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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docker-build-push
by mock-serverBuilds and pushes the MockServer Maven CI Docker image locally. Covers corporate CA certificate setup, architecture selection (amd64 vs arm64), buildx gotchas with corporate TLS proxies, and Docker Hub authentication. Use when the user says "build docker image", "push maven image", "rebuild CI image", "docker build", "push to docker hub", or needs to manually build/push the mockserver/mockserver:maven image outside of CI.
build-monitor
by mock-serverContinuously monitors Buildkite pipeline builds, detects failures, investigates root causes, fixes issues, and pushes fixes. Runs a polling loop that checks build status at configurable intervals for a configurable duration. Use when the user says "monitor builds", "watch pipeline", "watch CI", "continuous monitoring", "keep checking builds", or wants automated build-fix cycles.
browser-auth
by mock-serverDocuments how to use Chrome DevTools MCP to navigate authenticated web pages, extract data (tokens, configuration values), take screenshots, and handle login flows. Use when you need to interact with Buildkite, AWS Console, or other authenticated web UIs via the Chrome browser. Triggers when users ask about "browser auth", "extract token from browser", "navigate to buildkite", "chrome mcp", or need to scrape data from authenticated pages.
pipeline-investigation
by mock-serverInvestigates Buildkite pipeline failures to find root causes. Returns structured JSON to the parent for formatting. Triggers when users ask about failing pipelines, build errors, or need help debugging CI/CD issues. Accepts Buildkite build URLs or build numbers and performs deep investigation.
aws-investigation
by mock-serverInvestigates AWS infrastructure issues affecting Buildkite build agents (EC2, AutoScaling, Lambda). Returns structured JSON to the parent for formatting. Triggers when users ask about build agents not running, EC2 issues, ASG scaling problems, or infrastructure health.
ideate
by mock-serverClarifies a rough idea into a precise problem specification through structured dialogue. Asks targeted questions to surface assumptions, scope, constraints, and actors. Produces a specification document defining WHAT needs to change — not HOW to change it. Use when starting a new feature, brainstorming an idea, clarifying requirements, or when a user says "I have an idea", "let's think through", "ideate", "brainstorm", "I want to build", "help me think about", "what should we build".
issue-review
by mock-serverReviews a GitHub issue to determine validity, classify as user error or bug, check if already fixed, and take appropriate action. For user errors, improves error messages and documentation. For real bugs, implements a fix following the full commit workflow including tests and adversarial review. Closes the issue with a clear resolution message. Use when the user says "review issue", "triage issue", "is this a bug", "check issue", "issue review", or provides a GitHub issue URL or number.
terraform-tfvars
by mock-serverCreates the terraform.tfvars file for the Buildkite build agent Terraform stack. Documents how to retrieve the Buildkite agent token from Buildkite UI (via Chrome DevTools MCP) and AWS SSM Parameter Store, validate tokens, and populate the tfvars file. Use when users say "create tfvars", "set up terraform variables", "deploy buildkite agents", "configure buildkite token", or need to recreate the terraform.tfvars after a fresh checkout.
release-management
by mock-serverPrepares a MockServer release by recommending the release version from Semantic Versioning rules and `changelog.md`, checking release readiness, and listing the exact Buildkite release parameters. Use when users say "prepare release", "release version", "run the release pipeline", "which version should we release", or need to verify changelog and secret readiness before triggering the release pipeline.
dependabot-snyk-pr-management
by mock-serverInteract with Dependabot and Snyk pull requests for dependency upgrades and security fixes. Documents Dependabot commands, javax/jakarta compatibility checks, safe merge workflows, and troubleshooting. Use when managing dependency upgrade PRs or security fix PRs.
pr-monitor
by mock-serverMonitors Dependabot and Snyk dependency upgrade PRs, automatically merging them when builds pass. Handles javax/jakarta compatibility validation and provides detailed status reporting. Use when the user says "monitor PRs", "watch builds", "auto-merge PRs", "merge passing PRs", or "watch dependency PRs".
review-spec
by mock-serverDeep adversarial specification review using the 8-lens review constitution. Evaluates design documents, plans, and specs for ambiguity, completeness, feasibility, security, and MockServer-specific concerns. Loaded by review-cheap and review-final agents.
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.