Skill UI

A comprehensive Go implementation of ReactiveX for building declarative, type-safe pipelines for asynchronous data streams. It manages complex event sources, handling automatic backpressure, error propagation, and context integration. Ideal for real-time data processing, WebSockets, and event-driven architectures where manual goroutine and channel wiring is complex.

A robust CLI tool for connecting to Lark/Feishu real-time events. It allows streaming, subscribing, and consuming various events (e.g., IM messages, reactions, meeting updates) as NDJSON output. Ideal for developing Lark bots, building real-time message processors, or implementing long-running streaming webhook handlers, especially in AI agent subprocesses.

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 skill guides the end-to-end process of building automated evaluation pipelines for Python applications powered by Large Language Models (LLMs). It teaches how to define evaluation criteria, instrument the application, build golden datasets, and run real-world evaluations using tools like pixie, ensuring the app's logic (not just the LLM) is rigorously tested. Ideal for QA, benchmarking, and improving LLM-based services.

This guide provides proven architectural patterns for building complex, reliable, and scalable workflows using n8n. It covers core patterns like Webhook processing, HTTP API integration, database synchronization, AI agent design, and scheduled tasks. Learn how to select the right pattern and utilize advanced components for robust automation.

This tool acts as an expert prompt engineer, guiding users through a systematic discovery process to create highly structured, production-ready prompts for AI assistants like GitHub Copilot. It covers defining persona, specifying complex tasks, managing context variables, integrating tools, and enforcing strict output standards, ensuring the resulting prompt is comprehensive and actionable for advanced AI agents.

A comprehensive CLI for managing the entire lifecycle of Windows applications. It handles initialization, building, packaging into MSIX, signing, debugging, and advanced UI automation via Microsoft UI Automation. Supports multiple modern frameworks including .NET, C++, Electron, Rust, and Flutter, streamlining the deployment process to the Microsoft Store.

This skill guides the management of CI/CD pipelines within KubeSphere DevOps. It utilizes Kubernetes Custom Resources (CRs) to define, trigger, and monitor complex build workflows, seamlessly integrating with Jenkins concepts in a cloud-native manner. Use it for automating build, test, and deployment processes for applications running on Kubernetes.

Pyzotero is a powerful Python wrapper for the Zotero Web API v3. It enables developers to programmatically interact with Zotero libraries, allowing for CRUD operations on items, collections, tags, and attachments. Use this skill to build robust research automation workflows, manage bibliographic references, export citations (e.g., BibTeX), or perform bulk data processing within a Zotero environment.

This guide provides a comprehensive, reproducible workflow for setting up an Algolia local development environment. It covers creating separate development indices, seeding mock data, and implementing client mocking for unit tests. This process ensures fast, safe iteration cycles, allowing developers to build and test search features without ever touching production data.

Configure robust CI/CD pipelines for Apify Actor builds and deployments using GitHub Actions. This guide covers setting up unit tests on pull requests, running deep integration tests against the live Apify API, and automating the production deployment process upon merge. Ideal for maintaining reliable, high-quality scraping and automation projects.

A comprehensive guide for debugging complex Claude integration issues beyond basic error handling. It covers advanced patterns such as managing inconsistent outputs (temperature control), validating tool use calls, recovering from streaming connection drops, handling max_tokens truncation, and correctly processing image/vision inputs. Essential for building robust, production-grade AI applications with Anthropic's API.