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.
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Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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quartz-integration
by vitorpamplonaIntegration guide for using the Quartz Nostr KMP library in external projects. Use when: (1) adding Quartz as a Gradle dependency, (2) setting up NostrClient with WebSocket, (3) creating/signing/sending events, (4) building relay subscriptions with Filter, (5) handling keys with KeyPair/NostrSignerInternal, (6) using Bech32 encoding/decoding (NIP-19), (7) platform-specific setup (Android vs JVM/Desktop), (8) NIP-57 zaps, NIP-17 DMs, NIP-44 encryption in external projects.
kotlin-coroutines-structured-concurrency
by vitorpamplonaUse when writing or reviewing Kotlin code that stores CoroutineScope, launches from init/non-suspending APIs, calls runBlocking, or catches broad exceptions around suspend calls. Technique-layer skill — complements the codebase-specific kotlin-coroutines.
kotlin-coroutines
by vitorpamplonaAdvanced Kotlin coroutines patterns for AmethystMultiplatform. Use when working with: (1) Structured concurrency (supervisorScope, coroutineScope), (2) Advanced Flow operators (flatMapLatest, combine, merge, shareIn, stateIn), (3) Channels and callbackFlow, (4) Dispatcher management and context switching, (5) Exception handling (CoroutineExceptionHandler, SupervisorJob), (6) Testing async code (runTest, Turbine), (7) Nostr relay connection pools and subscriptions, (8) Backpressure handling in event streams. Delegates to kotlin-expert for basic StateFlow/SharedFlow patterns. Complements nostr-expert for relay communication.
kotlin-expert
by vitorpamplonaAdvanced Kotlin patterns for AmethystMultiplatform. Flow state management (StateFlow/SharedFlow), sealed hierarchies (classes vs interfaces), immutability (@Immutable, data classes), DSL builders (type-safe fluent APIs), inline functions (reified generics, performance). Use when working with: (1) State management patterns (StateFlow/SharedFlow/MutableStateFlow), (2) Sealed classes or sealed interfaces, (3) @Immutable annotations for Compose, (4) DSL builders with lambda receivers, (5) inline/reified functions, (6) Kotlin performance optimization. Complements kotlin-coroutines agent (async patterns) - this skill focuses on Amethyst-specific Kotlin idioms.
kotlin-flow-state-event-modeling
by vitorpamplonaUse when writing or reviewing Kotlin StateFlow/SharedFlow/Channel choices, sentinel default values, stateIn placement, WhileSubscribed staleness, or MutableStateFlow update patterns. Technique-layer skill — complements the codebase-specific kotlin-expert.
kotlin-multiplatform
by vitorpamplonaPlatform abstraction decision-making for Amethyst KMP project. Guides when to abstract vs keep platform-specific, source set placement (commonMain, jvmAndroid, platform-specific), expect/actual patterns. Covers primary targets (Android, JVM/Desktop, iOS — all mature) with web/wasm as possible future targets. Integrates with gradle-expert for dependency issues. Triggers on: abstraction decisions ("should I share this?"), source set placement questions, expect/actual creation, build.gradle.kts work, incorrect placement detection, KMP dependency suggestions.
kotlin-types-value-class
by vitorpamplonaUse when writing or reviewing Kotlin type declarations to choose @JvmInline value class over data class where appropriate, including Compose stability implications. Technique-layer skill — complements the codebase-specific kotlin-expert.
nostr-expert
by vitorpamplonaNostr protocol implementation patterns in Quartz (AmethystMultiplatform's KMP Nostr library). Use when working with: (1) Nostr events (creating, parsing, signing), (2) Event kinds and tags, (3) NIP implementations (80+ NIP packages in quartz/), (4) Event builders and TagArrayBuilder DSL, (5) Nostr cryptography (secp256k1, NIP-44 encryption), (6) Relay communication patterns, (7) Bech32 encoding (npub, nsec, note, nevent). Complements nostr-protocol agent (NIP specs) - this skill provides Quartz codebase patterns and implementation details.
compose-state-holder-ui-split
by vitorpamplonaUse when a Jetpack Compose screen-level composable takes a ViewModel/component/controller, collects state or effects, handles navigation/snackbars, or wires callbacks while also rendering layout. Technique-layer skill — complements the codebase-specific compose-expert and feed-patterns.
auth-signers
by vitorpamplonaSigner abstraction patterns in Amethyst. Use when working with event signing, choosing between a local keypair (`NostrSignerInternal`), a remote NIP-46 bunker signer (`NostrSignerRemote`), or a NIP-55 Android external-app signer (`NostrSignerExternal`). Covers the abstract `NostrSigner` base class, `SignerResult` contract, how to wire a new flow that needs to sign events, and the security/UX trade-offs between signer kinds.
account-state
by vitorpamplonaAccount state and in-memory event store patterns in Amethyst. Use when working with `Account.kt` (per-user state objects — `kind3FollowList`, `nip65RelayList`, `muteList`, `bookmarkState`, each exposing a `.flow` StateFlow), `LocalCache` (the object-level event store backed by `LargeCache`), `User`/`Note` model classes, or any ViewModel that reads user-specific state. Covers how account events cascade from relay arrival to UI state, how to add a new account-scoped setting, and when to read from `LocalCache` vs subscribe to a StateFlow.
amy-expert
by vitorpamplonaPatterns for extending `amy`, the Amethyst CLI in `cli/`. Use when adding an `amy <verb>` command, touching files under `cli/src/main/kotlin/…/cli/`, wiring a new subcommand into `Main.kt`, writing an interop test script that drives Amy, or extracting logic out of `amethyst/` into `commons/` so a CLI command can call it. Enforces the thin-assembly-layer rule (no Nostr protocol or business logic inside `cli/`), the dual-output contract (text by default, single-line JSON object on stdout under `--json`, exit codes 0/1/2/124), and the extract-from-Android recipe. Complements `nostr-expert` (protocol in Quartz), `kotlin-multiplatform` (expect/actual for extraction), and `feed-patterns` / `account-state` / `relay-client` (where the business logic should end up). NOT for general Nostr or Kotlin work — those have their own skills.
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.