Skill 全量检索

LLM 与 AI389,030discord-clawdSkillsMP@openclaw使用 talk Discord-backed OpenClaw agent/session; archive 搜索.Use to talk to the Discord-backed OpenClaw agent/session; not for archive search.#llm-aiopenclaw/openclawGit 克隆查看详情与安装步骤 → LLM 与 AI389,030configure-channelSkillsMP@openclaw配置 prove 聊天通道 non-可交互一个-liners; secrets SecretRefs.Configure and prove a chat channel with non-interactive one-liners; secrets only as SecretRefs.#llm-aiopenclaw/openclawGit 克隆查看详情与安装步骤 → LLM 与 AI389,030discordSkillsMP@openclawDiscord messaging 工作流程 OpenClaw's 消息 tool.Discord messaging workflows through OpenClaw's message tool.#llm-aiopenclaw/openclawGit 克隆查看详情与安装步骤 → LLM 与 AI389,030imsgSkillsMP@openclaw使用 imsg CLI OpenClaw 智能体 iMessage/SMS DMs, 分组, replies, reactions, polls, watching, 私有-API 操作.Use the imsg CLI from OpenClaw agents for iMessage/SMS DMs, groups, replies, reactions, polls, watching, and private-API actions.#llm-aiopenclaw/openclawGit 克隆查看详情与安装步骤 → LLM 与 AI389,030voice-callSkillsMP@openclaw启动人声调用通过 OpenClaw 人声-调用插件.Start voice calls via the OpenClaw voice-call plugin.#llm-aiopenclaw/openclawGit 克隆查看详情与安装步骤 → LLM 与 AI389,030geminiSkillsMP@openclawGemini CLI 一个-shot 提示词, 摘要, generation, 技能, 钩子, MCP, Gemma 路由.Gemini CLI one-shot prompts, summaries, generation, skills, hooks, MCP, or Gemma routing.#llm-aiopenclaw/openclawGit 克隆查看详情与安装步骤 → LLM 与 AI389,030openai-whisperSkillsMP@openclaw本地语音-to-文本 Whisper CLI ( API 关键).Local speech-to-text with the Whisper CLI (no API key).#llm-aiopenclaw/openclawGit 克隆查看详情与安装步骤 → LLM 与 AI389,030sagSkillsMP@openclawElevenLabs 文本-to-语音 mac-风格说明 UX.ElevenLabs text-to-speech with mac-style say UX.#llm-aiopenclaw/openclawGit 克隆查看详情与安装步骤 → LLM 与 AI253,169agent-introspection-debuggingSkillsMP@affaan-m结构化 self-调试工作流程 AI 智能体失败使用采集, 诊断, contained recovery, introspection 报告. 使用智能体运行 fails need reproducible 诊断 instead 重试.Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169dmux-workflowsSkillsMP@affaan-m多-智能体 orchestration 使用 dmux (tmux pane manager AI 智能体). 模式并行智能体工作流程跨 Claude 代码, Codex, OpenCode, 其他 harnesses. 使用 running 多个智能体 sessions 并行 coordinating mulMulti-agent orchestration using dmux (tmux pane manager for AI agents). Patterns for parallel agent workflows across Claude Code, Codex, OpenCode, and other harnesses. Use when running multiple agent sessions in parallel or coordinating mul#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169agent-harness-constructionSkillsMP@affaan-mAI エージェントのアクション空間、ツール定義、観測フォーマットを設計・最適化して完了率を向上させます。#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169ai-regression-testingSkillsMP@affaan-mAI 支援開発のためのリグレッションテスト戦略。データベース依存なしのサンドボックスモード API テスト、自動化されたバグチェックワークフロー、同じモデルがコードを書いてレビューする AI のブラインドスポットを捕捉するパターン。#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169autonomous-loopsSkillsMP@affaan-m自動Claude 代码ループのパターンとアーキテクチャ — シンプルな順序パイプラインからRFC駆動マルチエージェントDAGシステムまで。自動Claude Codeループのパターンとアーキテクチャ — シンプルな順序パイプラインからRFC駆動マルチエージェントDAGシステムまで。#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169openclaw-persona-forgeSkillsMP@affaan-m为 OpenClaw AI 智能体锻造完整的龙虾灵魂方案。根据用户偏好或随机抽卡， 输出身份定位、灵魂描述(SOUL. md)、角色化底线规则、名字和头像生图提示词。 如当前环境提供已审核的生图技能，可自动生成统一风格头像图片。 当用户需要创建、设计或定制 OpenClaw 龙虾灵魂时使用。 不适用于：微调已有 SOUL. md、非 OpenClaw 平台的角色设计、纯工具型无性格智能体。 触发词：龙虾灵魂、虾魂、OpenClaw 灵魂、养虾灵魂、龙虾角色、龙为 OpenClaw AI Agent 锻造完整的龙虾灵魂方案。根据用户偏好或随机抽卡， 输出身份定位、灵魂描述(SOUL.md)、角色化底线规则、名字和头像生图提示词。 如当前环境提供已审核的生图 skill，可自动生成统一风格头像图片。 当用户需要创建、设计或定制 OpenClaw 龙虾灵魂时使用。 不适用于：微调已有 SOUL.md、非 OpenClaw 平台的角色设计、纯工具型无性格 Agent。 触发词：龙虾灵魂、虾魂、OpenClaw 灵魂、养虾灵魂、龙虾角色、龙#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169strategic-compactSkillsMP@affaan-m任意の自動コンパクションではなく、タスクフェーズを通じてコンテキストを保持するための論理的な間隔での手動コンパクションを提案します。#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169team-builderSkillsMP@affaan-m並列チームを構成して派遣するためのインタラクティブなエージェント選択ツール#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169token-budget-advisorSkillsMP@affaan-m回答する前に、どれだけの回答深度を消費するかについてユーザーに情報に基づいた選択を提供する。ユーザーが回答の長さ、深さ、またはトークンバジェットを明示的に制御したい場合にこのスキルを使用する。トリガー条件："词元预算", "词元 count", "词元用法", "词元限量", "响应 length", "answer depth", "short 版本", "简要 answer", "detaile回答する前に、どれだけの回答深度を消費するかについてユーザーに情報に基づいた選択を提供する。ユーザーが回答の長さ、深さ、またはトークンバジェットを明示的に制御したい場合にこのスキルを使用する。トリガー条件："token budget", "token count", "token usage", "token limit", "response length", "answer depth", "short version", "brief answer", "detaile#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169iterative-retrievalSkillsMP@affaan-m서브에이전트 컨텍스트 문제를 해결하기 위한 점진적 컨텍스트 검색 개선 패턴#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169agent-introspection-debuggingSkillsMP@affaan-m针对AI代理故障的结构化自调试工作流程，包括捕获、诊断、受限恢复和内省报告。#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169ai-regression-testingSkillsMP@affaan-mAI辅助开发的回归测试策略。沙盒模式API测试，无需依赖数据库，自动化的缺陷检查工作流程，以及捕捉AI盲点的模式，其中同一模型编写和审查代码。#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169brand-voiceSkillsMP@affaan-m从真实的帖子、文章、发布说明、文档或网站文案中构建基于源材料的写作风格档案，然后在内容、外展和社交工作流中重复使用该档案。当用户希望保持声音一致性而不使用通用的AI写作套路时使用。#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169token-budget-advisorSkillsMP@affaan-m在回答前，为用户提供关于消耗多少响应深度的知情选择。当用户明确希望控制响应长度、深度或令牌预算时使用此技能。