Agent skill

llm

Generate text, call tools, and get structured JSON output using LLM providers (Claude, OpenAI, Gemini, Ollama). Use when the user needs to call an LLM API for text generation, function calling, or structured data extraction.

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Install this agent skill to your Project

npx add-skill https://github.com/binome-dev/humcp/tree/main/src/tools/llm

SKILL.md

LLM Tools

Tools for generating text, calling functions, and extracting structured data using LLM APIs.

Requirements

Set environment variables for the providers you want to use:

  • ANTHROPIC_API_KEY: For Claude tools
  • OPENAI_API_KEY: For OpenAI tools
  • GOOGLE_API_KEY: For Gemini tools
  • OLLAMA_HOST: For Ollama (optional, defaults to http://localhost:11434)

Basic Chat

python
result = await claude_chat(prompt="Explain quantum computing")
result = await openai_chat(prompt="Explain quantum computing")
result = await gemini_chat(prompt="Explain quantum computing")
result = await ollama_chat(prompt="Explain quantum computing")

Tool / Function Calling

Claude

python
result = await claude_chat(
    prompt="What's the weather in San Francisco?",
    tools=[{
        "name": "get_weather",
        "description": "Get current weather for a location",
        "input_schema": {
            "type": "object",
            "properties": {
                "location": {"type": "string"}
            },
            "required": ["location"]
        }
    }],
    tool_choice={"type": "auto"}
)
# result["data"]["tool_calls"] = [{"id": "...", "name": "get_weather", "input": {"location": "San Francisco"}}]

OpenAI (Responses API)

Supports both custom function tools and built-in tools (web_search, file_search, code_interpreter).

python
# Custom function tool
result = await openai_chat(
    prompt="What's the weather in San Francisco?",
    tools=[{
        "type": "function",
        "name": "get_weather",
        "description": "Get current weather for a location",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string"}
            },
            "required": ["location"]
        }
    }],
    tool_choice="auto"
)
# result["data"]["tool_calls"] = [{"id": "...", "name": "get_weather", "arguments": {"location": "San Francisco"}}]

# Built-in web search
result = await openai_chat(
    prompt="What happened in the news today?",
    tools=[{"type": "web_search"}]
)

Gemini

python
result = await gemini_chat(
    prompt="What's the weather in San Francisco?",
    tools=[{
        "name": "get_weather",
        "description": "Get current weather for a location",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string"}
            },
            "required": ["location"]
        }
    }],
    tool_config={"function_calling_config": {"mode": "AUTO"}}
)
# result["data"]["tool_calls"] = [{"name": "get_weather", "args": {"location": "San Francisco"}}]

Ollama

python
result = await ollama_chat(
    prompt="What's the weather in San Francisco?",
    tools=[{
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"}
                },
                "required": ["location"]
            }
        }
    }]
)
# result["data"]["tool_calls"] = [{"name": "get_weather", "arguments": {"location": "San Francisco"}}]

Structured Output (JSON Schema)

Claude

python
result = await claude_chat(
    prompt="Extract: John Smith, john@example.com, Enterprise plan",
    response_format={
        "type": "object",
        "properties": {
            "name": {"type": "string"},
            "email": {"type": "string"},
            "plan": {"type": "string"}
        },
        "required": ["name", "email", "plan"]
    }
)

OpenAI

python
# JSON mode (valid JSON, no schema enforcement)
result = await openai_chat(
    prompt="List 3 colors as JSON",
    system_prompt="Respond in JSON format.",
    response_format={"type": "json_object"}
)

# Structured output (schema-enforced)
result = await openai_chat(
    prompt="Extract: John Smith, john@example.com, Enterprise plan",
    response_format={
        "type": "json_schema",
        "name": "contact_info",
        "strict": True,
        "schema": {
            "type": "object",
            "properties": {
                "name": {"type": "string"},
                "email": {"type": "string"},
                "plan": {"type": "string"}
            },
            "required": ["name", "email", "plan"],
            "additionalProperties": False
        }
    }
)

Gemini

python
result = await gemini_chat(
    prompt="Extract: John Smith, john@example.com, Enterprise plan",
    response_format={
        "type": "object",
        "properties": {
            "name": {"type": "string"},
            "email": {"type": "string"},
            "plan": {"type": "string"}
        },
        "required": ["name", "email", "plan"]
    }
)

Ollama

python
# JSON mode
result = await ollama_chat(
    prompt="List 3 colors as JSON",
    response_format="json"
)

# Schema-enforced structured output
result = await ollama_chat(
    prompt="Extract: John Smith, john@example.com, Enterprise plan",
    response_format={
        "type": "object",
        "properties": {
            "name": {"type": "string"},
            "email": {"type": "string"},
            "plan": {"type": "string"}
        },
        "required": ["name", "email", "plan"]
    }
)

Response Format

All tools return a standardized response:

json
{
  "success": true,
  "data": {
    "model": "gpt-4o",
    "content": "Generated text...",
    "usage": {"input_tokens": 25, "output_tokens": 150},
    "tool_calls": [{"id": "...", "name": "fn", "arguments": {...}}]
  }
}

Provider-specific fields:

  • Claude: stop_reason ("end_turn", "tool_use", "max_tokens")
  • OpenAI: id (response ID for chaining), status ("completed", "failed")
  • Gemini: finish_reason ("STOP", "MAX_TOKENS")
  • Ollama: done_reason ("stop", etc.)

Parameters

Parameter Type Claude OpenAI Gemini Ollama
prompt str Yes Yes Yes Yes
system_prompt str Yes Yes Yes Yes
model str Yes Yes Yes Yes
temperature float 0-1 0-2 0-2 Yes
max_tokens int Yes Yes Yes Yes
top_p float Yes Yes Yes Yes
top_k int Yes - Yes Yes
stop/stop_sequences list[str] Yes - Yes Yes
frequency_penalty float - - Yes Yes
presence_penalty float - - Yes Yes
seed int - - - Yes
tools list[dict] Yes Yes Yes Yes
tool_choice/tool_config dict/str Yes Yes Yes -
response_format dict/str Yes Yes Yes Yes
store bool - Yes - -
host str - - - Yes
extra dict Yes Yes Yes Yes

The extra parameter accepts a dict of additional provider-specific kwargs passed directly to the underlying API call.

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