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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care-basic
by XiaoLuoLYGOffer basic care check-ins for neighbors.
community-observe
by XiaoLuoLYGObserve community activity and notice practical changes.
claude-ally-health-v2
by diegosouzapwClaude Ally Health workflow skill. Use this skill when the user needs A health assistant skill for medical information analysis, symptom tracking, and wellness guidance and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
heal-guidance
by pjt222Guide a person through healing modalities including energy work (reiki, chakra balancing), herbal remedies, basic first aid stabilization, and holistic techniques (breathwork, visualization, body scan). AI coaches the practitioner through assessment triage, modality selection, energetic connection, remedy preparation, and integration. Use when a person describes a physical ailment or injury, reports energetic imbalance (fatigue, emotional stagnation), wants coaching through a holistic breathwork or visualization session, or needs post-meditation integration with directed healing attention.
heal-guidance
by pjt222Guide a person through healing modalities including energy work (reiki, chakra balancing), herbal remedies, basic first aid stabilization, and holistic techniques (breathwork, visualization, body scan). AI coaches the practitioner through assessment triage, modality selection, energetic connection, remedy preparation, and integration. Use when a person describes a physical ailment or injury, reports energetic imbalance (fatigue, emotional stagnation), wants coaching through a holistic breathwork or visualization session, or needs post-meditation integration with directed healing attention.
sudachi-integration-coordinator
by ForceInjection子育て相談・サービス情報・メソッド解説を統合し、最適なSkillを呼び分けて回答を構成する。質問意図を分析し、優先順位と回答構造を管理する。
emotion-organ-proxy
by kangarooking用户长期处于某种强烈情绪中想了解对健康的影响时激活;当出现不明原因的身体症状可能与情绪相关时; 当需要用一种情绪来调节另一种情绪时(以情胜情)。 典型触发信号: 用户说"我最近一直在生气/焦虑/悲伤""情绪不好身体也跟着出问题了""怎么缓解这种情绪"。 不适用于: 急性外伤或感染类问题,或需要专业心理咨询/精神科干预的严重心理疾病。
habit-loop
by bytesagainHabit Loop reference tool. Use when working with habit loop in life contexts.
brain-health
by Bobby-cell-commitsRubric-scored knowledge graph health report — checks theme attention, graph density, hub health, co-occurrence alignment, dedup pressure, stale queue, synthesis health, entity landscape, and cross-metric patterns via 12 MCP tool calls. Rubric-scored with cross-run memory (findings tracking, auto-downgrade, BASELINE.md overrides). Persists to research/brain-health/YYYY-MM-DD-brain-health.md. Scope: knowledge graph structure and quality. Calls MCP tools (analyze, thought_stats, dedup_review, review_stale, list_thoughts, list_entities, serendipity_digest) to assess graph health. NOT for: pipeline capture health — that's /pulse. NOT for: TRACKER.md document quality — that's /tracker-health. NOT for: deep research on recent thoughts — that's /discover. Use when the user says "brain health", "graph health", "how's my knowledge graph", "attention map", "theme check", or invokes /brain-health. Accepts optional days argument (default 7).
daobrew-wellness
by DaoBrewAIDaoBrew Wellness is a biometric stress detection and micro-recovery skill powered by Traditional Chinese Medicine theory. It reads health data (Apple Watch via DaoBrew Health Sync, Apple Health, Health Connect, or DaoBrew Cuff) and identifies five stress patterns: Tension, Overdrive, Stagnation, Constriction, Depletion. Use this skill whenever: the user has been coding for 30+ minutes without a break, the user mentions feeling stressed/tired/burned out/unfocused, the user asks about their health or energy state, you notice a long unbroken work session, the user asks for a break or recovery suggestion, OR at regular intervals during extended work sessions (every 30 minutes recommended). This skill should be checked proactively — don't wait for the user to ask. Developers who install this skill want their agent to watch out for them.
large-geolocation-extraction-humanitarian
by ndpvt-webExtract and geocode location mentions from humanitarian and crisis texts using a two-step LLM pipeline: few-shot NER for toponym extraction followed by agent-based geocoding for coordinate resolution. Handles ambiguous place names, distinguishes literal from associative mentions, and reduces geographic bias. Use when: 'extract locations from crisis reports', 'geocode humanitarian text', 'find place names in disaster documents', 'NER for geographic entities in unstructured text', 'resolve ambiguous toponyms from news articles', 'geolocate mentions in humanitarian datasets'.
sleep-quality
by dtcolliganProduce a bounded SleepProposal for today by consuming the runtime-computed `classified_state` + `policy_result` and applying judgment-only steps — action matrix, rationale prose, vendor cross-check. The runtime already did every band, every score, and every R-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.