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...
isa-88
by Fuuz-PlatformISA-88 (S88) batch control standard reference for the Fuuz Industrial Operations Platform. Covers physical models, process models, procedural control, recipe management, PackML state machine, equipment hierarchy, and ISA-95 integration. Trigger when users mention ISA-88, S88, batch control, batch processing, PackML, state machine, state model, recipe management, process cell, unit procedure, equipment phase, procedural control, batch recipe, general recipe, master recipe, control recipe, equipment module, control module, workcenter state transitions, Hold/Unhold, Suspend/Unsuspend, or machine modes.
isa-95
by Fuuz-PlatformISA-95 (ANSI/ISA-95.00.01-05) enterprise-control system integration standard reference for the Fuuz platform. Use when requests mention "ISA-95", "ISA 95", "S95", "IEC 62264", "enterprise-control integration", "equipment hierarchy", "role-based equipment", "physical asset model", "functional hierarchy", "Level 0-4", "manufacturing operations management", "MOM", "MO&C", "work center", "work unit", "process segment", "product segment", "production capability", "object model", "personnel model", "material model", "process segment", "operations definition", "operations schedule", "operations performance", "operations capability", "work master", "work directive", "job order", "job response", "work calendar", "work alert", "B2MML", "transaction model", "PUSH", "PULL", "PUBLISH", or when designing asset hierarchies, UNS topic structures, work order models, or OEE systems aligned with ISA-95 on Fuuz.
fuuz-flows
by Fuuz-PlatformBuild data flows for the Fuuz Industrial Operations platform (fuuz.com). Trigger on fuuz flow, data flow, flow design, web flow, backend flow, gateway flow, rfc, remote function call, or industrial automation workflows (OEE calculation, warehouse logic, external API integration, telemetry aggregation, production tracking). Covers flow architecture, node configuration (82 node types across 12 categories), state model (payload, context, claims, batches), flow patterns (ETL, reactive, dashboard API, batch fan-out), concurrency protection, naming conventions, and debugging. Cross-references fuuz-graphql for query/mutation syntax, fuuz-jsonata for expressions, fuuz-javascript for script nodes.
scada-alarming
by Fuuz-PlatformSCADA alarm management reference based on ISA-18.2 / IEC 62682 for the Fuuz Industrial Operations Platform. Use when requests mention alarm management, alarm rationalization, alarm philosophy, alarm priority, alarm shelving, alarm suppression, alarm flooding, alarm deadband, alarm lifecycle, alarm states, alarm KPIs, alarm metrics, alarm audit, standing alarms, stale alarms, chattering alarms, nuisance alarms, first-out alarms, alarm rate, ISA-18.2, IEC 62682, EEMUA 191, SCADA alarms, HMI alarms, operator alarm load, or when designing alarm systems, alarm dashboards, alarm evaluation flows, or alarm rationalization workflows on Fuuz.
iso-22400
by Fuuz-PlatformISO 22400 MES KPI standard reference for the Fuuz Industrial Operations Platform. Covers Part 1 (concepts, terminology, time model) and Part 2 (34 KPI definitions). Use when requests mention ISO 22400, MES KPIs, manufacturing KPIs, OEE formula, OEE standard, availability, effectiveness, performance ratio, quality ratio, throughput rate, MTBF, MTTR, MTTF, worker efficiency, utilization efficiency, setup ratio, scrap ratio, rework ratio, first pass yield, process capability, machine capability, Cp, Cpk, Cm, Cmk, net equipment effectiveness, NEE, TEEP, inventory turns, production loss, equipment load, energy consumption KPI, time model, planned busy time, actual production time, or KPI calculation methodology for manufacturing operations.
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.