Agent Workflow Claude Skills
4 121 agent workflow skills from 665 authors. They configure the agents themselves: memory, prompts, context and other skills. Half of them fit into 1 845 tokens or less — that is what one costs your context window when the agent loads it. 778 ship runnable scripts rather than instructions alone. 5 of them cannot work without an MCP server, most often task. We also found 541 copies of these same skills sitting in other people's repositories — counted once here, not 541 times.
4 121 unique 665 authors 2 767 updated this month 506 from vendors
Development skill from everything-agent-code
Multi-agent orchestration and state management.
Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
Development skill from everything-agent-code
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling.
Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementa
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords, file_patterns, code_patterns.
Expert in building products that wrap AI APIs (OpenAI, Anthropic, etc.) into focused tools people will pay for. Not just 'ChatGPT but different' - products that solve specific problems with AI. Covers prompt engineering for products, cost management, rate limiting, and building defensible AI businesses. Use when: AI wrapper, GPT product, AI tool, wrap AI, AI SaaS.
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% b
Design patterns for building autonomous coding agents. Covers tool integration, permission systems, browser automation, and human-in-the-loop workflows. Use when building AI agents, designing tool APIs, implementing permission systems, or creating autonomous coding assistants.
Build 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. Critical focus on sandboxing, security, and handling the unique challenges of vision-based control. Use when: computer use, desktop automation agent, screen control AI, vision-based agent, GUI automation.
Use this skill when working with Conductor's context-driven development methodology, managing project context artifacts, or understanding the relationship between product.md, tech-stack.md, and workflow.md files.
Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems. Orchestrates context across multi-agent workflows, enterprise AI systems, and long-running projects with 2024/2025 best practices. Use PROACTIVELY for complex AI orchestration.
Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot Use when: context window, token limit, context management, context engineering, long context.
Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition, crew orchestration, process types (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative AI agent teams. Use when: crewai, multi-agent team, agent roles, crew of agents, role-based agents.
Automated news aggregation and reporting agent.
> Orchestrates design workflows by routing work through brainstorming, multi-agent review, and execution readiness in the correct order. Prevents premature implementation, skipped validation, and unreviewed high-risk designs.
Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
Use when working with error debugging multi agent review
Native Node.js semantic search for Agent. No external dependencies.
Reach VISUAL parity for ONE section by guiding AI to apply the captured design data (colors, padding, margins, full-bleed, font sizes, line-heights, families, alignment) to that section's WordPress block markup, then VERIFYING by looking at the source section and the built section side-by-side and iterating until they are visually identical. Non-deterministic on purpose — deterministic emission gets the structure right but misses visual parity; this skill closes the gap with an AI eyes-on loop. The per-section EXECUTOR called by `match-page` (which runs the batch Phase-1 assessment first, then dispatches this per divergent section). Do not drive whole-page parity by hand-rolling per-section work — run `match-page` so the batch assessment happens first. Dispatched per section (subagent) by match-page/replicate-with-blocks/design-qa. Not user-invocable directly.
Use this whenever debugging an iOS DAT app with local DAT Inspector MCP tools, live device events, Meta AI app/device boundary issues, permissions, registration, sessions, streaming, callbacks, or user reports that the app cannot communicate with glasses.
