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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ibm-mainframe
by DauQuangThanhProvides comprehensive IBM Mainframe administration, development, and modernization guidance including z/OS operations, JCL scripting, COBOL/PL/I programming, CICS/IMS configuration, DB2 administration, TSO/ISPF usage, system programming, batch processing, and mainframe-to-cloud migration strategies. Covers mainframe security (RACF, ACF2), performance tuning, capacity planning, disaster recovery, and DevOps integration. Use when working with mainframe systems, z/OS, JES2/JES3, VTAM, USS, or when users mention "mainframe", "z/OS", "MVS", "TSO", "ISPF", "JCL", "CICS", "IMS", "DB2 mainframe", "VSAM", "RACF", "mainframe modernization", "legacy mainframe", or "IBM z Systems".
ibm-cloud
by DauQuangThanhProvides comprehensive IBM Cloud platform guidance including compute services (VPC, Virtual Servers, IKS, OpenShift, Code Engine, Cloud Functions), storage (Object Storage, Block Storage, File Storage), databases (Db2, Cloudant, PostgreSQL, MySQL, MongoDB, Redis), IAM security (access groups, service IDs, Key Protect, Secrets Manager), networking (VPC, load balancers, Direct Link), CLI automation, Terraform/Schematics infrastructure as code, monitoring, and cost optimization. Covers infrastructure provisioning, application deployment, security configuration, multi-zone high availability, and operational best practices. Use when working with IBM Cloud services, deploying cloud infrastructure, managing cloud resources, configuring security and networking, or when users mention "IBM Cloud", "IKS", "Code Engine", "Db2", "Cloudant", "VPC", "cloud provisioning", "IBM Kubernetes", "OpenShift", "Terraform IBM", "Schematics", or "IBM cloud platform".
rpg-migration-analyzer
by DauQuangThanhAnalyzes legacy RPG (Report Program Generator) programs from AS/400 and IBM i systems for migration to modern Java applications. Extracts business logic from RPG III/IV/ILE source code, identifies data structures (D-specs), file operations (F-specs), program dependencies (CALLB/CALLP), and converts RPG constructs to Java equivalents. Generates migration reports, complexity estimates, and Java implementation strategies with POJO classes, JPA entities, and service methods. Use when modernizing AS/400 or IBM i legacy systems, analyzing RPG source files (.rpg, .rpgle, .RPGLE), converting RPG to Java, mapping data specifications to Java classes, planning legacy system migration, or when user mentions RPG analysis, Report Program Generator, RPG III/IV/ILE, AS/400 modernization, IBM i migration, packed decimal conversion, or mainframe application rewrite.
jcl-migration-analyzer
by DauQuangThanhAnalyzes legacy JCL (Job Control Language) scripts to assist with migration to modern workflow orchestration and batch processing systems. Extracts job flows, step sequences, data dependencies, conditional logic, and program invocations. Generates migration reports and creates implementation strategies for Spring Batch, Apache Airflow, or shell scripts. Use when working with mainframe job migration, JCL analysis, batch workflow modernization, or when users mention JCL conversion, analyzing .jcl/.JCL files, working with job steps, procedures, or planning workflow orchestration from JCL jobs.
cobol-migration-analyzer
by DauQuangThanhAnalyzes legacy COBOL programs and JCL jobs to assist with migration to modern Java applications. Extracts business logic, identifies dependencies, generates migration reports, and creates Java implementation strategies. Use when working with mainframe migration, COBOL analysis, legacy system modernization, JCL workflows, or when users mention COBOL to Java conversion, analyzing .cbl/.CBL/.cob files, working with copybooks, or planning Java service implementations from COBOL programs.
alibaba-cloud
by DauQuangThanhProvides comprehensive Alibaba Cloud (Aliyun) guidance including ECS, ApsaraDB, OSS, SLB, VPC, RAM, ACK (Kubernetes), Function Compute, API Gateway, CDN, and monitoring services. Covers infrastructure provisioning with Terraform/ROS, cloud architecture design, security best practices, cost optimization, and migration strategies. Produces infrastructure code, deployment scripts, architecture diagrams, and operational procedures. Use when working with Alibaba Cloud services, designing cloud architecture on Aliyun, migrating to Alibaba Cloud, setting up Chinese cloud infrastructure, implementing multi-region deployments in China, or when users mention Alibaba Cloud, Aliyun, ECS, OSS, ApsaraDB, ACK, RDS, SLB, or Chinese cloud computing.
kiss-docx-markdown
by DauQuangThanhTranslate between Word (.docx) files and Markdown with high-fidelity round-trip. Use this skill whenever the user wants to convert a Word document to Markdown, edit a .docx as text, regenerate a .docx from Markdown, diff/review Word content in git, or keep a canonical text version of a document alongside the .docx. Triggers: 'docx to markdown', 'word to md', 'convert this Word doc to markdown', 'rebuild this docx from markdown', 'edit my report in markdown', 'extract word text with formatting', 'round-trip my Word doc'. Preserves headings, lists, tables, images, footnotes, comments, tracked changes, and document styles via pandoc plus a sidecar metadata file. Not a replacement for the base docx skill when the user is authoring a new Word document from scratch — use that skill for new content, and use this one for round-trip translation.
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.