Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Agent workflows
This skill should be used when searching Claude Code session transcripts with semantic understanding. Triggers on queries like "find sessions about X", "when did I work on Y", "search previous conversations". Supports natural language queries with synonym matching.
Overrides default LLM truncation behavior. Enforces complete HTML generation with zero placeholder patterns. Every landing page must be delivered as a complete, production-ready file. No shortcuts, no skeletons, no “add more as needed” patterns
Interactively onboard a project to OpenSpec by running a structured interview and generating a complete QRSPI-configured openspec/config.yaml. Use this skill whenever a user mentions "openspec config", "config.yaml for openspec", "set up openspec", "onboard to openspec", "generate openspec config", "QRSPI config", or asks how to configure OpenSpec for their project — even if they just say "help me set up openspec" or "I want to use openspec". Always prefer this skill over ad-hoc config generation.
Use when implementing on-device AI with Apple's Foundation Models framework — prevents context overflow, blocking UI, wrong model use cases, and manual JSON parsing when @Generable should be used. iOS 26+, macOS 26+, iPadOS 26+, axiom-visionOS 26+
Ultra-compressed communication mode. Cuts token usage by about 75% by speaking like a caveman while preserving full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when the user says “caveman mode,” “talk like caveman,” “use caveman,” “less tokens,” “be brief,” or invokes /caveman. Also auto-triggers when token efficiency is requested.
Build software products autonomously via GSD headless mode. Handles the full lifecycle: write a spec, launch a build, poll for completion, handle blockers, track costs, and verify the result. Use when asked to "build something", "create a project", "run gsd", "check build status", or any task that requires autonomous software development via subprocess
Harbor intake for external work — the captain only handles unresolved decisions. Sweeps incoming issues and PRs, verifies every claim before disposition, reuses every decision already made, and hands the maintainer a docket whose pending items each carry one question with options, recommendation, impact and evidence. Agent-autonomous for facts and for actions covered by standing authorization; signed for every new judgment. Never merges.
TỰ BẢO TRÌ framework overstack trên máy user (self-maintain) — migrate llmwiki cũ lên harness (case B) hoặc update bản mới (case C). MỘT lệnh `install-harness.sh --self-heal` tự cài + tự backfill nợ wiki (Origin/index/OKF) + refresh bản đồ năng lực + health-check trong 1 process, không vòng lặp re-run. Trigger: "tự bảo trì framework", "self-maintain", "migrate harness", "update harness", "update overstack", "nâng cấp harness", "cài harness vào dự án cũ", "/harness-update".
Detect installed AI coding agents and append honest-feedback rules to their instruction files. Scans for config files (CLAUDE.md, copilot-instructions.md, .cursorrules, etc.), adapts output format per agent, and appends directives that disable sycophancy and enable constructive pushback. Use when setting up honest feedback, disabling people-pleasing, or enabling objective criticism. Triggers on honest agent, objective feedback, no sycophancy, honest criticism, contradict me, challenge assumptions, honest mode, brutal honesty
Use when the user asks to “create a hook,” “add a PreToolUse/PostToolUse/Stop hook,” “validate tool use,” “implement prompt-based hooks,” “use ${CLAUDE_PLUGIN_ROOT},” “set up event-driven automation,” “block dangerous commands,” or mentions hook events (PreToolUse, PostToolUse, Stop, SubagentStop, SessionStart, SessionEnd, UserPromptSubmit, PreCompact, Notification). Provides comprehensive guidance for creating and implementing Claude Code plugin hooks, focusing on the advanced prompt-based hooks API
Software implementation planning with optional file-based persistence. Use when asked to plan, when unresolved architecture or scope decisions need a durable record, or when multi-phase implementation needs recovery state. For the full research-and-issue workflow, use the ia-plan command (/ia-plan in Claude Code).
Use when a task may benefit from a bounded subagent for implementation, large-codebase discovery at task start, independent review, requested or pre-commit code simplification, or expert work on complex decisions, modeling, automation, or repeated failures. The main agent decides whether to delegate and accepts the result. Do not use for simple questions or short tasks that do not need meaningful review or delegation.
Build real-time Indic voice agents with Sarvam STT/TTS/LLM on LiveKit or Pipecat (Python), plus JS/TS custom-pipeline notes. Use this skill when creating phone bots, IVR, or always-on conversational voice apps. For one-shot in-chat voice reply or dubbing via MCP, use sarvam-mcp instead.
