Skills
96,770 packaged capabilities and playbooks for agents. Install any of them with one command.
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Systematically helps users learn a brand-new technical domain and outputs an interactive HTML concept map. Solves the core problem of users "not knowing what they don't know" — proactively expanding the user's cognitive boundaries through the "relatedness method" and the "same-category method", mapping dependency/causal/conditional relationships between concepts, planning a learning path, and ultimately delivering a visual learning map with a check-in feature. Use this skill when the user expresses the following intents: - Wants to systematically learn or get started with a new domain ("help me learn XX", "I want to systematically understand XX") - Wants a learning map, concept map, or knowledge graph ("help me make a learning map of XX") - Wants to plan a learning path ("I want to get started with XX, help me plan", "how should I learn the XX domain") - Wants a panorama view of a domain ("help me figure out the full picture of XX", "what don't I know about the XX domain") - Wants to fill in their knowledge blind spots ("help me fill in the learning approach for XX", "I only half understand XX") - Wants to do technical research with structured output ("help me research XX and turn it into a concept outline") Even if the user only vaguely says "help me understand XX", as long as it involves systematically learning a technical domain of some depth, this skill should be triggered. Do not trigger it merely because the user asks a specific question about a domain — this skill is designed for "panoramic learning", not for answering a single question.
For Solana program development with tokens and PDAs, Light is 200x cheaper than SPL/Solana and has minimal code differences (e.g. for any Solana program and DeFi such as AMMs, vaults, lending). Includes rent-free Light-PDAs, token accounts, and mints. Light SDK with Anchor or Pinocchio. Includes a DeFi Router integration guide. Minimal program logic changes.
For client development with tokens on Solana, Light Token is 200x cheaper than SPL and has minimal changes. Skill includes guides for create mints, associated token accounts, transfer, approve, burn, wrap, and more. @lightprotocol/compressed-token (TypeScript) and light_token_client (Rust).
Reconcile messy Linear-linked repository state after multiple agents, branches, worktrees, commits, PRs, or old session data: discover issue-tagged work, compare it with main, PRs, Linear issue content, comments, revisions, timeline evidence, and locally available agent session evidence; produce a reconciliation plan; safely delete only proven-merged local work; integrate finished work; rebase active work; diagnose common pipeline failures; and update Linear statuses, labels, descriptions, and marked comments to match verified repository reality.
Searches and discovers academic papers across multiple sources (Semantic Scholar, arXiv, Tavily, Exa, Gemini deep research, AMiner, Google Scholar) with adaptive engine selection based on query type. Returns ranked, deduplicated results with metadata (authors, venue, year, citations, abstract, PDF link). Use when the user asks to find papers / literature / publications / preprints / references on a topic, search for related work, look up a specific paper by title or DOI or arXiv ID, find papers by an author, find recent SOTA / state-of-the-art work, survey a research area, or run a deep / comprehensive literature search with synthesis.
Scaffold a well-designed agent loop with best-practice coaching and a cross-model review council. Use when the user wants to design, build, or set up an agent loop, iterative agent workflow, self-review loop, LLM-as-judge loop, multi-model council, reviewer/judge gate, or /goal-style looping process. Start from a named pattern template (security-scan, code-review, bug-hunt, docs-sync, research-synthesis) or from a blank interview. Guide goal refinement, typed verification criteria, reviewer and judge selection, privacy boundaries, termination guards, no-progress stops, and lightweight observability, then emit a RUN_IN_SESSION.md handoff prompt plus portable loop.yaml, loop.resolved.json, LOOP.md, and run-loop.py.
Maintains agent-facing documentation by mining current and recent conversations, transcripts, git context, and existing docs for durable reusable guidance. Use when updating AGENTS.md, agent rules, skills, workflows, project docs, or when the user asks to preserve recurring preferences, stable workspace facts, or documentation learnings.
