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.
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Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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build-thesis
by TraderAliceBuild a two-sided, falsifiable thesis on a specific name — the left side (does the number stand up, and where do you differ from consensus) and the right side (is the market itself favoring this — sector, capital, macro, trend). Use when the user has a ticker but no conviction yet: "is the NVDA thesis real", "build a thesis on X", "should I believe the X story", "bull and bear case for Y", "stress-test my view on Z", "is X already priced in", "left side or right side on X", "everyone's buying Y, should I". This is the have-a-name / no-conviction step — it picks up where a value-chain scan hands off ("the next question: is the thesis real?") and turns a name into a thesis you can act on and later monitor.
tool-audit
by TraderAliceAudit OpenAlice's AI tools end-to-end — call each one (using its declared example input as the starting point), judge whether it runs, whether its description / params / output are good, and write a review with concrete "how to change it" notes. Use when the developer wants to dogfood the tool surface, find tools that are broken / thin / confusing, or get an optimization to-do list: "audit the tools", "which tools are broken", "review all the MCP tools", "test the tool surface", "go use every tool and tell me what to fix". A half-automatic regression + tool-optimization input.
scan-value-chain
by TraderAliceScan an investment theme by decomposing its value chain, then surface the handful of names actually worth researching — each with why and the next question. Use when the user has a theme/sector/thread but no specific ticker yet: "what's worth looking at in semis", "scan the AI-infra space", "I'm curious about uranium / obesity drugs / power grid", "who are the picks-and-shovels in X", "map the supply chain for Y", "find the names worth watching in the X value chain". This is the have-a-theme / no-target step — it turns "I don't know what to look at" into a short, reasoned shortlist.
sector-rotation
by TraderAliceRead what's moving across the whole market right now — which sectors are surging vs crashing, and where capital is rotating between them, across long / medium / short timeframes. The right-side, top-of-funnel "what is the market actually doing" read. Use when the user has no specific target and wants the lay of the land: "what's hot right now", "what sectors are surging / crashing", "where is money rotating", "what's leading and lagging", "is this risk-on or risk-off", "what should I be looking at this week", "show me the rotation map". Hands the standout movers off to a value-chain scan to dig into.
opencli-reader
by TraderAliceRead-only access to the long tail of sites Alice's own tools do NOT cover, via the community `opencli` CLI (~160 site adapters): social sentiment (Reddit, HackerNews, Twitter/X, Bluesky, Xueqiu 雪球, Weibo), options flow (Barchart), crypto long-tail (CoinGecko, DeFiLlama, Binance), global news frontpages (Bloomberg, Reuters, BBC), CN money-flow (Eastmoney 东方财富 northbound / longhu / money-flow / hot-rank), research (arXiv, PubMed, Google Scholar), and a generic `web read` fallback. Triggers: "what's reddit / 雪球 saying about X", "unusual options flow on Y", "TVL of Z", "Bloomberg headlines", "northbound flow today", "search arXiv for…", any read from a site Alice has no tool for. READ-ONLY — never invoke write commands. opencli is NOT bundled with OpenAlice: if it's missing and a task would benefit, you MUST say so and ask the user — never install silently, never silently work with thinner data.
alice-workspace
by TraderAliceAgent collaboration on your shell PATH via the `alice-workspace` CLI: push finished work to the user's Inbox (`inbox push`, with repeatable `--doc` file attachments), read the inbox back (`inbox read`, `--self` for your own pushes), locate a peer workspace's files to read/edit them (`peer path`), and track entities across workspaces (`track`). Use for: "push my findings to the inbox", "surface this report to the user", "what did I already report?", "read the file another workspace sent", "track this ticker". Workspaces collaborate through git — commit before you push, and commit after you edit a peer's files. Discover flags with `alice-workspace --help` — do NOT guess.
