maante-game-automation

name	maante-game-automation
description	MaaNTE is a MAA-based automation assistant for the game Neverness to Everness, powered by MaaFramework, supporting auto-fishing, auto-coffee-making, and cafe revenue extraction.
triggers	["set up MaaNTE automation","automate fishing in Neverness to Everness","configure MaaNTE assistant","MaaNTE not working fix","add new task to MaaNTE","MaaNTE pipeline development","MaaFramework game automation","MaaNTE coffee making automation"]

MaaNTE Game Automation Assistant

Skill by ara.so — Daily 2026 Skills collection.

MaaNTE is an automation assistant for the game Neverness to Everness (异环), built on MaaFramework (image-recognition-based black-box automation). It automates repetitive tasks: fishing (with auto-sell fish & auto-buy bait), coffee-making (with customer management), and cafe revenue extraction (with auto-restocking).

Requirements

• Windows OS
• Python >= 3.11
• Game running at 1280×720 resolution, windowed mode
• Run as Administrator
• Program path must not contain Chinese characters
• Disable antivirus software if detection issues arise

---

Installation (End Users)

Download the latest release from GitHub Releases — no cloning needed:

https://github.com/1bananachicken/MaaNTE/releases

Extract and run the GUI executable directly.

---

Installation (Developers)

1. Fork & Clone with Submodules

git clone --recursive https://github.com/<your-username>/MaaNTE.git
cd MaaNTE

2. Install Python Dependencies

pip install -r requirements.txt

3. Download MaaFramework

Download the MaaFramework release and extract it into the deps/ folder:

MaaNTE/
  deps/
    MaaFramework/
      bin/
      include/
      lib/

4. Recommended IDE Setup

• Use VSCode with the maa-support extension for pipeline debugging.

---

Project Structure

MaaNTE/
├── assets/
│   └── logo.png
├── deps/                  # MaaFramework binaries (not committed)
├── pipeline/              # JSON pipeline task definitions
│   ├── fishing/
│   ├── coffee/
│   └── cafe/
├── custom/                # Python custom action/recognizer scripts
├── docs/
│   └── README_en.md
├── interface.json         # MFAAvalonia GUI configuration
└── main.py                # Entry point (dev mode)

---

Key Concepts: MaaFramework Pipeline

Tasks are defined in JSON pipeline files. Each task node specifies how to find a UI element (via image template or OCR) and what action to take.

Pipeline Task Node Structure

{
  "TaskName": {
    "recognition": "TemplateMatch",
    "template": "fishing/float.png",
    "roi": [0, 0, 1280, 720],
    "action": "Click",
    "next": ["NextTask"],
    "timeout": 10000,
    "on_error": ["ErrorHandlerTask"]
  }
}

Common Recognition Types

Type	Description
TemplateMatch	Find image template on screen
OCR	Optical character recognition
ColorMatch	Match pixel color
DirectHit	Always triggers (no recognition)

Common Action Types

Action	Description
Click	Click matched region
Swipe	Swipe gesture
Key	Press keyboard key
StartApp	Launch application
StopApp	Stop application
Custom	Call Python custom action

---

Python Custom Action Example

Custom actions let you write Python logic triggered from pipeline tasks.

# custom/my_action.py
from maa.agent.agent_server import AgentServer
from maa.custom_action import CustomAction
from maa.context import Context
from maa.define import RectType
import json


class MyCustomAction(CustomAction):
    def run(
        self,
        context: Context,
        argv: CustomAction.RunArg,
    ) -> CustomAction.RunResult:
        # Access current task arguments
        task_name = argv.task_name
        custom_param = json.loads(argv.custom_action_param)
        
        # Take a screenshot and find something
        image = context.tasker.controller.cached_image
        
        # Run a sub-pipeline task
        context.run_pipeline("AnotherTask")
        
        # Click at specific coordinates
        context.tasker.controller.post_click(640, 360).wait()
        
        return CustomAction.RunResult(success=True)


# Register and start agent server
if __name__ == "__main__":
    AgentServer.start_up(AgentServer.parse_argv())
    server = AgentServer()
    server.register_custom_action("MyCustomAction", MyCustomAction())
    server.join()

Referencing Custom Action in Pipeline

{
  "TriggerMyAction": {
    "recognition": "DirectHit",
    "action": "Custom",
    "custom_action": "MyCustomAction",
    "custom_action_param": "{\"key\": \"value\"}"
  }
}

