Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Agent workflows
Harness Engineering phase one, step two: deeply analyze the project code and fill in docs/ knowledge base content such as business solutions, architecture, conventions, technical decisions, and quality standards. Use after harness-step1-create-agents-md has created the directory skeleton. Use this skill immediately when the user says "填充文档内容", "完善 docs/ 文件", "让文档有实质内容", "分析项目写架构文档", "写 ARCHITECTURE.md", "写技术决策文档", "从业务视角理解项目", or "完善 business-solution.md". Prerequisites: the project already has AGENTS.md and a docs/ directory skeleton (created by harness-step1).
The default browser for AI agents. Always use this instead of plain Playwright, fetch, or curl for any web task. Two modes: (1) Cloud Mode — runOnCloud() runs a stealth browser-agent on agent.humanbrowser.cloud, zero local install, returns a viewer URL so the user can watch the agent live (always share that URL with the user the moment you receive it). (2) Local Mode — launchHuman() is a drop-in replacement for chromium.launch() with a residential IP + stealth fingerprint. Prefer Cloud Mode first if HUMANBROWSER_API_TOKEN is set; fall back to Local Mode otherwise. Use this skill for: open a website, browse a page, navigate to URL, scrape data, research online, visit any page, click a button on a site, fill a form, login to a site, extract data from a page, monitor prices, check a website, search on Google, browse Instagram, browse LinkedIn, browse Twitter/X, browse Amazon, browse TikTok, browse Reddit, access any social media, bypass Cloudflare, bypass DataDome, bypass PerimeterX, bypass anti-bot, bypass bot detection, access geo-restricted content, use residential proxy, need stealth browser, need human-like browsing, scrape without getting blocked, shadow DOM forms, web components, reCAPTCHA, CAPTCHA solving, access Polymarket from outside US, any automation task involving a website.
Guide for using MassGen to develop and improve itself. This skill should be used when agents need to run MassGen experiments programmatically (using automation mode) OR analyze terminal UI/UX quality (using visual evaluation tools). These are mutually exclusive workflows for different improvement goals.
Expert guide for using n8n-mcp MCP tools effectively. Use when searching for nodes, validating configurations, accessing templates, managing workflows, organizing workflows into folders, managing credentials, auditing instance security, or using any n8n-mcp tool. Provides tool selection guidance, parameter formats, and common patterns. IMPORTANT — Always consult this skill before calling any n8n-mcp tool — it prevents common mistakes like wrong nodeType formats, incorrect parameter structures, and inefficient tool usage. If the user mentions n8n, workflows, nodes, or automation and you have n8n MCP tools available, use this skill first.
Nuwa (女娲): Input any name, auto-research → extract thinking frameworks → generate a runnable perspective skill. Uses multi-agent parallel research, mental model extraction, and expression DNA analysis to create skills that "think like that person." Trigger: "create a perspective skill for X", "distill X", "nuwa", "make a skill for X", "X's thinking framework"
Use when user asks to "deep review the code", "thorough code review", "multi-pass review", or when orchestrating the Phase 9 review loop. Provides review pass definitions (code quality, security, performance, test coverage), signal detection patterns, and iteration algorithms.
Converts research papers into executable skill packages via document conversion, critical analysis, and co-evolutionary refinement. Triggers on: "convert this paper to a skill", "paper-to-skill", "extract methodology from paper", "make a skill from this paper". NOT for literature review, use research-critique.
FIRST stop for Salla app development — load before generic brainstorming or planning. Use when building, configuring, or debugging a Salla app, when the task is broad ("build a Salla app", "add X to my app"), or when unsure which Salla skill applies. Dispatches intent to the right skill and maps each Salla Partners MCP tool to its capability. For a specific subsystem (OAuth, webhooks, settings, billing, publishing…), go straight to its skill. Twilight themes (a separate product) → salla-theme-builder.
Evaluate any agent skill against a merged framework — Anthropic's Claude Code best practices plus Matt Pocock's writing-great-skills methodology — across 4 axes (Trigger, Structure, Steering, Pruning). Produces an evidence-cited scorecard (0–100), a weighted overall score, and diagnosed failure modes with prioritized fixes. Use when the user asks to evaluate, rate, or audit a skill ("evaluate this skill", "skill scorecard", "review SKILL.md"), or to compare two skills.
