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...
eptr2-api-discovery
by TideseedDiscover and explore available eptr2 API endpoints for Turkish electricity market data. List all 213+ API calls, find endpoints by category (GÖP, GİP, DGP, Üretim, Tüketim), search for specific data types, and get parameter requirements. Use when asking what data is available, how to find endpoints, or exploring the API. Triggers on: available endpoints, API calls, list endpoints, what data, which API, how to find, hangi veri, mevcut servisler.
eptr2-consumption-data
by TideseedQuery Turkish electricity consumption and demand forecast data including real-time consumption, UECM (settlement consumption/Uzlaştırmaya Esas Çekiş Miktarı), and load plan forecasts. Use when asking about electricity demand, consumption patterns, load forecasting, or UECM data in Turkey. Triggers on: elektrik tüketimi, talep tahmini, yük planı, UEÇM, consumption forecast.
eptr2-convenience-wrappers
by TideseedUse typed get_* convenience wrapper functions from eptr2.calls instead of eptr.call("key", ...). These wrappers provide type hints, bilingual (EN/TR) docstrings, auto-complete, and an optional eptr argument. Use when writing scripts with typed functions, generating wrapper-based code, asking which get_* function to use, or preferring IDE-friendly code. Triggers on: get_mcp, get_smp, get_rt_cons, get_rt_gen, convenience wrapper, typed function, eptr2.calls, get_ function, wrapper call.
eptr2-generation-tracking
by TideseedQuery Turkish electricity generation data including real-time generation by resource type, plant-level production, UEVM (settlement generation/Uzlaştırmaya Esas Veriş Miktarı), and generation forecasts. Use when asking about power generation, renewable energy output, plant production, solar/wind generation, or UEVM data in Turkey. Triggers on: elektrik üretimi, santral üretimi, rüzgar üretimi, güneş üretimi, UEVM, generation by fuel type.
eptr2-imbalance-costs
by TideseedCalculate and analyze Turkish electricity imbalance costs including KUPST (Kesinleşmiş Üretim Planından Sapma Tutarı/Production Plan Deviation Cost), positive/negative imbalance penalties, and DSG (Dengeden Sorumlu Grup) tolerance calculations. Use when asking about imbalance settlement, deviation costs, KUPST, energy surplus/deficit penalties, or imbalance calculations in Turkey. Triggers on: dengesizlik maliyeti, sapma tutarı, KUPST, KÜPST, enerji açığı, enerji fazlası, imbalance penalty.
eptr2-market-operations
by TideseedQuery Turkish electricity market operations data including Day-Ahead Market (GÖP) orders and clearing, Intraday Market (GİP) transactions and order books, bilateral contracts (İA), and Balancing Power Market (DGP) instructions. Use when asking about market volumes, trading activity, order books, block bids, flexible offers, or bilateral agreements in Turkey. Triggers on: GÖP, GİP, DGP, gün öncesi piyasası, gün içi piyasası, ikili anlaşmalar, market orders, block bids, YAL, YAT.
eptr2-price-analysis
by TideseedQuery and analyze Turkish electricity market prices including MCP (PTF/Piyasa Takas Fiyatı), SMP (SMF/Sistem Marjinal Fiyatı), WAP (Ağırlıklı Ortalama Fiyat), and imbalance prices. Use when asking about electricity prices, market clearing prices, day-ahead prices, system marginal prices, weighted average prices, or price comparisons in Turkey's energy market. Triggers on: PTF, SMF, GÖP fiyat, GİP fiyat, elektrik fiyatı.
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.