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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ubiquitous-language
by CodeAlive-AIMaintain a project thesaurus (domain glossary) following DDD ubiquitous language principles. Use PROACTIVELY when naming anything: variables, functions, classes, modules, database fields, API endpoints, events, files, or directories. Also use when the user asks to "create thesaurus", "update glossary", "add term", "rename to match domain", "check naming consistency", "what should I call this", "domain language", "ubiquitous language", or "naming conventions". Ensures all names in the codebase are consistent, descriptive, and aligned with the shared domain vocabulary. Not for general code style or linting — only for domain term consistency.
semantic-scholar-deep
by CodeAlive-AIDeep research over the Semantic Scholar Graph API. Covers endpoints missing from allenai's lookup skill — paper references (backward citations), recommendations, batch paper lookup (up to 500 IDs), snippet search, and multi-hop citation graph traversal (BFS forward/backward). Use when the user asks to build a citation graph, expand a literature seed, find related work, run a reference network traversal, explore what a paper cites or what cites it beyond simple lookup, or batch-resolve many DOI/arXiv/S2 IDs. For multi-step research questions, delegate to the deep-paper-researcher subagent to keep the main context clean. Not for single paper-by-ID lookups (use semantic-scholar-lookup) or topical discovery (use web_search_advanced_exa).
maintaining-windows-health
by CodeAlive-AIHands-on playbook for Windows 11 disk cleanup, dev-machine optimization, and proactive health alerting. Use when the PC is full or slow, when a BSOD / Kernel-Power 41 / crash dump / commit-memory pressure happened, when the user asks to free disk space, audit storage, set up disk/memory alerts, or restore the same monitoring on a new PC. Built around native Microsoft-supported tooling (Storage Sense, cleanmgr, DISM, pnputil, vssadmin, wevtutil, powercfg) as the safety floor, a drift-protected HTML cleanup UI, and a Task Scheduler + BurntToast alerter. Covers dev machines with heavy AI/Docker/WSL workloads. Not for general Windows support, hardware diagnostics, GPU/driver troubleshooting, antivirus/malware removal, Windows Update repair, networking, or app-specific performance problems unrelated to disk or memory pressure.
fpf-problem-solving
by CodeAlive-AIFirst Principles Framework (FPF) — thinking amplifier. Use when user wants to think through a complex problem, architect a system, evaluate alternatives, decompose complexity, classify problems, define quality attributes, plan rigorously, make decisions under uncertainty, establish causality, reason about time and trends, describe architecture or structural views, check mathematical model fit, or improve pattern quality. Also triggers on: FPF, bounded contexts, SoTA packs, assurance calculus, decision theory, causal reasoning, temporal reasoning, architecture description, quality gates, FPF Parts A-K. Not for simple task planning, general philosophy, or Agile unrelated to FPF.
skills-management
by CodeAlive-AISearch, find, discover, install, remove, update, review, list, move, optimise, and iterate on skills for AI coding agents. Use when user asks "find a skill for X", "search for a skill", "is there a skill for X", "install skill", "remove skill", "update skills", "list skills", "review skill quality", "move skill", "check for updates", "optimise skill", "train skill on tasks", "iterate skill", "audit skill edits", "log skill edit", "diff skill versions", "trigger test skill", "transfer skill across agents", or "how do I do X" where X might have an existing skill. THE tool for skill discovery, ecosystem search, and SkillOpt-style training loops. Do not use for creating skills from scratch (use /skill-creator instead).
fpf-problem-solving
by CodeAlive-AIFirst Principles Framework (FPF) — thinking amplifier. Use when user wants to think through a complex problem, architect a system, evaluate alternatives, decompose complexity, classify problems, define quality attributes, or plan rigorously. Also triggers on: FPF, bounded contexts, SoTA packs, assurance calculus, FPF Parts A-K. Not for simple task planning, general philosophy, or Agile unrelated to FPF.
codealive-context-engine
by CodeAlive-AISemantic search, grep, and Q&A across codebases and documentation indexed in CodeAlive. Use when the user mentions "CodeAlive", asks to list or get data sources, list indexed repositories, search code or docs across remote repos, fetch artifact content, or trace call graphs across repositories.
openclaw-guide
by CodeAlive-AIReference and consulting skill for OpenClaw — a messaging gateway that connects AI agents to multiple communication platforms (Telegram, Discord, Slack, WhatsApp, iMessage, and more). Use when working with OpenClaw configuration, channels, Gateway setup, skills, cron jobs, MCP servers, memory, OAuth, or troubleshooting. Also use when the user asks how to implement a use case on their OpenClaw bot (daily morning brief, research workflows, competitive radar, decision playbook), how to add a new channel, or how to connect the CodeAlive context engine. Triggers on requests like "configure openclaw", "add Discord to my bot", "set up morning brief", "gateway not starting", "connect CodeAlive search", "OAuth re-auth", or any close paraphrase. Companion of install-openclaw-to-yc — install both together.
person-lookup
by CodeAlive-AILook up a person's background: work history, education, social profiles, contact info. Use when asked 'who is [person]?' or need to research someone before a meeting. For finding their email specifically, see find-email-by-name. For enriching a lead with full contact data, see lead-enrichment.
founder-sales
by CodeAlive-AIHelp founders close their first customers and build repeatable sales processes. Use when someone is doing founder-led sales, trying to get their first customers, running early sales calls, or asking when to hire their first salesperson. For pipeline tracking and scoring, see pipeline-manager. For writing cold outreach, see cold-email.
pipeline-manager
by CodeAlive-AIManage the sales pipeline: add/update prospects, score with FIT+INTENT+TIMING, track stage transitions, generate status reports. Central orchestrator for all pipeline skills. For signal detection, see signal-scanner. For outreach execution, see outreach-sender.
product-led-sales
by CodeAlive-AIHelp users implement product-led sales motions. Use when someone is transitioning from pure PLG to sales-assisted, defining PQLs, building sales handoff processes, or trying to expand self-serve users into enterprise contracts.
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.