llm
Multi-provider LLM integration. Unified interface for OpenAI, Anthropic, Google, and local models.
Best use case
llm is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Multi-provider LLM integration. Unified interface for OpenAI, Anthropic, Google, and local models.
Teams using llm should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/llm/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How llm Compares
| Feature / Agent | llm | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Multi-provider LLM integration. Unified interface for OpenAI, Anthropic, Google, and local models.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# LLM 🔮 Multi-provider Large Language Model integration. ## Supported Providers - OpenAI (GPT-4, GPT-4o) - Anthropic (Claude) - Google (Gemini) - Local models (Ollama, LM Studio) ## Features - Unified chat interface - Model comparison - Token counting - Cost estimation - Streaming responses ## Usage Examples ``` "Compare GPT-4 vs Claude on this task" "Use local Llama model" "Estimate tokens for this prompt" ```
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