Skill UI

This guide outlines best practices for building highly flexible and maintainable React components. It addresses common architectural pitfalls, such as 'boolean prop proliferation,' by recommending advanced patterns like compound components, state lifting, and context providers. Use this when refactoring large component libraries, designing robust APIs, or reviewing component architecture, including considerations for React 19 APIs.

RWKV is an RNN+Transformer hybrid architecture with linear-time inference, infinite context, and GPT-style training. It delivers constant-memory, streaming generation on CUDA and CPU, ideal for long-context, memory-constrained deployments.

Suggests manual /compact runs at strategic workflow milestones by tracking PreToolUse calls so Claude retains context without unexpected truncation, with reminders after configurable thresholds.

Provides a structured workflow for defining agent-agnostic subagents with isolated context, covering purpose definition, metadata configuration, prompt writing, and common specialist patterns for verification, debugging, security auditing, and review.

Generates daily asynchronous standup notes by analyzing Obsidian context, Jira tickets, git commits, and calendar events, providing actionable summaries that keep remote teams aligned without synchronous meetings.

Provides production-ready Megatron-Core workflows for training 2B–462B parameter LLMs with tensor/pipeline/expert/context parallelism, maximizing GPU efficiency on H100 clusters.

Provides production-ready TypeScript and Python patterns for TwinMind AI memory and meeting intelligence REST APIs, covering authenticated clients, memory storage/retrieval, meeting context creation, batching, and rate-limit aware retries to standardize integrations.

Guides choosing and implementing Windsurf-validated workspace blueprints for single projects, focused monorepos, and multi-window microservice teams, aligning Cascade contexts, .windsurfignore rules, and migration decisions.

Creates customized Cascade agent configurations for domain-specific tasks using Windsurf tools, covering system prompts, context sources, activation triggers, and validation workflows for specialized AI behavior.

Manages Cascade AI context windows, prioritization, and memory persistence for large projects to keep multi-session navigation, documentation, and workflow optimization aligned.

Automates new Windsurf projects by configuring the Cascade AI agent, creating .windsurfrules, capturing project context, and standardizing team defaults so onboardings stay consistent and efficient.

Windsurf Debugging AI uses Cascade to analyze stack traces, error logs, and context so developers can identify root causes, apply fixes, and document prevention steps during troubleshooting.