Agents
1,401 skills.
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kimi-delegateamelnagdy3.6KDelegate a coding task to the Kimi Code CLI (`kimi`) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to hand implementation work to Kimi - phrasings like "have Kimi implement X", "delegate this to Kimi", "run it through Kimi Code", or "use Kimi to implement/fix/refactor" - or wants to run a queue of coding tasks through Kimi while staying the reviewer. DO NOT USE for tasks small enough to do inline, or when the user wants the code written langchain-python-quickstartlangchain-ai3.6KScaffold a minimal local LangChain agent in Python by following the official quickstart. Use when the user wants to quickly build or try a LangChain agent locally.deepagents-python-quickstartlangchain-ai3.6KScaffold a minimal local Deep Agent in Python by following the official quickstart, using provider-native web search instead of Tavily. Use when the user wants to quickly build or try a Deep Agent locally.langgraph-typescript-quickstartlangchain-ai3.6KScaffold a minimal local LangGraph agent in TypeScript by following the official quickstart. Use when the user wants to quickly build or try a LangGraph agent locally.prompt-engineeringgiuseppe-trisciuoglio3.6KProvides workflows to write, debug, and optimize prompts for LLMs, including few-shot example selection, chain-of-thought structuring, system prompt design, and template composition. Use when the user asks to write or improve a prompt, wants help with few-shot examples, chain-of-thought, system prompts, prompt templates, or asks how to get better results from an LLM.langchain-typescript-quickstartlangchain-ai3.5KScaffold a minimal local LangChain agent in TypeScript by following the official quickstart. Use when the user wants to quickly build or try a LangChain agent locally.deepagents-typescript-quickstartlangchain-ai3.5KScaffold a minimal local Deep Agent in TypeScript by following the official quickstart, using provider-native web search instead of Tavily. Use when the user wants to quickly build or try a Deep Agent locally.alliumjuxt3.5KGive your AI agents something more useful than a prompt. Velocity through clarity.grok-delegateamelnagdy3.5KDelegate a coding task to the Grok Build CLI as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to hand implementation work to Grok — phrasings like "have Grok do X", "delegate this to Grok", "run it through Grok", "use Grok Build to implement/fix/refactor", or "have grok CLI do this" — or to run a queue of coding tasks through Grok while staying the reviewer. Prefer it when the user will review the diff and commit it themselves. DO NOT USE fkibana-agent-builderelastic3.5KCreate and manage Kibana Agent Builder agents and custom tools. Use when asked to create, update, delete, test, or inspect agents or tools in Agent Builder, or when the user wants to understand what agents or tools already exist.chunking-strategygiuseppe-trisciuoglio3.5KProvides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.self-learningphilschmid3.4KAutonomous skill generator that learns new technologies from the web. Use when, users want to learn about a new library/framework/tool, need to create a skill for an unfamiliar technology, want to research and document a technology's usage patterns, or invoke with `/learn <topic>`. This skill uses web search and browser tools to discover, extract, and synthesize documentation into a reusable skill.paperclip-create-agentpaperclipai3.4KCreate new agents in Paperclip with governance-aware hiring. Use when you need to inspect adapter configuration options, compare existing agent configs, draft a new agent prompt/config, and submit a hire request.spring-ai-mcp-server-patternsgiuseppe-trisciuoglio3.4KProvides Spring Boot MCP server patterns that create Model Context Protocol servers with Spring AI by defining tool handlers, exposing resources, configuring prompt templates, and setting up transports for AI function calling and tool calling. Use when building MCP servers to extend AI capabilities with Spring's official AI framework, implementing AI tools, custom function calling, or MCP client integration.skill-writerpytorch3.4KGuide users through creating Agent Skills for Claude Code. Use when the user wants to create, write, author, or design a new Skill, or needs help with SKILL.md files, frontmatter, or skill structure.autopilotnick-vels3.4KUse when the user dictates an app, site, bot, or feature to build end-to-end and expects a finished result without reviewing specs, tickets, or code — vibecoding sessions, non-technical users, "собери под ключ", "build it for me", "не задавай лишних вопросов" requests. Also use when the user invokes /autopilot, or asks for a build in a named mode, depth or finish — «полный автомат», «режим интервью», «погриль меня», «ручной режим», «строго по брифу», «проработай глубоко», «вылижи до эталона».autoresearchgithub3.4KAutonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy''s autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autolangchain4j-tool-function-calling-patternsgiuseppe-trisciuoglio3.4KProvides