触发条件："词元预算", "词元 count", "词元用法", "词元限量", "响应 length", "answer depth", "short 版本", "简要 answer", "详细 answer", "exhaustive answer", "respuesta c在回答前，为用户提供关于消耗多少响应深度的知情选择。当用户明确希望控制响应长度、深度或令牌预算时使用此技能。触发条件："token budget", "token count", "token usage", "token limit", "response length", "answer depth", "short version", "brief answer", "detailed answer", "exhaustive answer", "respuesta c#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169iterative-retrievalSkillsMP@affaan-m模式 progressively refining 上下文 retrieval solve subagent 上下文问题Pattern for progressively refining context retrieval to solve the subagent context problem#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169agent-harness-constructionSkillsMP@affaan-m设计优化 AI 智能体操作 spaces, tool definitions, observation formatting higher completion rates. 使用 defining revising 智能体's tool 集合, 操作 space, observation 格式化.Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent's tool set, action space, or observation format.#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169agent-introspection-debuggingSkillsMP@affaan-m结构化 self-调试工作流程 AI 智能体失败使用采集, 诊断, contained recovery, introspection 报告. 使用智能体运行 fails need reproducible 诊断 instead 重试.Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169agentic-osSkillsMP@affaan-m构建持久化多-智能体 operating 系统 Claude 代码. 覆盖 kernel 架构, specialist 智能体, slash 命令, 文件-基于记忆, 定时自动化, 状态管理无需外部数据库. 使用构建 persBuild persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a pers#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169dmux-workflowsSkillsMP@affaan-m多-智能体 orchestration 使用 dmux (tmux pane manager AI 智能体). 模式并行智能体工作流程跨 Claude 代码, Codex, OpenCode, 其他 harnesses. 使用 running 多个智能体 sessions 并行 coordinating mulMulti-agent orchestration using dmux (tmux pane manager for AI agents). Patterns for parallel agent workflows across Claude Code, Codex, OpenCode, and other harnesses. Use when running multiple agent sessions in parallel or coordinating mul#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169gan-style-harnessSkillsMP@affaan-mGAN-inspired 生成器-Evaluator 智能体 harness 构建 high-质量应用 autonomously. 基于 Anthropic's March 2026 harness 设计论文. 使用功能构建 autonomously 生成器 evaluator iteratGAN-inspired Generator-Evaluator agent harness for building high-quality applications autonomously. Based on Anthropic's March 2026 harness design paper. Use when a feature should be built autonomously through generator and evaluator iterat#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169iterative-retrievalSkillsMP@affaan-m模式 progressively refining 上下文 retrieval solve subagent 上下文问题. 使用 subagent lacks 上下文需要 retrieval refined 跨 passes.Pattern for progressively refining context retrieval to solve the subagent context problem. Use when a subagent lacks the context it needs and retrieval must be refined across passes.#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169lead-intelligenceSkillsMP@affaan-mAI-原生销售线索 intelligence outreach 流水线. Replaces Apollo, Clay, ZoomInfo 智能体-powered signal scoring, mutual ranking, warm 路径 discovery, 来源-derived 人声 modeling, 通道-特定 outreach 跨邮件, LinkedAI-native lead intelligence and outreach pipeline. Replaces Apollo, Clay, and ZoomInfo with agent-powered signal scoring, mutual ranking, warm path discovery, source-derived voice modeling, and channel-specific outreach across email, Linked#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169openclaw-persona-forgeSkillsMP@affaan-m为 OpenClaw AI 智能体锻造完整的龙虾灵魂方案。根据用户偏好或随机抽卡， 输出身份定位、灵魂描述(SOUL. md)、角色化底线规则、名字和头像生图提示词。 如当前环境提供已审核的生图技能，可自动生成统一风格头像图片。 当用户需要创建、设计或定制 OpenClaw 龙虾灵魂时使用。 不适用于：微调已有 SOUL. md、非 OpenClaw 平台的角色设计、纯工具型无性格智能体。 触发词：龙虾灵魂、虾魂、OpenClaw 灵魂、养虾灵魂、龙虾角色、龙为 OpenClaw AI Agent 锻造完整的龙虾灵魂方案。根据用户偏好或随机抽卡， 输出身份定位、灵魂描述(SOUL.md)、角色化底线规则、名字和头像生图提示词。 如当前环境提供已审核的生图 skill，可自动生成统一风格头像图片。 当用户需要创建、设计或定制 OpenClaw 龙虾灵魂时使用。 不适用于：微调已有 SOUL.md、非 OpenClaw 平台的角色设计、纯工具型无性格 Agent。 触发词：龙虾灵魂、虾魂、OpenClaw 灵魂、养虾灵魂、龙虾角色、龙#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169orch-pipelineSkillsMP@affaan-m共享 orchestration 引擎 orch-* 技能 family. Defines gated 研究-计划-TDD-评审-提交流水线, size 分类器, 智能体映射, two human gates orch-* operation 技能 delegate. usually invokShared orchestration engine for the orch-* skill family. Defines the gated Research-Plan-TDD-Review-Commit pipeline, the size classifier, the agent map, and the two human gates that the orch-* operation skills delegate to. Not usually invok#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169plan-orchestrateSkillsMP@affaan-m读取计划文档, decompose 步骤, 设计按-步骤智能体 chain ECC 目录, emit 就绪-to-paste /orchestrate 自定义提示词. Generative — 调用 /orchestrate itself. 使用用户多-steRead a plan document, decompose it into steps, design a per-step agent chain from the ECC catalogue, and emit ready-to-paste /orchestrate custom prompts. Generative only — never invokes /orchestrate itself. Use when the user has a multi-ste#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169ralphinho-rfc-pipelineSkillsMP@affaan-mRFC-driven 多-智能体 DAG 执行模式质量 gates, merge 队列, 工作 unit orchestration. 使用 running RFC-driven 多-智能体执行质量 gates merge 队列.RFC-driven multi-agent DAG execution pattern with quality gates, merge queues, and work unit orchestration. Use when running RFC-driven multi-agent execution with quality gates and a merge queue.