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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pdf-processing
by Ming-Kai-LCComprehensive PDF processing techniques for handling large files that exceed Claude Code's reading limits, including chunking strategies, text/table extraction, and OCR for scanned documents. Use when working with PDFs larger than 10-15MB or more than 30-50 pages.
tar-umt-fyp-rds
by Ming-Kai-LCComprehensive guide for TAR UMT Data Science (RDS) students completing their Final Year Project. Use when RDS students need help with (1) understanding FYP processes and requirements, (2) structuring FYP reports and deliverables, (3) writing research-based chapters and thesis, (4) selecting appropriate data science projects, (5) understanding research methodology and theoretical frameworks, (6) conducting experiments and statistical analysis, (7) preparing for system testing and presentations, (8) meeting submission deadlines and formats, (9) report formatting and structure requirements, or (10) any other FYP-related guidance specific to Data Science students at TAR UMT.
tar-umt-academic-writing
by Ming-Kai-LCAcademic writing assistance for TAR UMT students focusing on APA referencing, plagiarism prevention, and Turnitin report interpretation. Use when students need help with (1) formatting APA citations and references, (2) avoiding plagiarism through proper paraphrasing and citing, (3) understanding Turnitin similarity scores, (4) learning TAR UMT's academic integrity policies, or (5) preparing assignments that meet university standards.
jupyter
by Ming-Kai-LCSpecialized assistant for working with Jupyter notebooks (.ipynb files). Use for analyzing, editing, debugging, or executing code in notebooks. Helps with data analysis, machine learning, deep learning, data visualization, and scientific computing workflows. Can read notebook contents, modify cells, execute Python code in the kernel, add documentation, and troubleshoot errors.
fyp-statistical-validator
by Ming-Kai-LCAutomate statistical validation, hypothesis testing, and confidence interval calculations required by TAR UMT thesis standards and top ML research conferences. Use when you need to (1) calculate 95% confidence intervals for model performance metrics, (2) perform hypothesis testing to compare models (McNemar's test, paired t-test, Bonferroni correction), (3) generate APA-formatted results tables for thesis chapters, (4) create reproducibility statements for experimental setup, (5) validate statistical significance of model improvements, or (6) format results according to academic publishing standards. Essential for FYP Chapter 4 (experimental setup documentation) and Chapter 5 (results with statistical validation).
crossvit-covid19-fyp
by Ming-Kai-LCComplete context for TAR UMT Data Science FYP implementing CrossViT for COVID-19 chest X-ray classification. Use when working on Jupyter notebooks, code implementation, data analysis, model training, or any task related to Tan Ming Kai's final year project. This skill provides dataset specs, model architecture details, hardware constraints (NVIDIA RTX 6000 Ada Generation (51GB VRAM) VRAM), preprocessing parameters, baseline models, evaluation metrics, hypotheses, and coding guidelines for reproducible research following TAR UMT academic requirements.
fyp-jupyter
by Ming-Kai-LCComplete data science research workflow for Jupyter notebooks covering CRISP-DM methodology from data loading through model validation, with MLflow experiment tracking integration, phase-based workflow guidance (Exploration, Systematic Experimentation, Analysis, Documentation), and skill integration points. Use when working on FYP data science projects requiring systematic data preprocessing, EDA, feature engineering, modeling, statistical validation, experiment tracking, or needing guidance on what to work on at each project phase. Includes MLflow setup for tracking 30+ experiment runs, weekly work planning for 10-week FYP timeline, and clear decision framework for when to use which skill (fyp-jupyter, crossvit-covid19-fyp, fyp-statistical-validator, tar-umt-fyp-rds, tar-umt-academic-writing).
my-skill-name
by Ming-Kai-LCClear description of what this skill does and when Claude should use it. Include trigger keywords users might mention.
skill-writer
by Ming-Kai-LCCreates and validates Claude Code skills with proper format. Use when creating new skills, writing SKILL.md files, validating existing skills, or when user mentions "skill format", "create skill", "skill template", or "validate skill".
skill-writer
by Ming-Kai-LCExpert guide for creating Claude Code skills. Use when asked to create a skill, write a SKILL.md file, or teach about skill creation. Provides templates, best practices, and validation guidance for personal, project, and plugin skills.
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.