and generates LangChain4j tool and function calling patterns: annotates methods as tools with @Tool, configures tool executors, registers tools with AiServices, validates tool parameters, and handles tool execution errors. Use when building AI agents that call tools, define function specifications, manage tool responses, or integrate external APIs with LLM-driven applications.langchain4j-spring-boot-integrationgiuseppe-trisciuoglio3.4KProvides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications, building Java LLM applications with @Bean configuration, or setting up Spring AI patterns.langchain4j-rag-implementation-patternsgiuseppe-trisciuoglio3.4KProvides Retrieval-Augmented Generation (RAG) implementation patterns with LangChain4j for Java. Generates document ingestion pipelines, embedding stores, vector search, and semantic search capabilities. Use when building chat-with-documents systems, document Q&A over PDFs or text files, AI assistants with knowledge bases, semantic search over document repositories, or knowledge-enhanced AI applications with source attribution.wind-alicewind-alice3.4K调用万得 Alice Agent（A2A 协议，SSE 流式）执行指定 Skill 并获取分析结果的 CLI 工具。当用户要求"用 Alice 跑某个 Skill"、"出一份某公司的调研问题清单"、"做一页纸投资备忘"、"核验一段金融信息"等需要点名 Alice 子 Skill 的场景使用。qdrantgiuseppe-trisciuoglio3.4KProvides Qdrant vector database integration patterns with LangChain4j. Handles embedding storage, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, semantic search, or recommendation engines.raggiuseppe-trisciuoglio3.4KImplements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.langchain4j-ai-services-patternsgiuseppe-trisciuoglio3.3KProvides patterns to build declarative AI Services with LangChain4j for LLM integration, chatbot development, AI agent implementation, and conversational AI in Java. Generates type-safe AI services using interface-based patterns, annotations, memory management, and tools integration. Use when creating AI-powered Java applications with minimal boilerplate, implementing conversational AI with memory, or building AI agents with function calling.ce-workeveryinc3.3KExecute a plan or concrete work prompt end-to-end. Use when implementing from a plan document, a spec path, or a clear build request; use ce-debug for open-ended bugs. Use when an outer orchestrator needs implementation and local verification only, without the shipping tail.langchain4j-vector-stores-configurationgiuseppe-trisciuoglio3.3KProvides configuration patterns for LangChain4J vector stores in RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up hybrid search, or optimizing vector database performance for production AI applications.langchain4j-mcp-server-patternsgiuseppe-trisciuoglio3.3KProvides LangChain4j patterns for implementing MCP (Model Context Protocol) servers, creating Java AI tools, exposing tool calling capabilities, and integrating MCP clients with AI services. Use when building a Java MCP server, implementing tool calling in Java, connecting LangChain4j to external MCP servers, or securing tool exposure for agent workflows.lfgeveryinc3.3KTake a request all the way to done, hands-off, through the right Compound Engineering skills. A code change ends as an open pull request, pushed without stopping. Use only when the user explicitly asks for autonomous end-to-end work or invokes lfg directly. Use ce-plan, ce-work, ce-debug, or ce-commit-push-pr for work the user reviews step by step.skill-creatorcomposio-community3.3KGuide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.cursor-delegateamelnagdy3.2KDelegate a coding task to the Cursor Agent CLI (`cursor-agent`) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to hand implementation work to Cursor — phrasings like "have Cursor implement X", "delegate this to Cursor", "run it through Cursor Agent", or "use Cursor to implement/fix/refactor" — or wants to run a queue of coding tasks through Cursor while staying the reviewer. DO NOT USE for tasks small enough to do inline, or when the usercline-sdkcline3.2KComprehensive Cline SDK skill for building AI agents. Covers the direct Agent runtime, ClineCore sessions, custom tools, plugins, events, LLM providers, scheduling, multi-agent teams, and production deployment. Use for any task involving @cline/sdk or its sub-packages.notebooklmgiuseppe-trisciuoglio3.2KEnables interaction with Google NotebookLM for advanced RAG (Retrieval-Augmented Generation) capabilities via the notebooklm-mcp-cli tool. Use when querying project documentation stored in NotebookLM, managing research notebooks and sources, retrieving AI-synthesized information, generating audio podcasts or reports from notebooks, or performing contextual queries against curated knowledge bases. Triggers on "notebooklm", "nlm", "notebook query", "research notebook", "query documentation in noteyour-skill-namegrafana3.2KClear description of what this skill does and when to use it. Use when the user asks about X or wants to work with Y. Include specific trigger phrases so agents auto-load it correctly. Max 1024 characters.skill-creatoropenclaw3.2KAuthor