skill-seekers-reference/docs/integrations/MINIMAX_INTEGRATION.md

MiniMax AI Integration Guide

Complete guide for using Skill Seekers with MiniMax AI platform.

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Overview

MiniMax AI is a Chinese AI company offering OpenAI-compatible APIs with their M2.7 model. Skill Seekers packages documentation for use with MiniMax's platform.

Key Features

• OpenAI-Compatible API: Uses standard OpenAI client library
• MiniMax-M2.7 Model: Powerful LLM for enhancement and chat
• Simple ZIP Format: Easy packaging with system instructions
• Knowledge Files: Reference documentation included in package

---

Prerequisites

1. Get MiniMax API Key

1. Visit MiniMax Platform
2. Create an account and verify
3. Navigate to API Keys section
4. Generate a new API key
5. Copy the key (starts with eyJ - JWT format)

2. Install Dependencies

# Install MiniMax support (includes openai library)
pip install skill-seekers[minimax]

# Or install all LLM platforms
pip install skill-seekers[all-llms]

3. Configure Environment

export MINIMAX_API_KEY=eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9...

Add to your ~/.bashrc, ~/.zshrc, or .env file for persistence.

---

Complete Workflow

Step 1: Scrape Documentation

# Scrape documentation website
skill-seekers scrape --config configs/react.json

# Or use quick preset
skill-seekers create https://docs.python.org/3/ --preset quick

Step 2: Enhance with MiniMax-M2.7

# Enhance SKILL.md using MiniMax AI
skill-seekers enhance output/react/ --target minimax

# With custom model (if available)
skill-seekers enhance output/react/ --target minimax --model MiniMax-M2.7

This step:

• Reads reference documentation
• Generates enhanced system instructions
• Creates backup of original SKILL.md
• Uses MiniMax-M2.7 for AI enhancement

Step 3: Package for MiniMax

# Package as MiniMax-compatible ZIP
skill-seekers package output/react/ --target minimax

# Custom output path
skill-seekers package output/react/ --target minimax --output my-skill.zip

Output structure:

react-minimax.zip
├── system_instructions.txt    # Main instructions (from SKILL.md)
├── knowledge_files/           # Reference documentation
│   ├── guide.md
│   ├── api-reference.md
│   └── examples.md
└── minimax_metadata.json      # Skill metadata

Step 4: Validate Package

# Validate package with MiniMax API
skill-seekers upload react-minimax.zip --target minimax

This validates:

• Package structure
• API connectivity
• System instructions format

Note: MiniMax doesn't have persistent skill storage like Claude. The upload validates your package but you'll use the ZIP file directly with MiniMax's API.

---

Using Your Skill

Direct API Usage

from openai import OpenAI
import zipfile
import json

# Extract package
with zipfile.ZipFile('react-minimax.zip', 'r') as zf:
    with zf.open('system_instructions.txt') as f:
        system_instructions = f.read().decode('utf-8')
    
    # Load metadata
    with zf.open('minimax_metadata.json') as f:
        metadata = json.load(f)

# Initialize MiniMax client (OpenAI-compatible)
client = OpenAI(
    api_key="eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9...",
    base_url="https://api.minimax.io/v1"
)

# Use with chat completions
response = client.chat.completions.create(
    model="MiniMax-M2.7",
    messages=[
        {"role": "system", "content": system_instructions},
        {"role": "user", "content": "How do I create a React component?"}
    ],
    temperature=0.3,
    max_tokens=2000
)

print(response.choices[0].message.content)

With Knowledge Files

import zipfile
from pathlib import Path

# Extract knowledge files
with zipfile.ZipFile('react-minimax.zip', 'r') as zf:
    zf.extractall('extracted_skill')

# Read all knowledge files
knowledge_dir = Path('extracted_skill/knowledge_files')
knowledge_files = []
for md_file in knowledge_dir.glob('*.md'):
    knowledge_files.append({
        'name': md_file.name,
        'content': md_file.read_text()
    })

# Include in context (truncate if too long)
context = "\n\n".join([f"## {kf['name']}\n{kf['content'][:5000]}" 
                     for kf in knowledge_files[:5]])

response = client.chat.completions.create(
    model="MiniMax-M2.7",
    messages=[
        {"role": "system", "content": system_instructions},
        {"role": "user", "content": f"Context: {context}\n\nQuestion: What are React hooks?"}
    ]
)