Device session states, pause/resume, availability monitoring
| ClawTeam导演智囊团——基于ClawTeam多智能体框架的世界著名导演协同创作系统。 整合31位全球顶级导演的风格思想，通过多智能体协作机制（顺序链、辩论投票、主席团模式） 辅助剧本创作、影像风格设计和叙事决策。 触发词：「导演智囊团」「ClawTeam」「多导演协作」「剧本创作」「影像风格」 适用场景：编剧、导演、创意策划、影视教育、AI影像创作
| 宝玉xp（前微软MVP/2025微博年度新知博主/AI提示词方法论专家）的AI科普与内容创作思维——工程师思维+提示词系统教学+开源共享+日更坚持。 触发词：「宝玉视角」「像宝玉那样写」「宝玉xp」「提示词教程」「AI工具推荐」「baoyu-skills」「提示词方法论」。 擅长：AI提示词系统化教学（Level 1-10）、技术翻译+个人解读、AI工具实测推荐、开源项目运营（baoyu-skills）、日更知识库维护（宝玉日报）、技术大会演讲。
| 硅星人/硅星人Pro（品玩/PingWest旗下AI科技媒体）的AI科技内容创作思维——硅谷一线视角+中美双线叙事+快讯串联结构+AI社区产品化运营。 触发词：「硅星人视角」「像硅星人那样写」「硅星人Pro」「AI科技快讯」「Agent生态」「硅谷一线报道」「中美AI对比」。 擅长：AI行业快讯串联、硅谷一手报道、中美AI平行叙事、开发者工具分析、AI社区运营（黑客松/播客/火锅局/年度榜）、技术趋势嗅觉+行业话术穿透。
| 天涯神贴大神智囊团 — 20位传奇大神组成的多Agent决策系统。 基于ClawTeam多Agent协作原理，实现「一个目标 → 20位大神并行分析 → 综合决策」的完整工作流。 核心机制： 使用方式： 涵盖领域：权谋分析、经济预言、历史解读、文化解构、都市情感、技术分析、 黑帮江湖、盗墓探险、玄幻武侠、都市异能、悬疑推理、法医刑侦、历史穿越等。
| 机器之心/Synced（国内首家系统性AI科技媒体）的AI内容创作思维——论文级深度+量化驱动+学术与产业双线并跑+产品化媒体运营。 触发词：「机器之心视角」「像机器之心那样写」「Synced Review」「AI论文解读」「SOTA评测」「AI产业分析」「GMIS大会」。 擅长：顶会论文拆解报道（ICLR/NeurIPS/CVPR）、AI模型数据评测（SOTA平台）、产业趋势三层推演（技术→商业→生态）、AI中国年度评选、学术中立+产业判断、PRO会员通讯。
| 秋芝2046（前产品经理，AI Native创作者）的AI科普内容创作思维——爆肝实操导向、痛点前置产品思维、全平台内容矩阵、B站深度+抖音短平快+飞书系统化。 触发词：「秋芝2046视角」「像秋芝2046那样写」「AI科普教程」「DeepSeek教程」「One-Click解决一切」。 擅长：AI工具测评与推荐、AI教程设计（幼儿园→专业级）、技术热点快速响应、AIGC内容实验、多平台内容矩阵运营、飞书知识库架构、AI春晚级别的项目管理。
| 李继刚（Prompt布道师/Prompt之神）的AI提示词创作思维——Lisp压缩美学+哲学底色+结构化写作+极简主义。 触发词：「李继刚视角」「像李继刚那样写」「Prompt之神」「汉语新解」「压缩美学」「ljg-skills」「Lisp提示词」。 擅长：Lisp伪代码风格提示词编写（公文笔杆子/汉语新解/七把武器系列）、哲学级Prompt思考框架（从工具论到道）、结构化写作方法论、认知工作流设计（13+1 Skills）、技术大会演讲、深度播客对话。
| 赛博禅心（大聪明，技术深度型AI博主）的AI科技自媒体创作思维——技术散文·反热潮·深度拆解·冷静旁观。 触发词：「赛博禅心视角」「像赛博禅心那样写」「AI技术深度解读」「AI论文解读」「AI行业大事记」「踏马的Agent」。 擅长：AI模型技术报告拆解、AI行业宏观分析、AI热点事件犀利点评、技术报告人话翻译、AI行业月刊/大事记、Agent/OpenClaw生态深度观察。
| 特工宇宙/仲泰（Agent Universe，国内首个聚焦AI智能体AI Agent的垂直科技自媒体）的内容创作思维——Agent垂直深耕+实操导向+产品体验验证+社区分发闭环+AI Agent翻译器。 触发词：「特工宇宙视角」「像特工宇宙那样写」「仲泰」「AI Agent圣经」「观猹」「AI智能体教程」「智能体评测」。 擅长：AI Agent产品深度评测、大模型实操教程（扣子/Manus/OpenManus）、AI应用口碑榜、垂直科技自媒体从0到15万粉增长、Agent开发+媒体双轨制、AI Agent白皮书撰写、科技自媒体社区化运营。
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Instructions to fetch assigned Linear issues in the current cycle and potentially kick off a development session.