Operate desktop applications on macOS, Windows, and Linux through native accessibility trees, using the agent-desktop CLI. Use when an AI agent must see or act on a desktop GUI: click buttons, fill forms, read UI state, navigate menus, toggle checkboxes, scroll, drag, type text, press shortcuts, take screenshots, manage windows, use the clipboard, launch or close apps, and read notifications. Triggers on: "click button", "fill form", "open app", "read UI", "computer use", "operate desktop", "accessibility tree", "snapshot app", "type into field", "navigate menu", "toggle checkbox", "take screenshot", "desktop automation", "agent-desktop", or any desktop GUI task on macOS, Windows, or Linux.
Compress an agent's routing file (RESOLVER.md or AGENTS.md) by converting granular skill-per-row tables into functional-area dispatchers. Each area lists sub-skills in a "(dispatcher for: ...)" clause. The LLM reads one area entry and routes to the correct sub-skill. Proven via held-out A/B eval: dispatcher pattern outperforms naive pipe-table compression.
Generate a /goal mega prompt for Claude Code or Codex CLI by interviewing the user about their task. Use when the user wants to define a long-horizon autonomous goal—migration, refactor, feature build, optimization loop, test fixing, research project, learning system, or any task where the agent should run end-to-end without hand-holding. Trigger on: “help me write a goal”, “I want Claude to keep working until...”, “run this autonomously”, “set a /goal”, or any request that implies sustained agentic execution toward a non-trivial outcome. The skill conducts a structured interview (one question at a time) to extract outcome, context, success criteria, constraints, and quality bar—then outputs a filled-in mega prompt ready to paste into Claude Code or Codex.
Search and recall past AI conversations across Claude Code, Codex, Devin CLI, Grok, OpenCode, Pi, and ZCode via the `trellis mem` CLI. Use whenever the user asks to remember, find, or look up anything discussed in previous AI sessions across platforms, projects, or time. Triggers on phrases like "我之前跟 Claude/Codex 讨论过 X", "上次怎么处理 Y", "翻一下历史对话", "我们当时怎么决定 X 的", "为什么我们选了 X 而不是 Y", "find what I said about Z", "what did I discuss last week", "the rationale for choosing X", or "find the brainstorm where we picked Z over alternatives". Use even when the user doesn't say "history" or "recall"—any reference to past AI-conversation content should trigger this skill. The tool reads sessions directly from each platform's local storage; nothing is uploaded.
Score any AI model on the DeepSWE coding-agent benchmark via the OpenRouter API. Use when the user wants an independent, reproducible coding-agent eval — "run DeepSWE", "benchmark this model on DeepSWE", "score model X on the coding benchmark", "test a model via OpenRouter on DeepSWE", or to verify vendor-reported coding scores. Covers setup, the OpenRouter wiring for mini-swe-agent, single-task / subset / full 113-task runs, and leaderboard submission.
Create, edit, deploy, and interact with agents on TerminalUse. Use when user mentions "tu", "terminaluse", "deploy agent", "create agent", "edit agent", "update agent", "add skills", "agent task", "filesystem", or wants to build/modify/test/run an agent.
Plan how to build with You.com. Use when adding web search, content extraction, or research to an app or agent: finds the right You.com API, MCP server, SDK, or docs path (for example Vercel AI SDK, LangChain, Python SDK, or REST) through the `you-discover` tool and Docs MCP.
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to “open a website”, “fill out a form”, “click a button”, “take a screenshot”, “scrape data from a page”, “test this web app”, “login to a site”, “automate browser actions”, or any task requiring programmatic web interaction.
Design autonomous coding loop prompts for Clay's Ralph Loop. Use this skill when the user says /clay-ralph, mentions "ralph loop", wants to set up an autonomous coding loop, or wants Clay to run tasks while they're AFK. Guides the user through creating .claude/PROMPT.md and .claude/JUDGE.md for hands-off iterative development. Even if the user just says "I want to automate this task" or "run this overnight", this skill applies.
This skill should be used when the user asks to "create a slash command", "add a command", "write a custom command", "define command arguments", "use command frontmatter", "organize commands", "create command with file references", "interactive command", "use AskUserQuestion in command", or needs guidance on slash command structure, YAML frontmatter fields, dynamic arguments, bash execution in commands, user interaction patterns, or command development best practices for Claude Code.