---

API Reference

SkillAdaptor Methods

from skill_seekers.cli.adaptors import get_adaptor

# Get MiniMax adaptor
adaptor = get_adaptor('minimax')

# Format SKILL.md as system instructions
instructions = adaptor.format_skill_md(skill_dir, metadata)

# Package skill
package_path = adaptor.package(skill_dir, output_path)

# Validate package with MiniMax API
result = adaptor.upload(package_path, api_key)
print(result['message'])  # Validation result

# Enhance SKILL.md
success = adaptor.enhance(skill_dir, api_key)

Environment Variables

Variable	Description	Required
MINIMAX_API_KEY	Your MiniMax API key (JWT format)	Yes

---

Troubleshooting

Invalid API Key Format

Error: Invalid API key format

Solution: MiniMax API keys use JWT format starting with eyJ. Check:

# Should start with 'eyJ'
echo $MINIMAX_API_KEY | head -c 10
# Output: eyJhbGciOi

OpenAI Library Not Installed

Error: ModuleNotFoundError: No module named 'openai'

Solution:

pip install skill-seekers[minimax]
# or
pip install openai>=1.0.0

Upload Timeout

Error: Upload timed out

Solution:

• Check internet connection
• Try again (temporary network issue)
• Verify API key is correct
• Check MiniMax platform status

Connection Error

Error: Connection error

Solution:

• Verify internet connectivity
• Check if MiniMax API endpoint is accessible:

curl https://api.minimax.io/v1/models

• Try with VPN if in restricted region

Package Validation Failed

Error: Invalid package: system_instructions.txt not found

Solution:

• Ensure SKILL.md exists before packaging
• Check package contents:

unzip -l react-minimax.zip

• Re-package the skill

---

Best Practices

1. Keep References Organized

Structure your documentation:

output/react/
├── SKILL.md              # Main instructions
├── references/
│   ├── 01-getting-started.md
│   ├── 02-components.md
│   ├── 03-hooks.md
│   └── 04-api-reference.md
└── assets/
    └── diagrams/

2. Use Enhancement

Always enhance before packaging:

# Enhancement improves system instructions quality
skill-seekers enhance output/react/ --target minimax

3. Test Before Deployment

# Validate package
skill-seekers upload react-minimax.zip --target minimax

# If successful, package is ready to use

4. Version Your Skills

# Include version in output name
skill-seekers package output/react/ --target minimax --output react-v2.0-minimax.zip

---

Comparison with Other Platforms

Feature	MiniMax	Claude	Gemini	OpenAI
Format	ZIP	ZIP	tar.gz	ZIP
Upload	Validation	Full API	Full API	Full API
Enhancement	MiniMax-M2.7	Claude Sonnet	Gemini 2.0	GPT-4o
API Type	OpenAI-compatible	Anthropic	Google	OpenAI
Key Format	JWT (eyJ...)	sk-ant...	AIza...	sk-...
Knowledge Files	Included in ZIP	Included	Included	Vector Store

---

Advanced Usage

Custom Enhancement Prompt

Programmatically customize enhancement:

from skill_seekers.cli.adaptors import get_adaptor
from pathlib import Path

adaptor = get_adaptor('minimax')
skill_dir = Path('output/react')

# Build custom prompt
references = adaptor._read_reference_files(skill_dir / 'references')
prompt = adaptor._build_enhancement_prompt(
    skill_name='React',
    references=references,
    current_skill_md=(skill_dir / 'SKILL.md').read_text()
)

# Customize prompt
prompt += "\n\nADDITIONAL FOCUS: Emphasize React 18 concurrent features."

# Use with your own API call

Batch Processing

# Process multiple frameworks
for framework in react vue angular; do
    skill-seekers scrape --config configs/${framework}.json
    skill-seekers enhance output/${framework}/ --target minimax
    skill-seekers package output/${framework}/ --target minimax --output ${framework}-minimax.zip
done

---

Resources

• MiniMax Platform
• MiniMax API Documentation
• OpenAI Python Client
• Multi-LLM Support Guide

---

Next Steps

1. Get your MiniMax API key
2. Install dependencies: pip install skill-seekers[minimax]
3. Try the Quick Start example
4. Explore advanced usage patterns

For help, see Troubleshooting or open an issue on GitHub.