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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xt-merge
by JaggerxtrmMerges queued PRs from xt worktree sessions in the correct order (FIFO), maintaining linear history by rebasing remaining PRs after each merge. Use this skill whenever the user has multiple open PRs from xt worktrees, asks to "merge my PRs", "process the PR queue", "drain the queue", "merge worktree branches", or says "what PRs do I have open". Also activate after any xt-end completion when other PRs are already open, or when the user asks "can I merge yet" or "is CI green". Handles the full sequence: list → sort → CI check → merge oldest → rebase cascade → repeat until queue is empty.
xt-end
by JaggerxtrmAutonomous session close flow for xt worktree sessions. Use this skill whenever the user says "done", "finished", "wrap up", "close session", "ship it", "I'm done", "ready to merge", or similar. Also activate when all beads issues in the session are closed, or when the user explicitly runs /xt-end. This skill is designed for headless/specialist use: it must make deterministic decisions, auto-remediate common anomalies, and avoid clarification questions unless execution is truly blocked.
xt-debugging
by JaggerxtrmComplete debugging workflow — error analysis, log interpretation, performance profiling, and GitNexus call-chain tracing. Use when investigating bugs, errors, crashes, or performance issues.
using-specialists
by JaggerxtrmUse this skill whenever you're about to start a substantial task — pause first and route the work through specialists instead of doing discovery or implementation yourself. Consult before any: code review, security audit, deep bug investigation, test generation, multi-file refactor, architecture analysis, or multi-chain specialist orchestration. Also use for the mechanics of delegation: --bead workflow, --context-depth, background jobs, MCP tool (`use_specialist`), or specialists doctor. Don't wait for the user to say "use a specialist" — proactively evaluate whether delegation makes sense.
find-docs
by JaggerxtrmRetrieves up-to-date documentation, API references, and code examples for any developer technology. Use this skill whenever the user asks about a specific library, framework, SDK, CLI tool, or cloud service -- even for well-known ones like React, Next.js, Prisma, Express, Tailwind, Django, or Spring Boot. Your training data may not reflect recent API changes or version updates. Always use for: API syntax questions, configuration options, version migration issues, "how do I" questions mentioning a library name, debugging that involves library-specific behavior, setup instructions, and CLI tool usage. Use even when you think you know the answer -- do not rely on training data for API details, signatures, or configuration options as they are frequently outdated. Always verify against current docs. Prefer this over web search for library documentation and API details.
using-specialists-auto
by JaggerxtrmOperator-offline autonomous orchestration overlay. Activate when the user says "auto mode", "full auto", "run autonomously", "I'll leave you alone", or similar — and hands over a multi-item priority list. Layers on top of `using-specialists-v3`: paranoid pacing, dispatch loop shape, dist-rebuild discipline, escalation triggers specific to unsupervised runs. Does NOT duplicate v3's bead contracts, sleep table, rebuttal patterns, escalation matrix, or session-end handoff — refers to v3 for those.
using-xtrm
by JaggerxtrmBehavioral operating manual for an xtrm-equipped Claude Code session. Covers when to use which tool, how to handle questions and triggers, workflow examples, and skill routing. Reference material (hook list, gate rules, full bd commands, git workflow) lives in CLAUDE.md. Injected automatically at session start via additionalSystemPrompt.
update-xt
by JaggerxtrmUpdate an xtrm-initialized project to match the current canonical install state. Use this skill whenever the user asks to update, upgrade, repair, or re-sync xtrm in a project — or when they say something like "xt is out of date", "skills aren't loading", "hooks aren't firing", "the install looks wrong", or "I just pulled new xtrm changes". Also triggers when the agent detects stale paths like .claude/skills → active/claude (old structure) or .pi/settings.json pointing to active/pi (old structure). Proactively suggest running this skill after any xtrm-tools upgrade.
using-service-skills
by JaggerxtrmService catalog discovery and expert persona activation. At session start, a catalog of registered expert personas is injected automatically. Use this skill to discover, understand, and activate the right expert for any task.
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.