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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arxivterminal
by AxectCLI tool (arxivterminal) for fetching, searching, and managing arXiv papers locally. Use when working with arXiv papers using the arxivterminal command - fetching new papers by category, searching the local database, viewing papers from specific dates, or managing the local paper database.
workshop-paper-review
by AxectProduce OpenReview-ready peer reviews for ICML/NeurIPS/ICLR workshop papers (4-8 page submissions). Covers PDF intake, evidence-grounded review drafting, anti-anchoring scoring calibration, parallel fact-check verification against the source PDF, AI-writing-pattern removal, and bilingual (Korean draft → English submission) workflow. Produces a tight ~400-word review with Title, Rating, Confidence, Summary, Strengths, Weaknesses, and Questions for Authors — without an Overall section. Use when the user mentions reviewing a workshop paper, drafting an OpenReview submission, preparing a referee report for ICML/NeurIPS/ICLR workshops, fact-checking a review draft, or translating a Korean review draft into a publication-ready English submission.
dropbox
by AxectUpload files to Dropbox, download from Dropbox, and create or reuse shared links. Use when the user mentions Dropbox, asks to upload/download/share a file via Dropbox, or wants a shareable link for a file already in Dropbox. Use `~/.config/dropbox-skill/credentials.json` as the only supported credentials path.
dropbox-skill
by AxectUpload files to Dropbox, download from Dropbox, and create or reuse shared links. Use when the user mentions Dropbox, asks to upload/download/share a file via Dropbox, or wants a shareable link for a file already in Dropbox.
vastai
by AxectManage Vast.ai GPU cloud instances via the vastai CLI. Use this skill whenever the user mentions Vast.ai, GPU rentals, cloud GPU instances, searching for GPU offers, creating/destroying instances, vast.ai billing, or any task involving the vastai command-line tool. Also trigger when the user wants to rent GPUs, find cheap GPUs, deploy Docker containers on remote GPUs, manage remote training infrastructure, or transfer data to/from cloud GPU machines. Even if the user just says "spin up a GPU" or "find me an A100", this skill likely applies.
xkcd-py
by AxectGenerate a Python matplotlib plot script that follows the user's mandatory xkcd style — `with plt.xkcd():` context, the `pparam = dict(...)` axis-config pattern, raw-string LaTeX labels, wide xkcd-canvas (`figsize=(10, 6)`), and `dpi=300` savefig. Supports parquet, CSV, and NumPy (`.npy` / `.npz`) data sources, and four plot variants: single line, multi-line + legend, scatter / errorbar, and multi-panel subplots. Produces the `.py` file only — does NOT execute it. The user runs it themselves (typically with `uv run`). Use when the user asks to: write an xkcd-style matplotlib script, draft a hand-drawn / sketch-style figure, plot data in xkcd style from parquet / CSV / `.npy`, scaffold a quick xkcd line / scatter / errorbar / subplot script, or set up an xkcd plot matching their lab template. Triggers on: "xkcd plot", "xkcd matplotlib", "xkcd style", "hand-drawn plot", "sketch plot", "plt.xkcd", "comic style plot", "xkcd 플롯", "xkcd 그래프", "손그림 그래프", "스케치 스타일 플롯", "xkcd 스크립트", "xkcd 코드".
md2pdf-typora
by AxectConvert Markdown to PDF using Typora's Whitey theme via pandoc + Chrome headless, replicating Typora's PDF export appearance
concept-explainer
by AxectCreate rigorous teaching explanations as a single markdown document with equations, assumptions, derivations, plots, optional schematic prompts, and PDF export. Use when the user asks to explain a physics, math, ML, statistics, or CS concept; write lecture, tutorial, or seminar notes; derive results step by step; build a kind but rigorous walkthrough; or turn a paper section into classroom-ready material. For Korean output, follow the skill's Dropbox PDF copy rule.
adversarial-review
by AxectRun an adversarial peer-review swarm on a paper draft, report, or manuscript by spawning parallel persona subagents (hostile theorist, experimentalist, statistician, journal editor, citation auditor, figure critic) and synthesizing their critiques into a ranked fix list with suggested defense experiments. Use when the user asks for a paper review, referee simulation, desk-reject check, pre-submission audit, citation audit, figure audit, novelty attack, or wants feedback on a draft report before submission or internal circulation.
research-report
by AxectCreate or revise a structured markdown research or experiment report with integrated plots, optional literature/reference support, plot manifest tracking, report version history, and report-body validation inside a single Harness without external Codex or Gemini calls. Use when you need to generate `report.md`, inventory or validate plots, create `plots/plot_manifest.json`, manage `report_versions.json`, connect report sections to supporting references, or turn an experiment or output directory into a reusable report workflow.
paperbanana
by AxectGenerate publication-quality academic diagrams and statistical plots from text descriptions using the paperbanana CLI. Supports methodology diagrams, statistical plots, diagram evaluation, and iterative refinement with user feedback. Use when the user asks to: create a diagram, generate a figure, make a plot from data, evaluate diagram quality, refine/improve a generated diagram, or anything related to academic illustration generation. Triggers on: "diagram", "figure", "plot", "paperbanana", "academic illustration", "methodology diagram", "evaluate diagram", "refine diagram", "improve figure".
vastai
by AxectManage Vast.ai GPU cloud instances via the vastai CLI. Use this skill whenever the user mentions Vast.ai, GPU rentals, cloud GPU instances, searching for GPU offers, creating/destroying instances, vast.ai billing, or any task involving the vastai command-line tool. Also trigger when the user wants to rent GPUs, find cheap GPUs, deploy Docker containers on remote GPUs, manage remote training infrastructure, or transfer data to/from cloud GPU machines. Even if the user just says "spin up a GPU" or "find me an A100", this skill likely applies.
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.