OpenAI ChatGPT Integration Guide

Complete guide for creating and deploying skills to OpenAI ChatGPT using Skill Seekers.

Overview

Skill Seekers packages documentation into OpenAI-compatible formats optimized for:

Assistants API for custom AI assistants
Vector Store + File Search for accurate retrieval
GPT-4o for enhancement and responses

Setup

1. Install OpenAI Support

# Install with OpenAI dependencies
pip install skill-seekers[openai]

# Verify installation
pip list | grep openai

2. Get OpenAI API Key

Visit OpenAI Platform
Navigate to API keys section
Click “Create new secret key”
Copy the key (starts with sk-proj- or sk-)

3. Configure API Key

# Set as environment variable (recommended)
export OPENAI_API_KEY=sk-proj-...

# Or pass directly to commands
skill-seekers upload --target openai --api-key sk-proj-...

Complete Workflow

Step 1: Scrape Documentation

# Use any config (scraping is platform-agnostic)
skill-seekers scrape --config configs/react.json

# Or use a unified config for multi-source
skill-seekers unified --config configs/react_unified.json

Result: output/react/ skill directory with references

Step 2: Enhance with GPT-4o (Optional but Recommended)

# Enhance SKILL.md using GPT-4o
skill-seekers enhance output/react/ --target openai

# With API key specified
skill-seekers enhance output/react/ --target openai --api-key sk-proj-...

What it does:

Analyzes all reference documentation
Extracts 5-10 best code examples
Creates comprehensive assistant instructions
Adds response guidelines and search strategy
Formats as plain text (no YAML frontmatter)

Time: 20-40 seconds Cost: ~$0.15-0.30 (using GPT-4o) Quality boost: 3/10 → 9/10

Step 3: Package for OpenAI

# Create ZIP package for OpenAI Assistants
skill-seekers package output/react/ --target openai

# Result: react-openai.zip

Package structure:

react-openai.zip/
├── assistant_instructions.txt  # Main instructions for Assistant
├── vector_store_files/        # Files for Vector Store + file_search
│   ├── getting_started.md
│   ├── hooks.md
│   ├── components.md
│   └── ...
└── openai_metadata.json       # Platform metadata

Step 4: Upload to OpenAI (Creates Assistant)

# Upload and create Assistant with Vector Store
skill-seekers upload react-openai.zip --target openai

# With API key
skill-seekers upload react-openai.zip --target openai --api-key sk-proj-...

What it does:

Creates Vector Store for documentation
Uploads reference files to Vector Store
Creates Assistant with file_search tool
Links Vector Store to Assistant

Output:

✅ Upload successful!
Assistant ID: asst_abc123xyz
URL: https://platform.openai.com/assistants/asst_abc123xyz
Message: Assistant created with 15 knowledge files

Step 5: Use Your Assistant

Access your assistant in the OpenAI Platform:

Go to OpenAI Platform
Find your assistant in the list
Test in Playground or use via API

What Makes OpenAI Different?

Format: Assistant Instructions (Plain Text)

Claude format:

---
name: react
---

# React Documentation
...

OpenAI format:

You are an expert assistant for React.

Your Knowledge Base:
- Getting started guide
- React hooks reference
- Component API

When users ask questions about React:
1. Search the knowledge files
2. Provide code examples
...

Plain text instructions optimized for Assistant API.

Architecture: Assistant + Vector Store

OpenAI uses a two-part system:

Assistant - The AI agent with instructions and tools
Vector Store - Embedded documentation for semantic search

Tool: file_search

The Assistant uses the file_search tool to:

Semantically search documentation
Find relevant code examples
Provide accurate, source-based answers

Using Your OpenAI Assistant

Option 1: OpenAI Playground (Web UI)

Go to OpenAI Platform
Select your assistant
Click “Test in Playground”
Ask questions about your documentation

Option 2: Assistants API (Python)

from openai import OpenAI

# Initialize client
client = OpenAI(api_key='sk-proj-...')

# Create thread
thread = client.beta.threads.create()

# Send message
message = client.beta.threads.messages.create(
    thread_id=thread.id,
    role="user",
    content="How do I use React hooks?"
)