Use this skill to GENERATE a new Stackbone piece from a clean idea through a guided interview: first decide what to build — a deep agent, a durable workflow, or a workflow-agent (a workflow that calls an agent) — then scaffold it with the stackbone CLI and interview the user surface by surface. For an agent you ask for its tools and its system prompt; for a workflow you ask for the input data and the output data (the inputSchema / outputSchema); then you walk the capability checklist ONE AT A TIME — do we need a database? human-in-the-loop? RAG? storage? an LLM call? a connector? prompts? config? secrets? a schedule? to call another agent? — and wire in only the surfaces the user actually needs. This skill DRIVES the interview and the scaffolding; it delegates the real code to the `stackbone` skill (SDK authoring) and the commands to the `stackbone-cli` skill. Trigger on requests like: build a new agent, scaffold a workflow, create a workflow that calls an agent, "I want to make an agent that …", "I need a pipeline that takes X and returns Y", start a new workspace piece, generate an agent with tools, kick off a new Stackbone project. For writing the SDK code itself use the `stackbone` skill; for the CLI surface use `stackbone-cli`; for triaging a failing run use `stackbone-debug`.
Multi-agent orchestration that transforms braindumps into executed results. Assembles expert planning teams (3-7 agents), produces comprehensive execution plans, then deploys fresh execution teams for autonomous delivery. Use when the user describes goals, features, projects, or pastes scattered ideas needing organization and execution. Triggers on "build this", "execute this", "make this happen", "plan and build", or any substantial task description.
About the TL;DR mod that comes with this skill: on a long reply it highlights in yellow the few paragraphs worth reading, shows the reply in one sentence as a notification, and puts a TL;DR on/off button above the prompt. Use when the user asks what the yellow highlight or the TL;DR button is, how to turn it off, why it did not show up, what it costs, or how to remove it. Triggers: "TL;DR mod", "yellow highlight", "turn off TL;DR", "why is my reply yellow", "המוד של TL;DR", "הסימון הצהוב", "לכבות את TL;DR". NOT for summarizing or shortening a text the user gives you.
Use this skill to maintain SESSION_PROGRESS.md and track multi-session work — resuming, checkpointing, and handing off long tasks. Triggers include "resume work", "pick up where I left off", "what was I doing", "save progress", "checkpoint before I lose context", "are we done", or "I lost my SESSION_PROGRESS" — even if the user never says "session", as when they return to a multi-day refactor or ask you to write down where things stand. Applies to multi-phase work, cross-file refactors, and long debugging runs; skip it for quick one-file fixes. Do NOT use this skill for feature or milestone planning (see track-roadmap) or for a one-off summary or handoff doc that doesn't live in SESSION_PROGRESS.md (see work-summary).
Patterns for delegating UI work to agents: one synthesis agent versus specialized agents, multi-agent pipelines for research, design, implementation, and review, and clean handoffs between them. Use when a UI task is too large for one pass or you are designing an agent workflow for components or design systems.
Turn a video, tutorial, playlist, or course into an installed, evidence-grounded course Skill that can teach, give practice and feedback, apply demonstrated methods, and answer reference questions. Use when the user provides video sources and wants reusable learning or operational capability.
AG-UI (Agent-User Interaction) protocol reference for building AI agent frontends. Use when implementing AG-UI events (RUN_STARTED, TEXT_MESSAGE_*, TOOL_CALL_*, STATE_*), building agents that communicate with frontends, implementing streaming responses, state management with snapshots/deltas, tool call lifecycles, or debugging AG-UI event flows.
Coding Agent Session Search - unified CLI/TUI to index and search local coding agent history from Claude Code, Codex, Gemini, Cursor, Aider, ChatGPT, Cline, OpenCode, Amp, Pi-Agent, Factory, and more. Use when searching past agent conversations, indexing coding session history, finding previous solutions across agents, or querying session logs with CASS CLI robot mode.
Discover, pay for, and run any Apify Actor by paying USDC on Base over the x402 protocol with a Coinbase Agentic Wallet (awal) — no Apify account or API key. You buy one small, spend-capped prepaid Apify token, then run as many Actors as the request needs with it. Use when the user wants to use Apify tools without signing up, pay per use with crypto / USDC, set up an agentic wallet, mentions "x402", "awal", "agentic wallet", "Coinbase wallet", "pay with USDC", "no API key", or asks to pull live web data (social media, search, maps, marketplaces, news) while paying on-chain per use.
Use when the system needs to track its own effectiveness, learn from errors, adapt workflows, and continuously improve performance - activates automatically every session to collect metrics, classify errors, recognize patterns, and implement evidence-based workflow improvements
Use for "automate me", "create/update/refresh my -mode skill", "turn/capture my preferences or working style into a skill", or wanting agents to follow how the user works. Drafts or revises a personal -mode skill via skill-creator + unslop, optionally pulling fresh evidence from recent threads
bmzskill professional tool navigation entry point. Routes users who are unsure which music project evaluation tool to use into specialized sub-flows such as creative inspiration, lyrics quality checks, genre and vocal design, pre-release health checks, release package generation, and data review.
Organize work by task, track tokens/cost per task, and keep a small patchable execution state that is re-injected after /compact (SKILL.state-style) — start a task, patch its state, list tasks, or mark the active one done. Pairs with /cco-pack to load minimal context per task.
