953 boosters for "agent" — open source, verified from GitHub, ready to install
BioMCP is an MCP server enabling agent-based biomedical research and development through Claude integration. It benefits biomedical researchers and developers automating computational biology workflows.
"description": "A curated collection of AI coding agent skills for browser automation, frontend design, performance auditing, task tracking, and structured problem-solving workflows.", "repository": "https://github.com/wunki/amplify", "browser-automation",
Agentic MCP enables AI assistants to interact with MCP servers through socket-based communication, allowing discovery and execution of external tools with progressive disclosure. Developers and AI builders benefit by gaining structured access to MCP server capabilities without manual integration.
Autarch Agent System is a multi-agent architecture that orchestrates AI workflows across scoping, research, planning, execution, and review stages, enabling developers to leverage AI for structured, traceable code generation and codebase analysis at scale.
"name": "claude-cognis", "description": "Persistent memory across Claude Code sessions using Cognis", "email": "support@lyzr.ai"
Skill-Based Agents provides documentation for 6 autonomous agent skills (archon-manager, framework-orchestrator, multi-agent-architect, codex-review-workflow, design-system-architect, security-architect) that handle strategic coordination, orchestration, and governance across projects. Developers building multi-agent systems or requiring automated governance workflows benefit from understanding these reusable agent patterns.
Goals is a Rust-based agentic CLI that enables Claude to delegate isolated tasks to subagents via the `invoke_subagent` tool, keeping the main agent's context lean and focused. Developers building multi-step AI workflows benefit from cleaner task decomposition and reliable single-request subagent execution.
The API Agent designs, implements, and manages APIs with best practices for consistency, scalability, and developer experience. Backend developers and API architects benefit from automated API lifecycle management and specification generation.
The Data Agent is a specialized assistant that handles database operations, data modeling, schema design, and quality assurance across projects. It benefits developers, data engineers, and architects who need expert guidance on data management workflows and optimization.
"name": "genomic-agent-discovery", "description": "AI agents that collaborate to analyze your DNA. 20+ MCP tools, 16 databases, real-time dashboard. Runs locally.", "main": "src/cli.mjs",
"name": "inclusive-design-skills", "description": "Agentic skills for designing accessible, inclusive products — from cognitive accessibility to adaptive interfaces, inclusive research, and accessibility decision-making.", "cognitive-accessibility",
Parallel Search MCP enables AI agents to perform concurrent web searches through a standardized MCP server interface. Developers building AI applications on Claude Desktop, Claude Code, or Cursor benefit from faster, more efficient information retrieval for their agents.
"name": "arkhe-claude-plugins", "description": "Supercharge Claude Code with 118 specialized components — from deep reasoning and autonomous dev loops to DDD architecture, design system enforcement, and git workflow automation. 29 agents, 33 commands, 56 skills across 14 modular plugins." "source":
"name": "adcp-client", "description": "Adds the /adcp skill for calling AdCP advertising agent tools over MCP or A2A, running protocol compliance scenarios, and querying the AdCP registry. Includes built-in test agents for zero-config use. Backed by the npm package (formerly ).", "name": "AdCP Comm
"name": "scaffolding", "description": "11 agents, 35 skills, 18 commands, 9 hooks — spec-driven multi-agent orchestration for Claude Code, with optional cross-device semantic memory.", "url": "https://github.com/komluk"
DynamicEndpoints Autogen_mcp enables creation and management of collaborative AI agents that solve problems through natural language interaction. Developers and AI teams benefit from orchestrating multi-agent workflows across Claude Desktop, Claude Code, and Cursor.
"name": "agentic-jumpstart-marketplace", "name": "Agentic Jumpstart", "email": "webdevcody@gmail.com"
Docs & Links automates documentation consistency checks, Markdown linting, and link validation to keep repository documentation in sync with actual code. Teams managing multiple projects benefit from automated enforcement of documentation quality standards.
A comprehensive guide to building sophisticated AI agents using the Robota SDK, covering architecture patterns, tool integration, and best practices. Developers building multi-agent systems and complex AI workflows benefit from its advanced techniques and reusable patterns.
"description": "LLM observability tooling for agent development and Claude Code", "url": "https://comet.com" "repository": "https://github.com/comet-ml/opik-claude-plugin",
A Windsurf-specific coding guidelines booster that enforces Python best practices including documentation standards, absolute imports, early returns, and design patterns for maintainable code. Ideal for Python developers using Windsurf who want to maintain consistent code quality across their projects.
AgentAsJudge is an agentic evaluation framework that enables AI to systematically assess and compare the quality of multiple-choice questions across educational value, clarity, and answerability. It benefits educators, content creators, and assessment teams looking to automate quality control of exam and quiz questions.
"name": "enterprise-team", "displayName": "Enterprise Team - Hire a Whole Company", "description": "Complete virtual company with 76 specialized AI agents across Engineering, Product, Infrastructure, Data, Security, Marketing, Sales, Finance, Legal, and People. Pipeline modes (Full/Sprint/Micro), De
AgentAsJudge is an agentic evaluation framework that enables AI systems to critically review educational introductions by validating them against specified quality metrics and providing constructive feedback. It benefits educators, instructional designers, and developers building AI-assisted learning platforms who need reliable, fair assessment of educational content.
