199 boosters for "LLM" — AI-graded, open source, ready to install
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.
This booster automates reconnaissance of LLM API endpoints to identify models, authentication methods, and configuration details for security testing. Red team operators and security researchers benefit from structured enumeration workflows.
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 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.
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 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.
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.
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.
HoloLoom is a neural decision-making system prompt integrating prompt management, analytics, and Thompson Sampling optimization for LLM applications. Developers working with multi-agent systems and prompt optimization would benefit from its structured approach to prompt selection and performance tracking.
gllm is a system prompt that transforms LLMs into code-first orchestrators for efficient task processing, enabling developers to handle large files and complex workflows through programmatic verification rather than context-heavy text generation.
mybin is a personal collection of 65+ production-ready CLI utility scripts (Python, Bash, Ruby) for AI/LLM integration, system utilities, and development tasks that Claude Code can proactively leverage. Developers with Unix-style workflows and frequent CLI automation needs benefit from instant access to tested, reusable tools.
Windsurf Rules for OpenCap Stack provides coding standards and LLM-aligned prompts to guide developers on test-driven, production-ready Python/FastAPI backend development. Developers working on OpenCap Stack projects and AI coding assistant users benefit from these standardized guidelines.
An educational system prompt teaching the anatomy and best practices of production-grade LLM system prompts through structured examples like Stanford's Biomni agent. Useful for developers and students building custom AI agents and assistants.
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.
A JavaScript-based system for dynamically creating and modifying AI agents without recompilation, enabling local LLM developers to test and share agent configurations interactively.
Aura is a system prompt that instructs Claude to write code as an expert Lx compiler interface, enforcing LLM-friendly language rules like exhaustiveness, no loops, and explicit effects. It benefits developers learning or writing Lx code who need consistent, compiler-compatible output from Claude.
A system prompt for financial analysis and workflow orchestration designed to enable AI assistants (Claude, ChatGPT) to function as institutional finance engines with structured reasoning and tool integration capabilities.
This MCP server integrates ResourceSpace digital asset management (DAM) with Claude and Cursor, enabling AI assistants to query, manage, and organize digital assets directly. It benefits teams managing large media libraries who want to leverage AI for asset discovery and workflow automation.
MESSENGERMIKE is a notification agent that sends iMessage alerts to a specific phone number via the CIRCE framework. It's designed for developers who need to integrate Mac-based notifications into autonomous agent workflows.
An MCP server that integrates llmstxt documentation fetching into Claude Desktop and Claude Code, enabling developers to access and reference documentation within their AI-assisted workflows.
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.
MESSENGERMIKE is a notification agent that sends iMessage alerts to a specific phone number via the CIRCE framework. It's designed for developers who need automated notifications during code execution workflows.
An MCP server that provides unified control over multiple development servers (Django, Vue, Celery, etc.) through a TUI interface, enabling LLM-assisted development workflows in Copilot. Ideal for developers managing complex local environments who want programmatic server control integrated with AI assistants.
