FastMCP FastMCP
Sign In

zhayujie / linkai-agent

Call LinkAI applications and workflows. Use bash command to execute like 'bash <base_dir>/scripts/call.sh <app_code> <question>'.

Productivity
0 views
0 installs
Source: https://github.com/zhayujie/chatgpt-on-wechat/tree/master/skills/linkai-agent

Skill Content

---
name: linkai-agent
description: Call LinkAI applications and workflows. Use bash with curl to invoke the chat completions API.
homepage: https://link-ai.tech
metadata:
  emoji: 🤖
  requires:
    bins: ["curl"]
    env: ["LINKAI_API_KEY"]
---

# LinkAI Agent

Call LinkAI applications and workflows through the chat completions API. Available apps are loaded from config.json.

## Setup

This skill requires a LinkAI API key.

1. Get your API key from [LinkAI Console](https://link-ai.tech/console/interface)
2. Set the environment variable: `export LINKAI_API_KEY=Link_xxxxxxxxxxxx` (or use env_config tool)

## Configuration

1. Copy `config.json.template` to `config.json`
2. Add your apps/workflows in config.json. The skill description is auto-generated from this config when loaded.

## Usage

Use the bash tool with curl to call the API. **Prefer curl** to avoid encoding issues on Windows PowerShell.

```bash
curl -X POST "https://api.link-ai.tech/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $LINKAI_API_KEY" \
  -d '{
    "app_code": "<app_code>",
    "messages": [{"role": "user", "content": "<question>"}],
    "stream": false
  }'
```

**Optional parameters**:

- Add `--max-time 120` to curl for long-running tasks (video/image generation)

**On Windows cmd**: Use `%LINKAI_API_KEY%` instead of `$LINKAI_API_KEY`.

**Example** (via bash tool):

```bash
bash(command='curl -sS --max-time 120 -X POST "https://api.link-ai.tech/v1/chat/completions" -H "Content-Type: application/json" -H "Authorization: Bearer $LINKAI_API_KEY" -d "{\"app_code\":\"G7z6vKwp\",\"messages\":[{\"role\":\"user\",\"content\":\"What is AI?\"}],\"stream\":false}"', timeout=130)
```

## Response

Success (extract `choices[0].message.content` from JSON):

```json
{
  "choices": [{
    "message": {
      "role": "assistant",
      "content": "AI stands for Artificial Intelligence..."
    }
  }],
  "usage": {
    "prompt_tokens": 10,
    "completion_tokens": 50,
    "total_tokens": 60
  }
}
```

Error:

```json
{
  "error": {
    "message": "Error description",
    "code": "error_code"
  }
}
```