Guidance
技能 已验证 活跃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
To provide developers with fine-grained control over LLM output, ensuring validity, structure, and reducing generation latency for tasks requiring precise formatting and multi-step reasoning.
功能
- Constrained LLM output generation via regex and grammars
- Guaranteed valid JSON, XML, and code generation
- Token healing for natural continuations
- Support for multiple LLM backends (API and local)
- Pythonic control flow for multi-step workflows
- Selection constraints for fixed choices
使用场景
- Generating valid JSON or XML outputs
- Enforcing specific data formats (emails, dates, phone numbers)
- Building structured agent workflows with decision trees
- Reducing LLM latency by constraining generation paths
- Creating reproducible LLM outputs through precise grammars
非目标
- Replacing the core LLM inference engine
- Providing a low-level interface for raw token generation
- Handling the execution of external tools (delegated to user code)
- Managing LLM agent orchestration beyond generation control
Execution
- info:Pinned dependenciesWhile dependencies are listed, specific version pinning and lockfiles are not explicitly detailed in the SKILL.md, relying on standard pip behavior.
安装
请先添加 Marketplace
/plugin marketplace add Orchestra-Research/AI-Research-SKILLs/plugin install AI-Research-SKILLs@ai-research-skills质量评分
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