All Skills

Constrain LLM output with grammars; guarantee valid JSON.

Outlines: structured JSON/regex/Pydantic LLM generation.

Structured LLM outputs validated with Pydantic.

DSPy: declarative LM programs, auto-optimize prompts, RAG.

Evolve prompts/regex/SQL/code with Imbue's evolution loop.

Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

Asks clarifying questions in multiple rounds before starting ambiguous tasks. Fires when 2+ task dimensions each have 3+ viable answers.

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming

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

Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library

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