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Found 103 Skills
Performance optimization rules for THREE.js and graphics programming. Covers mobile-first optimization, fallback patterns, memory management, render loop efficiency, and general graphics best practices for smooth 60fps experiences across devices.
Use when app feels slow, memory grows over time, battery drains fast, or you want to profile proactively - decision trees to choose the right Instruments tool, deep workflows for Time Profiler/Allocations/Core Data, and pressure scenarios for misinterpreting results
Flutter DevTools, Profiling, Logging & Memory Management
recon when mapping the current codebase, refreshing an earlier map, or re-orienting after repository changes.
The session loop that makes agentmemory pay off, recall before starting work, save at decision points, learn from corrections. Use when starting a nontrivial task, after settling a decision or debugging a gotcha, or whenever deciding if something belongs in memory.
Write, review, or improve UIKit code following best practices for view controller lifecycle, Auto Layout, collection views, navigation, animation, memory management, and modern iOS 18–26 APIs. Use when building new UIKit features, refactoring existing views or view controllers, reviewing code quality, adopting modern UIKit patterns (diffable data sources, compositional layout, cell configuration), or bridging UIKit with SwiftUI. Does not cover SwiftUI-only code.
Build AI agents with tools, memory, and multi-step reasoning - ChatGPT, Claude, Gemini integration patterns
Retrieve relevant episodic context from memory for informed decision-making. Use when you need past episodes, patterns, or solutions to similar tasks.
The basics of how to program GPUs using Mojo. Use this skill in addition to mojo-syntax when writing Mojo code that targets GPUs or other accelerators. Use targeting code to NVIDIA, AMD, Apple silicon GPUs, or others. Use this skill to overcome misconceptions about how Mojo GPU code is written.
Post-task review. Extract learnings, classify, write to memory layers, and reconcile GitHub issues.
Guidelines for backend development in C++ and Elixir/Phoenix, covering modern language features, best practices, and production-ready patterns.
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.