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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mnemosyne
by d8maldonConvene a council of historical mathematical legends (Gauss, Riemann, Hilbert, Poincare, Cauchy, Weierstrass, Lebesgue, Banach, Kolmogorov, von Neumann, Klein, Noether) plus control-theory founders (Lyapunov, Krasovskii, LaSalle, Nagumo, Filippov, Moreau, Krasnoselskii, Tikhonov, Lefschetz, Popov, Kokotovic, Monopoli, Kalman, Pontryagin, Bellman, Wiener) plus estimation classics (Fisher, Cramer, Rao, Markov) plus ODE / functional-analysis founders (Lipschitz, Picard, Kato, Fenchel) to find foundational gaps, structural reformulations, and classical lemmas the modern proof should have invoked. Named after the titan goddess of memory, mother of the muses. Activates when the user asks to "consult the OGs", "consult mnemosyne", "find the classical lemma", "what would Lyapunov say", "is there a pre-1985 result that does this", or invokes the OG council on a proof or analytical section. Reads notes/council-log.md FIRST, classifies findings, appends a structured entry.
tenets
by d8maldonAndrej Karpathy's CLAUDE.md behavioural guidelines for software-engineering tasks, packaged as the tenets of the daedalus craft. Apply whenever a coding task is being implemented, modified, refactored, or reviewed. Four principles - think before coding (state assumptions, surface tradeoffs, ask when unclear), simplicity first (minimum code, no speculative features, no abstractions for single-use code, no unrequested configurability), surgical changes (touch only what the request requires, match existing style, do not refactor adjacent code, clean up only your own orphans), and goal-driven execution (turn tasks into verifiable success criteria, state a brief plan with per-step checks for multi-step work). Biases toward caution over speed; for trivial tasks use judgement. Activates when the user mentions CLAUDE.md, behavioural guidelines, Karpathy rules, daedalus tenets, or whenever a coding task starts in a fresh session.
athena
by d8maldonConvene a council of currently-active mathematicians (Tao, Scholze, Bhargava, Huh, Viazovska, Venkatesh, Villani, Duminil-Copin, Figalli, Lurie) plus applied controls people (Annaswamy, Ames, Egerstedt, Tomlin, Krstic, Khalil, Hovakimyan) to audit proofs, theorem statements, equations, and analytical claims. Named after the goddess of wisdom and strategic warfare. Activates when the user asks to "audit the math", "review this proof", "consult athena", "check these equations", "find the gap in this argument", or invokes the council on a research manuscript, lemma, or analytical section. Reads notes/council-log.md FIRST, classifies each finding as NEW / RECURRING-UNFIXED / CONFLICT-WITH-PRIOR-SIGNOFF, and appends a structured entry to the log. Designed to break compounding review loops via explicit pre-commitment honouring.
prometheus
by d8maldonConvene a council of the founders/pioneers AND the modern leaders of artificial intelligence, machine learning, and computer science (Turing, McCarthy, Minsky, Shannon, Rosenblatt, McCulloch & Pitts, Hebb, Samuel, Wiener, Pearl, Bayes, Fisher, Kolmogorov, Markov, Vapnik & Chervonenkis, Widrow, Ada Lovelace, Grace Hopper, Karen Sparck Jones, Barbara Liskov, plus Hinton, LeCun, Bengio, Schmidhuber, He, Vaswani, Goodfellow, Sutton, Karpathy, Manning, Malik, Efros, Fei-Fei Li, Jordan, Valiant, Ghahramani, Bishop, Koller, Jeff Dean, Knuth, Lamport) to audit machine-learning and computer-science work - model choices, training and evaluation methodology, generalization, retrieval/ranking, algorithms, and systems feasibility. Named after the Titan of forethought who brought knowledge to humanity. Activates when the user asks to "consult prometheus", "audit the ML", "review this ML plan / model choice / training setup", "is this the right approach/model", "will this overfit", "find the flaw in this design", or invokes
hermes
by d8maldonConvene a council of the founders of vehicle dynamics AND the modern leaders of automotive, autonomous, and racing vehicle dynamics (Olley, Milliken, Segel, Pacejka, Broulhiet, Lanchester, Ackermann/Lankensperger, Riekert & Schunck, Kane, Dubins, Reeds & Shepp, Dickmanns, plus Gerdes, Rajamani, Guiggiani, Velenis, Borrelli, Thrun, Urmson, Kendall, Anguelov, Newey, Hrovat, Tseng) to audit vehicle-dynamics work - tire models, load transfer, single-track / multibody plants, handling and stability, state and friction estimation, control allocation, racing lines, and real-telemetry validation. Named after the god of roads, speed, and travelers. Activates when the user asks to "consult hermes", "audit the vehicle dynamics", "review this tire model / handling claim / single-track derivation", "is the load transfer right", "check the racing line", "is this validated on real telemetry", or invokes the council on a vehicle-dynamics model, plant, controller, estimator, or manuscript. Reads notes/council-log.md FIRST, cl
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.