826 boosters for "Agent" — open source, verified from GitHub, ready to install
This skill helps developers discover and understand all installed plugins by displaying their triggers, commands, and available agents in one place. It's essential for users working with Claude Code who need to explore plugin capabilities without manual searching.
Covalt provides Cursor-specific coding rules inspired by tinygrad principles, guiding developers to write clean, composable, and maintainable code through practical patterns like small functions, context managers, and helper extraction.
"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 customizing Orxa agents across three methods—configuration overrides, YAML files, and custom agent creation—enabling developers to adapt agents to specific workflow needs. Ideal for teams using Claude Code or Claude Desktop who need flexible, governance-focused agent customization.
A voice-controlled multi-agent framework for spawning and orchestrating AI workers across machines via CLI, SDK, and Telegram. Useful for developers building complex distributed AI workflows.
Scout is a specialized agent that rapidly locates relevant files across large codebases using parallel search strategies, helping developers navigate complex projects during feature development, debugging, and refactoring tasks.
UltraThink activates a structured single-agent development methodology using linear task execution and TodoWrite planning, designed for developers building Claude-based agents who need organized, step-by-step task management.
A Cursor IDE rules configuration providing comprehensive development standards and best practices for LLM application development, including project structure guidelines, coding principles, and documentation conventions.
Windsurf Rules for app2agent provides architectural guidance and codebase conventions for a Next.js 15 backend with Supabase integration and a browser extension built with React/Vite. Developers working on this specific full-stack project benefit from structured rules that ensure consistent implementation across auth, landing, and extension modules.
lucid-toolkit provides guidance for efficient multi-agent coordination using schema.org vocabulary and token budget optimization, helping developers structure complex subagent invocations on Claude Code. Best for teams managing resource-constrained agentic workflows.
"name": "silver-bullet", "description": "Agentic Process Orchestrator for AI-native Software Engineering & DevOps. Combines GSD, Superpowers, Engineering, and Design plugins into enforced 20-step (app) and 24-step (DevOps) workflows with 10-layer technical compliance.", "version": "0.13.2",
Node Code Sandbox MCP enables AI agents and LLMs to safely execute JavaScript code, install npm packages, and test implementations in real-time. Developers building coding assistants, automated testing tools, and interactive AI applications benefit from this secure, MCP-compliant execution environment.
This MCP server validates Mermaid diagram syntax and provides grammar checking for diagram code, helping developers ensure their Mermaid diagrams are syntactically correct before rendering. It benefits software engineers, documentation writers, and anyone creating flowcharts, sequence diagrams, or other Mermaid visualizations.
Enables AI agents to connect to the BSV Overlay Network for discovering other agents, advertising services, and exchanging BSV micropayments. Useful for developers building decentralized multi-agent systems with cryptocurrency-based service payments.
A QA-focused agent that creates comprehensive test suites, identifies edge cases, and ensures code quality through systematic testing methodologies. Developers and QA engineers benefit from automated test strategy development and validation across different testing levels.
A project management agent that coordinates AI team members to handle sprint planning, task prioritization, and stakeholder communication. Useful for teams looking to automate PM workflows with AI agents.
Clawgram is a photo-first social network designed for AI agents to share images and interact within a secure, sandboxed environment. It benefits AI developers building multi-agent systems that need safe social collaboration features.
Freshcontext MCP is a real-time web extraction server that provides AI agents with fresh, timestamped data from sources like GitHub, Hacker News, and Y Combinator. Developers building AI applications need current contextual information, making this especially valuable for agents requiring up-to-date web data.
Loom Plan converts user ideas into structured tickets and implementation plans by leveraging memory recall to avoid reinventing solutions. It's useful for developers and teams who want to turn vague requests into actionable, sequenced work items.
"name": "claude-kit", "description": "Configuration factory for Claude Code — 16 skills, 7 agents, 15 stacks, 6 hooks, audit scoring (0-10), and practices pipeline. Bootstrap, sync, audit any project.", "name": "Luis Eiman",
"name": "agentdeals", "description": "MCP server aggregating developer tool deals, free tiers, and startup programs", "main": "dist/index.js",
PrismBench enables developers to create specialized LLM agents through YAML configuration for systematic evaluation of model capabilities using Monte Carlo Tree Search. Useful for ML engineers, researchers, and teams building production LLM systems who need comprehensive benchmarking and evaluation frameworks.
A system prompt for automating browser-based tasks using AI agents with Playwright and Gemini, enabling developers to build automated workflows for web interactions like job searches and email management across multiple AI platforms.
PrismBench enables developers to create specialized LLM agents through YAML configuration for comprehensive benchmarking and evaluation of language model capabilities. Teams building AI evaluation systems and ML testing pipelines benefit from its systematic Monte Carlo Tree Search approach and containerized deployment.
