Agent skill · software engineering · davila7
outlines
Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library
Why this skill is useful
Adds executable scripts for structured text generation with type safety and local model support that aren't in public docs.
What it needs
Requires outlines, pydantic, transformers, vllm installed locally. About 7k tokens when loaded. Last updated 2026-08-06. 30,138 stars on the source repository.
What this skill does
Outlines: Structured Text Generation When to Use This Skill Use Outlines when you need to: Guarantee valid JSON/XML/code structure during generation Use Pydantic models for type-safe outputs Support local models (Transformers, llama.cpp, vLLM) Maximize inference speed with zero-overhead structured generation Generate against JSON schemas automatically Control token sampling at the grammar level GitHub Stars: 8,000+ From: dottxt.ai (formerly .txt) Installation Quick Start Basic Example: Classification With Pydantic Models Core Concepts 1. Constrained Token Sampling Outlines uses Finite State Machines (FSM) to constrain token generation at the logit level. How it works: 1. Convert schema (JSON/Pydantic/regex) to context-free grammar (CFG) 2. Transform CFG into Finite State Machine (FSM) 3. Filter invalid tokens at each step during generation 4. Fast-forward when only one valid token exists Benefits: Zero overhead: Filtering happens at token level Speed improvement: Fast-forward through deterministic paths Guaranteed validity: Invalid outputs impossible 2. Structured Generators Outlines provides specialized generators for different output types. Choice Generator JSON Generator Regex Generator Integer/Float Generators 3. Model Backends Outlines supports multiple local and API-based backends. Transformers (Hugging Face) llama.cpp vLLM (High Throughput) OpenAI (Limited Support) 4. Pydantic Integration Outlines has first-class Pydantic support with automatic schema translation. Basic Models Nested Models Enums and Literals Common Patterns Pattern 1: Data Extraction Pattern 2: Classification Pattern 3: Structured Forms Pattern 4: Multi-Entity Extraction Pattern 5: Code Generation Pattern 6: Batch Processing Backend Configuration Transformers llama.cpp vLLM (Production) Best Practices 1. Use Specific Types 2. Add Constraints 3. Use Enums for Categories 4. Provide Context in Prompts 5. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills davila7/prompt-engineering-outlines