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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cosid-spring-boot
by Ahoo-WangConfigure CosId in Spring Boot applications with cosid-spring-boot-starter. Use when the user works with application.yml, Gradle or Maven dependencies, starter feature variants, Redis/JDBC/MongoDB/ZooKeeper/proxy distributors, SnowflakeId, SegmentId, SegmentChainId, CosIdGenerator, @CosId, IdGeneratorProvider, ID converters, machine guarder settings, clock-backwards synchronization, or Actuator endpoints in a Spring Boot service.
cosid-strategy-guide
by Ahoo-WangChoose the right CosId ID generation strategy for Java distributed systems. Use when the user compares CosIdGenerator, SnowflakeId, SegmentId, or SegmentChainId; asks which generator to use; or evaluates ID type, ordering, throughput, clock sensitivity, machine ID allocation, JavaScript-safe IDs, coordination backends, or production tradeoffs.
cosid-manual-integration
by Ahoo-WangManually integrate CosId into Java or Kotlin applications without Spring Boot auto-configuration. Use when the user needs programmatic setup for SnowflakeId, SegmentId, SegmentChainId, CosIdGenerator, machine ID distribution, custom IdConverter wiring, non-Spring environments, library/framework integration, or production-safe generator lifecycle management.
cosid-sharding
by Ahoo-WangDesign and configure CosId sharding algorithms for database sharding and ShardingSphere. Use when the user mentions table or database sharding, ShardingSphere COSID_MOD or COSID_INTERVAL rules, modulo sharding, date/time interval sharding, range routing, SnowflakeId timestamp extraction, ModCycle, IntervalTimeline, CachedSharding, PreciseSharding, RangeSharding, or SnowflakeLocalDateTimeConvertor.
wow-development-workflow
by Ahoo-WangUse when developing, completing, restructuring, or enhancing Wow framework aggregate or saga behavior, including new domain capability, command/event/state design, cross-aggregate orchestration, aggregate tests, saga tests, KDoc, scenario documents, or design reports.
wow-code-review
by Ahoo-WangUse when reviewing Wow framework code, PR diffs, or pre-merge changes involving aggregates, commands, events, sourcing, sagas, projections, command gateway, Query DSL, starter configuration, or Wow tests
wow
by Ahoo-WangWow framework assistant for building reactive DDD + Event Sourcing + CQRS microservices in Kotlin/Java on JVM 17+ with Spring Boot. Use this skill when a task involves Wow framework semantics: - DDD aggregates, command/event/state modeling, bounded contexts, tenant/owner routing - CQRS, Event Sourcing, event stores, snapshots, projections, read models - Saga orchestration, event processors, retry policy, PrepareKey - Command gateway, wait plans, command bus, WebFlux command endpoints - Wow tests: AggregateSpec, SagaSpec, AggregateVerifier, SagaVerifier - Wow annotations such as @AggregateRoot, @OnCommand, @OnSourcing, @OnEvent, @StatelessSaga, @ProjectionProcessor, @EventProcessor, @AfterCommand, @OnError, @Retry, @BoundedContext, @CreateAggregate, @CommandRoute Do not trigger for unrelated Kotlin, Gradle, frontend, or documentation tasks unless Wow framework behavior or APIs are directly relevant.
wow-debugging
by Ahoo-WangUse when Wow commands, events, sourcing, sagas, projections, wait plans, Query DSL, retry policies, starter configuration, or tests fail, hang, skip handlers, produce unexpected state, or behave inconsistently
cosec-policy-author
by Ahoo-WangUse when writing, validating, explaining, or debugging CoSec policy JSON, policy statements, ALLOW/DENY rules, action matchers, condition matchers, role-based authorization, tenant scoping, or rate limiting policies.
cosec-custom-matcher
by Ahoo-WangUse when extending CoSec policy matching with custom ActionMatcher or ConditionMatcher implementations, condition types, matcher factories, ServiceLoader SPI registration, or Spring bean matcher registration.
cosec-integration
by Ahoo-WangUse when adding CoSec to a Spring Boot application, choosing WebFlux/WebMVC/Gateway modules, configuring JWT authentication, authorization filters, local policies, Redis policy caching, social login, IP enrichment, OpenAPI, or OpenTelemetry integration.
cosec-troubleshoot
by Ahoo-WangUse when diagnosing CoSec authentication or authorization failures such as unexpected 401/403 responses, denied requests that should be allowed, policies not loading, JWT token rejection, matcher mismatches, or unclear access decisions.
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.