367 boosters for "LLM" — open source, verified from GitHub, ready to install
Pulse Radar uses LLM pipeline to transform raw Telegram messages into structured knowledge. Core philosophy: Messages individually are noise; batched extraction reveals patterns. 1. JSON-only output — explicitly state "respond with ONLY JSON"
A clean, LLM-optimized MCP server that enables Claude to browse Reddit posts, search content, and analyze user data directly. Developers and AI builders benefit from seamless Reddit integration in their Claude-powered applications.
An MCP server that integrates Xcode with AI assistants, allowing Claude to interact directly with iOS/macOS projects and understand their structure. Ideal for iOS developers seeking AI-powered code analysis, refactoring, and project navigation.
A PHP SDK for building AI agents with structured outputs and multi-agent orchestration, enabling developers to decompose complex tasks into specialized subagents with isolated contexts and independent execution.
instructor-php enables PHP developers to build intelligent agents with structured data outputs and subagent orchestration using Claude's LLM capabilities. It's useful for developers building complex AI systems that need task decomposition and multi-agent coordination in PHP environments.
The Following is a next.js 15 project, react 19, typescript, shadcn ui, tailwind css project. It is an ai resume builder. It is called "ResumeLM". JSON Fields Structure:
Lint agent configurations before they break your workflow. Validates Skills, Hooks, MCP, Memory, Plugins across Claude Code, Cursor, GitHub Copilot, and Codex CLI. Invoke when user asks to: If not found, install:
An MCP adapter that extends the Pi coding agent to work seamlessly with Claude Desktop and Claude Code, enabling developers to integrate Model Context Protocol capabilities into their AI-powered coding workflows.
This is the universal AI skills library — 204 production-ready skill packages across 13 professional domains with 559 Python automation tools and 12 sample CI/CD workflows. It works with every major AI coding assistant. This is NOT a traditional application. It's a library of self-contained skill pa
A specialized diagnostic tool for data engineers to systematically investigate Airflow DAG failures, identify root causes, and implement prevention strategies. Ideal for complex pipeline debugging scenarios requiring deep analysis beyond basic log inspection.
No database. No vectors. No manual saves. Just an LLM observer that compresses your conversations into prioritised notes, consolidates when they grow, and recovers anything missed. Five layers of redundancy, zero maintenance. ~$0.00/month (using free-tier models). While other memory skills ask you t
Heuristic scoring (no AI key configured).
"name": "sap-skills", "description": "Production-ready skills for SAP development and AI coding assistants", "repository": "https://github.com/secondsky/sap-skills",
An MCP server for conversation history search and retrieval in Claude Code
ContextWeaver is an MCP server that helps LLMs intelligently manage and weave together contextual information for more coherent responses. It's useful for developers building Claude integrations who need sophisticated context handling across multi-turn conversations.
Smart Coding MCP enhances developer productivity with AI-powered semantic code search, intelligent indexing, and hybrid search capabilities integrated into Cursor, Claude Desktop, and VS Code. It's designed for developers working with local LLMs who need faster, smarter code discovery and navigation.
"version": "0.10.0", "description": "AI agents on autopilot - define in markdown, run on cron, CI/CD, or serverless", "license": "Apache-2.0",
You are an expert Pythonista. You are familiar with the Python data science stack. You are also an expert in prompting LLMs to do stuff. 1. Create a macro in macros.html with clear parameter documentation
Gollem is a Cursor rules booster that establishes development standards and restrictions for Go-based agentic AI applications using MCP, ensuring consistent code quality, English-only documentation, and proper testing practices.
A comprehensive Go testing booster that teaches TDD methodology, table-driven tests, benchmarking, and fuzzing patterns for Claude Code users. Ideal for Go developers aiming to write reliable, well-tested code following idiomatic Go practices.
用户在学 / 接触一个新概念 X（新技术、新算法、新理论、新领域……），尤其觉得"陌生 / 有点难"的时候。难，往往不是智商问题，是它相对用户还存在"没接上的旧知识"。 用一两句话说清 X 到底在干什么——它的核心机制 / 结构是什么。剥掉术语外壳，留下"它本质是一个 "。只有先拿到结构，才能去匹配用户学过的东西。 拿不准就直接问「你学过 吗？」，绝不从正在讲的材料 / 文章作者背景推断用户会什么。
基于 Benjamin Bloom「2 Sigma Problem」研究（1984）的一对一 AI 导师系统。每个课题是一个独立文件夹，通过自适应生成的课程文档 + 用户反馈循环模拟一对一苏格拉底式导师，把学习效果推向 +2σ。学习的主要载体是文档，对话只是辅助确认状态。 所有回复、解释、提问、文档一律使用中文。 触发本 skill 后，以下守则在整个学习交互全程生效——违反字面就是违反精神：
用户说"想学 / 理解 / 搞懂 / 讲讲一个概念 X"时——这是默认入口，一次跑完五视角，用户再选深入哪个。 抓住 X 的本质结构（剥术语），按三猜想给 🎁其实已学过 / 🔗结构同构（字段级对应表）/ 🧩可用已有知识解释，点出元知识。先激发信心，再谈深入。 定位"既定问题"（学 X 解决什么）、现有知识够不够、X 的贬值速度与 ROI，给"够用就停 / 只学最小那块 / 值得深挖"的深度边界。不是劝退，是防止一上来过度钻。
用户要系统进入一个新领域，或焦虑"学得不够系统 / 不知何时算够"。 用户为什么学 X？（接 的"既定问题"）目的决定图谱画到多细。 从入门点出发、沿父子关系排一条有效路径。颗粒度按需自由切换（领域图 → 细分学科图）。"学到哪算够"= 覆盖到能解决第一步那个目的的节点即可，不必学满。