# Run assistant
run = client.beta.threads.runs.create(
    thread_id=thread.id,
    assistant_id='asst_abc123xyz'  # Your assistant ID
)

# Wait for completion
while run.status != 'completed':
    run = client.beta.threads.runs.retrieve(thread_id=thread.id, run_id=run.id)

# Get response
messages = client.beta.threads.messages.list(thread_id=thread.id)
print(messages.data[0].content[0].text.value)

Option 3: Streaming Responses

from openai import OpenAI

client = OpenAI(api_key='sk-proj-...')

# Create thread and message
thread = client.beta.threads.create()
client.beta.threads.messages.create(
    thread_id=thread.id,
    role="user",
    content="Explain React hooks"
)

# Stream response
with client.beta.threads.runs.stream(
    thread_id=thread.id,
    assistant_id='asst_abc123xyz'
) as stream:
    for event in stream:
        if event.event == 'thread.message.delta':
            print(event.data.delta.content[0].text.value, end='')

Advanced Usage

Update Assistant Instructions

from openai import OpenAI

client = OpenAI(api_key='sk-proj-...')

# Update assistant
client.beta.assistants.update(
    assistant_id='asst_abc123xyz',
    instructions="""
You are an expert React assistant.

Focus on modern best practices using:
- React 18+ features
- Functional components
- Hooks-based patterns

When answering:
1. Search knowledge files first
2. Provide working code examples
3. Explain the "why" not just the "what"
"""
)

Add More Files to Vector Store

from openai import OpenAI

client = OpenAI(api_key='sk-proj-...')

# Upload new file
with open('new_guide.md', 'rb') as f:
    file = client.files.create(file=f, purpose='assistants')

# Add to vector store
client.beta.vector_stores.files.create(
    vector_store_id='vs_abc123',
    file_id=file.id
)

Programmatic Package and Upload

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

# Get adaptor
openai_adaptor = get_adaptor('openai')

# Package skill
package_path = openai_adaptor.package(
    skill_dir=Path('output/react'),
    output_path=Path('output/react-openai.zip')
)

# Upload (creates Assistant + Vector Store)
result = openai_adaptor.upload(
    package_path=package_path,
    api_key='sk-proj-...'
)

if result['success']:
    print(f"✅ Assistant created!")
    print(f"ID: {result['skill_id']}")
    print(f"URL: {result['url']}")
else:
    print(f"❌ Upload failed: {result['message']}")

OpenAI-Specific Features

1. Semantic Search (file_search)

The Assistant uses embeddings to:

Find semantically similar content
Understand intent vs. keywords
Surface relevant examples automatically

2. Citations and Sources

Assistants can provide:

Source attribution
File references
Quote extraction

3. Function Calling (Optional)

Extend your assistant with custom tools:

client.beta.assistants.update(
    assistant_id='asst_abc123xyz',
    tools=[
        {"type": "file_search"},
        {"type": "function", "function": {
            "name": "run_code_example",
            "description": "Execute React code examples",
            "parameters": {...}
        }}
    ]
)

Include images in your documentation:

Screenshots
Diagrams
Architecture charts

Troubleshooting

Issue: `openai not installed`

Solution:

pip install skill-seekers[openai]

Issue: `Invalid API key format`

Error: API key doesn’t start with sk-

Solution:

Get new key from OpenAI Platform
Verify you’re using API key, not organization ID

Issue: `Not a ZIP file`

Error: Wrong package format

Solution:

# Use --target openai for ZIP format
skill-seekers package output/react/ --target openai

# NOT:
skill-seekers package output/react/ --target gemini  # Creates .tar.gz

Issue: `Assistant creation failed`

Possible causes:

API key lacks permissions
Rate limit exceeded
File too large

Solution:

# Verify API key
python3 -c "from openai import OpenAI; print(OpenAI(api_key='sk-proj-...').models.list())"

# Check rate limits
# Visit: https://platform.openai.com/account/limits

# Reduce file count
skill-seekers package output/react/ --target openai --max-files 20

Issue: Enhancement fails

Solution:

# Check API quota and billing
# Visit: https://platform.openai.com/account/billing

# Try with smaller skill
skill-seekers enhance output/react/ --target openai --max-files 5

# Use without enhancement
skill-seekers package output/react/ --target openai
# (Skip enhancement step)

Issue: file_search not working

Symptoms: Assistant doesn’t reference documentation

Solution:

Verify Vector Store has files
Check Assistant tool configuration
Test with explicit instructions: “Search the knowledge files for information about hooks”

Best Practices

1. Write Clear Assistant Instructions

Focus on:

Role definition
Knowledge base description
Response guidelines
Search strategy

2. Organize Vector Store Files

Keep files under 512KB each
Use clear, descriptive filenames
Structure content with headings
Include code examples

3. Test Assistant Behavior

Test with varied questions:

1. Simple facts: "What is React?"
2. How-to questions: "How do I create a component?"
3. Best practices: "What's the best way to manage state?"
4. Troubleshooting: "Why isn't my hook working?"

4. Monitor Token Usage

# Track tokens in API responses
run = client.beta.threads.runs.retrieve(thread_id=thread.id, run_id=run.id)
print(f"Input tokens: {run.usage.prompt_tokens}")
print(f"Output tokens: {run.usage.completion_tokens}")

5. Update Regularly

# Re-scrape updated documentation
skill-seekers scrape --config configs/react.json

# Re-enhance and upload (creates new Assistant)
skill-seekers enhance output/react/ --target openai
skill-seekers package output/react/ --target openai
skill-seekers upload react-openai.zip --target openai

Cost Estimation

GPT-4o pricing (as of 2024):

Input: $2.50 per 1M tokens
Output: $10.00 per 1M tokens

Typical skill enhancement:

Input: ~50K-200K tokens (docs)
Output: ~5K-10K tokens (enhanced instructions)
Cost: $0.15-0.30 per skill

Vector Store:

$0.10 per GB per day (storage)
Typical skill: < 100MB = ~$0.01/day

API usage:

Varies by question volume
~$0.01-0.05 per conversation

Next Steps

✅ Install OpenAI support: pip install skill-seekers[openai]
✅ Get API key from OpenAI Platform
✅ Scrape your documentation
✅ Enhance with GPT-4o
✅ Package for OpenAI
✅ Upload and create Assistant
✅ Test in Playground

Resources

Status: ✅ Production Ready (v2.5.0+)

Found an issue or have suggestions? Open an issue

OpenAI ChatGPT Integration Guide

Overview

Setup

1. Install OpenAI Support

2. Get OpenAI API Key

3. Configure API Key

Complete Workflow

Step 1: Scrape Documentation

Step 2: Enhance with GPT-4o (Optional but Recommended)

Step 3: Package for OpenAI

Step 4: Upload to OpenAI (Creates Assistant)

Step 5: Use Your Assistant

What Makes OpenAI Different?

Format: Assistant Instructions (Plain Text)

Architecture: Assistant + Vector Store

Tool: file_search

Using Your OpenAI Assistant

Option 1: OpenAI Playground (Web UI)

Option 2: Assistants API (Python)

Option 3: Streaming Responses

Advanced Usage

Update Assistant Instructions

Add More Files to Vector Store

Programmatic Package and Upload

OpenAI-Specific Features

1. Semantic Search (file_search)

2. Citations and Sources

3. Function Calling (Optional)

4. Multi-Modal Support

Troubleshooting

Issue: openai not installed

Issue: Invalid API key format

Issue: Not a ZIP file

Issue: Assistant creation failed

Issue: Enhancement fails

Issue: file_search not working

Best Practices

1. Write Clear Assistant Instructions

2. Organize Vector Store Files

3. Test Assistant Behavior

4. Monitor Token Usage

5. Update Regularly

Cost Estimation

Next Steps

Resources

Issue: `openai not installed`

Issue: `Invalid API key format`

Issue: `Not a ZIP file`

Issue: `Assistant creation failed`