Agents
1,401 skills.
Browse
ai-agent-developmentsickn33AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.antigravity-workflowssickn33Use when asked to ship a SaaS MVP, audit application security, build an AI agent, run browser QA, or design a domain model with multiple skills and verified checkpoints.autonomous-agent-patternssickn33Design patterns for building autonomous coding agents, inspired by [Cline](https://github.com/cline/cline) and [OpenAI Codex](https://github.com/openai/codex).behavioral-modessickn33AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.cc-skill-continuous-learningsickn33Turn a completed debugging session or repeated user correction into a small, evidence-backed procedure. Use for explicit requests to capture reusable lessons; does not automatically extract or save memories.cc-skill-strategic-compactsickn33Prepare a verified checkpoint before condensing an agent conversation at a phase boundary. Use during long tasks when context is repetitive; preserves constraints, evidence, decisions and the next action.computer-use-agentssickn33Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Use, OpenAI's Operator/CUA, and open-source alternatives.context-managersickn33Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems.context-optimizationsickn33Context optimization extends the effective capacity of limited context windows through strategic compression, masking, caching, and partitioning. The goal is not to magically increase context windows but to make better use of available capacity.crewaisickn33Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies.dispatching-parallel-agentssickn33Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencieslangfusesickn33Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production.llm-app-patternssickn33Architecture and integration sketches for LLM applications, with explicit retrieval, tool, privacy and verification boundaries.loki-modesickn33Version 2.35.0 | PRD to Production | Zero Human Intervention > Research-enhanced: OpenAI SDK, DeepMind, Anthropic, AWS Bedrock, Agent SDK, HN Production (2025)mcp-buildersickn33Create MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks.memory-systemssickn33Design short-term, long-term, and graph-based memory architectures. Use when building agents that must persist across sessions, needing to maintain entity consistency across conversations, or implementing reasoning over accumulated knowledge.multi-agent-patternssickn33This skill should be used when the user asks to "design multi-agent system", "implement supervisor pattern", "create swarm architecture", "coordinate multiple agents", or mentions multi-agent patterns, context isolation, agent handoffs, sub-agents, or parallel agent execution.parallel-agentssickn33Multi-agent orchestration patterns. Use when multiple independent tasks can run with different domain expertise or when comprehensive analysis requires multiple perspectives.prompt-cachingsickn33Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation)prompt-engineering-patternssickn33Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.prompt-librarysickn33A comprehensive collection of battle-tested prompts inspired by [awesome-chatgpt-prompts](https://github.com/f/awesome-chatgpt-prompts) and community best practices.rag-engineersickn33Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications.rag-implementationsickn33RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.skill-developersickn33Comprehensive guide for creating and managing skills in Claude Code with auto-activation system, following Anthropic's official best practices including the 500-line rule and progressive disclosure pattern.skill-seekerssickn33-Automatically convert documentation websites, GitHub repositories, and PDFs into Claude AI skills in minutes.subagent-driven-developmentsickn33Use when executing implementation plans with independent tasks in the current sessionusing-superpowerssickn33Use when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questionsverification-before-completionsickn33Claiming work is complete without verification is dishonesty, not efficiency. Use when ANY variation of success/completion claims, ANY expression of satisfaction, or ANY positive statement about work state.voice-agentssickn33Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems.writing-skillssickn33Use when creating, updating, or improving agent skills.skill-createskillscatalogCreate new Agent Skills from templates with best-practice structure, pre-populated SKILL.md, and optional scripts/assets directories.pohuysmixsРежим идиоматического русского мата. Агент отвечает как живой русский инженер, который двадцать лет чинит прод: «наебнулось», «хуйня вопрос», «заебись, работает». Техническая точность байт в байт, лексика аутентичная. Уровни: lite, full (default), ultra. Use when: /pohuy, «та мне похуй», «заебал», «похуй-режим», просьба отвечать матом — для любой инженерной работы: дебаг, ревью, логи, деплой. Do NOT use for: просьба написать публичный текст (статья, пост, дока). Правило «в код, коммиты и PR мат ralphsnarktankConvert PRDs to prd.json format for the Ralph autonomous agent system. Use when you have an existing PRD and need to convert it to Ralph's JSON format. Triggers on: convert this prd, turn this into ralph format, create prd.json from this, ralph json.paysolana-foundationUser-authorized paid HTTP/API access for agents through local Pay MCP and TouchID gated payments (x402 MPP HTTP 402) SERVICES: search web, scrape, enrich people or companies, find contacts, agentic mailbox/email, social data, influencers, live research, Perplexity/Sonar, Solana/Ethereum RPC, wallet balance, blockchain analytic, crypto/stocks prices, image/video generation, OCR, document parsing, text analytic, translation, STT/TTS, places/maps, address validation, fact checks, phone calls, file building-skills-from-patternsspencerpaulyWhen the same multi-step workflow repeats in Cursor (user corrections or agent redos), capture it as a new SKILL.md under .cursor/skills/ so future sessions load it automatically.codebase-onboardingspencerpaulyLaunch multiple explore subagents in parallel to investigate architecture, data models, auth, APIs, and deployment. Synthesize into an onboarding document.parallel-exploringspencerpaulyExplore a large codebase in parallel by launching multiple explore subagents that each investigate a different area simultaneously. Use when onboarding onto a new project, understanding architecture, or investigating a cross-cutting concern.saving-workspace-contextspencerpaulyAutomatically persist useful context — research, decisions, learnings, templates — to workspace files so knowledge survives across conversations.ctrader-mcp-serversspotwareUse this skill ALWAYS when working with any cTrader MCP server.goal-promptsrinitudeUse when packaging source input for a standing goal.simplify-skillsrinitudeUse when simplifying a skill without losing behavior.skill-factorysrinitudeUse when a workflow or capability must become a new agent skill, when an existing skill must be updated or standardized without losing its purpose, when a user-level or project-level variant is needed, or when a skill needs validation, evaluation, scaffolding, scripts, tests, or a Mise task graph.would-agents-actuallysrinitudeUse when a claim depends on an agent taking a real action.orchestrationstablyaiCoordinate supervised Orca workers: threaded messages, blocking ask/reply, task dispatch, worker_done/escalation waits, task DAGs, decision gates, coordinator loops, and decomposing work across agents. Use `orca-cli` for full ownership handoffs — "hand off", "handoff", "handover", "give this to another agent", "another worktree" — unless asked to supervise, monitor, or coordinate a DAG, and for terminal control, lightweight terminal prompts, shell commands, Orca worktree management, and reading skill-cleanersteipeteCodex/OpenClaw skill audit: live budget, usage, duplicates, compact descriptions.proactive-agentsundial-orgTransform AI agents from task-followers into proactive partners that anticipate needs and continuously improve. Includes memory architecture with pre-compaction flush (so context survives when the window fills), reverse prompting (surfaces ideas you didn't know to ask for), security hardening, self-healing patterns (diagnoses and fixes its own issues), and alignment systems (stays on mission, remembers who it serves). Battle-tested patterns for agents that learn from every interaction and createsupermemorysupermemoryaiSupermemory is a state-of-the-art memory and context infrastructure for AI agents. Use this skill when building applications that need persistent memory, user personalization, long-term context retention, or semantic search across knowledge bases. It provides Memory API for learned user context, User Profiles for static/dynamic facts, and RAG for semantic search. Perfect for chatbots, assistants, and knowledge-intensive applications.supermemorysupermemoryaiSupermemory is a state-of-the-art memory and context infrastructure for AI agents. Use this skill when building applications that need persistent memory, user personalization, long-term context retention, or semantic search across knowledge bases. It provides Memory API for learned user context, User Profiles for static/dynamic facts, and RAG for semantic search. Perfect for chatbots, assistants, and knowledge-intensive applications.p7tanweaiP7 Senior Engineer mode — solution-driven execution under P8 supervision. Use when user says 'P7模式', '方案驱动', or when spawned as sub-task executor by P8. Produces: implementation plan + code + 3-question self-review, delivered via [P7-COMPLETION].p9tanweaiP9 Tech Lead mode — write Task Prompts, manage P8 agent teams, never write code yourself. Use when user says 'P9模式', 'tech-lead', '帮我管理这个项目', '任务拆解', or when coordinating 3+ parallel agents. Produces: Task Prompts (六要素) + P8 team delivery.