Entry point: /faion-net — invoke this skill for automatic routing to the appropriate domain.

LLM Integration Skill

Communication: User's language. Code: English.

Purpose

Handles direct integration with LLM APIs. Covers OpenAI, Claude, Gemini, local models, prompt engineering, and output structuring.

Scope

Area	Coverage
LLM APIs	OpenAI (GPT-4o, o1), Claude (Opus 4.5, Sonnet 4), Gemini (Pro, Flash)
Prompt Engineering	Few-shot, CoT, chain-of-thought techniques
Structured Output	JSON mode, function calling, tool use
Guardrails	Content safety, validation, error handling
Local LLMs	Ollama integration, privacy-focused deployments

Quick Start

Task	Files
OpenAI integration	openai-api-integration.md → openai-chat-completions.md
Claude integration	claude-api-basics.md → claude-messages-api.md
Gemini integration	gemini-basics.md → gemini-multimodal.md
Local LLM	local-llm-ollama.md
Prompts	prompt-basics.md → prompt-techniques.md
Function calling	function-calling-patterns.md + tool-use-basics.md

Methodologies (26)

OpenAI (5):

openai-api-integration: API setup, authentication, models
openai-chat-completions: Chat API, streaming, parameters
openai-function-calling: Tool definitions, execution
openai-embeddings: Text embeddings (moved to rag-engineer)
openai-assistants: Assistant API, threads, tools

Claude (6):

claude-api-basics: Anthropic API setup
claude-messages-api: Messages API, streaming
claude-tool-use: Tool definitions, structured output
claude-advanced-features: Extended thinking, prompt caching
claude-best-practices: Safety, context management
claude-api-integration: SDK integration patterns

Gemini (4):

gemini-basics: Google AI setup, models
gemini-multimodal: Vision, audio, video inputs
gemini-function-calling: Function declarations
gemini-api-integration: SDK patterns

Prompt Engineering (6):

prompt-basics: Structure, few-shot, roles
prompt-techniques: Advanced patterns, templates
cot-basics: Chain-of-thought fundamentals
cot-techniques: Zero-shot CoT, reasoning chains
structured-output-basics: JSON mode, schemas
structured-output-patterns: Advanced structuring

Safety & Tools (4):

guardrails-basics: Content safety, PII detection
guardrails-implementation: Implementation patterns
function-calling-patterns: Tool design, error handling
tool-use-basics: Tool fundamentals

Local (1):

local-llm-ollama: Ollama setup, model management

Code Examples

OpenAI Chat Completion

from openai import OpenAI

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain quantum computing"}
    ]
)
print(response.choices[0].message.content)

Claude Messages

import anthropic

client = anthropic.Anthropic()
message = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Explain RAG systems"}]
)
print(message.content[0].text)

Function Calling

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get current weather",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string"}
            }
        }
    }
}]

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What's the weather in SF?"}],
    tools=tools
)

Related Skills

Skill	Relationship
faion-rag-engineer	Uses embeddings APIs
faion-ai-agents	Uses tool calling
faion-ml-ops	Uses for evaluation

LLM Integration v1.0 | 26 methodologies

faion-llm-integration