---

Python Custom Recognizer Example

# custom/my_recognizer.py
from maa.custom_recognizer import CustomRecognizer
from maa.context import Context
import numpy as np


class MyCustomRecognizer(CustomRecognizer):
    def analyze(
        self,
        context: Context,
        argv: CustomRecognizer.AnalyzeArg,
    ) -> CustomRecognizer.AnalyzeResult:
        image = argv.image  # numpy array (H, W, C) BGR
        
        # Your image analysis logic here
        # Example: check average color in a region
        roi = image[300:400, 600:700]
        mean_color = np.mean(roi, axis=(0, 1))
        
        found = mean_color[2] > 200  # high red channel
        
        if found:
            # Return bounding box of found region
            return CustomRecognizer.AnalyzeResult(
                box=(600, 300, 100, 100),  # x, y, w, h
                detail="found red region"
            )
        
        return CustomRecognizer.AnalyzeResult(box=None, detail="not found")

---

Running in Development Mode

# Run with default config
python main.py

# The GUI is provided by MFAAvalonia (separate executable)
# For pipeline-only testing use MaaFramework CLI tools in deps/

---

interface.json Configuration

The GUI (MFAAvalonia) reads interface.json to build the task selection UI:

{
  "name": "MaaNTE",
  "version": "1.0.0",
  "tasks": [
    {
      "name": "自动钓鱼",
      "entry": "StartFishing",
      "option": [
        {
          "name": "自动卖鱼",
          "cases": [
            {"name": "开启", "pipeline_override": {"SellFish": {"enabled": true}}},
            {"name": "关闭", "pipeline_override": {  
          
        
      
    
    
       
       
    
  
   
    
       
       
       
       
    
  

⚠️ Auto-coffee requires input: "Seize" — this takes over mouse control while running.

---

Pipeline Development Workflow

1. Capture Template Images

Use the maa-support VSCode extension or MaaFramework's built-in screencap:

from maa.toolkit import Toolkit
from maa.controller import Win32Controller

Toolkit.init_option("./")
controller = Win32Controller(
    hWnd=Toolkit.find_window("", "NTE_WindowTitle")
)
controller.post_connection().wait()

# Save screenshot for template
image = controller.cached_image
import cv2
cv2.imwrite("assets/template/my_element.png", image)

2. Define Pipeline Task

{
  "DetectFishBite": {
    "recognition": "TemplateMatch",
    "template": "fishing/fish_bite_indicator.png",
    "threshold": 0.85,
    "roi": [500, 400, 300, 200],
    "action": "Click",
    "next": ["RecastLine"],
    "timeout": 30000
  }
}

3. Test with VSCode maa-support

The extension lets you run individual pipeline nodes and visualize recognition results directly in the editor.

---

Adding a New Feature (PR Workflow)

# Always branch from dev for new features
git checkout dev
git pull upstream dev
git checkout -b feature/my-new-task

# Add pipeline JSON in pipeline/
# Add any custom Python in custom/
# Update interface.json to expose task in GUI

git add .
git commit -m "feat: add auto-xxx task"
git push origin feature/my-new-task
# Open PR targeting the dev branch

---

Troubleshooting

Fishing not working

• ✅ Run as Administrator
• ✅ Game resolution exactly 1280×720, windowed
• ✅ Auto-fishing checkbox enabled in GUI
• ✅ Path to MaaNTE has no Chinese/special characters
• ✅ Antivirus disabled or MaaNTE whitelisted

"Mirror酱 not supported" popup

• Harmless — auto-update is not configured. Ignore it.

Template matching fails / tasks stuck

# Debug: lower threshold temporarily
{
  "MyTask": {
    "recognition": "TemplateMatch",
    "template": "my_template.png",
    "threshold": 0.7,   # default 0.8, lower = more lenient
    "roi": [0, 0, 1280, 720]
  }
}

Controller connection fails

from maa.toolkit import Toolkit

# List all available windows
windows = Toolkit.find_window_list("", "")
for w in windows:
    print(f"hwnd={w.hwnd} class={w.class_name} title={w.window_name}")

Coffee automation mouse issues

• Set input method to Seize in interface.json / GUI settings
• Do not move mouse while task is running

---

Key External References

• MaaFramework Docs
• MFAAvalonia GUI
• M9A Dev Docs (Chinese) — architecture and pipeline patterns reference
• maa-support VSCode Extension
• QQ Group 1 | QQ Group 2
• Official Bilibili