183 boosters for "llm" — AI-graded, open source, ready to install
An MCP Server that enables Claude to execute SQL queries and interact with databases (PostgreSQL via Sequelize). Developers building AI applications that need database access will find this useful for data retrieval and manipulation tasks.
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.
The MCP TypeScript SDK is a comprehensive toolkit for building and integrating Model Context Protocol servers and clients in TypeScript applications. Developers building AI-powered tools, Claude integrations, or context-aware LLM applications benefit from its standardized approach to managing resources, tools, and prompts.
MCP server that transforms linear AI reasoning into structured, auditable thought graphs
An MCP server that integrates Codex CLI with Claude Code and Claude Desktop, enabling developers to leverage Codex capabilities within Claude's ecosystem for enhanced code generation and AI-assisted development.
The MCP TypeScript SDK enables developers to build and integrate Model Context Protocol servers and clients, allowing applications to provide standardized context to LLMs. It's essential for developers building AI-powered applications that need to expose resources, tools, and prompts in a protocol-compliant way.
An MCP server that automates the installation and setup of other MCP servers, streamlining the process of extending Claude's capabilities. Developers and Claude users benefit by reducing manual configuration overhead when adding new MCP integrations.
An MCP server that provides structured access to adversarial tactics and cyber attack techniques for security research, penetration testing, and AI safety evaluation. Useful for security professionals, red teamers, and AI safety researchers studying attack vectors.
A system prompt that guides LLMs to act as rigorous sustainability and risk analysts for Polkadot governance proposals, evaluating treasury allocations through investment frameworks, fiscal precedent, and accountability mechanisms.
brosh is a Cursor-integrated MCP server that enables AI assistants to capture full-page browser screenshots with scrolling support using Playwright. Developers using Claude in Cursor benefit from automated visual webpage inspection for debugging, testing, and content analysis.
Atlas Guardrails provides context packing and duplicate detection tools to help AI coding assistants manage large codebases efficiently and avoid redundant code generation. Developers working on large projects benefit from cleaner context and reduced code duplication.
The ops agent automates infrastructure management, deployment, and cloud operations for DevOps teams. Developers and DevOps engineers use it to streamline CI/CD pipelines, monitor systems, and maintain production reliability.
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 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.
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.
Geist provides Cursor-integrated coding rules and best practices for Python, SQLAlchemy, logging, and server development using FastAPI and LLM models. It helps developers maintain consistent code standards and proper patterns when building AI-powered applications.
OpenCap Coding Standards v1.0 is a prompt-based guide for developers working on the OpenCap Stack, emphasizing test-driven development, consistency, and LLM-assisted coding for Python/FastAPI backends on Windsurf. It benefits backend developers and AI coding assistant users seeking standardized, production-ready code practices.
A system prompt that enables LLM-based news headline tagging by categorizing stories into People, Topic, and Geography tags with structured JSON output. Useful for developers building news processing pipelines or content classification systems.
An MCP server that enables intelligent context continuation across AI sessions, allowing developers to maintain conversation state and knowledge between separate Claude interactions. Useful for developers building multi-session AI workflows and applications requiring persistent context.
A Windsurf-specific security framework for detecting and testing OWASP LLM Top 10 vulnerabilities in LLM applications, with AWS integration and CI/CD automation. Ideal for security engineers and LLM developers building production-grade applications.
A comprehensive system prompt and structural guide for building production-ready GenAI applications with clear rules, templates, and best practices across multiple AI platforms. Developers building AI agents and structured LLM applications benefit from its emphasis on proper configuration, testing, and code standards.
A comprehensive guide to creating and configuring AI agents in the AgenticAI Core SDK, enabling developers to build autonomous agents with specific roles, LLM decision-making, and reusable tools. Essential for developers building multi-agent applications.
An MCP server that integrates Obsidian knowledge vaults with Claude, enabling AI-powered graph analysis, semantic search, and knowledge management across notes. Ideal for researchers, knowledge workers, and developers who want to leverage AI on their personal knowledge bases.
Intent Kit provides Cursor rules for building hierarchical intent-driven Python workflows with LLMs, enabling developers to create structured, context-aware automation systems. It's useful for developers building complex AI-driven applications that require intent classification and conditional execution.