alice
by TraderAliceResearch & data on your shell PATH via the `alice` CLI — THIS WORKBENCH's read surfaces: the collected-RSS archive (`alice rss`), cross-asset symbol search (`alice market search` → barIds), and K-line quant analysis (`alice analysis`). Use for: "grep the collected feeds for the Fed", "find the barId for AAPL", "compute RSI on this chart". Output is JSON; discover every flag with `alice --help` / `alice <group> <verb> --help` — do NOT guess. (Low-frequency market data — fundamentals, macro series, calendars, boards — is the separate `traderhub` CLI; the quant scripting manual is the `alice-analysis` skill.)
alice-uta
by TraderAliceTrading on your shell PATH via the `alice-uta` CLI — OpenAlice's trading surface. These commands MUTATE real broker state, so resolve the broker-native contract first and report every result. Use whenever you need to place / modify / cancel an order; close a position; check an account, portfolio, or order/trade history; resolve a contract or quote; or drive the trading-as-git approval flow: "place a buy order for AAPL", "what's my position in ETH", "close half my TSLA", "find the contract for this option", "show pending trades", "approve my orders". Discover every group, verb, and flag with `alice-uta --help` and `alice-uta <group> <verb> --help` — do NOT guess flags.
alice-analysis
by TraderAliceHow to compute technical analysis with OpenAlice's Quant Calculator (v2) via `alice analysis` — a small Python/pandas-subset scripting language over K-lines, keyed by barId so you compute on a SPECIFIC source (a broker's bars matching what you trade, or a named vendor) and can batch many timeframes/symbols/indicators in ONE call. Use whenever the task is technical/quantitative on price data: "RSI on BTC", "is AAPL above its 200-day", "50/200 golden cross check", "multi-timeframe momentum", "how extended is X (z-score)", "does this track the sector (correlation)", "trend strength", "compare 1h/4h/12h at once". Reach it with `alice analysis search-bars` (find a barId) then `alice analysis quant` (compute).
traderhub
by TraderAliceHow to pull LOW-FREQUENCY market data from the `traderhub` CLI: finished market boards (macro, movers, calendars, global macro, Fed, shipping, term structure, sector rotation), equity fundamentals (profile, financials, ratios, estimates, insiders, short interest), ETF drilldowns, FRED/BLS/EIA macro series, OECD cross-country indicators, IMF PortWatch shipping, and Deribit crypto curves. Use whenever you need a macro number, a fundamental, a calendar, or a ready-made board: "what's CPI", "AAPL ratios", "earnings this week", "which sectors are rotating in", "Suez canal traffic", "Fed balance sheet". Data is served hub-first (hosted TraderHub) with local fallback — no API keys needed. Discover flags live with `traderhub <group> <verb> --help`; do NOT guess flags.
openalice-cli
by TraderAliceHow to reach OpenAlice from your shell via the `alice*` CLIs. Two binaries: `alice` for MARKET DATA (news, symbol search, equity fundamentals, macro/economy series) and `alice-workspace` for AGENT COLLABORATION (push finished work to the user's inbox, track entities). Both print JSON and are discoverable with `--help`. Use whenever you need a number/headline/fundamental, or want to hand work back to the user, and this workspace exposes the `alice*` commands instead of (or alongside) the OpenAlice MCP tools: "look up AAPL", "what's Apple's revenue", "search news for the Fed", "push my findings to the inbox", "track this ticker". (For technical/quantitative analysis on price — RSI, moving averages, multi-timeframe — see the `openalice-quant` skill.) Discover everything live with `alice --help` / `alice-workspace --help` — do NOT guess flags.
openalice-quant
by TraderAliceHow to compute technical analysis with OpenAlice's Quant Calculator (v2) — a small Python/pandas-subset scripting language over K-lines, keyed by barId so you compute on a SPECIFIC source (a broker's bars matching what you trade, or a named vendor) and can batch many timeframes/symbols/indicators in ONE call. Use whenever the task is technical/quantitative on price data: "RSI on BTC", "is AAPL above its 200-day", "50/200 golden cross check", "multi-timeframe momentum", "how extended is X (z-score)", "does this track the sector (correlation)", "trend strength", "compare 1h/4h/12h at once". Two tools: `searchBars` (find a barId) then `calculateQuant` (compute) — or via CLI, `alice analysis search-bars` then `alice analysis quant`.
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.