Which is more popular, Semantic Kernel or Skills?

Skills has more GitHub stars (179,388 vs 28,622).

Which is more actively developed, Semantic Kernel or Skills?

Semantic Kernel had more commits in the last 90 days (59 vs 14).

Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.

Side-by-side comparison of two AI agent tools

Skills is growing faster: +26,013 GitHub stars in the last 30 days vs +166 for Semantic Kernel.
Pick Semantic Kernel for: integrate cutting-edge LLM technology quickly and easily into your apps. Pick Skills for: public repository for Agent Skills.

From GitHub data refreshed daily.

Integrate cutting-edge LLM technology quickly and easily into your apps

Public repository for Agent Skills

+Model-agnostic design supports multiple LLM providers including OpenAI, Azure OpenAI, Hugging Face, and local models
+Enterprise-ready with built-in observability, security features, and stable APIs for production deployments
+Multi-language support (Python, .NET, Java) with comprehensive agent orchestration and multi-agent system capabilities

+Official Anthropic implementation provides reliable, well-tested skill patterns and best practices for Claude AI development
+Extensive collection covering diverse domains from creative tasks to enterprise workflows, offering immediate practical value
+Self-contained modular design allows easy customization and extension of existing skills for specific organizational needs

-Requires significant programming knowledge and understanding of AI agent concepts
-Complex setup and configuration for advanced multi-agent workflows
-Learning curve for mastering the framework's extensive feature set and architectural patterns

-Skills are Claude-specific and may not be directly portable to other AI agents or platforms
-Some skills are source-available only (not open source), limiting modification rights for certain components
-Repository serves primarily as demonstration material, requiring thorough testing before production deployment

•Building enterprise chatbots and conversational AI applications with reliable LLM integration
•Creating complex multi-agent systems where specialized AI agents collaborate on business processes
•Developing AI applications that need flexibility to switch between different LLM providers and deployment environments

•Enterprise teams standardizing AI workflows with consistent document creation, branding, and communication processes
•Developers building Claude-powered applications needing reference implementations for complex multi-step tasks
•Organizations creating custom AI skills who need proven architectural patterns from Anthropic's production implementations

Which is more popular, Semantic Kernel or Skills?: Skills has more GitHub stars (179,388 vs 28,622).
Which is more actively developed, Semantic Kernel or Skills?: Semantic Kernel had more commits in the last 90 days (59 vs 14).
Should I use Semantic Kernel or Skills?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.