#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169team-builderSkillsMP@affaan-m可交互智能体 picker composing dispatching 并行 teams. 使用 composing dispatching 并行团队智能体任务.Interactive agent picker for composing and dispatching parallel teams. Use when composing and dispatching a parallel team of agents for a task.#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI253,169token-budget-advisorSkillsMP@affaan-mOffers 用户 informed choice much 响应 depth consume 在…之前 answering. 使用技能用户显式地需求控制响应 length, depth, 词元预算. TRIGGER: "词元预算", "词元 count", "Offers the user an informed choice about how much response depth to consume before answering. Use this skill when the user explicitly wants to control response length, depth, or token budget. TRIGGER when: "token budget", "token count", "to#llm-aiaffaan-m/eccGit 克隆查看详情与安装步骤 → LLM 与 AI243,171kanban-video-orchestratorSkillsMP@NousResearch计划运行多-智能体视频生产环境流水线.Plan and run multi-agent video production pipelines.#llm-ainousresearch/hermes-agentGit 克隆查看详情与安装步骤 → LLM 与 AI243,171inference-sh-cliSkillsMP@NousResearch运行 150+ AI 应用 (图片, 视频, LLM) 通过 inference. sh CLI.Run 150+ AI apps (image, video, LLM) via inference.sh CLI.#llm-ainousresearch/hermes-agentGit 克隆查看详情与安装步骤 → LLM 与 AI243,171chromaSkillsMP@NousResearch向量嵌入数据库 RAG 语义搜索.Embedding database for RAG and semantic search.#llm-ainousresearch/hermes-agentGit 克隆查看详情与安装步骤 → LLM 与 AI243,171serving-llms-vllmSkillsMP@NousResearchvLLM: high-吞吐量 LLM serving, OpenAI API, quantization.vLLM: high-throughput LLM serving, OpenAI API, quantization.#llm-ainousresearch/hermes-agentGit 克隆查看详情与安装步骤 → LLM 与 AI243,171llavaSkillsMP@NousResearch视觉-语言聊天: VQA, captioning, 图片 dialogue.Vision-language chat: VQA, captioning, image dialogue.#llm-ainousresearch/hermes-agentGit 克隆查看详情与安装步骤 → LLM 与 AI243,171qdrantSkillsMP@NousResearchVector 搜索引擎生产环境 RAG 系统.Vector search engine for production RAG systems.#llm-ainousresearch/hermes-agentGit 克隆查看详情与安装步骤 → LLM 与 AI243,171parallel-cliSkillsMP@NousResearch智能体-原生网页搜索, 深度研究, enrichment.Agent-native web search, deep research, and enrichment.#llm-ainousresearch/hermes-agentGit 克隆查看详情与安装步骤 → LLM 与 AI243,171humanizerSkillsMP@NousResearchHumanize 文本: strip AI-isms 添加真实人声.Humanize text: strip AI-isms and add real voice.#llm-ainousresearch/hermes-agentGit 克隆查看详情与安装步骤 → LLM 与 AI203,692n8n-create-instance-ai-evalSkillsMP@n8n-io作者新建 Instance AI 工作流程 eval case — 编写 locally JSON, calibrated 对照真实构建, pushed LangTracer 套件 CI 运行 — 构建 cases, behaviour/process cases, credential cases, seeded (mid-对话) casAuthors a new Instance AI workflow eval case — written locally as JSON, calibrated against a real build, then pushed to the LangTracer suite CI runs — build cases, behaviour/process cases, credential cases, and seeded (mid-conversation) cas#llm-ain8n-io/n8nGit 克隆查看详情与安装步骤 → LLM 与 AI203,692agent-builderSkillsMP@n8n-io加载在…之前 calling 构建-智能体新建现有 n8n 智能体. Governs prerequisite 创建, faithful handoff 用户's 请求, 智能体 targeting 跨 turns, 构建器问题, testing, 发布. 使用 directly 例行 folloLoad before calling build-agent for a new or existing n8n Agent. Governs prerequisite creation, faithful handoff of the user's request, agent targeting across turns, builder questions, testing, and publishing. Use directly for routine follo#llm-ain8n-io/n8nGit 克隆查看详情与安装步骤 → 自动化工具192,028initSkillsMP@microsoft生成更新聊天 customization 文件 AI 编码智能体Generate or update chat customization files for AI coding agents#automation-tools#ide-plugins#llm-aimicrosoft/vscodeGit 克隆查看详情与安装步骤 → LLM 与 AI192,028create-agentSkillsMP@microsoft创建自定义智能体 (. agent. md) 特定作业.Create a custom agent (.agent.md) for a specific job.#llm-aimicrosoft/vscodeGit 克隆查看详情与安装步骤 → LLM 与 AI192,028create-hookSkillsMP@microsoft创建钩子 (. JSON) enforce 政策自动化智能体生命周期事件.Create a hook (.json) to enforce policy or automate agent lifecycle events.#llm-aimicrosoft/vscodeGit 克隆查看详情与安装步骤 → LLM 与 AI192,028agent-host-chat-contributionsSkillsMP@microsoft构建评审 cross-cutting 智能体-主机聊天行为生命周期 contributions. 使用新增转换生命周期端 effects, 提示词上下文 injection, restored-历史记录转换, 协议-操作 observation, reviewiBuild and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewi#llm-aimicrosoft/vscodeGit 克隆查看详情与安装步骤 → LLM 与 AI175,110claude-apiSkillsMP@anthropics参考 Claude API / Anthropic SDK — 模型 ids, pricing, params, streaming, tool 使用, MCP, 智能体, 缓存, 词元 counting, 模型迁移. TRIGGER — 读取 BEFORE opening 目标文件; don't skip "looks 例如一个-liReference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-li#llm-aianthropics/skillsGit 克隆查看详情与安装步骤 → LLM 与 AI169,722prompt-lookupSkillsMP@fActivates 用户要求 AI 提示词, 需要提示词模板, 需求搜索提示词, 提及提示词. 聊天. 使用 discovering, retrieving, improving 提示词.Activates when the user asks about AI prompts, needs prompt templates, wants to search for prompts, or mentions prompts.chat. Use for discovering, retrieving, and improving prompts.#llm-aif/prompts.chatGit 克隆查看详情与安装步骤 → LLM 与 AI169,722skill-lookupSkillsMP@f搜索, 获取, 安装智能体技能提示词. 聊天注册中心使用 MCP 工具. 使用用户要求查找技能, browse 技能 catalogs, 安装技能 Claude, extend Claude's 能力可复用 AI 智能体 compSearch, retrieve, and install Agent Skills from the prompts.chat registry using MCP tools. Use when the user asks to find skills, browse skill catalogs, install a skill for Claude, or extend Claude's capabilities with reusable AI agent comp#llm-aif/prompts.chatGit 克隆查看详情与安装步骤 → LLM 与 AI154,917e2e-summary-skillSkillsMP@langgenius总结用户输入智能体构建器 E2E coverage.Summarize user input for Agent Builder E2E coverage.