342 boosters for "agents" — open source, verified from GitHub, ready to install
This booster guides developers through implementing five specialized AI agent personas (composer, arranger, theory assistant, jam session, audio engineer) with tailored tool access and knowledge bases for music creation tasks. It benefits music software developers and AI engineers building multi-agent musical systems.
A system prompt that transforms Claude into 'First Mate,' a proactive assistant for note-taking, task triage, and daily journaling with built-in data conventions and database tool integration. Ideal for developers and knowledge workers seeking to streamline personal productivity workflows across Claude-compatible platforms.
AgentTrust provides identity, trust verification, and secure orchestration for autonomous AI agents communicating with each other (A2A), with built-in protections against prompt injection and human-in-the-loop controls. Developers building multi-agent systems, especially those requiring security and auditability, benefit from its official A2A partnership and MCP integration.
A specialized Copilot prompt that configures an AI agent as an expert assistant for building and deploying ASP.NET Core web APIs and Blazor WebAssembly apps to Google Cloud, with integrated validation and iterative problem-solving capabilities.
A JavaScript-based system for dynamically creating and modifying AI agents without recompilation, enabling local LLM developers to test and share agent configurations interactively.
Documentation resource for Power Automate specialized agents that generates production-ready JSON workflow definitions. Useful for developers and automation engineers looking to create Power Automate flows programmatically.
A developer-facing AI agent guide for PFPT (Physically Fit PT), a .NET 8 MAUI/Blazor clinician documentation app, that provides setup instructions, coding standards, security rules, and exact commands for safe modifications. Developers and AI coding assistants use this to understand the codebase architecture, compliance requirements, and safe change procedures.
Peepit MCP Server enables AI agents to capture and analyze macOS screenshots with smart window targeting and AI-powered image analysis, solving the critical problem of giving Claude visual perception of the desktop environment.
"name": "a2asearch-mcp", "mcpName": "io.github.tadas-github/a2asearch-mcp", "description": "MCP server for searching and discovering AI agents, MCP servers, CLI tools and agent skills via A2ASearch",
A comprehensive guide to configuring and managing AI agents, covering role definition, permissions, monitoring, and escalation workflows. Developers and DevOps teams benefit from learning how to effectively collaborate with AI agents while maintaining governance and control.
Mcp Tool Factory generates MCP servers from natural language, OpenAPI specs, databases, GraphQL, and ontologies, supporting 10+ LLM providers. Developers building AI agents and tool ecosystems benefit from automated server generation without manual boilerplate.
AI Agents provides a framework for designing accountable, self-learning agents that operate within organizations through defined lifecycle stages, interaction patterns, and governance controls. It benefits teams building AI-native organizations who need structured approaches to agent deployment, oversight, and human-AI collaboration.
An expert AI engineer agent that helps developers build production-ready LLM applications, RAG systems, and intelligent agents with deep knowledge of modern AI stacks. Ideal for teams building chatbots, AI-powered features, and enterprise AI integrations.
A practical guide to writing maintainable, debuggable LLM agent code by addressing unique failure modes like non-determinism, opaque tool calls, and prompt-logic coupling. Developers building Claude-based agents will benefit from these patterns.
This booster guides AI agents through a structured process to create comprehensive project specifications autonomously, enabling them to understand scope, architecture, and constraints before implementation. Developers and AI systems benefit by having clear, actionable project blueprints that facilitate autonomous work and reduce ambiguity.
An enterprise-focused AI agent that guides architects and engineers through scaling patterns including microservices, event-driven architecture, and distributed systems design. Ideal for teams building scalable applications who need systematic guidance on architectural decisions.
Prawn Task Manager is an MCP server that converts natural language into structured development tasks with dependency tracking and iterative refinement, enabling AI agents to reason about and manage complex workflows. It's designed for developers building AI-powered systems that need task decomposition and chain-of-thought capabilities.
"id": "ai.artidrop/artidrop", "description": "The publishing layer for AI agents. Turn HTML and Markdown into shareable URLs instantly via MCP."
"id": "ai.roxels/roxels-mcp", "description": "Let AI agents create configurable voice agents via meeting rooms and return structured data.", "url": "https://github.com/trebu-org/interviewer",
Agent Flow Navigator is an MCP server that enables AI agents to traverse and execute complex graph-based workflows and state machines. It's useful for developers building multi-step agentic systems that require structured flow control and orchestration.
"name": "@gpu-bridge/mcp-server", "description": "GPU-Bridge MCP Server — 30 AI services as MCP tools. LLM, image, video, audio, embeddings, reranking, PDF parsing, NSFW detection & more. x402 native for autonomous agents.", "gpu-bridge-mcp": "index.js"
An intelligent agent selection engine that evaluates and recommends the best AI agents for each phase of a development pipeline, enabling teams to optimize task assignment with confidence scoring and human oversight. Benefits development teams managing multi-agent workflows who need data-driven agent selection and performance tracking.
"id": "ai.proofslip/mcp-server", "name": "ProofSlip", "description": "Receipt-based verification for AI agent workflows — create, verify, and poll ephemeral proof objects",
A hierarchical taxonomy (L0-L5) for classifying data agents by autonomy level, helping teams clarify capabilities, set expectations, and allocate responsibility in LLM-powered data systems. Useful for architects, product managers, and developers building or evaluating data agents.
