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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upgrade-changelog
by akash-networkGenerates CHANGELOG.md entries for upgrade versions found in upgrades/software/ by parsing init.go and upgrade.go
build-contracts
by akash-networkBuilds all cosmwasm contracts
vanity
by akash-networkRegisters node major version vanity URL in the sibling vanity repo
guidelines
by akash-networkBehavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
setup-env
by akash-networkSet up development environment by loading direnv. Must be run before any make targets (tests, builds, linting).
branch-namer
by akash-networkGenerate descriptive git branch names that follow the project's naming convention. Use this skill whenever the user asks to create a branch, name a branch, start working on a feature or fix, checkout a new branch, or when you're about to run `git checkout -b` or `git switch -c`. Also trigger when you see a vague branch name like `fix/auth` or `feature/billing` that lacks a description of what's actually changing — the branch name should always tell you *what* the change does, not just its category.
console-tests
by akash-networkWrite tests for the akash-network/console monorepo following established team patterns and reviewer expectations. Use this skill whenever you need to write, fix, review, or refactor tests in the console project — including unit tests, functional tests, integration tests, or E2E tests for both frontend (deploy-web) and backend (api, notifications, indexer, provider-proxy). Also trigger when the user mentions 'write tests', 'add tests', 'fix tests', 'test this', 'spec file', or asks about testing patterns in the console codebase. When in doubt about whether to use this skill for a testing task in this repo, USE IT.
linear-issue
by akash-networkPlan, create, and improve Linear issues with business-level clarity. Use this skill whenever the user wants to create a Linear issue, improve an existing one, file a bug, plan a feature, create a chore/enabler, or mentions "linear issue", "file an issue", "create a ticket", "log a bug", "new issue", "plan this work", "improve this issue", or "clean up this ticket". Also trigger when the user says "I found a bug", "we need a ticket for...", "can you create an issue for...", "break this down into issues", or pastes a Linear issue URL/ID. This is the required way to create and maintain issues — it ensures every issue follows the team's format and is scoped for small PRs.
akash
by akash-networkDEPRECATED — this standalone skill has been split into three skills and repackaged as the `akash-network` Claude Code plugin. Install the plugin and uninstall this standalone skill. See the repo README for migration instructions.
akash-node
by akash-networkRun an Akash Network full node or validator. Covers hardware and network requirements, full-node installation, state sync, becoming a validator, validator operations, slashing avoidance, key management (consensus key + operator key), monitoring, and sentry node patterns. Use for "Akash validator", "Akash full node", "Akash state sync", "Akash validator setup", "Akash sentry node", "Akash slashing", "Akash consensus key", "Akash node upgrade".
akash-provider
by akash-networkSet up and operate an Akash Network provider — the supply side of the decentralized cloud. Covers Kubernetes prerequisites, provider installation, attributes and pricing configuration, bid engine tuning, monitoring, and troubleshooting. Use for "run an Akash provider", "set up Akash provider", "Akash provider Kubernetes", "Akash provider attributes", "Akash provider pricing", "Akash provider bid engine", "Akash provider not getting bids", "Akash provider audit", "provider lease monitoring".
akash
by akash-networkBuild, validate, and deploy workloads to the Akash Network — the decentralized cloud marketplace. Covers SDL syntax & examples, choosing a deployment method (Console API, CLI, TypeScript/Go SDKs), authentication (API key, JWT, self-custody wallets), deployment lifecycle, fetching logs/events via the provider proxy, and fee grants/authz. Also covers AkashML — the managed inference surface for calling open-source LLMs on Akash compute via OpenAI/Anthropic-compatible APIs. Use for "deploy to Akash", "Akash SDL", "Akash Console API", "Akash CLI deploy", "Akash API key", "x-api-key", "Akash deploy logs", "stream Akash logs", "integrate Akash into my app", "@akashnetwork/chain-sdk", "@akashnetwork", "AkashML", "managed inference on Akash", "call an LLM on Akash", "playground.akashml.com", "api.akashml.com".
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.