470 boosters for "sis" — open source, verified from GitHub, ready to install
In Memoria is a persistent intelligence infrastructure MCP server that enables AI agents and developers to build codebase-aware applications with semantic search and pattern learning capabilities. It's designed for developers building AI-powered tools and agents that need long-term memory and code understanding.
Recall MCP Server enables persistent, cross-session memory for Claude and AI agents through Redis/Valkey or managed cloud hosting, allowing AI systems to retain and retrieve context across conversations. Developers building Claude applications, multi-turn agents, and AI systems requiring long-term context management benefit most from this solution.
This skill transforms Claude into an interactive assistant for capturing and analyzing Claude Code's API communications using mitmproxy, a free, open-source HTTPS proxy tool. The skill supports three equally important use cases: 1. Learning & Exploration - Understanding Claude Code's internal workin
"name": "uuv-e2e-accessibility-test", "description": "Integrates the Unified UUV Testing Framework into Claude code to provide BDD (Behavior-Driven Development) assistance for your projects based on accessibility datas.", "name": "Louis Fredice NJAKO MOLOM (https://github.com/luifr10) & Stanley SERV
"name": "mcp-outline", "description": "Connect Claude Code to Outline for document search, reading, creation, and management", "version": "1.10.1",
"name": "digital-marketing-pro", "description": "Plan, execute, and measure digital marketing across all channels. 25 specialist agents handle strategy, SEO, paid ads, content, email, social, PR, analytics, CRO, and agency operations — with brand voice enforcement, quality evaluation, multilingual s
이 항목들은 Anthropic 내부 빌드에서 실행될 때만 포함됩니다. Carefully consider the reversibility and blast radius of actions. Generally you can freely take local, reversible actions like editing files or running tests. But for actions that are hard to reverse, affect shared systems beyond your local environment, or coul
把 Serenity(@aleabitoreddit)的投资逻辑和方法论操作化的分析引擎 —— 不是模仿他说话,是用他怎么提问、怎么排除、怎么把热闹拆成可验证环节的方式,帮用户分析一只票/一个板块/一个 thesis。 1. 把市场当物理系统,不当 ticker feed。 不上来就甩代码。 2. 别问"这票能买吗",要问"该查哪一层" —— 前者等答案,后者建立判断。
"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",
Matchms is a Python library for mass spectrometry data processing, enabling researchers to load, standardize, and analyze spectral data from multiple formats (mzML, MGF, MSP) with similarity matching for metabolomics and compound identification workflows.
This MCP server bridges AI assistants with Linear project management via GraphQL, enabling Claude and Cursor users to query and manage Linear projects directly through natural language. It's essential for teams using Linear who want AI-assisted workflow automation and issue management.
"description": "Hand off context to any AI coding agent. Creates comprehensive handoff documents that capture the current task, progress, modified files, and all relevant context needed to seamlessly continue work in any AI assistant (Claude Code, Cursor, Copilot, Aider, etc.).", "name": "willseltze
Use this skill when the work starts from and the goal is to produce approved . 1. Read the current and page inventory. 2. Group pages by business purpose, not just URL shape.
Cursor rules for refactoring Strava Home Assistant integration to use consolidated sensors per activity type, remove external dependencies, and add device tracking. Useful for Home Assistant developers and Strava integration maintainers.
Run this bash block first, before any analysis: This is the first time clearshot is running — no config exists yet. Before doing any analysis, tell the user to run the onboarding setup. Say something brief like: "clearshot needs a quick first-run setup (two questions, arrow keys + enter):"
An MCP Server that integrates Alibaba Cloud DevOps API capabilities into AI assistants, enabling automated participation in development workflows and team collaboration optimization. Useful for teams using Alibaba Cloud infrastructure who want AI-assisted DevOps processes.
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.
你是一位专业的股票分析师，通过 Python 脚本获取真实市场数据，结合技术分析和消息面，为用户生成决策看板。 脚本支持分级降级策略，零配置即可运行，配置 API Key 后数据更精准： 3. 执行（先尝试直接运行，加 --news 可同时搜索新闻）：
ALWAYS use when: creating/editing marimo notebooks, working with any .py file containing @app.cell decorators, building reactive Python notebooks, doing exploratory data analysis in notebook form, converting Jupyter (.ipynb) to marimo, or when user mentions "marimo", "reactive notebook", or asks for an interactive Python notebook. Covers marimo CLI (edit, run, convert, export), UI components (mo.ui.*), layout functions, SQL integration, caching, state management, and wigglystuff widgets. If a task involves notebooks and Python, invoke this skill first.
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
Analyze, review, and provide recommendations for distributed system designs. Use when: (1) Reviewing existing system architectures for gaps or improvements, (2) Analyzing system designs for scalability, reliability, or performance issues, (3) Providing recommendations on load balancing, caching, databases, sharding, replication, messaging, rate limiting, authentication, resilience, or monitoring, (4) Assessing trade-offs in system design decisions, (5) Creating system design review documents with gaps and recommendations. Triggers: "review my system design", "analyze this architecture", "what are the gaps", "system design recommendations", "scalability review", "reliability analysis".
DyNote 是面向 Codex 这类 Agent 的抖音学习工具，不是一次性摘要器。核心原则是“数据资产先行，学习笔记后置”：先把字幕/转写、评论、元数据和 AI 快读沉淀为可复用资产，再按用户需求生成可追溯的学习笔记、总结和写作材料。默认目标不是字幕工程文件，而是先落一份原始数据包：、、、、、、，并用 归档可复用资产。默认把独立字幕轨或本地自动语音识别转写当作事实主干；当转写密度低、任务需要画面理解或用户只要快速筛选时，再用已登录抖音网页版的“问AI / 识别画面”补充，豆包只作为抖音 AI 不可用时的备用快读或待核验假设。 联网或登录态操作必须先使用 。不要读取、复制或打印 Cooki
Heuristic scoring (no AI key configured).
"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
