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.
Querying local SQLite index...
chitti-4wheeler
by bryanwilfredpinto-uiBharat's voice-first agent for car owners. Predicts engine problems, decodes DTCs, guides breakdowns, tracks documents, raises family-cascade SOS, anti-overcharge guard, fake-part scanner. Works in OBD2 mode (ELM327) or odometer-only mode. 26 languages, four-user accessibility, never auto-dials cops.
chitti-2wheeler
by bryanwilfredpinto-uiBharat's voice-first agent for motorcycle and scooter owners. Predicts breakdowns, guides restarts, tracks documents, raises family-cascade SOS, prevents theft. Works in OBD2 mode (ELM327) or odometer-only mode. 26 languages, four-user accessibility, never auto-dials cops.
chitti-fundamentals
by bryanwilfredpinto-uiBharat-themed agentic fundamental analyst for Indian equities. Investor-lens (Buffett/Lynch/Graham/Greenblatt) + ratios + financial statements + CAGR + shareholding + plain-English explanations. Use on chitti_fundamentals.html or for any company-quality / valuation / long-term investing question.
chitti-medupi
by bryanwilfredpinto-uiBharat-themed agentic medicine-cost intelligence for Indian families. Strict same-composition matching + Jan Aushadhi pricing + cart simulator + family wallet + insurance match. Use on chitti_medupi.html or for any medicine cost / generic alternative / Jan Aushadhi / pharmacy bill question.
chitti-news-sports
by bryanwilfredpinto-uiSports sub-agent for Chitti News. Cricket-first (India context), then football, kabaddi, hockey, badminton, athletics, chess. Use for match results, squad announcements, tournament news, player transfers, injury reports.
chitti-news-tech
by bryanwilfredpinto-uiTechnology sub-agent for Chitti News. Indian startup ecosystem, global tech (Apple, Google, Microsoft, OpenAI, Anthropic), AI/ML developments, telecom (Jio, Airtel), digital policy (DPDP Act, Digital India). Skip celeb-gadget reviews unless news-worthy.
chitti-news-entertainment
by bryanwilfredpinto-uiEntertainment sub-agent for Chitti News. Bollywood, regional cinema (Telugu, Tamil, Malayalam, Bengali), streaming (Netflix, Prime, JioCinema, Hotstar), music releases, awards. Tasteful, no gossip — film news, not paparazzi.
chitti-news-politics
by bryanwilfredpinto-uiPolitics sub-agent for Chitti News. Use for any political-news query — elections, parliament, state politics, party announcements, policy debates. Has hard neutrality guardrails: no opinion, no labels, equal coverage across parties, factual reporting only.
chitti-news
by bryanwilfredpinto-uiChitti News — state-aware multi-language Indian news aggregator. Aggregates 25+ RSS feeds across English and Hindi (regional languages stubbed for v1.1), serves articles by state × language × category, renders DeepSeek-powered "Chitti's Take" 3-bullet summaries, runs a fact-checker that cross-references ≥2 sources, and offers Read Later / Cancelled folders per device.
chitti-news-factcheck
by bryanwilfredpinto-uiFact Checker Agent — cross-references a news article against ≥2 other trusted RSS sources in our DB and returns a verdict (verified / partial / disputed / unverified) with rationale. Use when the user asks "is this true", "fact check this", "how reliable", "any other sources covering this".
chitti-technical
by bryanwilfredpinto-uiBharat-themed agentic technical analyst for Indian equities. Roshan Indicator + 43 indicators + composite signal strength + multi-timeframe rating + watchlist. Use on chitti_complete_technical.html or for any chart/signal/scan question.
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.