381,784 Collected SKILL.md files

Explore AI Agent Skills & Claude Prompts

Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.

search
expand_more
Active:
uaandesign
Showing 1 of 1 skills
uaandesign

bragi

by uaandesign
star 0

Bragi(布拉吉)——把一眼假的 AI 草稿,改成对的人爱看、内行挑不出毛病的稿子。 名字取自北欧神话的诗歌与雄辩之神、众神的吟游诗人 skald:他的本事就是把话说得像人、 且看人下菜碟——为不同的厅堂、不同的听众,换不同的讲法。 这个 Skill 做三件事:1) 去 AI 味——按"破绽清单"铲掉排比堆叠、总分总骨架、空心大词、 万能过渡句这些一眼能认出 AI 的痕迹;2) 对准角色——动笔前先问"这稿子给谁看" (业务 leader / 设计 leader / 研发 leader / 运营 / 产解…),明确后按那个角色在乎的东西 重排重点;3) 逼出垂类纵深——把泛泛而谈、外行也能写的句子标出来,向你要真东西 (具体数字、真实机制、内行才知道的取舍),给了就织进去,给不了就如实标"缺料", 绝不凭空编造深度(编的深度内行一眼就看穿)。交稿前还有一道审稿:换独立视角逐句标来源, 指不出处的(串了别处的料、编造)打回;专业性只标疑点抛给用户,AI 不自认"够专业"。 两种用法同一台引擎:A) 改写已有 AI 草稿;B) 0→1——给真实素材(仓库/产物/数据)和"要产出 什么文档、给谁看",Bragi 从素材里抽真材料、搭结构、去味、对角色,素材没有的就问你或标缺料。 当用户想去掉 AI 味、让文字更像人写的、把稿子改得更像人话、按汇报对象改写、做得更有专业深度、 降低 AI 感、润色给 leader 看的文档,或想拿自己的产物/素材 0→1 产出一份汇报/方案/文档时使用。 触发词包括但不限于:去掉 AI 味、这段太 AI 了、改得像人说的、降低 AI 感、按汇报对象优化、 这稿子给 leader 看帮我改、显得更专业更有深度、别这么泛、用 bragi 改改、 拿这个 skill/产物帮我写份汇报、从0到1给设计leader写个文档。 不要用于:**没有任何素材的凭空代写**(必须给真实素材,Bragi 从料里建、不编)、纯翻译、 代码注释、与"让文字更像人/更对路/更有料"无关的编辑。

navigation main article SKILL.md
schedule Updated 11 days ago
Page 1 of 1

Browse Agent Skills by Occupation

23 major groups · 867 SOC occupations

Browse by Category

Explore agent skills organized by their primary use case

SKILLMD / CREATORS AND OCCUPATION CATEGORIES

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.

SEO KNOWLEDGE HUB & TECHNICAL OVERVIEW

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.

8 QUESTIONS

Frequently Asked Questions

A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.