Guidance for building MIPS interpreters or emulators that execute MIPS binaries. This skill applies when implementing CPU emulation, ELF loaders, instruction decoders, or syscall handlers for MIPS architecture. Use when tasks involve creating virtual machines for MIPS executables, interpreting MIPS assembly instructions, or emulating MIPS system calls.
When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions "psychology," "mental models," "cognitive bias," "persuasion," "behavioral science," "why people buy," "decision-making," or "consumer behavior." This skill provides 70+ mental models organized for marketing application.
Design and audit marketplace trust and safety economics covering buyer/seller incentives, fraud, abuse, moderation cost, dispute handling, refunds, chargebacks, ratings, ranking incentives, creator payouts, quality gates, take rate, liquidity, supply quality, false positives, appeals, enforcement ladders, and unit economics. Use when a marketplace must grow without rewarding harmful behavior.
Analyze a toolbox folder and generate a toolbox.ignore file — detects files that should not ship to end users based on what actually exists in the folder. Only suggests patterns for files found. Advisory: presents suggestions with reasons before writing.
Cloud computing platform for running Python on GPUs and serverless infrastructure. Use when deploying AI/ML models, running GPU-accelerated workloads, serving web endpoints, scheduling batch jobs, or scaling Python code to the cloud. Use this skill whenever the user mentions Modal, serverless GPU compute, deploying ML models to the cloud, serving inference endpoints, running batch processing in the cloud, or needs to scale Python workloads beyond their local machine. Also use when the user wants to run code on H100s, A100s, or other cloud GPUs, or needs to create a web API for a model.
Write a monitoring setup guide for a service — defining what to measure, how to alert on it, and how to build the observability stack covering the four golden signals, business metrics, log strategy, distributed tracing, alerting rules, dashboard layout, and observability debt. Use when asked to set up monitoring for a service, define alerting strategy, write an observability plan, create a dashboard specification, or document logging standards for a team. Produces a metric definitions table, alert rules specification, dashboard layout wireframe, log schema, tracing setup checklist, and monitoring gap analysis.
UMPF structured prompt generation master — guides users through multi-turn deep dialogue to refine cinematic visual concepts. Supports 8 visual engines (photography/print/3D/oil painting/pixel/ink wash/stop motion/Pantone editorial illustration). Triggers: (1) the user says "generate prompt", "draw a...", "help me design a visual"; (2) Nano banana prompts are needed; (3) the user wants professional art direction to generate images; (4) UMPF or moonkite-maliang is mentioned; (5) style pantone + theme is specified, going straight into the Pantone flow
Based on the Meixiang intelligent stock screening API, screen A-shares, Hong Kong stocks, US stocks, and others by user-provided indicators, industry, or sector conditions. Returns complete CSV data with column descriptions, avoiding the use of outdated financial information.
Use this skill when the user asks to review or audit source code, a PR, diff, commit, or recent changes for bugs, security, performance, tests, compatibility, or maintainability, duplicated code or logic across files, or hard-coded values. Also use it after an AI agent finishes modifying code to inspect the current uncommitted working tree before handoff and report actionable findings with file and line evidence.
Use this skill when writing, reviewing, debugging, or modernizing C# code in .NET projects. Trigger for .cs/.csproj files, C# language-version questions, nullable reference types, records, pattern matching, async/cancellation, or version-appropriate syntax from C# 10+.
Use this skill when writing, reviewing, debugging, or modernizing Swift code for iOS, macOS, server-side Swift, or shared packages. Trigger for Swift 5+ language features, structured concurrency, memory safety, protocols/generics, SwiftUI integration, and performance-sensitive code.
Use this skill when building, debugging, refactoring, or reviewing SwiftUI apps or views for iOS 16+ and related Apple platforms. Trigger for NavigationStack, Observation/state flow, view composition, previews, performance, accessibility, and SwiftUI architecture.
