Skill UI

A comprehensive guide on using the nacos-cli tool to discover, install, update, merge, and publish AI skills stored in a Nacos registry. It covers key commands for listing, getting, uploading, reviewing, and releasing skills, ensuring version-aware publishing workflows for personal and team use.

Use the provided helper scripts to track and manage OpenCode sessions, list tasks, and inspect messaging status from the workspace; run them via `uv run python skills/opencode/<script>.py` with Python 3 available.

A comprehensive workflow for the full lifecycle management of AI skills. This tool guides users through creating skills from scratch, modifying existing ones, and rigorously benchmarking performance. It supports iterative refinement, quantitative evaluation, and optimizing skill descriptions for maximum triggering accuracy.

This meta-skill guides engineers through the entire software development lifecycle, from initial idea refinement and specification to implementation, testing, code review, and deployment. It emphasizes critical best practices like surfacing assumptions, managing ambiguity, enforcing simplicity, and maintaining scope discipline to prevent common failure modes.

A comprehensive workflow for discovering, integrating, and utilizing third-party APIs and SDKs. This skill guides the process by first detecting the project language, ensuring necessary guidelines and skills are present, systematically fetching API details, and providing structured guidance on authentication, model usage, and endpoint implementation. It tracks integration progress by recording specific development milestones (e.g., SDK setup, first call made).

This comprehensive guide provides a detailed framework for authoring effective and structured SKILL.md files for AI coding agents. It covers best practices for structuring content, including frontmatter fields, defining natural language triggers, establishing core principles, and incorporating advanced elements like decision trees and verification checklists, ensuring agents receive clear, actionable, and consistent technical guidance.

This is the foundational plumbing layer connecting AI agents to the Power Automate platform via FlowStudio MCP. It provides essential capabilities such as secure authentication setup, robust tool discovery (`list_skills`, `tool_search`), and handling complex, oversized API responses. It serves as the prerequisite layer for specialized workflow skills (e.g., building, debugging, monitoring).

Guides teams through creating a complete GitHub Copilot configuration by outlining required instructions, reusable skills, and agent/workflow files tailored to the chosen tech stack and workflow preferences.

Use terminal commands to discover, list, create, edit, move, copy, enable/disable, and delete AI agent skills across 11 tools, handling format conversions and scope overrides between global and project configurations.

This guide maps user questions and topics (e.g., configuration, memory, skills, CLI) to specific, relevant documentation paths and source code entry points within the QwenPaw ecosystem. It is designed for built-in QA Agents to eliminate aimless searching, ensuring the agent reads the most accurate files to provide comprehensive and precise answers about the system's features.

Skillshare provides centralized management and synchronization for AI Command Line Interface (CLI) skills and agents across dozens of AI tools (e.g., Claude, Cursor). It handles the entire skill lifecycle, including installation, updating, syncing, auditing, and complex configuration across both global and project environments. Ideal for developers building complex, multi-target AI workflows and integrating CI/CD pipelines.

Suggests relevant GitHub Copilot skills from the awesome-copilot repo that match current repository patterns and chat history, flags duplicates, and highlights outdated local skills for targeted updates.