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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spk-admin-upgrade
by Priivacy-aiUpgrade Spec Kitty installations and repair generated commands, skills, migrations, and compatibility shims.
spk-doctrine-profile-load
by Priivacy-aiLoad a Spec Kitty agent profile on demand for interactive sessions, including identity, governance scope, boundaries, and initialization.
ad-hoc-profile-load
by Priivacy-aiLoad an agent profile on demand to adopt a specific role for the current session. Applies the profile's identity, governance scope, boundaries, and initialization declaration without requiring a running mission. Triggers: "act as the architect", "load the reviewer profile", "switch to implementer", "use the researcher persona", "start a session as planner", "adopt the curator role", "initialize profile", "assume the designer identity". Does NOT handle: mission advancement (use runtime-next), charter interview/generation (use charter-doctrine), or profile creation (use the charter synthesize workflow / edit the profile YAML directly).
spec-kitty-runtime-next
by Priivacy-aiDrive the canonical spec-kitty next --mission <handle> control loop for mission advancement. Load agent profiles at init, apply action-scoped doctrine context at each step boundary, and pull specific tactics/directives on demand. Triggers: "run the next step", "what should runtime do next", "advance the mission", "what is the next task", "continue the workflow", "what step comes next". Does NOT handle: setup or repair requests, purely editorial glossary or doctrine maintenance, or direct code review.
spec-kitty-glossary-context
by Priivacy-aiCurate and apply canonical terminology across Spec Kitty missions. Triggers: "update the glossary", "use canonical terms", "check terminology", "add a term", "fix term drift", "glossary conflicts", "resolve ambiguity", "review terminology consistency". Does NOT handle: runtime loop advancement, setup or repair requests, agent configuration, or direct code implementation tasks.
spec-kitty-spdd-reasons
by Priivacy-aiDrive REASONS Canvas authoring and review for Spec Kitty missions that opted in to Structured-Prompt-Driven Development (SPDD) via charter selection. Triggers: "use SPDD", "use REASONS", "generate a REASONS canvas", "apply structured prompt driven development", "make this mission SPDD". Does NOT handle: enforcing SPDD on projects whose charter has not selected the doctrine pack (escalate to charter workflow instead). Does NOT mirror code as prose; code remains the source of truth for current behavior.
spk-start-agent-surface
by Priivacy-aiChoose the correct Spec Kitty workflow for Codex CLI, Claude Code, and supported slash-command or command-skill harnesses.
spec-kitty-charter-doctrine
by Priivacy-aiRun charter interview, generation, context, and sync workflows for project governance in Spec Kitty 3.x. Access doctrine artifacts programmatically via DoctrineService. Resolve agent profiles. Load action-scoped governance context iteratively, not all at once. Triggers: "interview for charter", "generate charter", "sync charter", "use doctrine", "set up governance", "charter status", "extract governance config", "load doctrine", "agent profile", "DoctrineService", "action index". Does NOT handle: generic spec writing not tied to governance, direct runtime loop advancement, setup/repair diagnostics, or editorial glossary maintenance.
spk-meta-skill-map
by Priivacy-aiDiscover the Spec Kitty 3.2.0 spk skill hierarchy, naming convention, legacy aliases, and the correct skill for a user intent.
spk-start-command-map
by Priivacy-aiMap Spec Kitty slash commands and CLI entry points to spk skills. Use when choosing /spec-kitty.* commands or explaining command-skill boundaries.
spec-kitty-bulk-edit-classification
by Priivacy-aiRecognize when a mission is a bulk edit and drive the occurrence-classification guardrail on the user's behalf. Triggers: user says any variant of "rename X to Y", "change the terminology", "migrate all occurrences", "replace across the codebase", "the X feature is now the Y feature", "sed everywhere", or any request that touches the same identifier/path/key in many files. Also triggers on gate errors mentioning "change_mode", "occurrence_map.yaml", "Bulk Edit Gate: BLOCKED", or "Bulk Edit Review: Diff Compliance". Does NOT handle: line-level semantic refactors inside one file, adding a new feature that creates new identifiers without changing existing ones, or reviewing finished missions for fidelity.
spec-kitty-git-workflow
by Priivacy-aiUnderstand how Spec Kitty manages git: what git operations Python handles automatically, what agents must do manually, worktree lifecycle, auto-commit behavior, merge execution, and the safe-commit pattern. Triggers: "how does spec-kitty use git", "worktree management", "auto-commit", "who commits what", "git workflow", "merge workflow", "rebase WPs", "worktree cleanup", "safe commit". Does NOT handle: runtime loop advancement (use runtime-next), setup or repair (use setup-doctor), mission selection (use mission-system).
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.