#llm-ailanggenius/difyGit 克隆查看详情与安装步骤 → LLM 与 AI132,003codexSkillsMP@garrytanOpenAI Codex CLI 封装 — 三个 modes. (gstack)OpenAI Codex CLI wrapper — three modes. (gstack)#llm-aigarrytan/gstackGit 克隆查看详情与安装步骤 → LLM 与 AI132,003design-shotgunSkillsMP@garrytan设计 shotgun: 生成多个 AI 设计 variants, 打开 comparison board, collect 结构化 feedback, iterate. (gstack)Design shotgun: generate multiple AI design variants, open a comparison board, collect structured feedback, and iterate. (gstack)#llm-aigarrytan/gstackGit 克隆查看详情与安装步骤 → LLM 与 AI132,003pair-agentSkillsMP@garrytanPair 远程 AI 智能体浏览器. (gstack)Pair a remote AI agent with your browser. (gstack)#llm-aigarrytan/gstackGit 克隆查看详情与安装步骤 → LLM 与 AI122,333openai-docsSkillsMP@openai使用 Codex models/pricing, 定时任务, 技能, 设置, 初始化设置, 故障排查, customization, automations, self-knowledge—包括 ',' ',' ' 应用,' ' 编码智能体' refer Codex— OpenAI APIsUse for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs#llm-aiopenai/codexGit 克隆查看详情与安装步骤 → LLM 与 AI106,860agent-tuiSkillsMP@google-geminiMain 智能体: NOT 使用技能 directly. need 测试 TUI, 调用 `tui_tester` subagent. 云端硬盘终端 UI (TUI) 应用 programmatically testing, 自动化, inspection. 使用: automating CLI/TUI 交互Main Agents: Do NOT use this skill directly. If you need to test the TUI, invoke the `tui_tester` subagent. Drive terminal UI (TUI) applications programmatically for testing, automation, and inspection. Use when: automating CLI/TUI interact#llm-aigoogle-gemini/gemini-cliGit 克隆查看详情与安装步骤 → LLM 与 AI94,749html-ppt-tech-sharingSkillsMP@nexu-ioOpenDesign internals: 智能体流, 沙箱, 产物工作 — 工程深度-dive talk. 构建决策-grade AI literacy 演示文稿 engineers, 开发社区.OpenDesign internals: how the agent stream, sandbox, and artifacts work — an engineering deep-dive talk. Built as a decision-grade AI literacy deck for engineers, dev community.#llm-ainexu-io/open-designGit 克隆查看详情与安装步骤 → LLM 与 AI94,749todo-writeSkillsMP@nexu-ioTodoWrite-driven 计划智能体提交在…之前 generation.TodoWrite-driven plan that the agent commits to before generation.#llm-ainexu-io/open-designGit 克隆查看详情与安装步骤 → LLM 与 AI94,749html-ppt-tech-sharingSkillsMP@nexu-ioOpenDesign internals: 智能体流, 沙箱, 产物工作 — 工程深度-dive talk. 构建决策-grade AI literacy 演示文稿 engineers, 开发社区.OpenDesign internals: how the agent stream, sandbox, and artifacts work — an engineering deep-dive talk. Built as a decision-grade AI literacy deck for engineers, dev community.#llm-ainexu-io/open-designGit 克隆查看详情与安装步骤 → LLM 与 AI94,749ve-terminal-monoSkillsMP@nexu-ioOpenDesign CLI: driving 完整设计工作流程 `od` 命令 — scripted, composable, 智能体-就绪. 构建决策-grade AI literacy 演示文稿 developers, power 用户.OpenDesign from the CLI: driving the full design workflow with the `od` command — scripted, composable, agent-ready. Built as a decision-grade AI literacy deck for developers, power users.#llm-ainexu-io/open-designGit 克隆查看详情与安装步骤 → LLM 与 AI94,749ai-music-albumSkillsMP@nexu-io完整-生命周期 AI 音乐 album 生产环境 — concept, lyric drafting, 跟踪 sequencing, 导出. Useful indie album 实验品牌 soundtracks.Full-lifecycle AI music album production — concept, lyric drafting, track sequencing, and export. Useful for indie album experiments and brand soundtracks.#llm-ainexu-io/open-designGit 克隆查看详情与安装步骤 → LLM 与 AI94,749creative-directorSkillsMP@nexu-ioAI creative director 递归 self-assessment: 20+ methodologies (SIT, TRIZ, Bisociation, SCAMPER, Synectics), 3-axis evaluation calibrated 对照 Cannes/D&AD/HumanKind, 5-阶段流程简要 presentation.AI creative director with recursive self-assessment: 20+ methodologies (SIT, TRIZ, Bisociation, SCAMPER, Synectics), 3-axis evaluation calibrated against Cannes/D&AD/HumanKind, 5-phase process from brief to presentation.#llm-ainexu-io/open-designGit 克隆查看详情与安装步骤 → LLM 与 AI94,749fal-lip-syncSkillsMP@nexu-io创建 talking head videos lip 同步音频视频通过 fal. ai. Useful explainer avatars, 多语言 dubbing previews, social cuts.Create talking head videos and lip sync audio to video via fal.ai. Useful for explainer avatars, multilingual dubbing previews, and social cuts.#llm-ainexu-io/open-designGit 克隆查看详情与安装步骤 → LLM 与 AI94,749fal-realtimeSkillsMP@nexu-io实时 streaming AI 图像生成通过 fal. ai. Suited moodboard exploration, 草稿 variations, rapid creative 迭代.Real-time and streaming AI image generation via fal.ai. Suited for moodboard exploration, draft variations, and rapid creative iteration.#llm-ainexu-io/open-designGit 克隆查看详情与安装步骤 → LLM 与 AI94,749nanobanana-pptSkillsMP@nexu-ioAI-powered PPT generation 文档分析 styled 图片通过 NanoBanana stack. Combines 图像生成结构化演示文稿输出.AI-powered PPT generation with document analysis and styled images via the NanoBanana stack. Combines image generation with structured deck output.#llm-ainexu-io/open-designGit 克隆查看详情与安装步骤 → LLM 与 AI94,749speechSkillsMP@nexu-io生成口语音频文本使用 OpenAI's API 构建- voices. Useful narrated explainers, lecture 音频, 快速 voiceover 跟踪.Generate spoken audio from text using OpenAI's API with built-in voices. Useful for narrated explainers, lecture audio, and quick voiceover tracks.