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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as2985-checker
by teddychenfeiyang-pngTechnical reference for AS 2985:2009 (respirable dust sampling method). Load this skill when rcs-report-generator or another action skill needs to verify or cite specific clauses of the gravimetric determination method for respirable dust. Not intended as a standalone user tool.
rcs-report-generator
by teddychenfeiyang-pngGenerate Safetysure RCS (respirable crystalline silica) dust survey reports with exact template fidelity. Use this skill when the user asks to "generate an RCS report", "write a dust monitoring report", "create an air monitoring report", "draft an RCS survey report", or provides monitoring data and asks for a compliance report. Also trigger on the /rcs-report command. Includes an HTML data capture intake form and produces reports section-by-section in markdown preview before final .docx assembly. Cross-references the safetysure-whs-legislation plugin for verified legislative provisions.
erp-reviewer
by teddychenfeiyang-pngReviews existing Emergency Response Plans against AS 3745:2010 compliance requirements. Assesses each section of an ERP against mandatory ("shall"), recommended ("should"), and optional ("may") requirements from the standard, identifying compliant items, non-compliant items, gaps (missing elements), and improvement opportunities. Produces a structured findings report as a Word document. Use this skill whenever reviewing an existing ERP, auditing an emergency response plan, checking ERP compliance with AS 3745, assessing the adequacy of emergency procedures, or conducting a gap analysis on emergency planning documentation.
as-3833
by teddychenfeiyang-pngTechnical reference for AS 3833:2024 (Storage and handling of mixed classes of dangerous goods). Use this skill when the user asks about mixed dangerous goods storage, segregation of incompatible dangerous goods classes, retail storage of dangerous goods, transit storage, storage cabinets for dangerous goods, separation distances for mixed stores, or fire protection for dangerous goods stores — or when an action skill needs to verify or cite specific clauses of AS 3833:2024.
ipl-qualification-assessor
by teddychenfeiyang-pngEvaluate whether a safeguard qualifies as an Independent Protection Layer (IPL) per CCPS (2001) criteria. Use this skill when the user asks to assess IPL qualification, evaluate safeguard independence, determine if a control measure can be credited in LOPA, check for common cause failures, assess BPCS independence (Approach A vs B), evaluate human action IPL criteria, or review an IPL register. Also trigger when users provide a list of safeguards and ask which can be credited as IPLs in a LOPA study.
sensitivity-analysis
by teddychenfeiyang-pngLOPA sensitivity analysis for IPL PFD variations, initiating event frequency ranges, and risk tolerance gap assessment. Use this skill when the user asks for sensitivity analysis of LOPA results, wants to understand how changes in IPL PFDs affect mitigated frequency, needs to evaluate the impact of using screening values vs site-specific data, or wants to assess the margin between mitigated frequency and risk tolerance criteria. Also trigger when users ask "what if" questions about LOPA parameters, request Monte Carlo or parametric analysis of LOPA scenarios, or need to justify that ALARP has been demonstrated.
aioh-compliance
by teddychenfeiyang-pngAIOH occupational hygiene exposure compliance decision framework based on the AIOH Compliance Decision Guide. Typically routed to by the request-router skill when occupational hygiene monitoring data is detected, or invoked directly by name (e.g. "run AIOH compliance analysis", "assess this SEG using aioh-compliance"). Use this skill for occupational exposure compliance assessment, AIOH compliance categories, UCL 95% compliance testing, U-parameter group compliance test, MVUE calculation, Land's exact method, exposure data treatment, lognormal distribution fitting, SEG compliance decisions, occupational hygiene statistics, exceedance fraction calculation, when to use UCL vs U-test, method disagreement guidance, or any question about interpreting occupational exposure monitoring data for compliance purposes. Also trigger when reviewing or generating occupational hygiene reports that reference AIOH methodology, workplace exposure standards, or statistical analysis of airborne contaminant data.
as-1319
by teddychenfeiyang-pngTechnical reference for AS 1319—1994 (Safety signs for the occupational environment), Reconfirmed 2018. Use this skill when the user asks about workplace safety signage, sign design requirements, symbolic signs, safety colours, sign classification, placement and maintenance of safety signs, or when an action skill needs to verify or cite specific clauses of AS 1319.
as-1657
by teddychenfeiyang-pngTechnical reference for AS 1657:2018 (Fixed platforms, walkways, stairways and ladders — Design, construction and installation). Use this skill when the user asks about design and safety requirements for fixed means of access, guardrailing systems, handrails, stairways, fixed ladders, platform design, walkway specifications, or when an action skill needs to verify or cite specific clauses of AS 1657:2018.
as-2359-2
by teddychenfeiyang-pngTechnical reference for AS 2359.2—2013 (Powered industrial trucks — Part 2: Operations). Use this skill when the user asks about powered industrial truck operations, forklift safety requirements, operator training and qualifications, truck maintenance, workplace design for truck operations, or when an action skill needs to verify or cite specific clauses of AS 2359.2—2013.
as-4024-1
by teddychenfeiyang-pngTechnical reference for the AS 4024.1 Series (Safety of machinery) — a comprehensive compilation of ~26 sub-standards covering machinery design, guarding, risk assessment, controls, ergonomics, and safety distances. Use this skill when the user asks about machinery safety design, machine guarding, risk assessment for machinery, safety-related control systems, interlocking devices, minimum safety distances, electrical equipment of machines, or any machinery safety design principle — or when an action skill needs to verify or cite specific clauses from the AS 4024.1 series.
as-4084-2
by teddychenfeiyang-pngTechnical reference for AS 4084.2:2023 (Steel storage racking — Part 2: Operation and maintenance), incorporating Amendment No. 1 (March 2024). Use this skill when the user asks about steel storage racking operation, maintenance requirements, inspection procedures, damage assessment, working load limits, signage, or compliance with AS 4084.2.
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.