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api-testing-enAPI 测试 EN

Agent Skill

用于辅助 API 设计、接口文档、请求响应结构和服务集成说明。它适合让 Agent 梳理 endpoint、生成 OpenAPI 草稿、检查字段命名、整理错误码或辅助前后端联调。使用时需要确认真实业务语义、鉴权方式、分页和错误处理规则;涉及生成接口文档时,应避免凭空补字段,最好从现有代码、schema 或接口样例中提取事实。

总安装

240

周安装

10

GitHub Stars

39

下载量

80
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:api-testing-en(API 测试 EN)
来源仓库:https://github.com/naodeng/awesome-qa-skills
仓库路径:skills/api-testing-en
安装命令:
npx skills add https://github.com/naodeng/awesome-qa-skills --skill api-testing-en
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/naodeng/awesome-qa-skills --skill api-testing-en

简介

提供英文版本的 API 测试指导,适用于需要国际化输出的场景。

  • 支持生成 CSV/JSON/Excel 格式测试用例,灵活适配不同交付需求。
  • 基于 prompts/api-testing_EN.md 模板扩展业务上下文与测试环境说明。
  • 安装方式:通过 GitHub 仓库安装,命令为 npx skills add <repo> --skill api-testing-en。
  • 中文用户可参考同仓库 api-testing 技能获取本地化支持。

SKILL.md

API Testing (English)

中文版: See skill api-testing.

Prompt: this directory's prompts/api-testing_EN.md.

When to Use

  • User mentions API testing, api-testing
  • Need to execute this testing type or produce deliverables per Standard-version
  • Trigger examples: "Generate/design/write API test cases for the following"

Output Format Options

Markdown by default. For Excel / CSV / JSON, add at the end of your request; see output-formats.md.

How to Use

  1. Open the relevant file in this directory's prompts/ and copy the content below the dashed line.
  2. Append your requirements and context (business flow, environment, constraints, acceptance criteria).
  3. If you need non-Markdown output, append the request sentence from output-formats.md at the end.

Code Examples

1. Postman + Newman REST API Testing

Complete user management API test example with 10 test cases.

Location: examples/postman-rest-api/

Includes:

  • Postman collection file (10 test cases)
  • Environment variable configuration
  • Newman automation run script
  • Detailed README documentation

Quick Start:

cd examples/postman-rest-api
npm install -g newman
./newman-run.sh

Test Coverage:

  • User CRUD operations (Create, Read, Update, Delete)
  • Authentication and authorization testing
  • Error handling and boundary conditions
  • Response time validation
  • Data format validation

See: examples/postman-rest-api/README.md

2. REST Assured (Java)

Coming soon - Java-based REST API testing with REST Assured framework.

Location: examples/rest-assured-java/

3. SuperTest (Node.js)

Coming soon - Node.js API testing with SuperTest framework.

Location: examples/supertest-nodejs/

Best Practices

API Test Design Principles

  1. Test Pyramid

- Unit tests: Test individual API endpoints - Integration tests: Test interactions between multiple endpoints - End-to-end tests: Test complete business workflows

  1. Test Data Management

- Use environment variables for different environment configurations - Use dynamic variables to avoid hardcoding - Clean up data after tests

  1. Assertion Strategy

- Verify status codes - Verify response structure (Schema Validation) - Verify response data - Verify response time

  1. Error Handling

- Test various error scenarios (4xx, 5xx) - Verify error message format - Test boundary conditions

Tool Selection Guide

ToolUse CaseAdvantages
Postman/NewmanREST API testingEasy to use, visual, CI/CD integration
REST AssuredJava projectsStrong typing, BDD style
Pytest + RequestsPython projectsFlexible, rich ecosystem
SuperTestNode.js projectsGood integration with Express
GraphQL PlaygroundGraphQL APIDesigned specifically for GraphQL

Common Pitfalls

  • ❌ Testing only happy paths → ✅ Cover validation, error handling, auth failures, and edge cases
  • ❌ Weak contract checks → ✅ Assert status, schema, business fields, and backward compatibility
  • ❌ Not isolating test data → ✅ Use deterministic seed data and clear teardown rules
  • ❌ Ignoring environment parity → ✅ Validate base URL, auth config, and dependency versions per environment

Troubleshooting

Detailed troubleshooting steps were moved to references/troubleshooting.md. Load it on demand to keep the main skill concise.

Reference Files

  • prompts/api-testing_EN.md — API testing Standard-version prompt
  • output-formats.md — Markdown / Excel / CSV / JSON request instructions
  • examples/postman-rest-api/ — Complete Postman + Newman example
  • quick-start.md — 5-minute quick start guide

Related skills: automation-testing-en, performance-testing-en, security-testing-en, functional-testing-en.

Target Audience

  • QA engineers and developers executing this testing domain in real projects
  • Team leads who need structured, reproducible testing outputs
  • AI users who need fast, format-ready deliverables for execution and reporting

Not Recommended For

  • Pure production incident response without test scope/context
  • Decisions requiring legal/compliance sign-off without expert review
  • Requests lacking minimum inputs (scope, environment, expected behavior)

Critical Success Factors

  • Provide clear scope, environment, and acceptance criteria before generation
  • Validate generated outputs against real system constraints before execution
  • Keep artifacts traceable (requirements -> test points -> defects -> decisions)

Output Templates and Parsing Scripts

  • Template directory: output-templates/

- template-word.md (Word-friendly structure) - template-excel.tsv (Excel paste-ready) - template-xmind.md (XMind-friendly outline) - template-json.json - template-csv.csv - template-markdown.md

  • Parser scripts directory: scripts/

- Parse (generic): parse_output_formats.py - Parse (per-format): parse_word.py, parse_excel.py, parse_xmind.py, parse_json.py, parse_csv.py, parse_markdown.py - Convert (generic): convert_output_formats.py - Convert (per-format): convert_to_word.py, convert_to_excel.py, convert_to_xmind.py, convert_to_json.py, convert_to_csv.py, convert_to_markdown.py - Batch convert: batch_convert_templates.py (outputs into artifacts/)

Examples:

python3 scripts/parse_json.py output-templates/template-json.json
python3 scripts/parse_markdown.py output-templates/template-markdown.md
python3 scripts/convert_to_json.py output-templates/template-markdown.md
python3 scripts/convert_output_formats.py output-templates/template-json.json --to csv
python3 scripts/batch_convert_templates.py --skip-same

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

39.62%
按下载量换算32

Claude

28.7%
按下载量换算23

Cursor

17.57%
按下载量换算14

Gemini CLI

10.82%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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