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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wiki-export
by Ar9avExport the Obsidian wiki's knowledge graph to structured formats for use in external tools. Use this skill when the user says "export wiki", "export graph", "export to JSON", "export to Gephi", "export to Neo4j", "graphml", "visualize wiki", "knowledge graph export", "export to OKF", "OKF bundle", "open knowledge format", "export as markdown bundle", or wants to use their wiki data in another tool. Outputs graph.json, graph.graphml, cypher.txt (Neo4j), and graph.html (interactive browser visualization) into a wiki-export/ directory at the vault root, plus an optional OKF (Open Knowledge Format) markdown bundle under wiki-export/okf/.
hermes-history-ingest
by Ar9avIngest Hermes agent history into the Obsidian wiki. Use this skill when the user wants to mine their past Hermes sessions for knowledge, import their ~/.hermes folder, extract insights from previous Hermes conversations, or says things like "process my Hermes history", "add my Hermes memories to the wiki", "ingest ~/.hermes", or "what have I worked on in Hermes". Also triggers when the user mentions Hermes memories, Hermes sessions, ~/.hermes/memories, or Hermes skill logs.
wiki-ingest
by Ar9avIngest any source into the Obsidian wiki by distilling its knowledge into interconnected wiki pages. Handles structured documents (PDFs, markdown, articles, papers, notes, folders), raw/unstructured text (chat exports, conversation logs, Slack/Discord threads, meeting transcripts, CSV/JSON data, journal entries, browser bookmarks, email archives, text dumps), AND web URLs. Use whenever the user wants to add new sources to their wiki: "add this to the wiki", "process these docs", "ingest this folder", "ingest this data", "process this export/logs", "import my chat history from X", "/ingest-url <url>", "add this URL", "save this page", or pastes a URL and says "add this" / "save this to my wiki". Also triggers when the user drops a file, or for raw mode: "process my drafts", "promote my raw pages", or any reference to the _raw/ staging directory. This is the general catch-all ingest skill for any document, text, or URL source not covered by a more specific ingest skill (claude-history-ingest, etc.).
wiki-import
by Ar9avImport a wiki knowledge graph into the current vault — either from a graph.json export file (stubs) or from an OKF (Open Knowledge Format) markdown bundle (full page bodies). Use this skill when the user says "import wiki", "import from export", "load graph.json", "import vault", "import OKF bundle", "import OKF", "load OKF", "import markdown bundle", "/wiki-import", or wants to transfer pages from one vault to another using the output of wiki-export.
pi-history-ingest
by Ar9avIngest Pi coding agent session history into the Obsidian wiki. Use this skill when the user wants to mine their past Pi sessions for knowledge, import their ~/.pi/agent/sessions folder, extract insights from previous coding sessions, or says things like "process my Pi history", "add my Pi sessions to the wiki", "ingest ~/.pi", or "what have I worked on in Pi". Also triggers when the user mentions Pi sessions, Pi agent history, ~/.pi/agent/sessions, or Pi conversation logs.
openclaw-history-ingest
by Ar9avIngest OpenClaw agent history into the Obsidian wiki. Use this skill when the user wants to mine their past OpenClaw sessions for knowledge, import their ~/.openclaw folder, extract insights from previous OpenClaw conversations, or says things like "process my OpenClaw history", "add my OpenClaw sessions to the wiki", "ingest ~/.openclaw", or "what have I worked on in OpenClaw". Also triggers when the user mentions OpenClaw session logs, MEMORY.md, daily notes, or ~/.openclaw/workspace.
impl-validator
by Ar9avValidate whether an implementation matches its stated goal. Use this skill when a skill or agent wants a second opinion on its own output, when the user says "check this implementation", "validate what you did", "is this correct?", "review the output", or "did you do this right?". Also spawned automatically as a subagent by other skills (memory-bridge, daily-update) to self-check their outputs before presenting to the user. Returns a structured pass/warn/fail verdict with specific actionable issues.
copilot-history-ingest
by Ar9avIngest GitHub Copilot CLI session history into an Obsidian wiki as distilled knowledge pages. Use this skill when the user wants to capture their Copilot CLI sessions into a personal wiki — extracting architecture decisions, debug notes, and patterns into searchable Obsidian pages. Triggers on phrases like "ingest my copilot sessions into obsidian", "add my copilot history to my wiki", "pull my copilot session history into the vault", "capture what I've learned from copilot into obsidian", "just the new sessions since last time", or "mine patterns across my copilot sessions". Also triggers when the user mentions session-store.db, ~/.copilot/session-state, or VS Code copilot-chat transcripts in the context of building a wiki or knowledge base. Does NOT trigger for general copilot usage questions, searching sessions, or backing up history.
codex-history-ingest
by Ar9avIngest Codex CLI conversation history into the Obsidian wiki. Use this skill when the user wants to mine their past Codex sessions for knowledge, import their ~/.codex folder, extract insights from previous coding sessions, or says things like "process my Codex history", "add my Codex conversations to the wiki", or "what have I discussed in Codex before". Also triggers when the user mentions .codex sessions, rollout files, session_index.jsonl, or Codex transcript logs.
claude-history-ingest
by Ar9avIngest Claude Code conversation history into the Obsidian wiki. Use this skill when the user wants to mine their past Claude conversations for knowledge, import their ~/.claude folder, extract insights from previous coding sessions, or says things like "process my Claude history", "add my conversations to the wiki", "what have I discussed with Claude before". Also triggers when the user mentions their .claude folder, Claude projects, session data, past conversation logs, local-agent-mode sessions, or audit logs.
wiki-synthesize
by Ar9avSystematically discover synthesis opportunities across the Obsidian wiki — pairs or clusters of concepts that co-occur frequently across pages but have no synthesis page connecting them. Creates new synthesis/ pages that draw explicit cross-cutting conclusions. Use when the user says "synthesize my wiki", "find connections", "what concepts keep coming up together", "/wiki-synthesize", or after a large ingest when the vault has grown significantly.
wiki-status
by Ar9avShow the current state of the wiki — what's been ingested, what's pending, and the delta between sources and wiki content. Use this skill when the user asks "what's the status", "how much is ingested", "what's left to process", "show me the delta", "what changed since last ingest", "wiki dashboard", or wants an overview of their knowledge base health and completeness. Also use before deciding whether to append or rebuild. Includes an insights mode triggered by "wiki insights", "what's central", "show me the hubs", "central pages", "what's connected", "wiki structure" — analyzes the shape of the wiki itself to surface top hubs, cross-domain bridges, and orphan-adjacent pages.
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.