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...
verify-paranoid
by easyinplayStage ④.c verify sub-workflow — gstack /review Paranoid Staff Engineer 关键模块 PR 前强制 (bundled gstack governance gate — mandatory before critical-module PR)。Gate: judgments.stage-routing.verify-paranoid-critical.fires (phase.is_critical_module == true) — 默认 critical fire only; 非关键模块 skip。 schema_version: harnessed.workflow.v3 with disciplines_applied (6 default) + tools_available (gstack-review) + 1 phase (gate ref is_critical_module conditional)。 Triggered by slash command `/verify-paranoid` after `harnessed setup`.
auto
by easyinplaySuper-master orchestrator — 一行命令跑完整 6-stage feature 开发流 (research conditional → discuss → plan → task → verify → retro mandatory), 适合 trivial / well-defined feature OR 你 想 hands-off。每 stage 内部仍 fan-out sub-workflow per 现有 stage-master orchestrator pattern。 v3.2.0 强化:Phase 0 AI 1-shot complexity assessment + Phase 0.5 understanding check prompt + Phase 5 `/retro` mandatory。 schema_version: harnessed.workflow.v3 with delegates_to (6 sub: research conditional order 0 + 4 stage-master order 1-4 + retro mandatory order 5) + disciplines_applied (6 default) + tools_available (agent-teams-create + planning-with-files)。Fail-fast default; opt-in `--staged` flag 重现 stage gate UX (每 stage 完停 user review)。 Triggered by slash command `/auto` (bare per ADR 0030 namespace policy D-02 LOCK) after `harnessed setup`.
verify
by easyinplayStage ④ Verify master orchestrator — 7 sub conditional per bundled Verify-stage cadence: progress 必跑 → code-review 并行 → paranoid 关键模块强制 → qa/security/design 可选 并行 conditional → simplify 末尾 → multispec 关键发布 Pattern C 4-specialist Agent Team。 schema_version: harnessed.workflow.v3 with delegates_to (7 sub: progress serial order 1 + 5 parallel conditional + simplify serial order 99) + disciplines_applied (6 default) + tools_available (10 entry)。Triggered by harnessed CLI `harnessed verify --phase <num>` or slash command `/verify` (bare per ADR 0030 namespace policy D-02 LOCK) after `harnessed setup`.
verify-simplify
by easyinplayStage ④.g verify sub-workflow — code-simplifier 末尾串行 (移除重复 / 多余逻辑; bundled verify-stage cadence — tail-step code-simplifier). schema_version: harnessed.workflow.v3 with disciplines_applied (6 default) + tools_available (code-simplifier) + 1 phase (gate ref is_final_step 末尾串行)。 Triggered by slash command `/verify-simplify` after `harnessed setup`.
task-clarify
by easyinplaytask-clarify workflow v3 — Stage ③.a 子任务澄清 sub-workflow (superpowers brainstorm + mattpocock /grill-with-docs conditional invoke)。Per-subtask repeat invoke 入口 — execute-task 每个 subtask 起步先走 task-clarify 评估 gate (judgments.subtask-gate.brainstorming.fires) 是否激活 brainstorming + 条件性 fire grill-with-docs (phase.spec_ambiguous == true)。 schema_version: harnessed.workflow.v3 with disciplines_applied [6] + tools_available [superpowers-brainstorming, grill-with-docs]. Triggered by harnessed CLI `harnessed task-clarify --task <text>` or slash command `/task-clarify` after `harnessed setup`.
task-code
by easyinplaytask-code workflow v3 — Stage ③.b 子任务编码 sub-workflow (karpathy 4 心法 always-on + mattpocock conditional route + planning-with-files progress.md update)。 2-phase composition: 01-code (karpathy 心法 + zoom-out 陌生模块 / improve-arch 周期审查 / diagnose bug conditional invokes_tools) → 02-progress (Claude Code plugin /plan 更新 progress.md 跨 session 进度同步)。 schema_version: harnessed.workflow.v3 with disciplines_applied [6] + tools_available [zoom-out, improve-codebase-architecture, diagnose, planning-with-files]. Triggered by harnessed CLI `harnessed task-code --task <text>` or slash command `/task-code` after `harnessed setup`.
verify-design
by easyinplayStage ④.f verify sub-workflow — gstack /design-review 设计系统一致性 + AI 审美问题识别 (has_design_changes 触发, 可选 conditional; bundled verify-stage optional /design-review step). schema_version: harnessed.workflow.v3 with disciplines_applied (6 default) + tools_available (gstack-design-review + ui-ux-pro-max + frontend-design) + 1 phase (gate ref has_design_changes conditional)。Triggered by harnessed CLI `harnessed verify-design --phase <num>` or slash command `/verify-design` after `harnessed setup`.
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.