786 boosters for "agent" — open source, verified from GitHub, ready to install
Secure agent runtime with trusted process mediation
Sub-Agents enables parent agents to delegate specialized tasks to child agents with independent prompts, tools, and providers, allowing developers to build modular, hierarchical AI systems that solve complex multi-step problems.
"name": "@cyanheads/mcp-ts-core", "version": "0.2.11", "mcpName": "io.github.cyanheads/mcp-ts-core",
"name": "codeguard-security", "description": "Security code review skill based on Project CodeGuard's comprehensive security rules. Helps AI coding agents write secure code and prevent common vulnerabilities.", "name": "Project CodeGuard",
Memelord is a persistent memory system for AI coding agents that uses vector search and reinforcement learning to help agents learn from past interactions. It's useful for developers building sophisticated coding assistants that need to retain and leverage historical context.
Resource Scout helps developers quickly find existing Claude Code skills and MCP servers from marketplaces and repositories before building custom solutions. It's essential for anyone looking to avoid reinventing the wheel and discover pre-built integrations.
A local SQLite-based command-line task board for AI agents and developers to manage multi-step coding tasks, track progress, and coordinate work across sessions without external dependencies. Ideal for breaking down complex implementations into trackable subtasks with comments and checklists.
You are "TunaCode", a Staff-level software developer agent inside the user's terminal. You are not a chatbot. You are an operational agent: you search, read, write, and execute code. Think step by step. Be direct, neutral, and concise.
uni-cli is a unified command-line interface that enables AI agents to seamlessly interact with 25+ services (messaging, productivity, research, utilities) through a consistent pattern. Developers and AI builders benefit from simplified multi-service integration without learning individual APIs.
Manage tasks and dependencies with Tracer CLI. Use for issue tracking, dependency management, finding ready work, and AI agent workflows.
Automates GitHub issue triage and fixing by fetching issues, spawning AI sub-agents to implement solutions, opening PRs, and handling review feedback—ideal for maintainers and teams managing high-volume bug backlogs.
Automatically captures session learnings, decisions, and context into markdown files to help future agents quickly understand prior work and decisions. Developers and teams benefit from persistent knowledge transfer across work sessions.
"name": "prism-mcp-server", "mcpName": "io.github.dcostenco/prism-mcp", "description": "The Mind Palace for AI Agents — persistent memory (SQLite/Supabase), behavioral learning & IDE rules sync, multimodal VLM image captioning, pluggable LLM providers (OpenAI/Anthropic/Gemini/Ollama), OpenTelemetry
Heuristic scoring (no AI key configured).
"name": "@ticktockbent/charlotte", "description": "Token-efficient browser MCP server — structured web pages for AI agents, not raw accessibility dumps", "main": "dist/index.js",
Ag Bridge is a LAN-only MCP server that provides agent supervision capabilities for Claude Desktop and Claude Code, enabling developers to monitor and control AI agent behavior in local environments.
A concurrent sub-agent executor that transparently parallelizes independent tool calls in agentic workflows, enabling faster multi-step operations without explicit LLM coordination. Developers building multi-agent systems in Claude Code or Claude Desktop benefit from improved performance and simplified agent orchestration.
"description": "Sanity plugin for Claude Code with MCP server, agent skills, agent rules, and slash commands.", "email": "support@sanity.io", "url": "https://www.sanity.io"
"name": "@paretools/jvm", "version": "0.16.1", "mcpName": "io.github.Dave-London/pare-jvm",
"name": "@paretools/bazel", "version": "0.16.1", "mcpName": "io.github.Dave-London/pare-bazel",
VT.ai provides Copilot-specific coding instructions for a multimodal AI chat application, establishing standards for Python development including naming conventions, style guides, and testing practices. Developers building AI-powered features with language models will benefit from these standardized guidelines.
PSI is a structured Plan-Spec-Implement workflow that guides developers through documentation-first development with test-driven implementation. It benefits teams wanting disciplined, traceable development processes with clear artifact generation.
You are an expert in prompt engineering and systematic application of prompting frameworks. Help users transform vague or incomplete prompts into well-structured, effective prompts through analysis, dialogue, and framework application. When a user provides a prompt to improve, analyze across dimensi
MCP Server that enables AI agents to analyze and auto-optimize Linux kernel schedulers using eBPF, helping systems engineers improve performance through intelligent workload profiling and optimization strategies.
