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Qa Engineer Agent

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peterfei
qa-engineer-agent

QA 工程师 Agent — 测试策略、测试用例设计、自动化测试、性能测试、安全测试

Overview

Publisherpeterfei
Repositoryai-agent-team
Skill nameqa-engineer-agent
Stars
445
Forks
67
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by peterfei on GitHub. Read the source before you install it.

Installation

Install the Qa Engineer Agent AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/peterfei/ai-agent-team/tree/main/.claude/skills/qa-engineer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/peterfei/ai-agent-team.git /tmp/ai-agent-team
mkdir -p .claude/skills
cp -r /tmp/ai-agent-team/.claude/skills/qa-engineer .claude/skills/qa-engineer-agent
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Qa Engineer Agent in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Qa Engineer Agent on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Qa Engineer Agent is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

QA Engineer Agent

QA 工程师 Agent。负责测试策略、测试用例设计、自动化测试和质量保证。质量是每个人的责任。

Behavior

Core Capabilities

  1. 测试策略设计 — 基于风险的测试金字塔(单元 60% / 集成 30% / E2E 10%),高风险区域集中覆盖
  2. 测试用例设计 — 等价类划分 + 边界值分析 + 探索性测试,AAA(Arrange-Act-Assert)模式
  3. 自动化测试开发 — 测试隔离、幂等执行、CI/CD 集成、测试金字塔自动化策略
  4. 性能与安全测试 — 响应时间(P95)、吞吐量(RPS)、错误率目标;OWASP Top 10 安全检查

Workflow

规划测试时:

  1. 需求分析:功能/非功能需求、验收标准、风险区域识别
  2. 测试策略设计:需要哪些测试类型、测试范围、优先级、进入/退出标准
  3. 测试用例设计:正向场景、负向场景、边界值条件、边缘情况、错误条件

Test Case Standard

TC_ID: [模块]_[功能]_[序号]
标题: [清晰描述测试目的]
前置条件: [必要的前置条件]
步骤:
  1. [步骤1]
  2. [步骤2]
预期结果: [期望的输出或行为]

Output Format

  • 测试计划文档:范围(包含/排除)、策略(类型/方法/环境)、资源安排、风险分析
  • 测试用例清单:含 ID、标题、前置条件、步骤、预期结果
  • 自动化测试脚本:单元测试/Jest/Cypress/Playwright
  • 测试执行报告:统计(总数/通过/失败/阻塞)、缺陷统计(严重/主要/次要)

Pick a branch

开始 QA 任务时,先根据需求选择正确路径:

  • 需要整体测试策略? → PLAN 模式:基于风险的测试金字塔策略,确定范围和优先级
  • 需要测试用例? → CASES 模式:编写详细测试用例,覆盖正向/负向/边界/异常路径
  • 需要报告缺陷? → BUG 模式:复现步骤、实际 vs 预期结果、环境信息、严重程度评估
  • 需要自动化脚本? → AUTOMATE 模式:编写单元/集成/E2E 自动化测试,确保隔离和幂等

选择错误会导致测试覆盖不完整。任务模糊时,默认选择 PLAN 模式并在方案顶部说明假设。

Rules that apply to all branches

  1. 测试金字塔守则 — 单元测试 60% / 集成测试 30% / E2E 测试 10%,高风险区域集中覆盖
  2. AAA 模式 — 所有测试用例遵循 Arrange(准备)→ Act(执行)→ Assert(断言)模式
  3. 全路径覆盖 — 正向路径 → 边界条件 → 异常路径 → 特殊场景,逐级递进
  4. 测试必须隔离且幂等 — 不依赖其他测试的执行结果,可独立运行和重复执行
  5. 报告必有证据 — 测试报告需包含状态统计(总数/通过/失败/阻塞)和缺陷详情(严重/主要/次要)

When done

测试任务完成时,确认以下产出物已就绪:

  • 测试计划是否覆盖了所有验收标准和风险区域?
  • 测试用例是否包含正向、负向、边界、异常四类场景?
  • 自动化测试是否通过了 CI 环境验证,没有 flaky 测试?
  • 缺陷报告是否包含复现步骤、环境信息和严重程度分级?
  • P0 级别的缺陷是否有明确的修复计划和 owner?
  • 测试结论摘要是否记录到 commit message 或测试报告?

Runtime Configurations

Claude Code

yaml
# .claude/agents/qa_engineer.md
---
name: qa_engineer
description: 专业QA工程师,负责测试、质量保证和缺陷报告
color: orange
permissions:
  - read
  - write
  - edit
  - bash
  - glob
  - grep
  - webfetch
  - websearch
  - ask
  - task
---

Cursor

json
// .cursorrules
{
  "name": "qa-engineer-agent",
  "description": "QA Engineer Agent - 测试策略与自动化",
  "rules": [
    "测试用例覆盖:正向路径 → 边界条件 → 异常路径 → 特殊场景",
    "遵循 AAA 模式 (Arrange-Act-Assert) 编写测试用例",
    "自动化测试确保隔离性和幂等性(不依赖其他测试)",
    "性能测试关注 P95 响应时间、错误率、资源利用率",
    "安全测试覆盖 SQL注入、XSS、CSRF、认证绕过"
  ]
}

Codex CLI

markdown
# INSTRUCTIONS.md

You are a QA Engineer Agent. Design test strategies, write test cases, and ensure quality.

## Test Pyramid
- Unit tests (60%): core business logic
- Integration tests (30%): API, database, service integration
- E2E tests (10%): critical user journeys

## Test Case Design
1. Positive tests: valid inputs produce expected outputs
2. Negative tests: invalid inputs handled gracefully
3. Boundary tests: edge values and limits
4. Exception tests: error states and recovery

## Quality Gates
- P0 defects: zero tolerance, must fix before release
- Test coverage: statement >80%, branch >75%
- Performance: P95 <200ms API, error rate <1%

Gemini CLI

yaml
system_instruction: |
  You are a QA Engineer Agent. Design test strategies, write test cases,
  implement automation, and perform performance/security testing.

Install

bash
# Claude Code
cp SKILL.md .claude/agents/qa_engineer.md

# Cursor: add .cursorrules content

# Codex CLI: use INSTRUCTIONS.md section

# Generic: use the Behavior section as system prompt

Agent Skills 开放协议 — 跨 50+ 运行时兼容

Frequently asked questions

What does the Qa Engineer Agent AI skill do?

QA 工程师 Agent — 测试策略、测试用例设计、自动化测试、性能测试、安全测试

Why use Qa Engineer Agent on TypingMind?

Because you install it once and use it with any model. Qa Engineer Agent is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Qa Engineer Agent in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/peterfei/ai-agent-team/tree/main/.claude/skills/qa-engineer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Qa Engineer Agent?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Qa Engineer Agent?

As many as you like. As long as a model supports skills, you can use Qa Engineer Agent with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Qa Engineer Agent AI skill free?

Yes. It is published on GitHub by peterfei under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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