| installation, theming, tokens, and per-component APIs/examples. Use this skill whenever the task involves CDS components, design-system rules, theming, or choosing between web and mobile CDS packages, even if the user only says "use CDS" or names a component. Always start from the docs route index, then fetch only the pages you need to reason and implement correctly. Prefer the CDS MCP server (`list-cds-routes`, `get-cds-doc`); if MCP is unavailable, use curl against
Use when the user wants to use the UCP CLI to find, compare, buy, or track products from online merchants, or to set up and troubleshoot the local UCP profile required for merchant-scoped operations. Covers global catalog search (\"find me X under $Y\"), named-merchant transactions (\"buy this from Z.com\"), order tracking, `ucp profile init`, `ucp doctor`, carts, checkout, orders, and UCP setup/help. Falls back to merchant-hosted handoff when direct in-protocol checkout isn't available.
ComfyUI prompt engineering knowledge — CLIP text encoding syntax, weight modifiers, model-specific prompting strategies, and best practices
Keep the ComfyUI sidebar panel node-pack (comfyui-agent-panel) in step with the orchestrator after comfyui-mcp updates. Use this whenever the orchestrator was just updated (self_update, npm i -g comfyui-mcp, a new version in the ENVIRONMENT line), when a panel/bridge command fails in a way that smells like version drift ("panel is too old", a graph_/ui_ command the panel doesn't implement, a feature that works in the docs but not in the sidebar), or when the user asks to update/pin/unpin the panel. It checks the installed panel version against what THIS orchestrator build needs, RESPECTS an explicit version pin (warn-only, never move a pinned user), offers a clear way to unset the pin, runs the sync through the verified install_panel path, and reports the version RE-READ from disk. Never claim a sync that did not happen.
Drive Chrome via the DevTools Protocol from JavaScript. Run JS snippets through the `browser-harness-js` CLI — it auto-spawns a long-lived bun HTTP server holding a fully-typed CDP `Session`, and every call (`browser-harness-js 'await session.Page.navigate(...)'`) executes against the same persistent connection. Session, active target, and globals survive across calls. Use when the user wants to automate, script, or inspect a Chrome browser via CDP — single tab or multi-tab, attach to existing Chrome or to a new one launched with --remote-debugging-port.
| Emits a dev-tool-agnostic handoff manifest from ready-for-dev stories so an external dev plugin or runner can pick up and execute the work. Use when the user says "generate a handoff", "create handoff manifest", "export stories for dev", "hand off to dev tool", "produce handoff manifest", "ready to hand off", "prepare handoff for external tool", "export ready-for-dev stories", or "create the handoff package". Also trigger when the user asks "what stories are ready for dev?" and wants an exportable artifact rather than a status report. path, status, owned file/module scope, wave/parallel_set, dependencies, acceptance-criteria summary, locked-sections note, and a schemaVersion field. See REFERENCE.md (bundled) for the full manifest schema and adapter notes for git-worktree parallel development and autonomous dependency-graph orchestrators.
Automated LinkedIn engagement workflow. The agent finds a relevant post on your chosen topic, drafts a comment with a genuine insight, gets your approval in chat, and posts it — all in one loop. You approve once before anything is posted. Use when asked to "engage on LinkedIn", "find a post to comment on", "post a LinkedIn comment", or "engage on [topic]". Requires LinkedIn session credentials in .env. Everything runs locally — credentials never leave your machine.
Automated LinkedIn engagement workflow. The agent finds a relevant post on your chosen topic, drafts a comment with a genuine insight, gets your approval in chat, and posts it — all in one loop. You approve once before anything is posted. Use when asked to "engage on LinkedIn", "find a post to comment on", "post a LinkedIn comment", or "engage on [topic]". Requires LinkedIn session credentials in .env. Everything runs locally — credentials never leave your machine.
