Generate structured outputs constrained by Pydantic models, JSON Schema, and regex.
npx skills add https://github.com/JKhyro/HERMES-AGENT --skill outlines-jkhyro
Please help me install this Agent Skill. Skill: outlines Source: https://github.com/JKhyro/HERMES-AGENT/tree/main/skills/mlops/inference/outlines Command: npx skills add https://github.com/JKhyro/HERMES-AGENT --skill outlines-jkhyro
Outlines removes the need to repair or re-ask for malformed model output by generating text that already matches a schema, pattern, or typed object from the start.
It supports Pydantic models, JSON Schema, enums, literals, regex-constrained text, nested structures, and high-throughput structured generation for extraction, classification, form parsing, SQL drafting, and code scaffolding. It is especially useful when you need reliable JSON or object outputs from local models such as Transformers, llama.cpp, or vLLM, while keeping inference fast and avoiding brittle post-processing.
Ask the model to extract your target fields into a structured JSON object that matches a Pydantic model.
High-intent search queries and answers about installing and using this skill.
To generate valid JSON without post-processing, you constrain the model's output using Pydantic models or JSON Schema. This structured generation prevents malformed outputs by ensuring the generated text matches your target schema from the start.
Structured generation forces language models to produce text that matches a specific schema, regex, or typed object. You need it when extracting data, parsing nested forms, or drafting code, ensuring reliable outputs while avoiding brittle repairs or re-asking.
Yes, you can use Pydantic models with vLLM and llama.cpp. The Skill supports backend-specific configurations for these platforms, allowing you to enforce structured outputs across local models while maintaining fast inference speeds.
The best way to enforce regex constraints during transformer inference is to apply structured generation directly within the backend. This guarantees the generated text conforms to your pattern, eliminating invalid outputs and avoiding slow post-processing.
Models output invalid JSON during high-throughput batch generation because they lack inherent structural constraints. By applying JSON Schema or Pydantic constraints before generation, you prevent malformed text and ensure valid structured outputs across all batches.
Structured generation works with both OpenAI API backends and local models. It supports backend-specific configurations for Transformers, llama.cpp, vLLM, and OpenAI, allowing you to enforce valid structured outputs across different inference environments.
jleechanorgConvert unstructured prompts into schema-conformant JSON outputs using Pydantic and JSON Schema.
1Mangesh1Parse and transform JSON and YAML data using jq and yq.
icyzhConstrain LLM token sampling to JSON, regex, or Pydantic schemas.
helix4uGenerate structured text from language models using Pydantic, JSON schemas, or regex.
