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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synthetic-data
by Sheldon-92Synthetic data & fine-tune dataset curation capability pack. Gives AI agents the judgment rules for pretraining/SFT data quality filtering, document-level deduplication, synthetic instruction generation, preference-pair curation, and benchmark contamination detection. Research-grounded rules from Self-Instruct, Evol-Instruct/WizardLM, LSHBloom, distilabel, Axolotl/Unsloth, DPO/RRHF/GRPO, and the ConTAM/CoDeC contamination literature. Use for any synthetic dataset build, fine-tune data prep, dedup pipeline, preference dataset, or contamination audit task.
knowledge-graph
by Sheldon-92Knowledge Graph & GraphRAG capability pack. Gives AI agents the judgment rules for building graph-enhanced retrieval systems — Microsoft GraphRAG indexing (Leiden communities, Global/Local/Drift search), LazyGraphRAG vs LightRAG cost selection, LLM knowledge-graph construction (ontology design, extraction prompting), entity resolution & deduplication, graph database selection (Neo4j/Memgraph/FalkorDB, LPG vs RDF-Star), and Text2Cypher/SPARQL-Star query translation. Research-grounded rules from Microsoft Research, Neo4j, LightRAG, OntoDup, and graph database benchmarks. Use for any GraphRAG pipeline, knowledge-graph construction, entity-resolution, graph-DB selection, or graph-query-translation task.
academic-research
by Sheldon-92Academic research methodology pack — systematic literature review, citation integrity, quality evaluation. Activates on: 学术, academic, 论文, paper, 文献, literature, meta-analysis, 元分析, PRISMA, systematic review, 系统性综述, PubMed, 文献综述, 学术研究, 科研
ai-prompt-engineering
by Sheldon-92Production prompt lifecycle toolkit. Gives AI agents the ability to design, test, optimize, version, and deploy prompts like a senior prompt engineer — with automated testing (promptfoo), programmatic optimization (DSPy), quality metrics (DeepEval), and CI/CD gates. Use for writing system prompts, testing prompt suites, diagnosing hallucination/drift, setting up CI/CD pipelines, or auditing existing prompts.
agent-memory
by Sheldon-92Agent memory and context engineering capability pack. Gives AI agents the judgment rules for memory architecture (CoALA working/episodic/semantic/procedural layers), context compaction strategy selection, MemGPT/Letta virtual context management, Mem0 extract-reconcile pipelines, LangGraph state persistence and time-travel debugging, and Anthropic prompt-caching topology. Research-grounded rules from MemGPT/Letta, Mem0, LangGraph, the CoALA framework, and Anthropic caching docs. Use for any agent memory design, context-window optimization, checkpointing, or long-horizon statefulness task.
agent-orchestration
by Sheldon-92Agent orchestration capability pack. Gives AI agents the judgment rules for building reliable multi-agent systems — framework selection (LangGraph / CrewAI / AutoGen v0.4+ / OpenAI Agents SDK / Claude Agent SDK), Supervisor vs Swarm topology, durable execution with Temporal event sourcing, human-in-the-loop interrupt/resume patterns, and tool-permission models. Research-grounded rules from framework docs, Temporal durable-execution patterns, and production complexity-cliff analysis. Use for any multi-agent architecture, orchestration framework choice, checkpoint/recovery design, HITL gating, or agent tool-permission task.
ai-agent-architecture
by Sheldon-92Decision navigator for designing reliable agent systems. Guides any AI agent through 10 architectural decisions derived from 3 production systems (Claude Code, OpenClaw, Hermes) and 7 real production disasters. Two modes: /design (new system) and /audit (existing system).
ai-evaluation
by Sheldon-92AI evaluation capability pack. Gives AI agents the judgment rules for professional benchmarking, regression testing, A/B comparison, adversarial red-teaming, CI/CD evaluation pipelines, evaluation framework design, and human evaluation calibration. Research-grounded rules from promptfoo, deepeval, deepteam, ragas, and enterprise evaluation practices. Use for any LLM/agent evaluation, benchmark design, safety testing, or evaluation pipeline task.
ai-guardrails
by Sheldon-92AI guardrails & LLM I/O security capability pack. Gives AI agents the judgment rules for defending LLM and agent pipelines against prompt injection (OWASP LLM01), improper output handling (OWASP LLM05), excessive agency, PII leakage, and unsafe content. Research-grounded rules from OWASP Gen AI Security, Microsoft Presidio, NVIDIA NeMo Guardrails, Meta Llama Guard, Lakera Guard, Rebuff, and Pydantic AI. Use for any guardrail design, prompt-injection defense, PII de-identification, output/tool-call validation, content-moderation, or LLM security review task.
ai-podcast-production
by Sheldon-92AI podcast production judgment for coding agents — script writing with Codex review, large-chunk TTS generation, dual-BGM music arrangement with envelope follower ducking, show notes, Colab deployment
ai-tool-integration
by Sheldon-92AI tool integration capability pack. Gives AI agents the judgment rules for MCP server development, CLI tool wrapping, API integration, tool schema design, permission models, testing, and documentation. Research-grounded rules from MCP TypeScript SDK, Anthropic cookbook, Claude Code source, and production MCP server patterns. Use for any MCP server build, CLI-to-MCP wrapping decision, API integration, tool schema review, or tool permission design task.
ai-voice-production
by Sheldon-92AI voice production judgment for coding agents — TTS tool selection, voice cloning, audiobook/podcast/dubbing pipelines, Apple Silicon optimization, licensing safety
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.