Use this skill when building, debugging, refactoring, or reviewing Vue 3 applications, SFCs, Composition API, Pinia stores, Vue Router, Vite, or Vue+TypeScript code. Trigger when the project has *.vue files or the user mentions Vue, Pinia, Composition API, Router, reactivity, or component architecture.
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use for network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
Neuropixels neural recording analysis. Load SpikeGLX/OpenEphys data, preprocess, perform motion correction, Kilosort4 spike sorting, quality metrics, Allen/IBL curation, and AI-assisted visual analysis for Neuropixels 1.0/2.0 extracellular electrophysiology. Use when working with neural recordings, spike sorting, extracellular electrophysiology, or when the user mentions Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, or unit curation.
Repurpose a newsletter issue into a high-level YouTube video outline. Use when asked to "create a YouTube outline from this newsletter", "turn this newsletter into a video", "repurpose for YouTube", "video outline from newsletter", or when converting any newsletter issue into video format.
Scaffolds or references a production-ready Node.js REST API with Express 5, TypeScript, Mongoose (MongoDB), Redis, Sentry, JWT auth, bcrypt, rate limiting, and centralized error handling. Use when the user wants to start a new observable and resilient backend, needs a Node.js API boilerplate with security and monitoring, or asks to clone or adapt this template repository.
Explore and understand Nx workspaces. Use when answering questions about the workspace, projects, or tasks. Also use when an nx command fails or you need to check available targets/configuration before running a task. Examples: "What projects are in this workspace?", "How is project X configured?", "What depends on library Y?", "What targets can I run?", "Cannot find configuration for task", "debug nx task failure"
Write an on-call runbook for a service — covering alert definitions, escalation paths, common incident responses, and on-call handoff procedures. Use when asked to write an on-call guide, create alert runbooks, document escalation procedures, or prepare an on-call handoff document. Produces a structured on-call runbook with per-alert response procedures, escalation matrix, diagnostic commands, and handoff template.
Guide to OpenAI's Assistants API v2: stateful conversational AI with Code Interpreter, File Search and function calling, vector stores for RAG up to 10,000 files, thread and run lifecycle management, and streaming, using the Node.js SDK or plain fetch. Notes the planned H1 2026 sunset and the migration path to the Responses API. Use when building stateful OpenAI chatbots, implementing RAG with vector stores, running Python via Code Interpreter, doing document Q&A with file search, managing conversation threads and run polling, streaming assistant responses, maintaining legacy Assistants code, or debugging errors such as thread already has active run, run status polling timeouts, vector store indexing delays, and file upload failures.
Expert guide for configuring, customizing, and creatively leveraging OpenClaw — the self-hosted AI gateway that connects LLMs to messaging channels (Telegram, WhatsApp, Discord, Slack, iMessage, etc.). Use when the user wants to: (1) Set up or modify their openclaw.json configuration, (2) Write or edit bootstrap files (SOUL.md, USER.md, AGENTS.md, IDENTITY.md, TOOLS.md), (3) Configure messaging channels, (4) Set up models and providers, (5) Create multi-agent routing, (6) Build skills, hooks, or cron jobs, (7) Troubleshoot OpenClaw issues, (8) Get creative ideas for leveraging OpenClaw in non-obvious ways. Triggers on: openclaw, gateway, SOUL.md, USER.md, AGENTS.md, IDENTITY.md, channels setup, agent routing, heartbeat, cron jobs, openclaw hooks, openclaw skills, openclaw config, openclaw.json, personal assistant setup.
Set up OpenClix in a mobile app by detecting the platform, copying source templates into a dedicated namespace, wiring initialization/event/lifecycle touchpoints, and running build verification. TRIGGER when the user asks to "integrate OpenClix", "set up local notifications", "add engagement logic", or mentions OpenClix for the first time in a project without an existing integration — even if they don't mention this skill by name. DO NOT trigger when OpenClix is already integrated and the user is asking about campaigns, analytics, or template updates — those belong to other openclix-* skills.
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.