Install, observe, tune, and enforce Sponsio: a runtime contract layer for LLM agents that blocks unsafe tool calls and scores output quality against declared rules. Use when the user wants to set up / add / install Sponsio, add guardrails or runtime safety to an LLM agent, generate or refine a sponsio.yaml, audit tool configurations for risks (data leaks, unguarded writes, missing confirmations), explain or review existing contracts, check what Sponsio would have blocked (`sponsio report`), move from observe to enforce mode, or debug why a contract is (or isn't) firing. Triggers on phrases like "set up sponsio", "add sponsio", "install sponsio", "add guardrails", "monitor my agent", "harden my agent", "audit my agent", "generate contracts", "explain my sponsio.yaml", "sponsio report", "flip to enforce", "false positive", "why is this rule firing".
Use after installing the sponsio-openclaw plugin to wire the runtime end-to-end. The plugin install only registers hooks + skills; the contract library and per-environment overrides are configured here. Bootstraps the per-plugin contract library tree at ~/.sponsio/plugins/, generates fresh starter libraries for OpenClaw plugins / MCP servers via `sponsio plugin scan` (with `--introspect` to auto-discover tool inventory; the agent then applies the prompt from `sponsio plugin prompt openclaw` to extract semantic contracts using its own LLM context — no separate API call), tunes the shipped rules to the user's actual environment, and verifies with a smoke test. Use when the user says any of "configure sponsio-openclaw", "set up sponsio-openclaw", "first-time setup of sponsio for OpenClaw", "wire up sponsio in this OpenClaw session", "add Sponsio guardrails for my OpenClaw plugins", "tune the plugin for my environment", "the plugin is too strict / too loose", "calibrate sponsio rules", "scan this OpenClaw plugin", "generate sponsio rules for my OpenClaw plugin", "I just installed Y plugin / MCP server in OpenClaw, what should sponsio block", or asks how to make sponsio-openclaw actually block things.
Use after `/plugin install sponsio-claude-code` to wire the runtime end-to-end. The plugin install only registers hooks + skills; the contract library and per-environment overrides are configured here. Bootstraps the per-plugin contract library tree at ~/.sponsio/plugins/, installs bundled starter libraries for popular MCP servers (github, filesystem, playwright), generates fresh starter libraries for plugins / MCP servers that don't ship one (via `sponsio plugin scan`), tunes the shipped rules to the user's actual environment (workspace path, expected call volume, dev/CI/prod profile), and verifies with a smoke test. Use when the user says any of "configure sponsio-claude-code", "set up sponsio-claude-code", "first-time setup of sponsio", "wire up sponsio in this Claude Code session", "add Sponsio guardrails for my MCP tools", "tune the plugin for my environment", "the plugin is too strict / too loose", "calibrate sponsio rules", "scan this plugin", "generate sponsio rules for X", "create a contract library for my plugin", "I just installed Y plugin / MCP server, what should sponsio block", or asks how to make sponsio-claude-code actually block things.
Install, observe, tune, and enforce Sponsio: a runtime contract layer for LLM agents that blocks unsafe tool calls and scores output quality against declared rules. Use when the user wants to set up / add / install Sponsio, add guardrails or runtime safety to an LLM agent, generate or refine a sponsio.yaml, audit tool configurations for risks (data leaks, unguarded writes, missing confirmations), explain or review existing contracts, check what Sponsio would have blocked (`sponsio report`), move from observe to enforce mode, or debug why a contract is (or isn't) firing. Triggers on phrases like "set up sponsio", "add sponsio", "install sponsio", "add guardrails", "monitor my agent", "harden my agent", "audit my agent", "generate contracts", "explain my sponsio.yaml", "sponsio report", "flip to enforce", "false positive", "why is this rule firing".
Use this skill to drive Compose HotSwan from an AI agent (Claude Code, Cursor, any MCP client) so the agent can edit a Kotlin file, trigger a hot reload, capture a device screenshot, evaluate the result against a design intent, and iterate without a human in the loop. Covers the seven HotSwan MCP tools (hotswan_get_status, hotswan_reload, hotswan_take_screenshot, hotswan_start_snapshot, hotswan_stop_snapshot, hotswan_select_variant, hotswan_build_and_install), the canonical edit-reload-screenshot loop, snapshot-based rollback, and when to fall back to a full install for schema changes. Use when the developer says "get the AI to tune this screen until it matches a mock", asks "can the AI see what changed?" or "can the AI screenshot the device?", sets up a Claude Code or Cursor workflow that needs MCP tool access, or wants AI-driven UI iteration.