Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
Use the same catalog through the API
Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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x402
by tenequmBuild internet-native payments with the x402 open protocol - HTTP 402 Payment Required for on-chain micropayments with no accounts or API keys. Use when developing paid APIs, paywalled content, AI agent payment flows, or MCP tools that charge per call. Covers the TypeScript, Python, and Go SDKs across EVM, Solana, Stellar, and Aptos.
effect-ts
by tenequmComprehensive Effect-TS development guide for TypeScript, focused on Effect v4 (the recommended default) with full v3 (stable) support for existing codebases. Use when building, debugging, reviewing, or generating Effect code: typed errors, fibers, Context/Layers, Scope, Schedule, streams, Schema, observability, HTTP, Config, SQL, CLI, RPC, STM, and Effect AI. Includes exhaustive wrong-vs-correct API tables to prevent hallucinated Effect code. Triggers when code imports from 'effect', '@effect/platform', '@effect/ai', or '@effect/sql', or the user mentions Effect-TS, functional TypeScript, Context, Layer, or Schema from Effect.
grafana-foundation-sdk
by tenequmBuild Grafana dashboards as code with the grafana-foundation-sdk typed builders (TypeScript or Go). Use when creating, modifying, or generating Grafana dashboard JSON programmatically, converting hand-written dashboard JSON to typed code, building monitoring dashboards, or working with Prometheus/Loki queries in dashboards.
typescript-dev
by tenequmBuild full-stack TypeScript apps with Vite 8, React 19, Tailwind CSS v4, shadcn/ui, Biome, Vitest, and Hono. Covers the frontend (Vite/Rolldown build + dev server, type-safe React 19, strict TypeScript 6.0, Tailwind/shadcn styling, Biome lint/format, Vitest) and the Hono 4 backend/edge layer (routing, middleware, Zod validation, end-to-end type-safe RPC, OpenAPI, multi-runtime deploy). Use when setting up or working in a TypeScript project: configuring Vite, writing components, the React Compiler, Tailwind/shadcn, dev server/HMR, bundles, tests, lint/format/CI, or building a Hono API and wiring its RPC client to React. Triggers on vite, rolldown, react, tsx, typescript, tsconfig, react compiler, tailwind, shadcn, cva, biome, vitest, hmr, dev server, hono, hono rpc, hc client, cloudflare workers, edge api, zod validator, zod-openapi.
founder-playbook
by tenequmDecision validation and thinking frameworks for startup founders. Use when you need to pressure-test a decision, validate your next steps, think through strategic options, or sanity-check your approach. Triggers on phrases like "should I", "help me think through", "is this the right move", "validate my thinking", "what am I missing". Covers fundraising, customer development, runway management, prioritization, and crypto/web3 founder challenges.
solana-development
by tenequmBuild, test, deploy, and audit Solana programs with Anchor or native Rust, and build with ZK Compression (Light Protocol). Use when developing Solana smart contracts, implementing token operations, optimizing compute, deploying to networks, auditing programs for vulnerabilities, or creating compressed tokens/PDAs.
mpp
by tenequmBuild with MPP (Machine Payments Protocol) - the open protocol for machine-to-machine payments over HTTP 402. Use when developing paid APIs, payment-gated content, AI agent payment flows, MCP tool payments, pay-per-token streaming, or any service using HTTP 402 Payment Required. Covers the mppx TypeScript SDK with Hono/Express/Next.js/Elysia middleware, pympp Python SDK, and mpp Rust SDK. Supports Tempo stablecoins, Stripe cards, Lightning Bitcoin, and custom payment methods. Includes charge (one-time) and session (streaming pay-as-you-go) intents. Make sure to use this skill whenever the user mentions mpp, mppx, machine payments, HTTP 402 payments, Tempo payments, payment channels, pay-per-token, paid API endpoints, or payment-gated services.
erc-8004
by tenequmBuild with ERC-8004 Trustless Agents - on-chain agent identity, reputation, validation, and discovery on EVM chains. Use when registering AI agents on-chain, building agent reputation systems, searching/discovering agents, working with the Agent0 SDK (agent0-sdk), or implementing the ERC-8004 standard. Triggers on ERC-8004, Agent0, agent identity, agent registry, agent reputation, trustless agents, agent discovery.
lance-format
by tenequmReference for Lance v8 - the open columnar lakehouse format for multimodal AI - and its Rust crate workspace (`lance`, `lance-table`, `lance-file`, `lance-encoding`, `lance-index`, `lance-io`, `lance-namespace`, and more). Use when building directly on the Lance crates - creating or reading `.lance` datasets, manifests, fragments, deletion files, the 2.x file format and structural encodings, vector / scalar / full-text / FM-Index / geo indexes, MemWAL streaming writes, optimistic-concurrency commits and commit handlers, schema evolution, versioning, time-travel, tags, branches, stable row IDs, namespaces, or object-store config. Triggers on lance crate, .lance file, lance dataset, lance file format, structural encoding, IVF_PQ, IVF_HNSW, IVF_RQ, RaBitQ, FM-Index, lance FTS, zonemap, MemWAL, OCC retry, lance schema evolution, lance namespace, pylance. This is the Lance format and engine (the `lance-format/lance` repo), not LanceDB the database product - but also the right reference for what LanceDB builds on.
web3-protocol-gtm
by tenequmGo-to-market strategy for web3 builders - protocols, products, services, and solo founders. Use when planning growth for a crypto protocol, building developer community, crafting CT narrative, planning ecosystem partnerships, preparing grant applications, launching tokens, pricing crypto-native products, or growing as a solo founder in web3. Covers community-led growth, CT strategy, developer relations, hackathon playbooks, standards adoption, token launch tactics, micropayment pricing, and agent-as-customer models.
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.