SEO/GEO Agent Operations SOP v2 — Rebuilt from real-world operations (analook.com 0→launch, GSC incident, DataForSEO monitoring, gingiris.tools post-migration recovery). What's new in v2: • Step-by-step onboarding (Q1→Q6), multiple choice wherever possible — no big forms • Mandatory daily SEO log — first item is always GSC token status check • Data cross-validation rule: any external metric must come from ≥2 sources before use • Backlink progress tracking (6-week wait → check → alert if not indexed) • New vs. established site strategy matrix (new site <6mo: KD≤20; mature: KD up to 35+) Real incidents baked in: GSC token expired silently for 2 weeks (analook.com), P0 keywords dropped out of Top 100 after domain migration (gingiris.tools), traffic data off by 17× from single-source DataForSEO. Triggers: "SEO agent" | "SEO daily report" | "GSC monitoring" | "DataForSEO" | "GEO agent" | "backlink tracking" | "IndexNow" | "competitor comparison page" | "SEO SOP" | "autonomous SEO" | "GEO triple combo" SEO/GEO Agent 运营 SOP v2 — 基于真实运营经验重写(analook.com 从零建站 + GSC 断档 2 周 + DataForSEO 监控 + gingiris.tools 迁移权重恢复)。 对话式开局、强制日报(GSC token 是第一项)、数据交叉验证铁律、外链进度追踪、新老站区分策略。
Two scopes: (1) Drives Kimi.app via computer-use for Work/Agent data-plugin queries (天眼查、同花顺 iFinD、SEC、IMF etc.) and Chat's built-in 深度研究 report; (2) kimi-cu macOS desktop-automation playbook — AX set_value / keyboard / click channel selection, foregrounding, AX-tree recovery, form & dialog recipes. Use for "用 Kimi 查" / "操作 Kimi 客户端" / "Kimi 插件" / "Kimi 深度研究" / driving any macOS desktop app (Electron/webview/native) via kimi-cu. Not for kimi.com browser automation (use kimi-webbridge) or direct iFinD API access.
Persist, schedule, resume, and hand off monitoring or ownership campaigns that outlive one agent session or disposable workspace. Use when work must be checked repeatedly over days, when creating a new long-term campaign, when transferring ongoing work to a future agent, or when producing a delayed evidence-based report
(NS) Spec-driven delivery — clarify requirements, define tasks (including unit/e2e), implement, and resume/continue a version from disk. Entry priority 2: feature specs, version work, multi-day scope, or “continue” / “resume version” when docs/versions/ has partial artifacts. Bare quick fixes: ns-coder. Do NOT use for brownfield onboarding, architecture rules, or /ns-harness prepare (never auto-run Prepare).
Use when the question is what the agent is told, not how its loop is wired: system prompt, tool choice, workflow or agent, harness audits. Triggers: “system prompt” / “системный промпт,” “аудит агента.” Not for the loop (agent-orchestrator), evals (agent-evals), or protocols (agent-interop)
Builds AI agents on Cloudflare using the Agents SDK with state management, real-time WebSockets, scheduled tasks, tool integration, and chat capabilities. Generates production-ready agent code deployed to Workers. Use when: user wants to "build an agent", "AI agent", "chat agent", "stateful agent", mentions "Agents SDK", needs "real-time AI", "WebSocket AI", or asks about agent "state management", "scheduled tasks", or "tool calling". Biases towards retrieval from Cloudflare docs over pre-trained knowledge.
Multi-agent collaborative debate producing visible round-by-round transcripts with real intellectual friction — members are topic-briefed custom agents, run as a 3-round DEBATE or a 1-round QUICK check, to find the best path. USE WHEN council, debate, multiple perspectives, weigh options, deliberate, get different views, what would experts say, pros and cons. NOT FOR pure adversarial attack (use RedTeam).
Use when you need a deep, implementation-ready engineering plan for a code change — built from GitNexus graph intelligence, statement-level PDG analysis, and targeted source verification, compact enough for an implementation agent to start without re-investigating. Also strengthens existing plans via Deepen mode. Examples: "/gitnexus-plan Add retry support to the ingestion pipeline", "/gitnexus-plan deepen docs/plans/<plan>.md", "plan this change using the knowledge graph".
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.
