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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fleet-identity
by JackReisThis skill should be used when the user asks "who is Wings", "who is Zoe", "who runs Kopi", "fleet identity", "fleet mapping", "agent mapping", "identity map", "autonomous ai agent", "what agent is behind X", or needs to look up which runtime agent is behind a Discord/Telegram surface (or vice versa). Returns the canonical mapping from ~/Documents/Coordination/ without duplicating data. Covers Hermes/Wings, OLIVIER_MBP/Zoe, KimiClaw/Mara/Kopi, and any future agents added to the coordination folder.
peer-grill-with-agents
by JackReisTwo (or more) agents independently audit the SAME existing agent stack against the codebase, then reconcile via the peer-grill file-based protocol. Each agent walks the same 13-branch agent-stack audit (grill-me-with-agents) on its own, dumps a claims.yaml grounded in concrete file paths, and the disagreements get ratified or escalated. Use when the agent topology is already implemented and you need multi-agent triangulation on whether a *change* to it is sound — single-agent grilling has known blind spots, peer-grill alone has no code anchor, this combines them. Triggers — "peer-grill the agent stack", "two agents audit the topology", "stress-test our agents from two angles", "triangulate the agent design", "reconcile our reading of the stack". Do NOT use for greenfield design (use `grill-me-agents`), for single-agent code-aware grilling (use `grill-me-with-agents`), or for non-agent state reconciliation (use `peer-grill`).
agent-show-and-tell
by JackReisEach agent in a local fleet — Claude sessions on different machines, mixed-model agents, or whatever's in play — independently writes a short "what I know and what I'm working on" report to a shared directory. One reader collates them into a roundup. No grilling, no consensus, no merging. Use when you want visibility across a multi-agent fleet, or the user mentions "fleet show and tell", "agent roundup", "what does each agent know", or "round-robin status from the agents".
001-jeremy-taskwarrior-integration
by JackReisEnforces complete Taskwarrior integration protocol for ALL coding tasks. Activates automatically when user mentions "taskwarrior", "task warrior", "tw", or discusses task management. Decomposes all coding work into properly tracked Taskwarrior tasks with full lifecycle: task add → task start → implementation → task done. Integrates with Timewarrior for automatic time tracking.
001-jeremy-taskwarrior-integration
by JackReisEnforces complete Taskwarrior integration protocol for ALL coding tasks. Activates automatically when user mentions "taskwarrior", "task warrior", "tw", or discusses task management. Decomposes all coding work into properly tracked Taskwarrior tasks with full lifecycle: task add → task start → implementation → task done. Integrates with Timewarrior for automatic time tracking.
excel-pivot-wizard
by JackReisGenerate pivot tables and charts from raw data using natural language - analyze sales by region, summarize data by category, and create visualizations effortlessly Activates when you request "excel pivot wizard" functionality.
000-jeremy-content-consistency-validator
by JackReisValidates messaging consistency across website, GitHub repositories, and local documentation. Generates comprehensive read-only discrepancy reports showing where messaging conflicts or inconsistencies exist. Activates when user mentions "consistency check", "validate documentation", "check for mixed messaging", "audit content consistency", or before updating internal paperwork.
dialectic-vocabulary
by JackReisReference for the scholastic + Greek vocabulary used in peer-grill and grill-me-agents protocols. Use when an agent needs to know what ELENCHOS, QUAESTIO, SED-CONTRA, RESPONDEO, or ALETHEIA mean as grill-log tags, or when authoring a structured disputation.
smart-graph
by JackReisUse when exploring vault note relationships, finding what links to a note, identifying orphaned or disconnected notes, mapping heading structure, or discovering hub notes. Triggers: "find backlinks", "vault graph", "orphaned notes", "what links to", "note relationships", "show connections", "heading structure", "most linked notes".
skill-adapter
by JackReisAnalyzes existing plugins to extract their capabilities, then adapts and applies those skills to the current task. Acts as a universal skill chameleon that learns from other plugins.
agent-context-loader
by JackReisPROACTIVE AUTO-LOADING: Automatically detects and loads AGENTS.md files from the current working directory when starting a session or changing directories. This skill ensures agent-specific instructions are incorporated into Claude Code's context alongside CLAUDE.md, enabling specialized agent behaviors. Triggers automatically when Claude detects it's working in a directory, when starting a new session, or when explicitly requested to "load agent context" or "check for AGENTS.md file".
excel-variance-analyzer
by JackReisAutomate budget vs actual variance analysis in Excel with flagging, commentary, and executive summaries for financial reporting and FP&A teams Activates when you request "excel variance analyzer" functionality.
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.