Popular GitHub Repositories for Yaml Validation

structured outputs for llms

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

Evidently is ??an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.

? Open-Source Evaluation & Testing library for LLM Agents

? Open-Source Evaluation & Testing for AI & LLM systems

Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.

A high-level machine learning and deep learning library for the PHP language.

A multilingual dialog corpus

Zero-boilerplate REST API framework for PHP 8+. Auto-generates routes, docs, validation.

Automatically find issues in image datasets and practice data-centric computer vision.

AI 开发者的 AI 工具包

Perception toolkit for sim2real training and validation in Unity

Claude Code Skill for browser automation with Playwright. Model-invoked - Claude autonomously writes and executes custom automation for testing and validation.

jsontt ? - AI JSON Translator with GPT + other FREE translation modules to translate your json/yaml files into other languages ? Check Readme ? Supports GPT / DeepL / Google / Bing / Libre / Argos

The AI toolkit for the AI developer

? Autonomous AI development with 19 specialized sub-agents coordinated through Claude Code. Production-ready applications from natural language requests with browser validation.

The toolkit to test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data for labeling.

This methodology provides a structured approach for collaborating with AI systems on software development projects. It addresses common issues like code bloat, architectural drift, and context dilution through systematic constraints and validation checkpoints.

A chatbot framework written in Go. All configurations are made in YAML files, or inside scripts written in your favorite language.