#llm-ainexu-io/open-designGit 克隆查看详情与安装步骤 → LLM 与 AI94,749venice-audio-musicSkillsMP@nexu-io音乐 generation queueing, retrieval, completion 接口通过 Venice. ai. Suited jingles, 后台循环, 原型 scoring.Music generation queueing, retrieval, and completion endpoints via Venice.ai. Suited for jingles, background loops, and prototype scoring.#llm-ainexu-io/open-designGit 克隆查看详情与安装步骤 → LLM 与 AI94,749venice-audio-speechSkillsMP@nexu-io文本-to-语音模型, voices, 格式, streaming 通过 Venice. ai. Useful narration, voiceover, conversational 智能体 voices.Text-to-speech models, voices, formats, and streaming via Venice.ai. Useful for narration, voiceover, and conversational agent voices.#llm-ainexu-io/open-designGit 克隆查看详情与安装步骤 → LLM 与 AI93,436knowledge-agentSkillsMP@thedotmack构建查询 AI-powered knowledge bases Claude-mem observations. 使用用户需要创建聚焦 "brains" observation 历史记录, 要求问题 past 工作模式, 编译 expertise 特定 topics.Build and query AI-powered knowledge bases from claude-mem observations. Use when users want to create focused "brains" from their observation history, ask questions about past work patterns, or compile expertise on specific topics.#llm-aithedotmack/claude-memGit 克隆查看详情与安装步骤 → LLM 与 AI93,436mode-creatorSkillsMP@thedotmackInteractively 创建, 安装, activate, 核验自定义 Claude-mem modes, 包括域名-特定 observation types, concept 标签, 可选 Telegram 告警, bot 初始化设置, 工作进程 restart, startup-上下文核验. 使用Interactively create, install, activate, and verify custom claude-mem modes, including domain-specific observation types, concept tags, optional Telegram alerts, bot setup, worker restart, and startup-context verification. Use this whenever#llm-aithedotmack/claude-memGit 克隆查看详情与安装步骤 → LLM 与 AI93,436weekly-digestsSkillsMP@thedotmack生成 serial week-by-week 叙事 digest 项目's 完整 Claude-mem 时间线. Splits 时间线按-ISO-week 文件, 运行一个 consecutive subagent 按 week — receiving prior week's carry-forward block — proGenerate a serial week-by-week narrative digest of a project's full claude-mem timeline. Splits the timeline into per-ISO-week files, then runs one consecutive subagent per week — each receiving the prior week's carry-forward block — to pro#llm-aithedotmack/claude-memGit 克隆查看详情与安装步骤 → LLM 与 AI93,077agentdb-memory-patternsSkillsMP@ruvnet实现持久化记忆模式 AI 智能体使用 AgentDB. 包含会话记忆, long-term 存储, 模式学习, 上下文管理. 使用构建 stateful 智能体, 聊天系统, 智能助手.Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.#llm-airuvnet/ruviewGit 克隆查看详情与安装步骤 → LLM 与 AI93,077agentdb-vector-searchSkillsMP@ruvnet实现语义 vector 搜索 AgentDB 智能文档 retrieval, similarity 匹配, 上下文-aware querying. 使用构建 RAG 系统, 语义搜索 engines, 智能 knowledge bases.Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.#llm-airuvnet/ruviewGit 克隆查看详情与安装步骤 → LLM 与 AI93,077browserSkillsMP@ruvnet网页浏览器自动化 AI-已优化 snapshots Claude-流程智能体Web browser automation with AI-optimized snapshots for claude-flow agents#llm-airuvnet/ruviewGit 克隆查看详情与安装步骤 → LLM 与 AI93,077github-multi-repoSkillsMP@ruvnet多-仓库 coordination, synchronization, 架构管理 AI swarm orchestrationMulti-repository coordination, synchronization, and architecture management with AI swarm orchestration#llm-airuvnet/ruviewGit 克隆查看详情与安装步骤 → LLM 与 AI93,077ruview-rvagentSkillsMP@ruvnet探索原型 rvAgent + RVF 集成 RuView agentic 流程. 使用处理 cross-cog coordination, 运维者-facing 智能体读取 BFLD / pose / vitals 事件线上, persisting 智能体状态 alongside sensing 数据 sExplore and prototype rvAgent + RVF integration for RuView agentic flows. Use when working on cross-cog coordination, operator-facing agents reading BFLD / pose / vitals events live, or persisting agent state alongside sensing data in the s#llm-airuvnet/ruviewGit 克隆查看详情与安装步骤 → LLM 与 AI86,975odysseusSkillsMP@odysseus-dev使用用户要求 Claude 代码读取编写 Odysseus 数据 (待办, 邮件, 日历, 记忆, 文档) launch/monitor/stop Cookbook 模型-serve 任务限定范围 Claude 智能体 API. 需要 ODYSSEUS_URL ODYSSEUS_API_Use when the user asks Claude Code to read or write Odysseus data (todos, email, calendar, memory, documents) or to launch/monitor/stop a Cookbook model-serve task through the scoped Claude Agent API. Requires ODYSSEUS_URL and ODYSSEUS_API_#llm-aiodysseus-dev/odysseusGit 克隆查看详情与安装步骤 → LLM 与 AI85,798fetch-country-briefSkillsMP@koala73获取当前 AI-生成 strategic intelligence 简要 country, keyed ISO 3166-1 内测版-2 代码. 使用用户要求摘要当前 geopolitical, economic, 安全情况特定 country.Retrieve the current AI-generated strategic intelligence brief for a country, keyed by ISO 3166-1 alpha-2 code. Use when the user asks for a summary of the current geopolitical, economic, or security situation in a specific country.#llm-aikoala73/worldmonitorGit 克隆查看详情与安装步骤 → LLM 与 AI85,798fetch-country-briefSkillsMP@koala73获取当前 AI-生成 strategic intelligence 简要 country, keyed ISO 3166-1 内测版-2 代码. 使用用户要求摘要当前 geopolitical, economic, 安全情况特定 country.Retrieve the current AI-generated strategic intelligence brief for a country, keyed by ISO 3166-1 alpha-2 code. Use when the user asks for a summary of the current geopolitical, economic, or security situation in a specific country.#llm-aikoala73/worldmonitorGit 克隆查看详情与安装步骤 → LLM 与 AI82,309bootstrapSkillsMP@bytedance生成个性化 SOUL. md warm, adaptive 新手引导对话. 