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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configure-sonarcloud-coverage
by ansible-communityConfigures CI coverage reporting for SonarCloud on Ansible collections. Use this skill when Sonar already runs but coverage is missing and you need to wire XML reports, aggregator workflows, and scanner report paths.
aws-terminator-workflow
by ansible-communityUse this skill when creating an end-to-end aws-terminator PR for new AWS modules in Ansible collections. Orchestrates analysis, implementation, validation, and PR submission, linking the companion PR back to the original collection PR. Invoke for "aws terminator workflow", "create aws-terminator pr", or "terminator pr for" a collection PR.
changelog-fragment
by ansible-communityCreates or updates changelog fragments for documenting changes in Ansible collections. Use when asked to create a changelog fragment, add a fragment, or update fragments with PR URLs. Automatically detects repository context from git.
configure-sonarcloud-collection
by ansible-communityAdds SonarCloud (SonarQube Cloud) static analysis to an Ansible collection repo: sonar-project.properties, GitHub Actions scanner workflow, XML coverage for Sonar, and contributor-facing docs; includes fork/secret and assistant-safe patterns (see Security section). Use when onboarding SonarCloud, wiring CI secrets, producing coverage.xml, or mirroring ansible-collections setups like amazon.aws.
get-pr-zuul-results
by ansible-communityFetches Zuul CI build status and log URLs for ansible-collections pull requests. Use this skill when you need to check Zuul results, summarize failing builds, or retrieve log links for a PR.
sanity
by ansible-communityRun Ansible sanity tests with smart change detection. Use when testing Ansible collections, validating module code, running pre-commit checks, or preparing releases. Supports smart mode (tests only changed files - fast), full mode (all files), and changed-only mode (custom range).
ansible-zen
by ansible-communityDisplay the Zen of Ansible principles and review Ansible code against them. Use when the user wants to see the Zen of Ansible, get philosophical guidance on their automation approach, or review code for simplicity, readability, and clarity. Use when user says "zen of ansible", "simplify my playbook", "is this too complex", or "clean code review". Do NOT use for strict rule compliance (use ansible-cop-review instead).
network-collection-triage
by ansible-communityTriage bug reports, CI failures, and GitHub issues across Ansible network collections (cisco.ios, cisco.iosxr, cisco.nxos, arista.eos, ansible.netcommon, ansible.utils, etc.). Two modes: scan mode for bulk weekly triage across all repos, and direct mode for deep triage of a single issue. Network-specific: uses cross-collection cascade detection for shared dependencies (netcommon, utils) and known network CI failure patterns. Outputs structured JSON and markdown. Use when asked to triage network issues, scan network issues, weekly triage, triage CI failure, or triage collection issue. Do not use for non-network collections or general Ansible questions.
ansible-markdown-docs
by ansible-communityFetch Ansible documentation from docs.ansible.com as Markdown. Use when the user asks about an Ansible topic, references a docs.ansible.com URL, or when the agent needs to look up official Ansible documentation to complete a task or verify information.
create-branch
by ansible-communityCreates a new feature branch following project conventions. Use this skill when you need to start new work by fetching latest from origin, basing off origin/main, and configuring fork-friendly upstream tracking.
commit
by ansible-communityThis skill should be used when the user asks to 'commit', 'create a commit', or 'git commit'. It creates conventional commits with FQCN scopes for Ansible collection content (roles, modules, plugins).
stable-release-analyze
by ansible-communityAnalyzes Ansible collection stable branches to determine pending releases and calculate appropriate SemVer versions. Checks for unreleased commits, analyzes changelog fragments, and recommends next version. Use when asked to check which collections need releases or what version to release.
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.