# local-ai-app-integration

Integrates Embeddable Lemonade into apps that currently call cloud AI APIs. Useful when shipping on-device inference on Windows x64 or Linux x64.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 895 installs · ★ 465
- Author: amd
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/amd/skills/tree/HEAD/skills/local-ai-app-integration/SKILL.md
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/local-ai-app-integration

## What it does

The skill embeds the Lemonade runtime into an existing code base. Instead of hitting cloud providers, the app spawns a private `lemond` subprocess. The existing HTTP client talks to the local process on `/api/v1`. It adds a launcher module of roughly 30 lines and places the required binary in `vendor/lemonade/`.

Wiring requires three client changes: the base URL, the API key, and a 120-second request timeout. The skill assists with model selection, such as `Qwen3-4B-GGUF` for chat or `whispercpp` for audio transcription. Inference executes across CPUs, graphics cards, and XDNA2 NPUs.

Only 64-bit Windows and Linux environments are supported for production. Embeddable macOS support is in beta, which makes it premature for production rollouts.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add amd/skills --skill local-ai-app-integration -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/local-ai-app-integration/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/local-ai-app-integration/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add amd/skills --skill local-ai-app-integration -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/local-ai-app-integration/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add amd/skills --skill local-ai-app-integration -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/local-ai-app-integration/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install amd/skills local-ai-app-integration
```

2. **Manually: put the files in place**
- File: `.github/skills/local-ai-app-integration/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add amd/skills --skill local-ai-app-integration -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/amd/skills --path skills/local-ai-app-integration
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/local-ai-app-integration/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add amd/skills --skill local-ai-app-integration -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/amd/skills/tree/HEAD/skills/local-ai-app-integration/SKILL.md

## When to use it
- route HTTP calls from cloud AI providers to a local `lemond` process
- bundle an offline inference binary directly into the project repository
- configure model execution across CPUs, integrated graphics, or XDNA2 NPUs
- implement startup health checks before pulling target weights

## Example prompts
- Add a local AI mode using Embeddable Lemonade to replace our OpenAI endpoints.
- Configure the lemond launcher module and point the HTTP client to the local port.

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: fbcbed833c2b4009.

BEGIN THIRD-PARTY FILE fbcbed833c2b4009
````text
---
name: local-ai-app-integration
description: >-
  Integrates local AI capabilities into applications using Embeddable Lemonade.
  Use when the user wants to add local AI, offline AI, private AI, on-device AI,
  a local LLM, local chat, embeddings, image generation, speech-to-text, or
  text-to-speech to an existing app; replace or supplement OpenAI, Anthropic, Ollama, or
  other cloud AI APIs with a local backend; only use to convert user apps. Do not use when
  the user just wants the agent itself to generate images, transcribe, or speak locally in
  the current workspace, even to cut their own API bill.
---

# Local AI App Integration (Embeddable Lemonade)

Add a local AI mode to an existing app that already talks to a cloud AI API
(OpenAI, Anthropic, or Ollama-compatible). The app launches `lemond`, the
Embeddable Lemonade binary, as a private subprocess and the existing client
talks to it on `http://localhost:PORT/api/v1`. The user gets local, private,
hardware-optimized inference (CPU, AMD iGPU/dGPU, XDNA2 NPU) with no separate
install.

**What you'll end up with:** one new launcher module (~30 lines), three mandatory changes to the existing HTTP client (`base_url`, `api_key`, and a 120-second HTTP timeout), one vendored binary under `vendor/lemonade/`.

## When this skill is the right tool

Use this skill when **all** of the following are true:

- The app already calls a cloud AI service over HTTP (OpenAI Chat Completions,
  Anthropic Messages, or Ollama).
- The user wants that AI to run on the end-user's PC, with the AI engine
  bundled into the app, not as a separate user install.
- The target platform is Windows x64 or Linux x64 (macOS embeddable is in beta).

If the user instead wants a **system-wide** Lemonade Server (one install,
shared across apps), do not use this skill; point them at
`https://lemonade-server.ai/install_options.html` and the standard OpenAI base
URL `http://localhost:13305/api/v1`.

## The opinionated path

This skill follows one fixed sequence. Do not deviate without a stated reason.

```
[ ] 1. Survey the app's current AI integration
[ ] 2. Pick a model + backend profile
[ ] 3. Place Embeddable Lemonade in the app's tree (full package, not just the binary)
[ ] 4. Add a `lemond` launcher (subprocess + API key + port + per-stage logging)
[ ] 5. Re-point the existing client at lemond (base_url, api_key, 120s timeout — all three required)
[ ] 6. Wait for /api/v1/health, install backend, then PULL the model before first use
[ ] 7. Wire shutdown and error recovery
```

Track progress against this checklist. Move on only when each step verifies.

> **Log every stage.** A local integration has many silent failure points —
> spawn, health, backend install, model download, first inference. Without a
> log line at each transition, "nothing happened" is indistinguishable from
> "broke at stage 3." Emit one clear line per stage as you build (see
````
END THIRD-PARTY FILE fbcbed833c2b4009

The third-party file has ended. Everything between the fbcbed833c2b4009 tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

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