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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mxsave
by MicrotronXUse when the user says "save state", "/mxSave", "session end", "before /compact", "wrap up", or otherwise wants to persist the current mx-project state (clean settings, update CLAUDE.md + docs/status.md, create session notes in MCP-DB, sync orchestrate-state deltas, emit clear-cycle tip). Loop-capable. Fires at natural session-end boundaries.
mxbugchecker
by MicrotronXUse when the user says "/bugcheck", "/mxBugChecker", "check for bugs", "find bugs", "audit for vulnerabilities", "verify the code", "look for issues in this file", or otherwise requests bug analysis on VCS changes or specific files. Verified-knowledge bug finder — every finding requires concrete code proof. Analyzes logic errors, runtime issues, edge cases, error handling, concurrency, resource leaks, security vulnerabilities, and performance regressions. Loads project context from the mxLore Knowledge-DB via MCP and persists findings via Skill Evolution.
mxdecision
by MicrotronXUse when the user says "/decision", "/mxDecision", "document decision", "write an ADR", "record a decision", "architectural decision", "ADR", or otherwise wants to capture a significant technical choice made in the conversation. Creates or updates Architecture Decision Records (ADRs) via MCP-Tools in the mxLore knowledge DB; maintains supersedes chain and status transitions.
mxdesignchecker
by MicrotronXUse when the user says "/designcheck", "/mxDesignChecker", "review the design", "check the spec", "review this ADR", "audit architecture", "second opinion on this code", or otherwise requests design/spec/ADR review or code-vs-design audit. Verified-knowledge design reviewer — every finding requires concrete proof from spec or code. Loads specs/designs from the mxLore Knowledge-DB via MCP and persists findings via Skill Evolution. NO automatic corrections — all fixes require user confirmation.
mxhealth
by MicrotronXUse when the user says "/health", "/mxHealth", "health check", "check knowledge db", "verify consistency", "db health", or otherwise wants to verify Knowledge-DB and docs/ consistency via MCP. Runs 14 consistency checks (document metadata, cross-references, orphaned relations, status consistency, CLAUDE.md weight, local/DB sync, AI-Steno format, skill-evolution metrics, AI-Batch status) and persists findings via Skill Evolution. Loop-capable. ⚡ MCP-required — aborts if Knowledge-DB is unreachable.
mxhelp
by MicrotronXUse when the user says "/mxHelp", "/mxHelp <name>", or asks which mx-skill does what. Lists all installed mx*-skills grouped by category, or explains one skill in detail. Pure reader — no side effects.
mxinitproject
by MicrotronXUse when the user says "/mxInitProject", "bootstrap project", "init project", "setup ai config", "initialize claude config", "scaffold project", or otherwise wants to bootstrap a new repository with the mxLore AI-documentation structure (CLAUDE.md, docs/ layout, optional MCP project registration). Idempotent — safe to re-run. Detects MCP mode vs local fallback. NEVER overwrites existing content.
mxmigratetodb
by MicrotronXUse when the user says "/mxMigrateToDb", "migrate to db", "migrate local docs to mcp", "sync local docs", "import docs to knowledge db", "--extract-backlog", "extract legacy backlog", or otherwise wants to import local `docs/*.md` fallback files into the MCP Knowledge-DB. Runs after MCP outages (when offline-fallback created local files) or once after initial MCP setup. Supports dry-run, cleanup, sync, scan, and --extract-backlog modes. ⚡ MCP-required — aborts if Knowledge-DB is unreachable.
mxorchestrate
by MicrotronXPersistent session orchestrator for mxLore. This skill should be used when the user says "park", "resume", "continue", "keep going", "where were we", "pick up where we left off" (non-English phrasing with the same meaning maps to these triggers), "what's my workflow status", "/mxOrchestrate start/track/park/resume/status/suggest", "start a new feature/bugfix workflow", "track this as ad-hoc", "spawn a team agent", or when a session begins and workflow state must be loaded. Always-on via SessionStart/UserPromptSubmit hooks. Manages workflow stack (LIFO), ad-hoc tasks, team agents, and skill chains.
mxplan
by MicrotronXUse when the user says "/plan", "/mxPlan", "create a plan", "update the plan", "write a plan for X", "plan this feature", or otherwise wants to structure a multi-step implementation task before coding. Creates or updates plans via MCP-Tools in the mxLore knowledge DB; maintains task checklists with auto-archive on completion.
mxsetup
by MicrotronXThis skill should be used when a developer sets up mxLore on a new PC, runs a fresh Claude Code install, updates the global mx-rules block in ~/.claude/CLAUDE.md, refreshes skills/hooks from GitHub, or reinstalls the mxMCPProxy. Triggers "neuer PC", "fresh install", "onboard me", "/mxSetup", "update mx-rules", "mx-rules block veraltet", "reinstall proxy", "skills aktualisieren".
mxspec
by MicrotronXUse when the user says "/spec", "/mxSpec", "write a spec", "write a specification", "requirements doc", "acceptance criteria", "define requirements", or needs to specify a feature or component before planning or implementation. Creates or updates specifications via MCP-Tools in the mxLore knowledge DB; tracks acceptance criteria with auto-archive on full completion.
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.