How to Auto‑Generate Test API Docs with Trae Skill in Seconds (Step‑by‑Step Guide)

The article explains why manual API documentation drains testers' time, then demonstrates how Trae Skill’s AI‑driven, customizable rules can generate accurate, real‑time interface docs in about ten seconds, complete with examples, curl commands, and reusable configurations.

Advanced AI Application Practice
Advanced AI Application Practice
Advanced AI Application Practice
How to Auto‑Generate Test API Docs with Trae Skill in Seconds (Step‑by‑Step Guide)

Test engineers spend far more time maintaining API documentation than writing automation cases or running regression tests. Inconsistent formats, missing fields, and delayed updates cause repeated back‑and‑forth with developers, breaking automated scripts and slowing quality work.

Trae Skill offers an AI‑powered solution that turns the documentation process into a one‑click operation. By defining a reusable Skill that encodes the required sections—core goal, scanning rules, format, examples, parameters, error codes, and version notes—the tool parses interface code or captured traffic and emits a complete Markdown spec that matches testing needs.

Key Advantages

Test‑oriented compliance : Custom rules force inclusion of request/response structures, error codes, curl examples, and test‑case hints, eliminating gaps that make manually written docs useless.

Massive time savings : Providing the interface source or a packet to Trae triggers a full document in roughly ten seconds, replacing the half‑day effort of manual verification.

Real‑time synchronization : Re‑run the Skill after any code change and the doc updates instantly, preventing stale specifications that cause test failures.

Team‑wide reusability : Once a Skill is saved, it can be shared across projects, enforcing a uniform output format and reducing onboarding friction for new testers.

Practical Walk‑through

1. Open TRAE, navigate to Rules and Skills , and create a new Skill.

2. Add the Skill definition, which consists of the following eight parts:

Core Goal

Scanning & Identification Features

Document Format

Complete Example

Response Parameters

Error‑Code Recognition

Notes (Data Dependencies)

Version Changes

3. Paste the following Markdown template into the Skill (the code block is kept exactly as shown):

# 接口文档规则 (简洁版)
## 1. 核心目标
当用户要求“生成接口文档”,分析代码并按照以下的格式输出Markdown接口文档
## 2. 扫描与识别
自动判断语言,根据AST语法自动解析接口特征
## 3. 接口文档格式
```markdown
# X.X {接口名称}(如:1.1 用户登录接口)
## 1. 基本信息
**接口地址:** `/api/xxx/xxx`
**请求方式:** GET/POST/PUT/DELETE
**权限要求:** `{权限标识/角色范围}`
**接口版本:** v1.0
## 2. 功能说明
- 详细描述接口的业务逻辑、适用场景、核心处理流程;
- 复杂流程可插入Mermaid图示。
```
## 4. 请求参数
### 3.1 Query参数
| 参数名 | 类型 | 必填 | 描述 | 验证规则 |
|--------|------|------|------|----------|
| param1 | string | 是 | 用户ID | 长度1-50 |
| param2 | int | 否 | 页码 | 1-100 |
### 3.2 Body参数
| 参数名 | 类型 | 必填 | 描述 | 示例值 |
|--------|------|------|------|--------|
| name | string | 是 | 用户名 | "zhangsan" |
| age | int | 否 | 年龄 | 25 |
## 5. 完整请求示例
```bash
curl -X GET "https://xxx.com/api/user?param1=123¶m2=1" \
-H "Token: Bearer eyJhbGci..." \
-H "Content-Type: application/json"
```
## 6. 响应参数
| 字段名 | 类型 | 必返 | 描述 | 示例值 |
|--------|------|------|------|--------|
| code | int | 是 | 状态码(200 为成功) | 200 |
| message | string | 是 | 提示信息 | "success" |
| data | object/array | 否 | 业务数据 | - |

4. In the chat window, enter a prompt such as “Generate API documentation for the hotel‑system login interface.” Trae scans the project directory, identifies the login endpoint, and returns a fully populated document matching the template.

The generated document includes request headers, body schema, example curl command, and response fields, ready to be copied into Postman or used directly in test case design.

5. Import the curl snippet into Postman to verify the endpoint instantly, eliminating manual transcription.

Conclusion

By configuring a single Skill, testers compress the documentation cycle from over 30 minutes to about 10 seconds, achieve near‑zero synchronization cost, and maintain a consistent, standards‑compliant spec across the whole team. The approach is especially suited to agile projects with frequent interface changes, allowing engineers to focus on defect detection rather than repetitive document upkeep.

software testingtest automationAPI documentationAI generationTrae Skill
Advanced AI Application Practice
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Advanced AI Application Practice

Advanced AI Application Practice

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