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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start-feature
by DeL-TaiseiOzakiStart a new feature with multi-agent collaboration (Opus 4.6 + Agent Teams). Phase 1: Codebase understanding (Opus subagent 1M context + Claude user interaction). Phase 2: Parallel research & design (Agent Teams: Researcher + Architect). Phase 3: Plan synthesis & user approval. Implementation is handled separately by /team-implement.
tdd
by DeL-TaiseiOzakiImplement features using Test-Driven Development (TDD) with Red-Green-Refactor cycle.
team-implement
by DeL-TaiseiOzakiParallel implementation using Agent Teams. Spawns teammates per module/layer, each owning separate files to avoid conflicts. Uses shared task list with dependencies for autonomous coordination. Run after /start-feature plan approval.
team-review
by DeL-TaiseiOzakiParallel code review using Agent Teams. Spawns specialized reviewers (security, quality, test coverage) to review implementation from different perspectives simultaneously. Run after implementation.
troubleshoot
by DeL-TaiseiOzakiDiagnose and plan fixes for errors/bugs with Codex-first multi-agent collaboration (Codex + Opus 4.6 + Agent Teams). Codex CLI is consulted in EVERY phase for deep code reasoning, hypothesis evaluation, and fix validation. Phase 1: Error reproduction & context gathering (Opus subagent 1M context + Codex initial analysis + Claude user interaction). Phase 2: Parallel diagnosis (Agent Teams: Root Cause Analyst [Codex-driven] + Impact Investigator [Opus + Codex risk analysis]). Phase 3: Fix plan synthesis, Codex validation & user approval. Fix implementation is handled separately by /team-implement.
update-lib-docs
by DeL-TaiseiOzakiUpdate library documentation in .claude/docs/libraries/ with latest information from web search.
context-loader
by DeL-TaiseiOzakiALWAYS activate this skill at the start of every task. Load project context from .claude/ directory including coding rules, design decisions, and documentation before executing any task.
context-refresh
by DeL-TaiseiOzakiMaintenance skill that compacts long-running working state so future sessions stay context-light. Prunes stale `Current Project|Feature|Bug Fix` work blocks from CLAUDE.md Zone C, compresses the live conversation, and (optionally) archives old research notes. Cross-session progress lives in PROGRESS.md and the per-session detail in `.claude/checkpoints/` — both are owned by /checkpointing and are NOT regenerated here. Run when Zone C has grown (typical trigger: CLAUDE.md > ~400 lines or multiple stale work blocks) or as the final step of a /checkpointing run.
design-tracker
by DeL-TaiseiOzakiPROACTIVELY track and document project design decisions without being asked. Activate automatically when detecting architecture discussions, implementation decisions, pattern choices, library selections, or any technical decisions. Also use when user explicitly says "record this", "what's our design status", or equivalent. Do NOT wait for user to ask - record important decisions immediately.
init
by DeL-TaiseiOzakiAnalyze project structure, write the thick requirements doc (.claude/docs/DESIGN.md), populate the thin "Repository Identity" pointer in CLAUDE.md (Zone B), and mirror identity into AGENTS.md.
plan
by DeL-TaiseiOzakiCreate a detailed implementation plan for a feature or task. Use when user wants to plan before coding.
research-lib
by DeL-TaiseiOzakiResearch a library and create comprehensive documentation in .claude/docs/libraries/.
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.