guidance
Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework
npx skills add Orchestra-Research/AI-Research-SKILLs --skill guidance --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Guidance: Constrained LLM Generation ## When to Use This Skill Use Guidance when you need to: - **Control LLM output syntax** with regex or grammars - **Guarantee valid JSON/XML/code** generation - **Reduce latency** vs traditional prompting approaches - **Enforce structured formats** (dates, emails, IDs, etc.) - **Build multi-step workflows** with Pythonic control flow - **Prevent invalid outputs** through grammatical constraints **GitHub Stars**: 18,000+ | **From**: Microsoft Research ## Installation ```bash # Base installation pip install guidance # With specific backends pip install guidance[transformers] # Hugging Face models pip install guidance[llama_cpp] # llama.cpp models ``` ## Quick Start ### Basic Example: Structured Generation ```python from guidance import models, gen # Load model (supports OpenAI, Transformers, llama.cpp) lm = models.OpenAI("gpt-4") # Generate with constraints result = lm + "The capital of France is " + gen("capital", max_tokens=5) print(result["capital"]) # "Paris" ``` ### With Anthropic Claude ```python from guidance import models, gen, system, user, assistant # Configure Claude lm = models.Anthropic("claude-sonnet-4-5-20250929") # Use context ma
- When to Use This Skill
- Installation
- Quick Start
- Basic Example: Structured Generation
- With Anthropic Claude
- Core Concepts
- 1. Context Managers
- 2. Constrained Generation
- 3. Token Healing
- 4. Grammar-Based Generation
- 5. Guidance Functions
- Backend Configuration
- Anthropic Claude
- OpenAI
Base installation pip install guidance With specific backends pip install guidance[transformers] # Hugging Face models pip install guidance[llama_cpp] # llama.cpp models
What does the guidance skill do?
Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill guidance --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.