SkillKit is a developer-centric toolkit for constructing modular, reusable AI agent skills and integrating them into workflows, platforms, and applications with minimal overhead. It provides a set of abstractions, templates, helper utilities, and patterns that help developers define intents, actions, context handling, memory management, and multi-step logic so that skills can be built once and reused everywhere. Instead of reinventing the wheel every time a new conversational or automation feature is needed, SkillKit encourages engineers to encapsulate logic into coherent skill units that can be registered, tested, and composed together. It supports integration with common agent runtimes and toolkits, allowing skills to be plugged into existing architectures without requiring deep infrastructure rewrites. The kit also includes example skills, documentation on best practices, and mechanisms for handling edge cases such as error states, fallbacks, and contextual switches.
Features
- Modular skill definition framework
- Templates for intents, actions, and context handling
- Memory and state management utilities
- Integration support for agent runtimes
- Example skills and best practice patterns
- Error handling and fallback support