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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rubric-writer
by WILLOSCARUse when `paper-review` has claims plus evidence gaps and needs the final referee-style report. **Trigger**: rubric review, referee report, peer review write-up, 审稿报告, REVIEW.md. **Use when**: `paper-review` pipeline 的最后阶段(C3),已有 `output/CLAIMS.md` + `output/MISSING_EVIDENCE.md`(以及可选 novelty matrix)。 **Skip if**: 上游产物未就绪(claims/evidence gaps 缺失)或你不打算输出完整审稿报告。 **Network**: none. **Guardrail**: 给可执行建议(actionable feedback),并覆盖 novelty/soundness/clarity/impact;避免泛泛而谈。
section-merger
by WILLOSCARDeterministically merge per-section files under `sections/` into `output/DRAFT.md`, preserving outline order and weaving transitions from `outline/transitions.md`. **Trigger**: merge sections, merge draft, combine section files, sections/ -> output/DRAFT.md, 合并小节, 拼接草稿. **Use when**: you have per-unit prose files under `sections/` and want a single `output/DRAFT.md` for polishing/review/LaTeX. **Skip if**: section files are missing or still contain scaffolding markers (fix `subsection-writer` first). **Network**: none. **Guardrail**: deterministic merge only (no new facts/citations); preserve section order from `outline/outline.yml`.
thesis-citation-enhance-review
by WILLOSCAR为中文毕业论文补强并核验引用:找出必须有引文支撑的句子,扩展候选文献,检查引用与论断是否匹配,并回写参考文献与正文引用。 **Trigger**: 引用补强, citation enhance, 文献补充, 引用核验, 毕业论文参考文献检查. **Use when**: 正文已有一定稳定度,需要系统补足背景、定义、对照工作和关键结论的引用支撑,并检查是否存在误引或漏引。 **Skip if**: 还没有稳定正文,或当前仅在做早期结构重构。 **Network**: optional. **Guardrail**: 不为了堆引用而堆引用;引用必须与论断匹配;先补对,再补多。
exercise-builder
by WILLOSCARUse when a tutorial module plan exists but each module still needs a verifiable teaching loop. **Trigger**: exercises, practice, verification checklist, 教程练习, 可验证作业. **Use when**: tutorial 的 C2,已有 `outline/module_plan.yml`,需要为每个模块补齐 exercise / expected output / verification steps。 **Skip if**: 还没有 module plan。 **Network**: none. **Guardrail**: 练习必须可验证,不能只给开放式思考题。
module-source-coverage
by WILLOSCARUse when a tutorial module plan exists and the run needs an auditable module-to-source grounding file before prose. **Trigger**: module coverage, source coverage, tutorial grounding, 模块覆盖, 来源覆盖. **Use when**: `source-tutorial` 的 C2,已有 `outline/module_plan.yml`,需要确认每个模块都能回指到 sources。 **Skip if**: module plan 或 source ingest 不完整。 **Network**: none. **Guardrail**: 只做 grounding audit,不写教程正文。
source-tutorial-spec
by WILLOSCARUse when a `source-tutorial` workspace has ingested sources and needs a grounded tutorial contract before structure planning. **Trigger**: source tutorial spec, tutorial from sources, learner profile, 教程规格, 从资料生成教程. **Use when**: `source-tutorial` 的 C2,需要根据 `sources/index.jsonl` / `sources/provenance.jsonl` 锁定 audience、prerequisites、learning objectives、source scope 和 running example policy。 **Skip if**: source ingest 还没完成,或 tutorial scope 已被人工冻结。 **Network**: none. **Guardrail**: 不要发明 sources 没支持的内容;running example 不稳时要明确写无统一 running example。
thesis-source-role-mapper
