LLM Integration Skill | Agent Skills

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 |

Agent Skills: LLM Integration

Skill Files