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...
oak
by goondocks-coFind out what happened, what was decided, and what depends on what in your codebase. Use this skill whenever you need to: recall past decisions or discussions ("what did we decide about X?"), check what might break before refactoring ("what depends on this module?"), find conceptually similar code that grep would miss ("all the retry/backoff logic"), look up past bugs, gotchas, or learnings, query session history or agent run costs, store observations about the codebase, or understand how components connect end-to-end. Powered by semantic search, memory lookup, and direct SQL against the Oak CI database (.oak/ci/activities.db). Also use when the user mentions oak_search, oak_context, oak_remember, oak_resolve_memory, or asks to run queries against activities.db or oak.
oak
by goondocks-coFind out what happened, what was decided, and what depends on what in your codebase. Use this skill whenever you need to: recall past decisions or discussions ("what did we decide about X?"), check what might break before refactoring ("what depends on this module?"), find conceptually similar code that grep would miss ("all the retry/backoff logic"), look up past bugs, gotchas, or learnings, query session history or agent run costs, store observations about the codebase, or understand how components connect end-to-end. Powered by semantic search, memory lookup, and direct SQL against the Oak CI database (.oak/ci/activities.db). Also use when the user mentions oak_search, oak_context, oak_remember, oak_resolve_memory, or asks to run queries against activities.db or oak.
myco-three-tier-config-architecture
by goondocks-coComprehensive procedures for implementing and managing Myco's three-tier configuration architecture with Machine/Grove/Project scope hierarchy. Covers config storage design with scope enforcement patterns, TypeScript compile-time scope validation, multi-tier settings UI development, hierarchical config merging and override resolution strategies, and migration workflows for scope boundary evolution. Use this when implementing new configuration settings, refactoring config scope boundaries, building scope-aware editing interfaces, or migrating configuration data between tiers, even if the user doesn't explicitly ask for three-tier architecture guidance.
myco-operate-skill-lifecycle-pipeline
by goondocks-coUse this skill when working with Myco's skill lifecycle system — whether generating a new skill from an approved candidate, reviewing the candidate queue, updating a stale skill, retiring an outdated one, or debugging why a skill wasn't triggered. Activates whenever you touch vault_skill_candidates, vault_skill_records, or vault_write_skill — or whenever the user asks about skills, the Skills dashboard, skill generation tasks, or the .agents/skills/ directory. Apply this skill even if the user doesn't explicitly say "skill lifecycle" — any time a task involves producing or updating a SKILL.md file, evaluating candidates, or managing skill status, this procedure applies.
myco-notification-system-operations
by goondocks-coUse when extending the Myco notification system with a new domain, configuring notification modes, diagnosing missing or misbehaving notifications, or fixing React UI issues related to notifications — even if the user doesn't explicitly ask for notification system help. Covers: domain self-registration and default configuration; the mode resolution priority chain (payload > domain override > global default > registry default) and its critical gotcha; emission point discipline with a coverage checklist; the SystemNotifications.tsx banner-only filter requirement; dismissed-vs-deleted semantics and the full debug protocol; React Router same-URL navigation fix for notification clicks; and schema design with on-write pruning. Complements extend-myco-daemon (which covers wiring steps) with operational semantics and failure-mode playbooks.
myco-daemon-process-lifecycle-management
by goondocks-coComprehensive procedures for managing Myco daemon process lifecycle including startup robustness, unified eviction and restart workflows, process identity management, multi-instance coordination, health checking, update application, npm package upgrade handling, daemon binary version mismatch detection, event-loop safety patterns, lag monitoring, yield points, and resource cleanup. Covers operational daemon management patterns from auto-spawn and migration tasks through SIGTERM/SIGKILL sequences to port release verification, cross-runtime coordination, and event loop responsiveness protection. Use when starting, restarting, updating, or coordinating daemon processes, even if the user doesn't explicitly ask for daemon lifecycle management.
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.