触发用户需求创建, 集合 up, 初始化 AI partner's identity — e. g., "创建 SOUL. md", "bootstrap 智能体", "集合 up AI 部分Generate a personalized SOUL.md through a warm, adaptive onboarding conversation. Trigger when the user wants to create, set up, or initialize their AI partner's identity — e.g., "create my SOUL.md", "bootstrap my agent", "set up my AI part#llm-aibytedance/deer-flowGit 克隆查看详情与安装步骤 → LLM 与 AI82,309video-generationSkillsMP@bytedance使用技能用户请求生成, 创建, imagine videos. 支持结构化提示词参考图片 guided generation.Use this skill when the user requests to generate, create, or imagine videos. Supports structured prompts and reference image for guided generation.#llm-aibytedance/deer-flowGit 克隆查看详情与安装步骤 → LLM 与 AI82,305agent-runtime-hooksSkillsMP@lobehub使用智能体生命周期钩子, tool mocks, intervention, sub-智能体调用上下文 compression.Use for agent lifecycle hooks, tool mocks, intervention, sub-agent calls and context compression.#llm-ailobehub/lobehubGit 克隆查看详情与安装步骤 → LLM 与 AI82,305agent-signalSkillsMP@lobehub使用智能体 Signal sources, 操作, 政策, 中间件, 工作流程 handoff deduplication.Use for Agent Signal sources, actions, policies, middleware, workflow handoff and deduplication.#llm-ailobehub/lobehubGit 克隆查看详情与安装步骤 → LLM 与 AI82,305chat-sdkSkillsMP@lobehub使用多-平台 bots 构建聊天 SDK: Webhook, 提及, slash 命令, cards, modals streaming.Use for multi-platform bots built with chat SDK: webhooks, mentions, slash commands, cards, modals and streaming.#llm-ailobehub/lobehubGit 克隆查看详情与安装步骤 → LLM 与 AI82,305heterogeneous-agentSkillsMP@lobehub使用 Claude Code/Codex 外部-智能体适配器, IPC, 事件映射, sessions, 持久化 tool-调用 chains.Use for Claude Code/Codex external-agent adapters, IPC, event mapping, sessions, persistence and tool-call chains.#llm-ailobehub/lobehubGit 克隆查看详情与安装步骤 → LLM 与 AI82,305i18nSkillsMP@lobehub使用用户-facing 字符串 React-i18next locale 关键点, namespaces, interpolation, 翻译 bun 运行国际化.Use for user-facing strings and react-i18next locale keys, namespaces, interpolation, translations or bun run i18n.#llm-ailobehub/lobehubGit 克隆查看详情与安装步骤 → LLM 与 AI82,305llm-generationSkillsMP@lobehub使用应用提示词, generateObject/generateText, 模型选择 generation 链路追踪. Excludes 提供方适配器智能体 snapshots.Use for application prompts, generateObject/generateText, model selection and generation tracing. Excludes provider adapters and agent snapshots.#llm-ailobehub/lobehubGit 克隆查看详情与安装步骤 → LLM 与 AI76,302agent-builderSkillsMP@shareAI-lab设计构建 AI 智能体域名. 使用用户: (1) 要求 "创建智能体", "构建助手", "设计 AI 系统" (2) 需要 understand 智能体架构, agentic 模式, 自主 AI (3) need 帮助 capabilDesign and build AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabil#llm-aishareai-lab/learn-claude-codeGit 克隆查看详情与安装步骤 → LLM 与 AI74,043deploying-scalable-agentsSkillsMP@microsoftTake 处理智能体原型可扩展, observable 生产环境部署 Microsoft Foundry. 覆盖部署模式 (客户端-hosted, hosted 智能体, 智能体工作流程), 智能体生命周期, 模型路由, 响应缓存, evaluatioTake a working agent prototype to a scalable, observable production deployment on Microsoft Foundry. Covers deployment patterns (client-hosted, hosted agents, agent workflows), the agent lifecycle, model routing, response caching, evaluatio#llm-aimicrosoft/ai-agents-for-beginnersGit 克隆查看详情与安装步骤 → LLM 与 AI74,043local-ai-agentsSkillsMP@microsoft构建本地-首先 AI 智能体运行 entirely developer workstation Microsoft Foundry 本地 Qwen 函数-calling 模型. 覆盖 Small 语言模型 (SLMs), OpenAI-compatible 本地接口, sandboxed 本地工具, 本地Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local #llm-aimicrosoft/ai-agents-for-beginnersGit 克隆查看详情与安装步骤 → LLM 与 AI74,043deploying-scalable-agentsSkillsMP@microsoftTake 处理智能体原型可扩展, observable 生产环境部署 Microsoft Foundry. 覆盖部署模式 (客户端-hosted, hosted 智能体, 智能体工作流程), 智能体生命周期, 模型路由, 响应缓存, evaluatioTake a working agent prototype to a scalable, observable production deployment on Microsoft Foundry. Covers deployment patterns (client-hosted, hosted agents, agent workflows), the agent lifecycle, model routing, response caching, evaluatio#llm-aimicrosoft/ai-agents-for-beginnersGit 克隆查看详情与安装步骤 → LLM 与 AI74,043local-ai-agentsSkillsMP@microsoft构建本地-首先 AI 智能体运行 entirely developer workstation Microsoft Foundry 本地 Qwen 函数-calling 模型. 覆盖 Small 语言模型 (SLMs), OpenAI-compatible 本地接口, sandboxed 本地工具, 本地Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local #llm-aimicrosoft/ai-agents-for-beginnersGit 克隆查看详情与安装步骤 → LLM 与 AI74,043deploying-scalable-agentsSkillsMP@microsoftTake 一个处理智能体原型 go 可扩展, observable 生产环境部署 Microsoft Foundry. E 覆盖部署模式 (客户端-hosted, hosted 智能体, 智能体工作流程), 智能体生命周期, 模型路由, 响应缓存, evaluatTake one working agent prototype go scalable, observable production deployment for Microsoft Foundry. E cover deployment patterns (client-hosted, hosted agents, agent workflows), the agent lifecycle, model routing, response caching, evaluat#llm-aimicrosoft/ai-agents-for-beginnersGit 克隆查看详情与安装步骤 → LLM 与 AI74,043local-ai-agentsSkillsMP@microsoft构建本地-首先 AI 智能体 wey dey 运行 fully developer workstation wit Microsoft Foundry 本地 Qwen 函数-calling 模型. E 覆盖 Small 语言模型 (SLMs), OpenAI-compatible 本地接口, sandboxed 本地工具, 本地 RBuild local-first AI agents wey dey run fully for developer workstation wit Microsoft Foundry Local and Qwen function-calling models. E cover Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local R#llm-aimicrosoft/ai-agents-for-beginnersGit 克隆查看详情与安装步骤 → LLM 与 AI74,043deploying-scalable-agentsSkillsMP@microsoftDalhin ang isang gumaganang 原型 ng 智能体 sa isang 可扩展, observable na 生产环境部署 sa Microsoft Foundry. Saklaw nito ang mga 部署模式 (客户端-hosted, hosted 