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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build-native-image-maven
by oracleBuild GraalVM native images using the native-maven-plugin (org.graalvm.buildtools). Use this skill to build Java applications with Maven, configure pom.xml native image settings, run native tests, collect metadata, or resolve build or runtime issues.
building-native-image
by oracleBuild and troubleshoot GraalVM Native Image applications. Use this skill to build Java applications with GraalVM Native Image, configure CLI options, or resolve build or runtime issues.
build-native-image-gradle
by oracleBuild GraalVM native images using Gradle Native Build Tools. Use this skill to build Java applications with Gradle, configure native-image build.gradle settings, or resolve build or runtime issues.
jira
by oracleDeal with Jira tickets autonomously
rota-bench-regression-analysis
by oracleAnalyze recent GraalPy benchmark regressions on `master` as part of the weekly rota. Use when asked to analyze benchmarks for rota.
rota-check-periodic-jobs
by oracleAnalyze current GraalPy periodic job failures for ROTA. Use when asked to triage, summarize, or plan work for current periodic job failures, starting from scripts/rota_ci_failures.py output, validating linked Jira issues, inspecting logs, forming hypotheses, reproduction commands, and implementation order.
rota-update-import
by oracleRun the GraalPy ROTA import update workflow. Use when asked to refresh imports, create the standard Graal import update pull request, inspect generated commits, and hand off to the shared GraalPython Bitbucket PR flow for tasks, gates, and failure fixes.
graalpython-bitbucket-pr
by oracleCreate or continue a GraalPython Bitbucket pull request and drive it through Graal Bot tasks, gate start, gate monitoring, failure investigation, fixes, pushes, and gate reruns. Use after a branch is ready for internal GraalPython review, or when an automation command has already created the PR and the remaining work is task cleanup and gate follow-up.
windows
by oracleWindows-specific command guidance. Use when working on Windows and a required CLI tool, especially gdev-cli, is not available directly in PowerShell or cmd.
third-party-package-patches
by oracleCreate or update GraalPy third-party package compatibility patches under graalpython/lib-graalpython/patches, including PyPI source preparation, rebasing existing patches, metadata.toml updates, license checks, version-range validation, and verify_patches.py validation.
apex
by oracleOracle APEX skills for Oracle APEX application development.
oci
by oracleOracle Cloud Infrastructure guidance for designing, operating, and troubleshooting OCI services, including OCI Kubernetes Engine (OKE) and Enterprise AI workflows for OCI Generative AI models, Responses API agents, RAG, cost estimation, governance, private endpoints, hosted agentic applications, and Oracle platform integrations. Use when the user asks about OKE cluster design, Terraform or Resource Manager planning, OKE incident troubleshooting, Generic VNIC Attachment, Multus, pod networking, node pools, add-ons, ingress, load balancers, OCIR image pulls, Workload Identity, Kubernetes workloads on OCI, OCI Generative AI, Enterprise AI Models, Enterprise AI Agents, governed GenAI applications, agentic workflows, RAG on Oracle Cloud, or OCI Generative AI pricing.
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.