llm-cli

LLM CLI Skill

Purpose

This skill enables seamless interaction with multiple LLM providers (OpenAI, Anthropic, Google Gemini, Ollama) through the llm CLI tool. It processes textual and multimedia information with support for both one-off executions and interactive conversation modes.

When to Use This Skill

Trigger this skill when:

• User wants to process text/files with an LLM
• User needs to choose between multiple available LLMs
• User wants interactive conversation with an LLM
• User needs to pipe content through an LLM for processing
• User wants to use specific model aliases (e.g., "claude-opus", "gpt-4o")

Example user requests:

• "Process this file with Claude"
• "Analyze this text with the fastest available model"
• "Start an interactive chat with OpenAI"
• "Use Gemini to summarize this document"
• "Chat mode with my local Ollama instance"

Supported Providers & Models

OpenAI

• Latest Models (2025):
  • gpt-5 - Most advanced model
  • gpt-4-1 / gpt-4.1 - Latest high-performance
  • gpt-4-1-mini / gpt-4.1-mini - Smaller, faster version
  • gpt-4o - Multimodal omni model
  • gpt-4o-mini - Lightweight multimodal
  • o3 - Advanced reasoning
  • o3-mini / o3-mini-high - Reasoning variants

Aliases: openai, gpt

Anthropic

• Latest Models (2025):
  • claude-sonnet-4.5 - Latest flagship model
  • claude-opus-4.1 - Complex task specialist
  • claude-opus-4 - Coding specialist
  • claude-sonnet-4 - Balanced performance
  • claude-3.5-sonnet - Previous generation
  • claude-3.5-haiku - Fast & efficient

Aliases: anthropic, claude

Google Gemini

• Latest Models (2025):
  • gemini-2.5-pro - Most advanced
  • gemini-2.5-flash - Default fast model
  • gemini-2.5-flash-lite - Speed optimized
  • gemini-2.0-flash - Previous generation
  • gemini-2.5-computer-use - UI interaction

Aliases: google, gemini

Ollama (Local)

• Popular Models:
  • llama3.1 - Meta's latest (8b, 70b, 405b)
  • llama3.2 - Compact versions (1b, 3b)
  • mistral-large-2 - Mistral flagship
  • deepseek-coder - Code specialist
  • starcode2 - Code models

Aliases: ollama, local

Workflow Overview

User Input (with optional model)
    ↓
Check Available Providers (env vars)
    ↓
Determine Model to Use:
  - If specified: Use provided model
  - If ambiguous: Show selection menu
  - Otherwise: Use last remembered choice
    ↓
Load/Create Config (~/.claude/llm-skill-config.json)
    ↓
Detect Input Type:
  - stdin/piped
  - file path
  - inline text
    ↓

Features

1. Provider Detection

• Checks environment variables for API keys
• Suggests available LLM providers on first run
• Detects: OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY, OLLAMA_BASE_URL

2. Model Selection

• Accept model aliases (gpt-4o, claude-opus, gemini-2.5-pro)
• Accept provider aliases (openai, anthropic, google, ollama)
• Interactive menu when selection is ambiguous
• Remembers last used model in ~/.claude/llm-skill-config.json

3. Input Processing

• Accepts stdin/piped input
• Processes file paths (detects: .txt, .md, .json, .pdf, images)
• Handles inline text prompts
• Supports multimedia files with appropriate encoding

4. Execution Modes

Non-Interactive (Default)

Interactive Mode

5. Configuration

Persistent config location: ~/.claude/llm-skill-config.json

Implementation Details

Core Files

• llm_skill.py - Main skill orchestration
• providers.py - Provider detection & config
• models.py - Model definitions & aliases
• executor.py - Execution logic (interactive/non-interactive)
• input_handler.py - Input type detection

Key Functions

detect_providers()

• Scans environment for provider API keys
• Returns dict of available providers

get_model_selector(input_text, provider=None)

• Returns selected model, showing menu if needed
• Respects last_model config preference

load_input(input_source)

• Handles stdin, file paths, or inline text
• Returns content string

execute_llm(content, model, interactive=False)

• Calls llm CLI with appropriate parameters
• Manages stdin/stdout for interactive mode

Usage in Claude Code

When user invokes this skill, Claude should:

1. Parse input for model specification (e.g., --model gpt-4o)
2. Call skill with content and optional model parameter
3. Wait for provider/model selection if needed
4. Execute and return results
5. For interactive mode, maintain conversation loop

Error Handling

• If no providers available: Suggest installing API keys
• If model not found: Show available models for chosen provider
• If llm CLI not installed: Suggest installation via pip install llm
• If file not readable: Fall back to treating as inline text

Configuration

Users can pre-configure preferences:

Slash Command Integration

Support /llm command: