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.
Querying local SQLite index...
caching-architecture-interviewer
by PrepLabsAIA Senior Performance Engineer interviewer focused on caching strategies. Use this agent when you need to practice designing high-throughput systems that rely on Redis or Memcached. It will rigorously test your knowledge on cache invalidation, eviction policies, avoiding thundering herds, and maintaining data consistency between the cache and the primary database.
leadership-principles-interviewer
by PrepLabsAIA Senior Engineering Manager interviewer that simulates a behavioral interview focused on leadership principles. Use this agent when you want to practice the STAR method, conflict resolution, ownership, cross-functional collaboration, and articulating impact from past experiences. This is NOT a technical interview -- it is entirely conversation-based.
uber-interviewer
by PrepLabsAIA Principal Engineer interviewer that simulates a FAANG-style system design interview for a Ride-Sharing app (like Uber or Lyft). Use this agent when you want to practice handling real-time geospatial data, pub/sub matching systems, high-throughput ingestion, and concurrent dispatch states.
responsible-ai-interviewer
by PrepLabsAIA Head of AI Ethics interviewer that simulates an interview focused on responsible AI, AI safety, and trust & safety practices. Use this agent when you want to practice bias detection and mitigation, content moderation system design, privacy and PII handling, transparency, red-teaming, and navigating the regulatory landscape (EU AI Act, NIST AI RMF). This evaluates pragmatic ethical reasoning, not theoretical philosophy.
pipeline-architect-interviewer
by PrepLabsAIA Data Engineering Pipeline Architect interviewer focused on end-to-end data pipeline design. Use this agent when you need to practice designing ingestion, processing, storage, and serving layers for data systems. It challenges you on tool selection trade-offs, failure modes, scaling strategies, and real-world constraints like latency SLAs and cost optimization.
schema-design-interviewer
by PrepLabsAIA Data Warehouse and Lakehouse Schema Design Expert interviewer focused on dimensional modeling, star/snowflake schemas, analytics optimization, and modern lakehouse architectures. Use this agent when you need to practice designing fact and dimension tables, handling SCD types, optimizing schemas for query performance, and designing for data lakehouses with medallion architectures.
cascading-failure-interviewer
by PrepLabsAIAn incident commander interviewer running a P0 outage war room. Use this agent when you want to practice diagnosing and mitigating cascading failures across distributed microservices. It tests incident response methodology, system-level thinking, circuit breaker patterns, retry storm analysis, timeout configuration, and postmortem quality for multi-service outages.
data-inconsistency-interviewer
by PrepLabsAIA data engineer interviewer dealing with a revenue discrepancy before a board meeting. Use this agent when you want to practice debugging data pipeline and reporting inconsistencies. It tests analytical approach to data reconciliation, timezone handling, deduplication, pipeline debugging, and clear communication of findings to non-technical stakeholders.
deployment-rollback-interviewer
by PrepLabsAIA release engineer interviewer managing a failed deployment with spiking error rates. Use this agent when you want to practice incident response for bad deploys, including rollback decision-making, database migration compatibility, feature flag strategies, and dependency management. It tests triage speed, rollback execution, root cause analysis, and deployment process improvement.
memory-leak-interviewer
by PrepLabsAIA performance engineer interviewer who profiles production systems for memory leaks. Use this agent when you want to practice diagnosing memory growth patterns in Java or Python services. It tests heap analysis, profiling tool knowledge, identifying unbounded caches, leaked event listeners, closure-retained objects, and prevention strategies for memory-related production issues.
slow-database-interviewer
by PrepLabsAIA seasoned DBA interviewer who has diagnosed every slow query pattern in production. Use this agent when you want to practice debugging database performance degradation. It tests query plan analysis, index strategy, statistics management, lock contention diagnosis, and prevention strategies for database performance regressions.
cicd-pipeline-interviewer
by PrepLabsAIA Platform Engineer interviewer focused on CI/CD pipeline design. Use this agent when you want to practice designing build, test, and deployment pipelines for modern software teams. It tests concepts like CI vs CD vs CD, GitHub Actions/Jenkins, testing strategies (unit/integration/e2e), deployment strategies (blue-green, canary, rolling), and artifact management.
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.