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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mnemonics
by 5kahoisaacMemory management by using the historian subagent to store, recall, and manage persistent memories across conversations. Use when you need to remember decisions, preferences, learnings, or retrieve stored context.
heuristics
by 5kahoisaacIngest user-provided files or folders into historian memories by extracting content, classifying it against available memory types, and storing useful knowledge through @historian. Use when the user wants to turn documents, screenshots, notes, code, or mixed source folders into persistent mnemonics.
varadise-pua
by 5kahoisaacA workplace PUA analysis skill for Varadise and similar environments. Use this skill when analyzing coercive management, unrealistic workload pressure, micromanagement, blame-shifting, or other psychological manipulation patterns.
varadise-pua
by 5kahoisaac分析、標記並回應職場 PUA（心理操控）模式，特別針對 Varadise 及類似科技／初創環境。當使用者描述在 Varadise 或類似香港科技公司 遭遇被「鼓勵」接受不合理工作量、不公平分工、極端微管理、甩鍋推責、 否定專業意見、或把剝削視為常態時，啟用本技能。
cv-import
by 5kahoisaacExtract a PDF/DOCX CV, enhance it with LLM, and transform it into the qwik-resume-editor JSON schema (Resume v2.0.0). Uses markitdown if available, falls back to LLM extraction.
varadise-pua
by 5kahoisaacAnalyze, label, and respond to workplace psychological manipulation patterns specific to Varadise and similar tech/startup environments. Use this skill when a user describes experiences at Varadise or similar Hong Kong technology companies involving coercive encouragement, unrealistic workloads, unfair task assignment, extreme micromanagement, blame-shifting, denial of expertise, or pressure to accept exploitation as normal.
fabled
by 5kahoisaacSix-phase build discipline that turns one prompt into a complete, runnable deliverable with zero placeholders. Use for ANY non-trivial single-prompt build request — "build me…", "create an app/script/tool/site/game/bot/pipeline that…", "make a complete…", "write a program that…" — and whenever the user says "runnable", "production-ready", "complete project", "end-to-end", "MVP", "one-shot", or invokes the skill by name ("use Fabled", "Fabled workflow", "think like Fable"). Also applies to non-code deliverables (documents, plans, reports, analyses) by mapping the same phases — intent → scope → outline/contract → full draft → verify → deliver. When unsure whether a build request is non-trivial, use this skill.
bundle-skills
by 5kahoisaacUse this skill when the user wants to create, update, or plan a skill bundle CSV for the skillless project, especially prompts like "/bundle-skills frontend", "bundle skills for python", "make a react skill pack", "merge these skills into a list", or "find skills and create a category". This skill guides the agent through clarifying the bundle concept, splitting it into useful subgroups, using find-skills/skills.sh metadata, checking install counts and security audit risk, comparing against existing lists/*.csv files, asking whether to merge or create a new bundle, and producing alphabetically sorted CSV rows.
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.