LLM Integration Skill Skill

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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.

Context Discovery

Auto-Investigation

Check these project signals before asking questions:

| Signal | Where to Check | What to Look For | |--------|----------------|------------------| | Dependencies | package.json, requirements.txt, go.mod | openai, anthropic, google-generativeai, langchain | | Config files | .env, config/*.{yml,json} | API keys (OPENAI_API_KEY, ANTHROPIC_API_KEY) | | Existing code | Grep for "openai", "anthropic", "genai" | Existing LLM integrations | | Documentation | README.md, docs/ | LLM usage patterns |

Discovery Questions

question: "What LLM task are you building?"
header: "Task Type"
multiSelect: false
options:
  - label: "Chat/completion API"
    description: "Direct LLM API calls for text generation"
  - label: "Function calling / tool use"
    description: "LLM selects and executes functions"
  - label: "Structured output (JSON mode)"
    description: "Constrain LLM to return valid JSON/schemas"
  - label: "Prompt optimization"
    description: "Improve existing prompts (few-shot, CoT, templates)"

question: "Which LLM provider(s)?"
header: "Provider"
multiSelect: true
options:
  - label: "OpenAI (GPT-4o, o1)"
    description: "OpenAI API integration"
  - label: "Claude (Anthropic)"
    description: "Claude Opus/Sonnet via Anthropic API"
  - label: "Gemini (Google)"
    description: "Gemini Pro/Flash via Google AI"
  - label: "Local LLM (Ollama)"
    description: "Self-hosted models for privacy"

question: "Do you need safety/content moderation?"
header: "Guardrails"
multiSelect: false
options:
  - label: "Yes - content filtering/PII detection"
    description: "Implement guardrails for safety"
  - label: "No - internal use only"
    description: "Skip guardrails"

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

Agent Skills: LLM Integration Skill

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