LLM CLI Tool Skill

A CLI tool and Python library for interacting with Large Language Models including OpenAI, Anthropic's Claude, Google's Gemini, Meta's Llama, and dozens of others via remote APIs or locally installed models.

When to Use This Skill

Use this skill when:

• Running prompts against LLMs from the command line
• Managing conversations and chat sessions
• Working with embeddings for semantic search
• Extracting structured data using schemas
• Installing and configuring LLM plugins
• Managing API keys for various providers
• Using templates for reusable prompts
• Logging and analyzing LLM interactions

Quick Reference

Basic Commands

# Run a prompt
llm "Your prompt here"

# Use a specific model
llm -m claude-4-opus "Your prompt"

# Chat mode
llm chat -m gpt-4.1

# With attachments (images, audio, video)
llm "describe this" -a image.jpg

# Pipe content
cat file.py | llm -s "Explain this code"

Key Management

llm keys set openai
llm keys set anthropic
llm keys set gemini

Plugin Management

llm install llm-anthropic
llm install llm-gemini
llm install llm-ollama
llm plugins

Documentation Index

Core Documentation

• README.md - Project overview and quick start guide
• docs/setup.md - Installation and initial configuration
• docs/usage.md - Comprehensive CLI usage guide (prompts, chat, attachments, conversations)
• docs/help.md - Complete command reference and help text

Model Configuration

• docs/openai-models.md - OpenAI model configuration and features
• docs/other-models.md - Configuration for other model providers

Advanced Features

• docs/tools.md - Tool use and function calling with LLMs
• docs/schemas.md - Structured data extraction from text and images
• docs/templates.md - Creating and using prompt templates
• docs/fragments.md - Long context support using fragments
• docs/aliases.md - Creating model aliases

Embeddings

• docs/embeddings/index.md - Embeddings overview
• docs/embeddings/cli.md - Embeddings CLI commands
• docs/embeddings/python-api.md - Embeddings Python API
• docs/embeddings/storage.md - Embeddings storage system
• docs/embeddings/writing-plugins.md - Writing embedding plugins

Plugins

• docs/plugins/index.md - Plugin system overview
• docs/plugins/installing-plugins.md - Installing and managing plugins
• docs/plugins/directory.md - Plugin directory listing
• docs/plugins/tutorial-model-plugin.md - Tutorial: Creating a model plugin
• docs/plugins/advanced-model-plugins.md - Advanced plugin development
• docs/plugins/plugin-hooks.md - Plugin hooks reference
• docs/plugins/plugin-utilities.md - Plugin utility functions

Python API & Development

• docs/python-api.md - Python library API reference
• docs/logging.md - Logging system and SQLite storage
• docs/contributing.md - Contributing to LLM development

Reference

• docs/related-tools.md - Related tools and ecosystem
• docs/changelog.md - Version history and changes

Common Workflows

Starting a Conversation

# Start chat with context
llm chat -m gpt-4.1 -s "You are a helpful coding assistant"

# Continue a previous conversation
llm -c "Follow up question"

Working with Files

# Analyze code
cat script.py | llm "Review this code for bugs"

# Process multiple files
cat *.md | llm "Summarize these documents"

Structured Output

# Extract data with schema
llm -m gpt-4.1 "Extract person info" -a photo.jpg --schema name,age,occupation

Template Usage

# List templates
llm templates

# Use a template
llm -t summarize < article.txt