Integrate LLMs into applications - APIs, prompting, fine-tuning, and context management
Connect to LLM APIs like Anthropic and OpenAI with streaming responses, optimized prompts, and cost management. Use when integrating models into applications or handling API calls.
/plugin marketplace add pluginagentmarketplace/custom-plugin-ai-agents/plugin install custom-plugin-ai-agents@pluginagentmarketplace-ai-agentsThis skill inherits all available tools. When active, it can use any tool Claude has access to.
assets/config.yamlassets/schema.jsonreferences/GUIDE.mdreferences/PATTERNS.mdscripts/multi_provider_client.pyscripts/validate.pyIntegrate Large Language Models with production-grade reliability.
Invoke this skill when:
| 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 |
# 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!"}]
)
SYSTEM = """You are {role}, an expert in {domain}.
Your task: {task}
Constraints: {constraints}
Output format: {format}"""
COT = """Think step by step:
1. Understand the problem
2. Break it down
3. Solve each part
4. Combine results"""
| 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 |
| Issue | Solution |
|---|---|
| 429 Rate Limited | Exponential backoff |
| Context overflow | Truncate/summarize |
| Poor output quality | Add examples, lower temp |
| High costs | Use cheaper model, cache |
ai-agent-basics - Agent architecturerag-systems - Retrieval augmentationtool-calling - Function callingThis skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
This skill should be used when the user asks to "create a slash command", "add a command", "write a custom command", "define command arguments", "use command frontmatter", "organize commands", "create command with file references", "interactive command", "use AskUserQuestion in command", or needs guidance on slash command structure, YAML frontmatter fields, dynamic arguments, bash execution in commands, user interaction patterns, or command development best practices for Claude Code.
This skill should be used when the user asks to "create a hook", "add a PreToolUse/PostToolUse/Stop hook", "validate tool use", "implement prompt-based hooks", "use ${CLAUDE_PLUGIN_ROOT}", "set up event-driven automation", "block dangerous commands", or mentions hook events (PreToolUse, PostToolUse, Stop, SubagentStop, SessionStart, SessionEnd, UserPromptSubmit, PreCompact, Notification). Provides comprehensive guidance for creating and implementing Claude Code plugin hooks with focus on advanced prompt-based hooks API.