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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dev-auto
by Jason-chen-coderUse ONLY when the user explicitly wants end-to-end guided workflow through the dev-skills toolchain. Triggers on phrases like "用 dev-auto / 帮我串起来 / 从需求到 commit / 完整跑 / end to end / 走完整流程 / 下一步该做什么 / what's next". Reads `.claude/artifacts/{designs,plans,fixes}/` and `.design-context.md` existence to detect current phase (no deep parsing) and recommends next step across dev-design-context, dev-grill-docs, dev-plan, dev-tdd, dev-fix, dev-verify, dev-code-review, dev-commit-writer, and dev-finish. Does NOT invoke other skills, write code, or produce artifacts. Optional arguments — `[slug]`; `--status [slug]`; `--next [slug]`; `--recover [slug]`.
dev-code-review
by Jason-chen-coderUse when reviewing uncommitted or staged git changes before commit. Trigger on: 帮我 commit, 我要 commit, commit 一下, 准备提交, pre-commit, 提交前检查, 看下这次修改, commit 前看一下, I want to commit, let's commit, review my changes. Checks conventions, functionality, wiring, comments, and dead code; emits a commit message only when READY. Does not mutate the working tree. Route to dev-commit-writer only when the user explicitly asks to skip review or wants only a commit message.
dev-commit-writer
by Jason-chen-coderUse only when the user explicitly asks for a commit message without review. Trigger on: 帮我写 commit message, 给个 commit message, 生成 commit message, 这次 commit message 怎么写, write a commit message, skip review, 跳过 review, 我自审过了, only message, 只要 message. Writes a message from the current git diff in repository style. Does not review code quality or mutate the working tree; ambiguous commit requests like 帮我 commit route to dev-code-review.
dev-design-context
by Jason-chen-coderUse as a one-time design setup for UI, product, landing-page, or brand-heavy work. Scans the project for design context, asks only the UX questions that cannot be inferred, then writes persistent design guidelines to .design-context.md. Does not implement features or review code.
dev-finish
by Jason-chen-coderUse when implementation and verification are complete and the user needs to finish a development branch by merging locally, creating a PR, keeping the branch, or discarding work. Applies after tests pass and code review is ready.
dev-fix
by Jason-chen-coderUse when the user reports a bug, broken behavior, regression, failing test, or asks to investigate/root-cause/debug. Trigger on: 修个 bug, 这个 bug, 排查一下, 复现, 这个错怎么回事, 为什么 X 不工作, debug, RCA, fix this bug, investigate, reproduce. Guides root-cause debugging: reproduce with a failing test, list hypotheses, trace backward, fix only the confirmed cause, verify red-green-red, and leave a regression test. Does not symptom-patch or write commit messages.
dev-grill-docs
by Jason-chen-coderUse as the main intake for fuzzy or under-specified feature work before coding. Trigger on: dev-grill-docs, grill-with-docs, grill with docs, 拷问需求, 拷问方案, 压测方案, 术语沉淀, 帮我设计, 写个方案, spec 一下, design this, scope this out. Reads existing docs/code first, asks one focused question at a time, produces the spec artifact at .claude/artifacts/designs/<feature>.md, and may also update CONTEXT.md or docs/adr/ for stable domain language and durable decisions. Does not implement code, fix bugs, or write implementation plans.
dev-plan
by Jason-chen-coderUse when a spec or scoped requirement exists and the user wants a concrete implementation plan before coding. Trigger on: 出个 plan, 做个实施方案, 怎么做这件事, 给我个方案, plan this, make a plan, consensus plan, ralplan. Produces a RALPLAN-DR plan with principles, decision drivers, viable options, ADR, and Planner-Architect-Critic validation. Does not gather requirements, fix bugs, review code, or write code.
dev-spec
by Jason-chen-coderCompatibility alias for dev-grill-docs spec-only mode. Use when the user explicitly says dev-spec, spec 一下, 设计文档, 帮我设计, design this, or scope this out and expects a feature intent artifact at .claude/artifacts/designs/<feature>.md. Do not maintain a separate workflow here: load dev-grill-docs and follow it with --spec-only, preserving dev-spec trigger compatibility for existing docs and downstream tools.
dev-tdd
by Jason-chen-coderUse when implementing a feature, refactor, direct hotfix, or scoped behavior change before writing production code. For bug reports that need root-cause investigation, use dev-fix instead; dev-fix already owns failing regression tests.
dev-verify
by Jason-chen-coderUse before claiming work is complete, fixed, ready, passing, or before handing off to commit review, PR, merge, or branch cleanup. Requires fresh verification commands and evidence before any success claim.
redesign-existing-projects
by Jason-chen-coderUpgrades existing websites and apps to premium quality. Audits current design, identifies generic AI patterns, and applies high-end design standards without breaking functionality. Works with any CSS framework or vanilla CSS.
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.