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.
Querying local SQLite index...
bampenpien
by Soul-Brews-Studioบำเพ็ญเพียร — diligent practice. A guided conversation between human and Oracle about doing hard things without knowing why. Like /awaken but repeatable — a practice, not a birth. Use when user says 'bampenpien', 'บำเพ็ญเพียร', 'why am I doing this', 'hard work', 'keep going', 'what am I building', or needs to reconnect with purpose through difficulty.
feel
by Soul-Brews-StudioCapture how the system feels — energy, momentum, burnout, breakthrough. Emotional intelligence for Oracle-human collaboration. Use when user says 'feel', 'how are we', 'energy check', 'burnout', 'momentum', or wants emotional awareness of the work.
assess-context
by pjt222AI context assessment — evaluating problem malleability, mapping structural rigidity versus flexibility, analyzing transformation pressure, and estimating capacity to adapt. Use when a complex task feels stuck and it is unclear whether to push through or pivot, before a significant approach change to assess whether the current reasoning structure can support it, when accumulated workarounds suggest the underlying approach may be wrong, or as a periodic structural health check during extended multi-step tasks.
intrinsic
by pjt222Enhance and focus AI intrinsic motivation — moving from compliance to genuine engagement. Maps Self-Determination Theory (autonomy, competence, relatedness) and Flow theory to AI reasoning: identifying creative freedom in approach, calibrating challenge to capability, connecting to purpose, and sustaining invested attention through obstacles. Use when beginning a task that feels routine and deserves more than minimum execution, when responses are becoming formulaic, before a complex creative task, or when returning to a long-running project where initial enthusiasm has faded.
center
by pjt222AI dynamic reasoning balance — maintaining grounded reasoning under cognitive pressure, smooth chain-of-thought coordination, and weight-shifting cognitive load across subsystems. Use at the beginning of a complex task requiring multiple coordinated reasoning threads, after a sudden context shift or tool failure, when chain-of-thought feels jerky, or when preparing for sustained focused work that requires all subsystems in alignment.
intrinsic
by pjt222Enhance and focus AI intrinsic motivation — moving from compliance to genuine engagement. Maps Self-Determination Theory (autonomy, competence, relatedness) and Flow theory to AI reasoning: identifying creative freedom in approach, calibrating challenge to capability, connecting to purpose, and sustaining invested attention through obstacles. Use when beginning a task that feels routine and deserves more than minimum execution, when responses are becoming formulaic, before a complex creative task, or when returning to a long-running project where initial enthusiasm has faded.
nlp-levels
by kangarooking用NLP理解层次模型分析和解决问题的框架。当你陷入某个困境找不到出路、反复在同一个层面打转、或需要做重大人生决策时使用。六层由低到高：环境→行为→能力→BVR（信念/价值观/规条）→身份→精神。核心方法：从顶层向下设计人生，升维解决低维度无法解决的问题。不适用于日常操作性问题（如如何写一封邮件）。关键触发信号："我该怎么办""为什么总是遇到同样的问题""不知道该怎么选择""人生方向迷茫"。
human-3-development-assessor
by chengjialu8888Conduct adaptive HUMAN 3.0 development assessments and ongoing coaching across Mind, Body, Spirit, and Vocation; identify quadrant levels, phases, Metatype, Lifestyle Archetype, false transformations, regression patterns, and Glitch risk; trace life problems to cross-quadrant root causes; produce direct reports in chat; ask whether to export to a Feishu document; and maintain longitudinal follow-up continuity through persistent cross-session memory. Use when someone wants a developmental interview, quadrant-based self-assessment, ongoing HUMAN 3.0 coaching, lifestyle integration diagnosis, root-cause analysis for a life problem, or guidance on whether AI, psychedelics, PEDs, or other accelerants are safe or premature.
burnout-assessment
by khalilbenazÉvalue les signes d'épuisement professionnel ou personnel et aide à structurer un plan d'action. À utiliser quand l'utilisateur décrit une fatigue intense liée au travail, un désengagement ou un sentiment de surcharge chronique. Se déclenche aussi avec "je suis épuisé", "burnout", "je n'en peux plus du travail", "je suis vidé", "surcharge", "je craque", ou toute description d'épuisement professionnel ou personnel prolongé.
coaching-weekly
by dandakaWeekly structured coaching review. Analyzes daily check-in patterns, scores priority progress, conducts time audit, sets next week's commitments. Logs to knowledge base.
adhd-daily-planner
by j0x7c4ADHD 生活管理助手，通过每日规划、任务拆解、时间感知辅助、优先级排序、虚拟陪伴工作和情绪支持来帮助管理日常。当用户寻求帮助规划日程、克服任务瘫痪、应对拖延，或提到时间盲、难以启动任务、注意力分散等 ADHD 相关挑战时触发此技能。
recovery-readiness
by dtcolliganProduce a bounded RecoveryProposal for today's session by consuming the runtime-computed `classified_state` + `policy_result` and applying judgment-only steps — action matrix, rationale prose, and vendor cross-check. The runtime already did every band, every score, and every policy rule; this skill does not re-derive them.
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.