by WILLOSCAR将中文毕业论文已有材料映射到“毕业论文角色”:把论文、模板、Overleaf 源稿、PDF、图表和实验材料按章节角色、研究问题和证据用途重新归位。 **Trigger**: 毕业论文材料映射, source role map, paper to chapter, 章节角色映射, 论文归章, 材料归位. **Use when**: 已经完成材料盘点,需要决定各份材料在毕业论文里扮演什么角色,而不是继续按原 paper 叙事直接写。 **Skip if**: 当前只是在修一个已经稳定的单章措辞,且没有新的来源材料进入。 **Network**: none. **Guardrail**: 不是做翻译;不是简单 `paper -> chapter` 分桶;必须显式说明“在毕业论文里的角色”。
thesis-workspace-init
by WILLOSCAR初始化中文毕业论文工作区:检查学校模板与已有材料的放置位置,明确提示当前还缺什么,建立 `codex_md/` / `claude_md/` / `tmp_layout*/` 等中间层目录,并生成材料盘点与初始工作文件。 **Trigger**: 毕业论文初始化, thesis workspace, 中文毕业论文准备, 模板归位, 材料盘点, 初始化论文工程. **Use when**: 你要开始一条毕业论文重构流程,手头已经有学校模板、旧 `tex`、PDF、Overleaf 源稿、bib 或图表材料,需要先把工程和中间层搭起来。 **Skip if**: 工作区已经稳定,且 `codex_md/material_index.md`、`codex_md/question_list.md`、`codex_md/00_thesis_outline.md` 都已存在并在使用。 **Network**: none. **Guardrail**: 不改正文内容;不把 `chapters/` 当思考区;不在 repo root 散落毕业论文工件。
idea-direction-generator
by WILLOSCARGenerate a compact pool of discussion-worthy research directions from the signal table, writing `output/trace/IDEA_DIRECTION_POOL.md`. **Trigger**: idea direction pool, brainstorm directions, research directions, 研究方向池, brainstorm pool. **Use when**: you already have a signal table and want a small, non-isomorphic set of candidate directions. **Skip if**: `output/trace/IDEA_DIRECTION_POOL.md` already exists and is refined. **Network**: none. **Guardrail**: no invented papers; directions must stay anchored to the current signal table and core set.
protocol-writer
by WILLOSCARUse when `evidence-review` needs an operational protocol before screening and extraction. **Trigger**: protocol, PRISMA, systematic review, inclusion/exclusion, 检索式, 纳入排除. **Use when**: `evidence-review` pipeline 的起点(C1),需要先锁定 protocol 再开始 screening/extraction。 **Skip if**: 不是做 evidence/systematic review(或 protocol 已经锁定且不允许修改)。 **Network**: none. **Guardrail**: protocol 必须包含可执行的检索与筛选规则;需要 HUMAN 签字后才能进入 screening。
human-checkpoint
by WILLOSCARRecord a human sign-off at a declared checkpoint (tick `Approve C*` in `DECISIONS.md`) so the pipeline can resume. **Trigger**: approve checkpoint, human approval, sign off, HITL, Approve C2, 审批, 签字, 人类检查点. **Use when**: A unit has `owner=HUMAN` and is BLOCKED waiting for a checkbox in `DECISIONS.md`. **Skip if**: The approval is already recorded (the checkbox is ticked). **Network**: none. **Guardrail**: Do not modify any content artifacts; only update `DECISIONS.md` (and optionally append a short sign-off note).
artifact-contract-auditor
by WILLOSCARAudit the workspace against the pipeline artifact contract (DONE outputs + pipeline target_artifacts). Writes `output/CONTRACT_REPORT.md`. **Trigger**: contract audit, artifact contract, missing artifacts, target_artifacts, CONTRACT_REPORT. **Use when**: you want an auditable PASS/FAIL view of whether a workspace is complete and self-contained (end of run or before sharing). **Skip if**: you are still intentionally mid-run and don’t care about completeness yet (but it’s still useful as a snapshot). **Network**: none. **Guardrail**: analysis-only; do not edit content artifacts; only write the report.
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.