智能体, 智能体工作流程), ang 生命周期 ng 智能体, 模式Dalhin ang isang gumaganang prototype ng agent sa isang scalable, observable na production deployment sa Microsoft Foundry. Saklaw nito ang mga deployment pattern (client-hosted, hosted agents, agent workflows), ang lifecycle ng agent, mode#llm-aimicrosoft/ai-agents-for-beginnersGit 克隆查看详情与安装步骤 → LLM 与 AI74,043local-ai-agentsSkillsMP@microsoftBumuo ng mga 本地-首先 AI 智能体 na tumatakbo nang buong-buo sa isang developer workstation gamit ang Microsoft Foundry 本地 Qwen 函数-calling 模型. Saklaw nito ang Small 语言模型 (SLMs), ang OpenAI-compatible na lokal naBumuo ng mga local-first AI agents na tumatakbo nang buong-buo sa isang developer workstation gamit ang Microsoft Foundry Local at Qwen function-calling models. Saklaw nito ang Small Language Models (SLMs), ang OpenAI-compatible na lokal na#llm-aimicrosoft/ai-agents-for-beginnersGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-adaptive-coordinatorSkillsMP@ruvnet智能体技能 adaptive-coordinator - 调用 $智能体-adaptive-coordinatorAgent skill for adaptive-coordinator - invoke with $agent-adaptive-coordinator#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-agentSkillsMP@ruvnet智能体技能智能体 - 调用 $智能体-智能体Agent skill for agent - invoke with $agent-agent#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-agentic-paymentsSkillsMP@ruvnet智能体技能 agentic-支付 - 调用 $智能体-agentic-支付Agent skill for agentic-payments - invoke with $agent-agentic-payments#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-analyze-code-qualitySkillsMP@ruvnet智能体技能分析-代码-质量 - 调用 $智能体-分析-代码-质量Agent skill for analyze-code-quality - invoke with $agent-analyze-code-quality#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-app-storeSkillsMP@ruvnet智能体技能应用-存储 - 调用 $智能体-应用-存储Agent skill for app-store - invoke with $agent-app-store#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-arch-system-designSkillsMP@ruvnet智能体技能 arch-系统-设计 - 调用 $智能体-arch-系统-设计Agent skill for arch-system-design - invoke with $agent-arch-system-design#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-architectureSkillsMP@ruvnet智能体技能架构 - 调用 $智能体-架构Agent skill for architecture - invoke with $agent-architecture#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-automation-smart-agentSkillsMP@ruvnet智能体技能自动化-智能-智能体 - 调用 $智能体-自动化-智能-智能体Agent skill for automation-smart-agent - invoke with $agent-automation-smart-agent#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-base-template-generatorSkillsMP@ruvnet智能体技能基础-模板-生成器 - 调用 $智能体-基础-模板-生成器Agent skill for base-template-generator - invoke with $agent-base-template-generator#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-benchmark-suiteSkillsMP@ruvnet智能体技能基准测试-套件 - 调用 $智能体-基准测试-套件Agent skill for benchmark-suite - invoke with $agent-benchmark-suite#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-byzantine-coordinatorSkillsMP@ruvnet智能体技能 byzantine-coordinator - 调用 $智能体-byzantine-coordinatorAgent skill for byzantine-coordinator - invoke with $agent-byzantine-coordinator#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-challengesSkillsMP@ruvnet智能体技能 challenges - 调用 $智能体-challengesAgent skill for challenges - invoke with $agent-challenges#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-code-analyzerSkillsMP@ruvnet智能体技能代码-analyzer - 调用 $智能体-代码-analyzerAgent skill for code-analyzer - invoke with $agent-code-analyzer#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-code-goal-plannerSkillsMP@ruvnet智能体技能代码-goal-planner - 调用 $智能体-代码-goal-plannerAgent skill for code-goal-planner - invoke with $agent-code-goal-planner#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-code-review-swarmSkillsMP@ruvnet智能体技能代码-评审-swarm - 调用 $智能体-代码-评审-swarmAgent skill for code-review-swarm - invoke with $agent-code-review-swarm#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-coderSkillsMP@ruvnet智能体技能 coder - 调用 $智能体-coderAgent skill for coder - invoke with $agent-coder#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-collective-intelligence-coordinatorSkillsMP@ruvnet智能体技能 collective-intelligence-coordinator - 调用 $智能体-collective-intelligence-coordinatorAgent skill for collective-intelligence-coordinator - invoke with $agent-collective-intelligence-coordinator#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-consensus-coordinatorSkillsMP@ruvnet智能体技能 consensus-coordinator - 调用 $智能体-consensus-coordinatorAgent skill for consensus-coordinator - invoke with $agent-consensus-coordinator#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-coordinationSkillsMP@ruvnet智能体 spawning, 生命周期管理, coordination 模式. 管理 60+ 智能体 types specialized 能力. 