Skill UI

A robust, production-tested pattern for coordinating multiple specialized AI agents. The orchestrator decomposes complex tasks, routes them to the most suitable specialist, prevents redundant or conflicting work (anti-duplication), and enforces mandatory quality gates (e.g., running tests, checking file diffs) to verify results before marking a task as complete. It also includes heartbeat monitoring for task progress.

A conversational onboarding skill designed to deeply understand the user and define the core identity of an AI partner. Through adaptive, multi-phase dialogue, it extracts key behavioral traits, motivations, and relationship dynamics, culminating in the generation of a comprehensive SOUL.md file that governs the AI's personality and interaction style. Use this skill when setting up, personalizing, or updating your AI agent.

This skill provides comprehensive interaction with the DeerFlow AI agent platform via its robust HTTP API. It is designed for managing complex, multi-step AI workflows, including deep academic research, code execution, web browsing, and file analysis. Users can initiate and continue conversation threads, manage agent memory, upload documents (PDF, etc.), and delegate complex research tasks to the autonomous agent system.

A disciplined workflow that forces you to build in thin vertical slices, test, verify, and commit each working increment before moving on, ideal for multi-file features or large refactors.

This skill guides the creation and editing of Obsidian Bases (.base files). It enables users to build dynamic, database-like views of notes by applying complex filters (AND, OR, NOT), defining computed properties using formulas, and configuring multiple views (table, cards, list). Use this when you need to aggregate, filter, or summarize information across multiple notes in a structured way, simulating a lightweight, navigable knowledge database.

This skill automates the comprehensive localization and translation of an entire MkDocs documentation suite. It translates all Markdown files from source English folders into a specified target language, preserving the exact folder structure and Markdown formatting. Additionally, it updates include references and modifies the core mkdocs.yml configuration file to properly support the new locale, ensuring a seamless multilingual documentation setup.

A structured planning system that uses markdown files (task_plan.md, progress.md) to manage complex, multi-step projects, research tasks, or workflows requiring extensive steps. It provides robust session recovery and includes file tampering detection, treating all plan contents as immutable data. Ideal for highly structured project management.

This tool automates the creation of robust Playwright end-to-end tests. Given a detailed user scenario, it executes a multi-step process using the Playwright MCP, generating high-quality, executable Playwright TypeScript code. It saves the test file and automatically runs and iterates the test until it passes, ensuring comprehensive functional coverage and reliable validation.

The GitHub Copilot SDK empowers developers to embed advanced, agentic AI workflows directly into their proprietary applications. It provides a programmatic interface to Copilot's production-tested agent runtime, allowing you to define complex agent behaviors, manage tool invocation, process file edits, and implement real-time streaming responses. Ideal for building custom, highly intelligent tools and automating complex tasks across multiple languages (Python, TypeScript, Go, .NET).

This guide provides comprehensive best practices for creating optimized, secure, and efficient multi-stage Dockerfiles. Learn how to separate build environments (builder stage) from minimal runtime environments, significantly reducing image size and attack surface. Topics covered include layer caching optimization, security hardening (non-root users), and using minimal base images for reproducible builds.

A sophisticated, file-based system designed for organizing and tracking complex, multi-step projects. It utilizes structured Markdown files (task_plan.md, progress.md, findings.md) to maintain a complete record of plans, research findings, and progress updates. Ideal for project analysis, workflow management, and ensuring data integrity throughout the planning lifecycle.

This file-based system provides a structured method for organizing and tracking the progress of complex, multi-stage projects or extensive research tasks. It automatically generates and manages key files like `task_plan.md`, `findings.md`, and `progress.md`. It is ideal for users needing to deconstruct, plan, and maintain comprehensive visibility over large, complex workflows, supporting automatic session recovery and integrity checks.