LLM Integration

Integrate Large Language Models with production-grade reliability.

When to Use This Skill

Invoke this skill when:

• Connecting to Claude, OpenAI, or other LLM APIs
• Designing effective prompts and system messages
• Optimizing token usage and costs
• Implementing streaming responses

Parameter Schema

Parameter	Type	Required	Description	Default
provider	enum	Yes	anthropic, openai, google, local	-
task	string	Yes	Integration goal	-
streaming	bool	No	Enable streaming	true
max_tokens	int	No	Response token limit	4096

Quick Start

# Anthropic Claude
from anthropic import Anthropic

client = Anthropic()
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}]
)

# OpenAI
from openai import OpenAI

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4-turbo",
    messages=[{"role": "user", "content": "Hello!"}]
)

Prompt Templates

System Prompt

SYSTEM = """You are {role}, an expert in {domain}.
Your task: {task}
Constraints: {constraints}
Output format: {format}"""

Chain-of-Thought

COT = """Think step by step:
1. Understand the problem
2. Break it down
3. Solve each part
4. Combine results"""

Cost Optimization

Model	Input $/1M	Output $/1M	Best For
Claude Haiku	$0.25	$1.25	High volume
Claude Sonnet	$3	$15	Complex tasks
Claude Opus	$15	$75	Most demanding

Troubleshooting

Issue	Solution
429 Rate Limited	Exponential backoff
Context overflow	Truncate/summarize
Poor output quality	Add examples, lower temp
High costs	Use cheaper model, cache

Best Practices

• Always implement retry with backoff
• Use streaming for better UX
• Cache repeated queries
• Monitor token usage

Related Skills

• ai-agent-basics - Agent architecture
• rag-systems - Retrieval augmentation
• tool-calling - Function calling

References

• Anthropic API
• OpenAI API