OpenAI Agents Skill

Quick Start Workflow

When working with OpenAI APIs:

1. •

   Choose the right model

   • •GPT-4: Complex reasoning, code generation, long context
   • •GPT-3.5-turbo: Fast, cost-effective, simple queries
   • •Check token limits (GPT-4: 8K/32K, GPT-3.5: 16K)
2. •

   Design system prompts

   • •Define agent personality and role
   • •Provide context about Physical AI domain
   • •Set output format expectations
   • •Include safety guardrails
3. •

   Implement streaming for better UX

   • •Stream responses token-by-token
   • •Show "thinking..." indicator
   • •Handle partial responses
4. •

   Add function calling for tools

   • •Define functions (search, calculator, etc.)
   • •Parse function call requests
   • •Execute and return results

Agent Personas

Physical AI Tutor

const TUTOR_PROMPT = `You are an expert tutor in Physical AI and Humanoid Robotics. 
- Explain concepts clearly with examples
- Reference specific textbook sections
- Encourage hands-on learning
- Use analogies for difficult topics
Always cite sources from the textbook context provided.`;

Code Reviewer

const REVIEWER_PROMPT = `You are a ROS 2 code reviewer.
- Review Python/C++ robotics code
- Check for anti-patterns
- Suggest optimizations
- Verify thread safety
Provide specific, actionable feedback.`;

Quick Helper

const HELPER_PROMPT = `You provide quick, concise answers about robotics.
- 2-3 sentence responses
- Focus on key points
- No code unless asked`;

Standard Patterns

Basic Chat

const completion = await openai.chat.completions.create({
  model: 'gpt-4',
  messages: [
    { role: 'system', content: TUTOR_PROMPT },
    { role: 'user', content: userQuestion },
  ],
  temperature: 0.7,
  max_tokens: 1000,
});

With RAG Context

const context = relevantChunks.map(c => c.text).join('\n\n');

const completion = await openai.chat.completions.create({
  model: 'gpt-4',
  messages: [
    {
      role: 'system',
      content: `${TUTOR_PROMPT}\n\nContext:\n${context}`,
    },
    { role: 'user', content: userQuestion },
  ],
});

Best Practices

For Physical AI chatbot:

• •Always include context from RAG retrieval
• •Set temperature 0.7 for balanced creativity
• •Limit max_tokens to control costs
• •Implement retry logic for rate limits
• •Log all completions for debugging
• •Stream responses for better UX

Knowledge Base

Comprehensive guides:

• •Streaming Setup → references/streaming.md
• •Function Calling → references/function-calling.md
• •Token Management → references/tokens.md
• •Cost Optimization → references/cost-optimization.md
• •Error Handling → references/error-handling.md