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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api-development
by hmislkREST API development guide for the HMIS project. Use when creating or extending any REST API endpoint — covers file structure, auth pattern, response format, ApplicationConfig registration, CapabilityStatementResource update, AnthropicApiService integration (system prompt module + tool handler), and the full post-implementation checklist.
app-configuration
by hmislkApplication configuration options reference for the HMIS project. Use when working with feature toggles, configuration keys, configOptionApplicationController, bill number configuration, printer configuration, or any configurable application behavior.
commit-code
by hmislkCommit code following HMIS project conventions. Use when committing changes with proper issue closing keywords, message format, and co-author attribution.
database-guide
by hmislkMySQL database development guide for the HMIS project. Use when working with database queries, SQL debugging, database schema investigation, MySQL performance tuning, credential management, or database connection troubleshooting.
deploy-qa
by hmislkDeploy to QA environment (QA1 or QA2). Use when deploying code to the QA testing environments via GitHub Actions. Includes pre-deployment checks for persistence.xml.
dto-implementation
by hmislkDTO implementation guidelines for the HMIS project. Use when creating or modifying DTOs, writing JPQL constructor queries, implementing DTO-based reports, converting entity code to DTO patterns, or troubleshooting DTO query issues. Covers constructor rules, facade methods, null relationship handling, and navigation patterns.
hotfix-deploy
by hmislkFull hotfix workflow for deploying urgent fixes to a production branch (coop-prod, ruhunu-prod, southernlanka-prod, etc.). Covers branch creation, fix, commit, push, and PR targeting the production branch. Use when you need to apply an urgent fix directly to a production environment without going through the normal development → QA → prod pipeline.
jsf-ajax
by hmislkJSF AJAX update rules for the HMIS project. Use when working on AJAX updates, p:commandButton update attributes, PrimeFaces AJAX callbacks, partial page rendering, or debugging AJAX update failures. Also covers JSF navigation patterns: why f:viewAction must not be used on @SessionScoped beans, and how initialization belongs in navigation methods. Critical rules to prevent silent AJAX failures and refresh/back-button state corruption.
performance-optimization
by hmislkPerformance optimization patterns for the HMIS project. Use when optimizing slow queries, improving autocomplete performance, tuning database queries, reducing page load times, optimizing report generation, or investigating performance issues.
pharmacy-dev
by hmislkPharmacy module development guide for the HMIS project. Use when working on pharmacy features including GRN, purchase orders, stock transfers, disbursements, retail sales, pharmacy reports, stock management, item substitution, or pharmacy billing workflows.
publish-wiki
by hmislkPublish pending changes in the sibling hmis.wiki repository to GitHub. Use when wiki documentation has been created or edited in the sibling ../hmis.wiki directory and needs to be committed and pushed to https://github.com/hmislk/hmis/wiki.
review-code
by hmislkReview code changes following HMIS project standards. Use when reviewing a pull request, verifying code changes, or checking code quality. Covers CodeRabbit verification, backward compatibility, persistence checks, and project-specific patterns.
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.