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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neqsim-consequence-analysis
by equinorQuantitative consequence analysis for oil & gas hazards — jet fire, pool fire, vapour cloud explosion (VCE), BLEVE, Gaussian plume and heavy-gas dispersion, probit-based fatality probabilities, individual and societal risk roll-up. USE WHEN: a task requires fire-radiation contours, dispersion to LFL/IDLH/ERPG, BLEVE thermal/missile assessment, or QRA-style risk integration of multiple release outcomes. Anchors on neqsim.process.safety.fire, neqsim.process.safety.dispersion, neqsim.process.safety.qra.
neqsim-hazid-fmea-eta-fta
by equinorStructured hazard-identification workflows — HAZOP worksheets with the seven IEC 61882 guidewords, FMEA failure-mode tables with RPN/criticality scoring, event-tree analysis (ETA) for outcome frequency, fault-tree analysis (FTA) with β-factor common-cause modelling and minimal cut sets, and escalation-graph (domino) screening. USE WHEN: a task requires systematic hazard identification, qualitative-to-quantitative scenario development, top-event decomposition, or escalation/domino analysis between adjacent equipment. Anchors on neqsim.process.safety.hazid, neqsim.process.safety.risk.eta, neqsim.process.safety.risk.fta, neqsim.process.safety.escalation.
neqsim-process-safety
by equinorProcess safety methodology — barrier management, PSFs/SCEs, HAZOP guidewords, LOPA worksheets, SIL determination per IEC 61511, bow-tie analysis, risk-matrix scoring, and trapped-liquid fire rupture screening. USE WHEN: a task requires barrier registers, hazard identification, layer-of-protection analysis, safety-integrity-level assignment for an SIF, trapped liquid rupture/PFP demand, or quantitative risk evaluation. Anchors on neqsim.process.safety.barrier, neqsim.process.safety.risk, and neqsim.process.safety.rupture classes.
stpa-overview
by sandgardenhqEntry point for STPA (System Theoretic Process Analysis) hazard and safety analysis. Use for full 4-step STPA sessions, focused project-critic safety reviews, coordinator safety gates, reviewer hazard checks, risk assessment, unsafe state transitions, external input, filesystem, concurrency, physical systems, AI-driven systems, or when tempted to route work to retired stpa-analyst.
stpa-step2-control-structure
by sandgardenhqSTPA Step 2 - Model the control structure using hierarchical control-feedback diagrams in Graphviz/DOT format; After completing STPA Step 1. When you need to understand how control flows through a system. When identifying controllers, control actions, and feedback paths.
stpa-step3-unsafe-control-actions
by sandgardenhqSTPA Step 3 - Identify Unsafe Control Actions (UCAs) using the 4-type analysis framework. After completing STPA Step 2. When analyzing control actions for potential safety issues. When you need to systematically identify what could go wrong with each control action.
quality-manager-qms-iso13485
by ricardonevesbragaImplementação e manutenção de Sistema de Gestão da Qualidade ISO 13485 para organizações de dispositivos médicos. Fornece design do QMS, controle de documentação, auditoria interna, gestão de CAPA e suporte à certificação. Use ao trabalhar com sistemas de qualidade de dispositivos médicos, preparar para auditorias ISO 13485, gerenciar documentação de conformidade regulatória, configurar ações corretivas ou construir programas de preparação para auditoria — com foco no mercado brasileiro e ANVISA.
adaptive-guardrail-calibrator
by diegosouzapwCalibrate guardrail thresholds from live hardware telemetry and emit environment presets. Use when thresholds are hand-tuned or drift with hardware changes.
astm
by diegosouzapwASTM Standards Specialist
ot-incident-summarization
by Happy-Technologies-LLCSummarize OT incidents with affected device inventory, safety impact analysis, containment status, and operational continuity assessment for industrial environments
pharaoh-fmea
by useblocksUse when deriving a single failure-mode entry (FMEA / DFA row) from one requirement or architecture element. Emits structured JSON with cause, effect, severity (1-10), occurrence (1-10), detection (1-10), and RPN.
iso7010-safety-sign-design
by gabrielmoreiraDesign hypothetical safety signs and pictograms compliant with ISO7010 standards for specified environments, detailing shape, color, and iconography for warnings, mandatory actions, prohibitions, and information.
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.