Skill UI

This guide outlines best practices for configuring IDEs and AI context management across complex, multi-service monorepos. It is essential for onboarding new teams, standardizing coding standards, and ensuring consistent development workflows for services across multiple deployment environments (Development, Staging, Production). Focuses on architectural rules, shared settings, and environment-specific deployment context.

This comprehensive guide offers detailed steps to optimize the Windsurf IDE experience. It addresses common performance bottlenecks such as slow indexing, high memory consumption, and inefficient AI context building (Cascade). Users will learn best practices for configuring exclusion patterns, managing focused workspaces, and tuning IDE settings for maximum development efficiency.

This guide outlines the Windsurf reference architecture, providing an optimal project structure for development, configuration, and AI context management. It covers rules hierarchy, workflow organization, monorepo strategies, and context pinning to maximize the effectiveness of AI assistants and development tools.

A comprehensive guide to establishing production-ready coding standards and context for the Windsurf AI environment. Learn how to utilize root-level rules (.windsurfrules), granular workspace rules, Model Context Protocol (MCP) server integration, and advanced prompt engineering to guide the AI and maintain consistent code quality across projects.

Expert skill for building modern, production-ready Chrome Extensions using Manifest V3 standards. Covers advanced topics including service worker management, content script injection, secure cross-context communication (message passing), and implementing best practices like `declarativeNetRequest` and secure storage APIs.

Reasoning-Driven Image Briefs skill transforms agent prompts into logic-based Gemini 3 style instructions for muapi.ai, adding structured creative briefs (subject/action/context/composition/lighting), precise text cues, and physics-grounded constraints before invoking generation scripts.

This skill guides users through a comprehensive, multi-stage process for creating high-quality documentation. It systematically moves through Context Gathering (understanding the background and audience), Refinement & Structure (iteratively building and editing sections), and Reader Testing (simulating external review to catch blind spots). Ideal for drafting complex specifications, proposals, or design documents that need to be robust and clear for a wide audience.

Self-hosted open-source alternative to NotebookLM for organizing research, ingesting diverse sources, generating AI notes, running context-aware chats, performing vector search, producing multi-speaker podcasts, and automating workflows via a REST API.

Hierarchical Agent Memory (HAM) scopes Claude Code agent context by generating per-directory CLAUDE.md files, tracking token savings, and surfacing analytics via a dashboard so multi-folder projects avoid repeated full scans.

This tool is designed for defining and scaffolding production-grade, multi-agent workflows. It allows developers to move beyond single prompts by structuring complex tasks into explicit steps. Core capabilities include selecting workflow patterns (e.g., sequential, parallel, orchestrator), defining handoff contracts, implementing error recovery strategies, and enforcing context and cost discipline across long-running AI processes.

Guides execution of implementation plans by dispatching isolated subagents per task, enforcing a spec-first then quality review loop, and preserving overall coordination context for faster, high-quality iterations.

Creates detailed implementation plans for multi-step tasks by outlining file responsibilities, concrete coding/testing steps, and commit guidance so developers can execute with no prior context.