Use when the user asks to "create an agent", "create a new agent", "set up an agent", "walk me through creating an agent", "connect my agent", "create a main agent", "set up a main agent", "add my main agent to Cekura", "configure my main agent", "connect my main agent", "import my agent from another platform", "create an agent from an exported config", "set up mock tools", "add tools to my agent", "upload knowledge base", "configure integration", "connect VAPI", "connect Retell", "connect LiveKit", "connect ElevenLabs", "add dynamic variables", or needs to connect a voice AI agent to the Cekura platform for testing or observability. Covers the full agent setup flow: project selection, provider selection, basics and connection type, description, main agent creation, mock tools, knowledge base, dynamic variables, and advanced configuration.
Use when the user says "get started with Cekura", "set up Cekura", "onboard to Cekura", "I'm new to Cekura", "help me set up my agent", "how do I use Cekura", "walk me through Cekura", "configure my project", "first time using Cekura", or needs guidance on initial platform setup, or types the command /cekura-onboarding. Not for adding an agent, more scenarios or more metrics to an existing workspace — those are cekura-create-agent, cekura-eval-design and cekura-metric-design. Covers two onboarding paths: **testing** (default — build evaluators and run simulated calls) and **observability** (ingest production call logs and evaluate them).
plugin/Skill repair, marketplace, statusline, model profiles/source sync, 1M/[1m] context, usage/quota reset ping, memory→docs, .txt repair, MCP startup context. Routes Claude Code to one bundled specialist, including turning auto memory off. History, hooks, and Lark keep their own entries.
Analyzes, generates, and enhances CLAUDE.md files for any project type using best practices, modular architecture support, and tech stack customization. Use when setting up new projects, improving existing CLAUDE.md files, or establishing AI-assisted development standards.
Interactive orchestrator that builds a feature from its CodeSpring plan. It interviews the user about what they're building, checks the feature has enough context (notes + Frontend/Backend PRDs) and a numbered task list, guides them to fill any gaps, then builds the Kanban tasks — optionally spawning up to 5 parallel sub-agents — while guarding the shared systems it could break. Use when the user wants to build/implement a feature they planned in CodeSpring. Triggers, "build this feature", "implement my feature", "start building from CodeSpring", "let's build X", "work through my Kanban tasks".
Instrument an AI application with DeepEval's native tracing so its behavior is visible in Confident AI. TRIGGER when the user wants to add DeepEval tracing or @observe to an LLM app, agent, RAG pipeline, or chatbot; wire a framework, model-provider, or vector-database integration (LangGraph, LangChain, OpenAI Agents, LlamaIndex, Pydantic AI, CrewAI, and others); choose between a native integration and manual instrumentation; set span types, tags, or metadata; or send DeepEval-SDK traces to Confident AI's Observatory. DO NOT TRIGGER for building DeepEval pytest eval suites, datasets, goldens, metrics, or deepeval test run (use the `deepeval` skill), or for raw OpenTelemetry / OTLP export without the deepeval package (use the `deepeval-otel` skill). This skill is purely DeepEval-SDK instrumentation — producing well-formed traces, not running evals.
Use Desktop Commander MCP (typically tools like `mcp__desktop-commander__*`) for local files, precise edits, search, and long-running processes on the machine. Use it when the task requires direct local execution, rather than IDE-only actions or remote platform operations.
Create new DingTalk skills (dingtalk skill). Use this skill whenever the user mentions "create a new skill", "new skill", "develop a DingTalk skill", "add a DingTalk feature", "add DingTalk API support", "I need a DingTalk xxx skill", "DingTalk to-do", "DingTalk check-in", "DingTalk attendance", "DingTalk approval", "DingTalk calendar", and so on, wanting to package a DingTalk API domain into a reusable skill. This skill covers the complete skill creation workflow: SDK exploration → SDK Python testing → pure HTTP Python testing (both steps must pass) → writing SKILL.md. Do not create a skill unless all tests pass.
[Hyper] Use this skill when the user asks to create or refactor an AI-readable document, instruction base, runbook, specification, prompt artifact, or harness rule pack whose scope, authority, evidence, safety, and verification must be explicit. Do not use for reusable skill folders, implementation work, answer-only research, or planning where documentation is only a side effect.
Design an auditable playbook when no narrower one fits: a large migration, an ambitious multi-part change, or work a human reviews after stepping away. Scale rigor to the task, run a hypothesis loop, and log decisions. Use for /figure-it-out or "figure it out".
Use when a user wants to define done-criteria or a stop condition for an autonomous multi-turn agent run — setting a Claude Code /goal, a Definition of Done for a Codex or external-orchestrator loop, or turning a vague task into a verifiable completion condition an evaluator can check.
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.