Designs provider-neutral LLM applications with structured output validation.
npx skills add https://github.com/stevenke1981/python_skills --skill py-ai
Please help me install this Agent Skill. Skill: py-ai Source: https://github.com/stevenke1981/python_skills/tree/main/py-ai Command: npx skills add https://github.com/stevenke1981/python_skills --skill py-ai
This skill solves the challenge of building production-grade AI applications that are resilient to provider changes, secure against prompt injection, and capable of handling complex orchestration without unnecessary framework bloat.
Use the py-ai skill to design a provider-neutral chat service that validates model output against a Pydantic schema.
High-intent search queries and answers about installing and using this skill.
Build provider-neutral LLM applications by decoupling business logic from specific SDKs using protocols. This architecture allows seamless switching between LLM providers while maintaining strict schema validation for structured outputs.
Validate structured output from LLMs by enforcing strict schema validation against Pydantic models. This ensures model outputs meet reliability requirements for automated workflows without framework bloat.
Pydantic enforces strict schema validation on LLM generated outputs by validating model responses against defined structures. This guarantees reliable production integration and automated workflow consistency.
This approach supports building secure RAG pipelines by implementing robust guardrails including permission filtering, citation verification, and prompt-injection defense. It maintains strict security boundaries during retrieval-augmented generation.
Implement observability and cost control for production-ready AI agents by satisfying strict boundary requirements within the application architecture. This ensures reliable production integration without unnecessary framework bloat.
Structured output validation for local model inference is needed when executing complex workflows requiring reliable automated processing. It guarantees that local outputs conform to strict schemas before downstream consumption.
HouseGarofaloRun local inference, embeddings, and fine-tuning with Hugging Face Transformers.
ivanshamaevExport Apache Airflow scheduler, task, and DAG signals to metrics, traces, and logs.
michaelalberScaffold end-to-end RAG pipelines for local or cloud LLMs.
pluginagentmarketplaceImplements layered defenses for LLM vulnerabilities across input, processing, and output stages.
