How to Give AI a ‘User Manual’: Understand Anthropic’s Skill Open Standard in 3 Minutes

When you ask an AI to write a weekly report, you may start with a simple prompt, but after months of adding dozens of rules and custom prompts, the AI can drift: formats go wrong, focus is lost, and promised actions are forgotten.

Anthropic released the Skill open standard in December 2025. It is not a proprietary feature; more than sixteen mainstream AI tools—including Claude Code, Cursor, GitHub Copilot, Trae, and Windsurf—already support it, and the ecosystem continues to grow.

A Skill is essentially a SKILL.md markdown document that describes four items: the skill’s name, when to use it, how to operate it, and any cautions. Think of it like opening only the red‑braised‑pork page of a massive cookbook instead of spreading every recipe on the table.

Progressive disclosure mechanism

First layer (idle): The AI sees only the skill’s title and a one‑sentence summary, a few dozen tokens, so hundreds of skills impose no load.

Second layer (matching): When the AI judges the current task matches the skill, it opens the full description, similar to deciding to cook a specific dish.

Third layer (execution): If the skill references templates or example code, the AI loads those resources only at execution time.

This on‑demand loading avoids the “bulky rule” approach where all rules are loaded at once, which dilutes the model’s attention and degrades performance. The new philosophy is “you need me, then I appear; otherwise I stay silent.” Some industry voices even claim that prompt engineering is fading while skill engineering is emerging.

Data supporting the trend: over sixteen AI tools have announced support for Agent Skills, more than 5,000 Skills are listed in public marketplaces, and Claude Code achieved an 80.9 % score on the SWE‑bench benchmark—an improvement the author attributes to the reduced context interference from Skills.

Practical use cases for non‑developers

Scenario 1 – Weekly reports: Create a “Write weekly report” Skill that defines a fixed template (e.g., “1. This week… 2. Next week… 3. Risks…”). Once installed, the AI will always follow this structure without extra prompting.

Scenario 2 – Team‑wide standards: Encode shared conventions—code naming, document headings, commit message format—into a Skill file. Any team member using Claude Code, Cursor, or Trae can install the same Skill and produce uniformly formatted output.

A minimal Skill example (YAML‑style markdown) is shown below:

---
name: 代码审查
description: 对提交的代码进行规范化审查，检查命名、注释和结构
---
## 使用场景
当用户请求 code/review/代码审查 时触发。
## 审查标准
1. 变量和函数命名使用驼峰格式
2. 所有注释使用中文
3. 接口参数必须包含校验逻辑

To use the Skill, place the file in the tool‑specific directory: .claude/skills/ for Claude Code, .cursor/skills/ for Cursor, and .trae/skills/ for Trae. The format is identical across tools, just like a PDF can be opened by any viewer.

From a broader perspective, if the Model Context Protocol (MCP) is the “USB interface” of the AI era, Agent Skills act as the universal driver that tells the AI how to perform tasks after the connection is made. This emerging standard aims to prevent each vendor from locking users into proprietary rule sets, much like PDF, HTTP, and USB enable cross‑vendor interoperability.

Finally, the author encourages readers to try it: open any Skill‑compatible AI, create a SKILL.md with ten lines (e.g., a simple email greeting), and see the AI respond more accurately within five minutes.