使用: spawning 智能体, coordinating 多-智能体任务, 管理智能体 pools. Skip: 单个-智能体工作, coordiAgent spawning, lifecycle management, and coordination patterns. Manages 60+ agent types with specialized capabilities. Use when: spawning agents, coordinating multi-agent tasks, managing agent pools. Skip when: single-agent work, no coordi#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-coordinator-swarm-initSkillsMP@ruvnet智能体技能 coordinator-swarm-初始化 - 调用 $智能体-coordinator-swarm-初始化Agent skill for coordinator-swarm-init - invoke with $agent-coordinator-swarm-init#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-crdt-synchronizerSkillsMP@ruvnet智能体技能 crdt-synchronizer - 调用 $智能体-crdt-synchronizerAgent skill for crdt-synchronizer - invoke with $agent-crdt-synchronizer#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-data-ml-modelSkillsMP@ruvnet智能体技能数据-机器学习-模型 - 调用 $智能体-数据-机器学习-模型Agent skill for data-ml-model - invoke with $agent-data-ml-model#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-dev-backend-apiSkillsMP@ruvnet智能体技能开发-后端-接口 - 调用 $智能体-开发-后端-接口Agent skill for dev-backend-api - invoke with $agent-dev-backend-api#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-docs-api-openapiSkillsMP@ruvnet智能体技能文档-接口-openapi - 调用 $智能体-文档-接口-openapiAgent skill for docs-api-openapi - invoke with $agent-docs-api-openapi#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-github-modesSkillsMP@ruvnet智能体技能 GitHub-modes - 调用 $智能体-GitHub-modesAgent skill for github-modes - invoke with $agent-github-modes#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-github-pr-managerSkillsMP@ruvnet智能体技能 GitHub-拉取请求-manager - 调用 $智能体-GitHub-拉取请求-managerAgent skill for github-pr-manager - invoke with $agent-github-pr-manager#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-goal-plannerSkillsMP@ruvnet智能体技能 goal-planner - 调用 $智能体-goal-plannerAgent skill for goal-planner - invoke with $agent-goal-planner#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-gossip-coordinatorSkillsMP@ruvnet智能体技能 gossip-coordinator - 调用 $智能体-gossip-coordinatorAgent skill for gossip-coordinator - invoke with $agent-gossip-coordinator#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-hierarchical-coordinatorSkillsMP@ruvnet智能体技能 hierarchical-coordinator - 调用 $智能体-hierarchical-coordinatorAgent skill for hierarchical-coordinator - invoke with $agent-hierarchical-coordinator#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-implementer-sparc-coderSkillsMP@ruvnet智能体技能 implementer-sparc-coder - 调用 $智能体-implementer-sparc-coderAgent skill for implementer-sparc-coder - invoke with $agent-implementer-sparc-coder#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-issue-trackerSkillsMP@ruvnet智能体技能工单-跟踪器 - 调用 $智能体-工单-跟踪器Agent skill for issue-tracker - invoke with $agent-issue-tracker#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-load-balancerSkillsMP@ruvnet智能体技能加载-balancer - 调用 $智能体-加载-balancerAgent skill for load-balancer - invoke with $agent-load-balancer#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-matrix-optimizerSkillsMP@ruvnet智能体技能 matrix-optimizer - 调用 $智能体-matrix-optimizerAgent skill for matrix-optimizer - invoke with $agent-matrix-optimizer#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-memory-coordinatorSkillsMP@ruvnet智能体技能记忆-coordinator - 调用 $智能体-记忆-coordinatorAgent skill for memory-coordinator - invoke with $agent-memory-coordinator#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-mesh-coordinatorSkillsMP@ruvnet智能体技能 mesh-coordinator - 调用 $智能体-mesh-coordinatorAgent skill for mesh-coordinator - invoke with $agent-mesh-coordinator#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-migration-planSkillsMP@ruvnet智能体技能迁移-计划 - 调用 $智能体-迁移-计划Agent skill for migration-plan - invoke with $agent-migration-plan#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-multi-repo-swarmSkillsMP@ruvnet智能体技能多-仓库-swarm - 调用 $智能体-多-仓库-swarmAgent skill for multi-repo-swarm - invoke with $agent-multi-repo-swarm#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-neural-networkSkillsMP@ruvnet智能体技能 neural-网络 - 调用 $智能体-neural-网络Agent skill for neural-network - invoke with $agent-neural-network#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-ops-cicd-githubSkillsMP@ruvnet智能体技能 ops-cicd-GitHub - 调用 $智能体-ops-cicd-GitHubAgent skill for ops-cicd-github - invoke with $agent-ops-cicd-github#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-orchestrator-taskSkillsMP@ruvnet智能体技能 orchestrator-任务 - 调用 $智能体-orchestrator-任务Agent skill for orchestrator-task - invoke with $agent-orchestrator-task#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-pagerank-analyzerSkillsMP@ruvnet智能体技能 pagerank-analyzer - 调用 $智能体-pagerank-analyzerAgent skill for pagerank-analyzer - invoke with $agent-pagerank-analyzer#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-performance-analyzerSkillsMP@ruvnet智能体技能 performance-analyzer - 调用 $智能体-performance-analyzerAgent skill for performance-analyzer - invoke with $agent-performance-analyzer#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-performance-benchmarkerSkillsMP@ruvnet智能体技能 performance-benchmarker - 调用 $智能体-performance-benchmarkerAgent skill for performance-benchmarker - invoke with $agent-performance-benchmarker#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-performance-monitorSkillsMP@ruvnet智能体技能 performance-监控 - 调用 $智能体-performance-监控Agent skill for performance-monitor - invoke with $agent-performance-monitor#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 → LLM 与 AI71,554agent-performance-optimizerSkillsMP@ruvnet智能体技能 performance-optimizer - 调用 $智能体-performance-optimizerAgent skill for performance-optimizer - invoke with $agent-performance-optimizer#llm-airuvnet/rufloGit 克隆查看详情与安装步骤 →