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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proof-engine
by yaniv-golanCreate formal, verifiable proofs of claims with machine-checkable reasoning. Use when asked to prove, verify, fact-check, or rigorously establish whether a claim is true or false — mathematical, empirical, or mixed. Trigger phrases: "is it really true", "can you prove", "verify this", "fact-check this", "prove it", "show me the logic". Do NOT use for opinions, essays, or questions with no verifiable answer.
geekmagic-smalltv-ultra
by yaniv-golanThis skill should be used when the user asks to "control the SmallTV", "change SmallTV theme", "upload image to SmallTV", "set SmallTV brightness", "configure SmallTV weather", "install alternative firmware", "flash ESPHome on SmallTV", "install bvweerd firmware", "write custom firmware for ESP8266 display", "build firmware for SmallTV", "update SmallTV firmware", "push image to display", "send text to SmallTV", "connect SmallTV to Home Assistant", "SmallTV WiFi recovery", or mentions the GeekMagic SmallTV Ultra, SmallTV-Ultra, TinyTV, HACS SmallTV integration, or an ESP8266-based 240x240 TFT display device. Covers stock firmware HTTP API usage, alternative firmware installation (bvweerd, ESPHome, Tasmota), and custom ESP8266 firmware development.
familiar-help
by yaniv-golanUse familiar companion tools for creative buddy/companion interactions. Handles fortunes, code roasts, haiku, stats, mood, lore, focus timers, and personality changes. Triggers when user addresses their companion by any name — "buddy" (Anthropic's UI label), "familiar", "companion", or whatever name the user uses for their companion — or by companion-specific intent: "tell me a fortune", "roast my code", "write a haiku", "show stats", "start a focus timer", "pomodoro", "be like Marvin", "change personality", "who are you", "what can you do". Do NOT use for normal code review, debugging, general coding help, or questions directed at Claude itself rather than the companion.
eml-fit
by yaniv-golanDeterministic library-first regression — fit a CSV against the calculator-primitive witness library (unary, affine `a·w(x)+b` with constant snapping, depth-2 composite `w(v(x))`, binary `w(x,y)`) and emit a machine-checkable JSON verdict ranked by max |residual| and R². Use when you need a reproducible, audit-able answer to "which elementary law generated this data?" — the JSON output is downstream-consumable (no LLM in the loop), exit codes encode the verdict, complex-plane evaluation via cmath catches branch-cut hazards. Snaps to π, e, 1/ln(10), Catalan G, ζ(3), Khinchin K, log₂(e), e^π, γ, etc. Optional `--noise-sigma σ` for measured data; reports SE(a)/SE(b).
eml-fit
by yaniv-golanDeterministic library-first regression — fit a CSV against the calculator-primitive witness library (unary, affine `a·w(x)+b` with constant snapping, depth-2 composite `w(v(x))`, binary `w(x,y)`) and emit a machine-checkable JSON verdict ranked by max |residual| and R². Use when you need a reproducible, audit-able answer to "which elementary law generated this data?" — the JSON output is downstream-consumable (no LLM in the loop), exit codes encode the verdict, complex-plane evaluation via cmath catches branch-cut hazards. Snaps to π, e, 1/ln(10), Catalan G, ζ(3), Khinchin K, log₂(e), e^π, γ, etc. Optional `--noise-sigma σ` for measured data; reports SE(a)/SE(b).
math-identity-check
by yaniv-golanNumerically check whether two elementary-function expressions are equal. Use when someone asks "is sin(x)^2 + cos(x)^2 = 1?", "does log(x*y) equal log(x)+log(y)?", "verify this identity", "is this trig/log/algebraic identity true?", or when reviewing an LLM-generated proof, textbook answer, or student submission that asserts two closed-form expressions are equal. Handles sympy-parseable Python-style expressions and LaTeX (`\frac`, `\sqrt`, etc.). Produces a `verified` / `refuted` / `branch-dependent` / `cannot-verify` verdict with a concrete counterexample when the identity fails. Backs onto the EML proof engine when both sides compile to its witness library; falls back to sympy lambdify otherwise. NOT a symbolic proof — for that use sympy.simplify or a CAS.
skill-packager
by yaniv-golanPackage AI agent skills into deployment formats — .zip, .skill, Claude plugin, Claude marketplace, Cursor plugin, Cursor marketplace, ChatGPT/Manus zip, Codex CLI, NanoClaw marketplace, OpenClaw/ClawHub, Agent Skills standard (.agents/), or a universal repo with all formats + CI/CD. Use when the user says "package this skill", "deploy my skill", "create a plugin from this skill", "make this work on Cursor/ChatGPT/Codex/NanoClaw/OpenClaw", "set up a repo for my skill", or wants to distribute a SKILL.md to any platform.
proof-engine
by yaniv-golanCreate formal, verifiable proofs of claims with machine-checkable reasoning. Use when asked to prove, verify, fact-check, or rigorously establish whether a claim is true or false — mathematical, empirical, or mixed. Trigger phrases: "is it really true", "can you prove", "verify this", "fact-check this", "prove it", "show me the logic". Do NOT use for opinions, essays, or questions with no verifiable answer.
deck-review
by yaniv-golanScores and strengthens startup pitch decks (pre-seed through Series A) against 35 investor-grade criteria grounded in Sequoia, DocSend, YC, a16z, and Carta data.
cap-table
by yaniv-golanModels cap-table mechanics for founders modeling dilution before signing — SAFE/note conversion, priced rounds with BBWA / narrow-based / full-ratchet anti-dilution, option-pool top-ups, warrants (cash and net-share exercise of vested outstanding warrants, deterministic pre-round pump), Israeli ↔ Delaware flips (1:1 share-for-share), MFN chains, pay-to-play, dual-class structures with voting-power render, Israeli §102 / IIA cap-table interactions, multi-scenario chained or independent rounds, and counsel-handoff packets citing NVCA, YC SAFE primer, Cooley GO. Use when a founder shares a SAFE, convertible note, term sheet, option plan, warrant, Articles of Association, or Carta XLSX. NOT for waterfall modeling, cumulative dividends, RSUs, 83(b), 409A, SPAC, or warrant repricing — see scope notes.
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.