand up the project's live app and hand it to the user to try a change firsthand, then act on their verdict once they reply. Use when the user asks to "preview the change", "let me try it", "spin up the app so I can test it", "set it up so I can poke at it", or before finalizing a UI/UX change that needs human eyes.

Overview

Publishertobihagemann
Repositoryturbo
Skill namepreview
Stars
417
Forks
32
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 tobihagemann on GitHub. Read the source before you install it.

Installation

Install the Preview 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/tobihagemann/turbo/tree/main/claude/skills/preview
  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/tobihagemann/turbo.git /tmp/turbo
mkdir -p .claude/skills
cp -r /tmp/turbo/claude/skills/preview .claude/skills/preview
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Preview 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 Preview 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 Preview 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.

Preview

Bring up the running app and let the user drive it themselves to judge the change, then act on their verdict.

Step 1: Determine Scope

Resolve what to preview using the first match:

  1. User-specified — the user says what to look at. Use that.
  2. PR — a PR URL or number is provided. Fetch its details and read the changed code.
  3. Conversation context — prior conversation contains recent work. Extract what changed, where it lives, and the expected behavior.
  4. App-level discovery — fresh context with no prior work. Examine entry points, routes, and the README to identify the app's core user-facing flows.

If the resolved scope has no user-visible surface to try (a CLI-only change, a library with no entry point, backend work with no UI to look at), present this message: "Nothing to preview — ." Then use the TaskList tool and proceed to any remaining task.

Step 2: Determine Launch Approach

Check for a project-specific skill or MCP tool that launches the app, and use it if present. Otherwise use the fallback for the surface type:

  • Web app → start the dev server; the access point is the local URL and port
  • Desktop/native app → build and launch the app so its window is open

Step 3: Bring Up the Stack

Start backend services and frontend together — a frontend-only change still needs the backend running to be exercised. When a service runs at an address other than its default, find the settings elsewhere in the stack that name that default, such as allowed origins and sign-in callback URLs, and bring each in line through runtime overrides, leaving the working tree unchanged: add the new address beside the default in a list, and replace the default only where no process outside this skill reads the setting. Build first if the project requires a build step.

Start long-running processes with the Bash tool (run_in_background: true, timeout: 7200000) and wait until each reports ready. Leave a trailing & off a run_in_background call: backgrounding twice reports success within seconds while the servers are either dead with the wrapper or orphaned still holding the port. Confirm each process bound to its port before sending it traffic, since a readiness probe passes just as well against an orphan from an earlier attempt. Capture each process's PID and stop it by that PID and its process group, rather than by port or command-line pattern, which also match a concurrent agent's server. Tail their logs with the Monitor tool so backend errors and warnings surface while the user is trying the app.

If a required service cannot be stood up in this session (missing auth provider, external dependency, seed data), or a process fails to start or never reports ready, use AskUserQuestion to surface the blocker and let the user choose how to proceed.

Step 4: Point the User at the Running App

Output as text:

  • The access point — the local URL and port for a web app, or confirmation that the window is open for a native app
  • When the surface sits behind a sign-in, each account to use with its password and the role that account holds, plus every key or code generated while bringing up the stack that the app requests during or after sign-in, such as on a new browser
  • What changed
  • Each scenario worth trying, as many as the change needs: numbered steps naming the exact controls and inputs, the result to expect after each step, and the judgment the user is being asked to make. Write each scenario so one person can perform it at one device, one action after another. Start it from the state the user finds at the access point, and give everything else a later step relies on, whether something that must exist, be selected, or hold focus, a step of its own that produces it. When a scenario depends on a state the user would not reach in ordinary use, open it with what that state is in the user's own terms and why it is worth looking at
  • Each case left out of the scenarios because one person cannot perform it that way
  • When a verification pass preceded this hand-over, what it could not cover: paths needing real credentials, external services, or state unavailable in this session
  • How to reply once they have tried it: say it looks good, adding whether to keep the app running or shut it down, or describe what needs changing

Then end the turn, leaving every process this skill started running.

Step 5: Act on the User's Reply

  • Needs changes — make the change the user describes, then rebuild or refresh the running app so it is live. When the reply or session context surfaces further open issues, fix and re-verify each. Once no known issue remains, output what changed and the scenarios worth retrying, close with Step 4's reply guidance, and end the turn again.
  • Looks good — when the user asks to shut the app down, stop every process this skill started, and revert every override this skill made in a service that keeps running. Otherwise leave everything running.

Then use the TaskList tool and proceed to any remaining task.

Frequently asked questions

What does the Preview AI skill do?

Stand up the project's live app and hand it to the user to try a change firsthand, then act on their verdict once they reply. Use when the user asks to "preview the change", "let me try it", "spin up the app so I can test it", "set it up so I can poke at it", or before finalizing a UI/UX change that needs human eyes.

Why use Preview on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tobihagemann/turbo/tree/main/claude/skills/preview. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Preview?

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 Preview?

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

Is the Preview AI skill free?

Yes. It is published on GitHub by tobihagemann 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 👇