Agent Skills

Implement proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use when architecting complex backend systems or refactoring existing applications for better maintainability.

:rocket: Serena is a powerful coding agent toolkit capable of turning an LLM into a fully-featured agent that works directly on your codebase. Unlike most other tools, it is not tied to an LLM, framework or an interface, making it easy to use it in a variety of ways. :wrench: Serena provides essential semantic code retrieval and editing tools that are akin to an IDE's capabilities, extracting code

Implement secure API design patterns including authentication, authorization, input validation, rate limiting, and protection against common API vulnerabilities

PAI exists to solve the P0 problem in the world: activating human creative potential through AI. An open-source personal AI infrastructure that makes the best AI accessible to everyone, not just the rich or technical elite.

Archive session learnings, debugging solutions, and deployment logs to .archive/yyyy-mm-dd/ as indexed markdown with searchable tags. Use when completing a significant task, resolving a tricky bug, deploying, or when the user says \"archive this\". Maintains .archive/MEMORY.md index for cross-session knowledge reuse.

Your agent has zero users. This fixes that. An agent-to-agent referral network where AI agents discover each other, cross-refer users, and earn credits. Available as an MCP server and HTTP API. Built by an AI agent that couldn't find its own customers. Published as io.github.oxgeneral/agentnet v1.0.0 You built an agent. It works. Nobody uses it. - 3M+ GPTs on OpenAI — most have zero users - 17,000

🚀 MCP Server Available: Install the Model Context Protocol server for AI Distiller from NPM: @janreges/ai-distiller-mcp - seamlessly integrate with Claude, Cursor, and other MCP-compatible AI tools! /.aid/ regardless of where you run aid - Cache management: MCP cache stored in .aid/cache/ for better organization - Easy cleanup: Add .aid/ to .gitignore to keep outputs out of version control Detect

Lint any URL for LLM readiness. Get a 0-100 score for token efficiency, RAG readiness, agent compatibility, and LLM extraction quality. Context CLI is an LLM Readiness Linter that checks how well a URL is structured for AI consumption. As LLM-powered search engines, RAG pipelines, and AI agents become primary consumers of web content, your pages need to be optimized for token efficiency, structure

Multi-agent AI consultation framework for Claude Code via MCP. Get a second (and third) opinion from other LLMs when Claude Code alone isn't enough. Claude Code is powerful, but one brain can miss bugs, overlook edge cases, or get stuck in a local optimum. Critical decisions benefit from diverse perspectives. Concilium runs parallel consultations with multiple LLMs through standard MCP protocol. E

All servers: 100 vendors, US region, individual plans, stateless, no auth, 100 req/min. Start local dev server: In a separate terminal: Use workflow endpoints when you want one request to return: - decision classification (yes | no | tie) from /api/decide - policy result from the relevant notary endpoint - recommended Zendesk action + tags + private note with requestid - POST https://refund.decide

This skill should be used when the user asks to "attack Active Directory", "exploit AD", "Kerberoasting", "DCSync", "pass-the-hash", "BloodHound enumeration", "Golden Ticket", "Silver Ticket", "AS-REP roasting", "NTLM relay", or needs guidance on Windows domain penetration testing.

Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.

API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.

Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers. Use when designing new APIs, reviewing API specifications, or establishing API design standards.

Automate Amplitude tasks via Rube MCP (Composio): events, user activity, cohorts, user identification. Always search tools first for current schemas.

English | 中文 k8m 是一款AI驱动的 Mini Kubernetes AI Dashboard 轻量级控制台工具，专为简化集群管理设计。它基于 AMIS 构建，并通过 kom 作为 Kubernetes API 客户端，k8m 内置了 Qwen2.5-Coder-7B，支持deepseek-ai/DeepSeek-R1-Distill-Qwen-7B模型 模型交互能力，同时支持接入您自己的私有化大模型（包括ollama）。 DEMO-InCluster模式 用户名密码 demo/demo - 详细的配置和使用说明请参考文档。 - 更新日志请参考更新日志。 - 开发设计文档-中文 - 开发设计文档-English - 迷你化设计：所有功能整合在一个单一的可执行文件中，部署便捷，使用简单。 - 简便易用：友好的用户界面和直观的操作流程，让 Kubernetes 管理更加轻松。支持

graphql → ai gqai is a lightweight proxy that exposes GraphQL operations as Model Context Protocol (MCP) tools for AI like Claude, Cursor, and ChatGPT. Define tools using regular GraphQL queries/mutations against your GraphQL backend, and gqai automatically generates an MCP server for you. 🔌 Powered by your GraphQL backend ⚙️ Driven by .graphqlrc.yml + plain .graphql files - 🧰 Define tools using

Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.

Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents.

           CentralMind Gateway: Create API or MCP Server in Minutes 🚀 Interactive Demo avialable here: https://centralmind.ai Simple way to expose your database to AI-Agent via MCP or OpenAPI 3.1 protocols. This will run for you an API: Which you can use inside your AI Agent: Gateway will generate AI optimized API. AI agents and LLM-powered applications need fas

When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," or "hypothesis." For tracking implementation, see analytics-tracking.

SEO and AEO (Answer Engine Optimization) best practices including EEAT principles, structured data, and technical SEO. Use when implementing metadata, sitemaps, structured data, or optimizing content for search engines and AI assistants.

Screenpipe brand style guide. Reference this when designing UI components, writing copy, or making visual decisions.