Skill UI

A comprehensive, generics-first utility library for Go, providing over 500 type-safe, functional helpers (like Map, Filter, Reduce, GroupBy) for slices, maps, and channels. Designed to replace manual, verbose loops with declarative code. It supports immutable operations by default and offers progressive complexity variants (lo, lop, lom, loi) to handle different performance needs, from standard transforms to CPU-bound parallelism and lazy iteration. Ideal for writing clean, maintainable, and performant Go code.

This CLI alias implements the PUA Loop protocol, designed for running structured, iterative verification cycles. It helps developers set up and manage complex coding tasks that require explicit verification commands and defined stop conditions. It serves as a powerful wrapper for AI coding assistants like Codex/Claude, ensuring robust and traceable development workflows.

This skill provides comprehensive management for annotation schemas (configs) and human review workflows (queues) within the Arize platform. It supports defining categorical, continuous, and freeform label types. Users can programmatically apply these annotations to project spans and datasets using the Python SDK, facilitating structured human feedback loops and improving model monitoring.

A comprehensive skill guide for building complex, feature-rich web games and game engines using modern web technologies like HTML5, Canvas, WebGL, and JavaScript. Covers full 2D and 3D game development workflows, including implementing the game loop, physics simulation (collision detection, Newtonian physics), handling diverse input methods (touch, gamepad), managing assets, and integrating multiplayer features via WebRTC. Ideal for creating everything from simple platformers to advanced 3D simulations.

A comprehensive guide detailing best practices for building, reviewing, and deploying Salesforce Flows. This skill covers selecting the correct Flow type (e.g., Screen, After-Save), ensuring bulk safety by preventing DML/SOQL in loops, implementing robust fault handling paths, and performing automation density checks to ensure scalable and resilient automation.

Provides patterns and techniques for building robust evaluation loops for AI agents. This skill guides the implementation of self-critique, evaluator-optimizer pipelines, and test-driven refinement workflows. Use it to significantly enhance the accuracy and quality of complex outputs like code, reports, and analyses by implementing structured, iterative improvement cycles (Generate → Evaluate → Critique → Refine).

This skill provides a comprehensive framework for optimizing, improving, and debugging Large Language Model (LLM) prompts. It leverages production trace data, system evaluations, and human annotations to extract prompts from spans. Users can run a data-driven optimization loop, ensuring the AI responses meet desired quality standards. Ideal for prompt tuning, system prompt refinement, and improving overall AI output quality.

A Socratic mentoring workflow that coaches junior developers and AI newcomers through questions, encouraging reflection, Copilot-assisted loops, and safe learning practices without handing out solutions.

This micro-prompt guides AI agents to adopt the mindset of an interactive programmer. It emphasizes using the REPL (Read-Eval-Print Loop) for systematic system exploration and modification, treating the system state as the ultimate source of truth. It also reminds the agent to utilize structural editing tools and maintain todo lists, significantly improving the accuracy and efficiency of the development workflow in live coding environments.

PyTorch Lightning is an advanced deep learning framework built on PyTorch. It simplifies the entire ML workflow, allowing users to organize models into LightningModules, automate training loops using the Trainer, and manage complex data pipelines with DataModules. It supports advanced features like multi-GPU/TPU scaling (DDP, FSDP), integrated logging (W&B, MLflow), and custom callbacks, making large-scale neural network development efficient and reproducible.

This guide details setting up a robust, local development loop for Adobe integrations (Firefly, PDF, Photoshop). It covers using the `aio` CLI for App Builder, implementing hot reload, and establishing unit/integration testing with mocking to ensure rapid, reliable iteration cycles for Adobe APIs.

A comprehensive guide to setting up a full-cycle local Web3 development workflow. This skill utilizes Alchemy as the RPC provider, Hardhat for local unit testing, and integrates Mainnet forking and Sepolia testnet deployment. It covers initializing the project, configuring smart contract interactions, and implementing advanced testing patterns.