writing-react-effects
writing-react-effects is a skill that helps developers write React components with optimized useEffect calls for synchronizing with external systems.
Browse and install thousands of AI Agent skills in the Killer-Skills directory. Supports Claude Code, Windsurf, Cursor, and more.
writing-react-effects is a skill that helps developers write React components with optimized useEffect calls for synchronizing with external systems.
batch-tasks is a task automation skill that orchestrates multiple independent tasks in parallel, designed for overnight or async workflows.
google-apps-script is a cloud-based JavaScript platform for automating Google Workspace services, including Sheets, Docs, Gmail, Drive, and Calendar.
coverage-expansion is a systematic approach to expanding test coverage for the Mycelia Julia package, utilizing environment flags and coverage files to identify gaps and improve accuracy.
🤖 Team onboarding kit for Claude Code AI coding assistant. Pre-configured with agents, skills, slash commands, and MCP integrations for Java 21/Spring Boot WebFlux, Angular, Flutter, PostgreSQL, and
langchain4j-vector-stores-configuration is a setup for configuring vector stores in LangChain4J, used for Retrieval-Augmented Generation applications and semantic search in Java.
validate-parser is a skill that validates parsers within the lib directory, utilizing source code from lib/parser/, lib/crypto/, and lib/source/.
Figment: MCP integration between AI Agent (Claude Code) and Figma. This allows AI agents trained for coding to have similar abilities in a Figma file.
gwt-project-index is a distributed LLM router with load balancing and automatic model distribution, designed to streamline AI coding workflows and spec integration.
A comprehensive Copier template for modern Python projects with quality enforcement, AI-assisted development, Docker support, and extensive tooling. Features TDD workflows, multi-AI integration, and production-ready configurations.
Benchmark Manager is a skill that manages AILANG evaluation benchmarks with features like prompt integration, debugging, and best practices for AI model evaluation.
Observability-guidelines is a set of principles and guidelines for ensuring comprehensive visibility into distributed systems and microservices, promoting modular design and test-driven development