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| Name | Contains | Agent success |
|---|---|---|
maria/fastapiv0.1.0 FastAPI framework with Pydantic v2 patterns, PII sanitisation, and practical workflows Contains: run-check-server Start a FastAPI dev server, verify docs and OpenAPI schema, test endpoints, and run pytest. Use when running, checking, or debugging a FastAPI application. scaffold-project Scaffold a new FastAPI project with an opinionated directory layout, pydantic-settings config, and starter files. Use when creating a new FastAPI application from scratch. | SkillsDocsRules | — |
Multi-module test support framework for Embabel Agent applications providing integration testing, mock AI services, and test configuration utilities | Docs | — |
Interactive Spring Shell-based command-line interface for the Embabel Agent platform, providing terminal interaction, chat sessions, and agent management commands. | Docs | — |
Spring Boot starter providing auto-configuration for Model Context Protocol (MCP) client with Spring WebFlux, enabling reactive AI applications to connect to MCP servers via SSE and Streamable HTTP transports | Docs | — |
Spring AI integration for Azure OpenAI services providing chat completion, text embeddings, image generation, and audio transcription with GPT, DALL-E, and Whisper models | Docs | — |
ONNX-based Transformer models for text embeddings within the Spring AI framework | Docs | — |
Spring Boot Starter for OpenAI integration providing auto-configuration for chat completion, embeddings, image generation, audio speech synthesis, audio transcription, and content moderation models. Includes high-level ChatClient API and conversation memory support. | Docs | — |
Spring Boot-compatible Ollama integration providing ChatModel and EmbeddingModel implementations for running large language models locally with support for streaming, tool calling, model management, and observability. | Docs | — |
Spring Boot auto-configuration module providing observability capabilities for Spring AI vector store operations through Micrometer integration. Automatically configures observation handlers for monitoring, tracing, and logging vector store queries and responses. | Docs | — |
Spring Boot auto-configuration for observability of Spring AI chat model operations through Micrometer metrics and distributed tracing | Docs | — |
Spring Boot auto-configuration for the ChatClient API in Spring AI applications | Docs | — |
Base starter module for the Embabel Agent Framework providing core dependencies for building agentic flows on the JVM with Spring Boot integration and GOAP-based intelligent path finding. | Docs | — |
RAG (Retrieval-Augmented Generation) framework for the Embabel Agent platform providing content ingestion, chunking, hierarchical navigation, and semantic search capabilities | Docs | — |
Discover and Export available Agent(s) as MCP Servers | Docs | — |
Core domain type definitions for the Embabel Agent Framework, providing foundational data classes and interfaces for agent-based AI workflows including content assets, research entities, and person types with Jackson serialization and PromptContributor capabilities. | Docs | — |
Common AI framework utilities for the Embabel Agent system including LLM configuration, output converters, prompt contributors, and embedding service abstractions. | Docs | — |
A2A protocol integration for Embabel Agent Framework enabling agent-to-agent communication | Docs | — |
Spring Boot auto-configuration for chat memory functionality in Spring AI applications | Docs | — |
Spring Boot auto-configuration for AI retry capabilities with exponential backoff and intelligent HTTP error handling | Docs | — |
Spring Boot Starter for building Model Context Protocol (MCP) servers with auto-configuration, annotation-based tool/resource/prompt definitions, and support for STDIO, SSE, and Streamable-HTTP transports | Docs | — |