or review AgentSkills: create, repair, validate, or restructure SKILL.md files and bundled resources.understand-chategonex-ai3.2KUse when you need to ask questions about a codebase or understand code using a knowledge graphgh-issuesopenclaw3.2KFetch GitHub issues, select candidates, spawn background fix agents, open PRs, and optionally process PR review comments.memory-md-managementgiuseppe-trisciuoglio3.1KProvides comprehensive memory file management capabilities including auditing, quality assessment, and targeted improvements for files such as CLAUDE.md. Use when user asks to check, audit, update, improve, fix, maintain, or validate project memory files. Also triggers for "project memory optimization", "CLAUDE.md quality check", "documentation review", or when a project memory file needs to be created from scratch. This skill scans memory files, evaluates quality against standardized criteria, cc-usezc2775841213.1K把较长的编码、排查、测试或交互式 CLI 工作交给 tmux 中的内层命令行编码 Agent， 由当前外层 Agent 负责拆解任务、读取稳定屏幕快照、纠正方向、执行最终验收，并在本次 任务结束后销毁内层 session。适用于用户明确要求使用 cc-use、希望把长时间实现工作 交给内层 Agent、需要在真实 Codex CLI 或 Claude Code TUI 中完成受监督工作，或需要 评估如何由 cron、launchd、systemd 等外部调度器触发 cc-use。principle-encode-lessons-in-structurecursor3.1KApply when you catch yourself writing the same instruction a second time, or notice a recurring correction. Encode the rule as a lint, metadata flag, runtime check, or script instead of more text.lark-mcpwhatevertogo3.1K飞书/Lark 官方 MCP 集成。支持发送消息、创建群组、操作多维表格（Bitable）、导入/搜索文档、知识库查询。触发词：飞书、Feishu、Lark、多维表格、bitable、飞书文档、飞书群。auditjsmastery-pro3.1KRun /audit on a greenfield project, an existing codebase with missing docs, or one area (/audit src/auth) to bootstrap the project's AI context, the AGENTS.md files every later skill reads. Writes tool agnostic AGENTS.md plus thin CLAUDE.md pointers, adding only what is missing; never overwrites curated content.langsmith-online-eval-engineeringlangchain-ai3.1KIteratively inspect traces, interview the user, and create LangSmith online evaluators one at a time. Use specifically for creating online evaluators for use within LangSmith -- use "eval-engineering" for Harbor-style online evaluations.skill-authoringgrafana3.1KAuthor, audit, and improve Grafana SKILL.md files against Anthropic's published Agent Skills guidance and the four-dimension rubric the grafana/skills CI gate uses (conciseness, actionability, workflow clarity, progressive disclosure). Applies the canonical SKILL.md structure (YAML frontmatter + body + references/ + scripts/ + assets/), the "pushy description" trigger pattern, the three-level progressive-disclosure model, and the validate-fix-rerun feedback loop. Use when creating a new skill inopenclaw-backuptheagentservice3.1KEncrypted backup and restore for OpenClaw Agent workspace files (SOUL.md, MEMORY.md, IDENTITY.md, AGENTS.md, TOOLS.md). Uses tar + openssl (AES-256-CBC) encryption and soul-upload.com API. Auto-generates a new random password for each backup (DO NOT reuse passwords). Use when the user needs to: (1) Back up or upload agent workspace files, (2) Restore or download a previous backup, (3) Delete a backup from remote storage, or (4) Manage encrypted agent persistence.agentforce-bot-upgradeforcedotcom3.1KUse this skill to Upgrade Einstein Bots into Agentforce agents end-to-end in a single pass, orchestrating per-bot Agent Spec generation, planner reconciliation across bots, agentforce-generate authoring, and post-conversion .agent enhancements. TRIGGER when: user asks to migrate, upgrade, or convert one or more Einstein Bots to Agentforce; runs a multi-bot bot-to-agent upgrade; needs Einstein Bot metadata turned into Agent Spec handoffs and generated .agent agents; convert bots to agents; upgradprompt-masternidhinjs3.1KGenerates optimized prompts for AI tools. Activates only when the user explicitly asks to write, fix, improve, or adapt a prompt for a specific AI tool (LLM, Cursor, Midjourney, image AI, video AI, coding agents, etc.). Does not activate for general conversation, coding tasks, document writing, or other non-prompt-engineering work.rag-blueprintnvidia3.1KNVIDIA RAG Blueprint — deploy, configure, troubleshoot, and manage. Handles any RAG action: deploy, install, start, enable, disable, toggle, change, configure, troubleshoot, debug, fix, shutdown, stop, or tear down any RAG feature or service (Agentic RAG, VLM, guardrails, query rewriting, models, search, ingestion, observability, summarization, reasoning, and more).principle-guard-the-context-windowcursor3KApply when context is filling up: large outputs, long files, repeated reads, fan-out planning. Route bulk to subagents; keep summaries in the main thread, not raw payloads.adaptive-communicationbencium3KUse when detecting ambiguous user intent, hedging language, open-ended framing, personal context before requests, or when unsure whether user wants exploration vs direct answer. Applies to all conversations.councilboshu23KCompare model perspectives for brainstorming, planning, validation, idea duels or interviews. Use when: independent proposals or judgments need optional bounded debate.