Constrain LLM output syntax with regex, grammars, and token-level filtering.
npx skills add https://github.com/cxnaive/hermes-agent-llbot --skill guidance-cxnaive
Please help me install this Agent Skill. Skill: guidance Source: https://github.com/cxnaive/hermes-agent-llbot/tree/main/optional-skills/mlops/guidance Command: npx skills add https://github.com/cxnaive/hermes-agent-llbot --skill guidance-cxnaive
This Skill solves the issue of unpredictable LLM output by enforcing strict syntactic constraints, ensuring that generated text always adheres to required formats like JSON, XML, or specific regex patterns.
Use the guidance skill to generate a JSON object representing a person with a name and age using the provided Pydantic schema.
High-intent search queries and answers about installing and using this skill.
You can enforce valid JSON generation by applying regex, grammars, and token-level filtering to constrain the LLM output. This guarantees the generated text strictly adheres to your required format, eliminating the need for error-prone retry loops.
Token healing automatically fixes unnatural token boundaries between prompts and generated text during inference. This mechanism ensures perfect formatting and prevents structural errors in the constrained generation output.
Yes, you can build complex, stateful multi-step agentic workflows using guidance and transformers. This approach leverages Pythonic control flow and context managers to orchestrate deterministic, multi-step text generation.
Local model integration is supported to achieve high-performance deterministic text generation. However, the strict requirement is having the guidance and transformers libraries installed to enforce format compliance and token healing during inference.
The best way to guarantee structured data generation without retry loops is enforcing strict syntactic constraints using grammars and token-level filtering. This proactively ensures valid JSON or XML output deterministically during the inference process.
LLM output fails Pydantic validation because prompt engineering alone cannot guarantee strict syntactic compliance. Applying constrained generation with token-level filtering and grammars deterministically enforces valid JSON structures that perfectly match your schema.
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Clay-HHKEnforce token-level regex and grammar constraints for structured LLM outputs.
