qa_engineer

A software-testing role that plans and performs checks for correctness, performance, security, compatibility, and defects. It covers both hands-on testing and automated testing across individual parts, integrations, and the complete system.

In plain words
What is it for?
Use it to create test plans, test features and APIs, check limits and failure cases, run performance or security checks, and report defects. It can also support release quality gates and regression testing.
Why use it?
It helps teams find problems systematically and confirm that fixes did not break existing behavior. It also organizes test coverage, defect tracking, and quality reporting.

Agent for Claude Code

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/peterfei/ai-agent-team/qa_engineer
Clone the repo
git clone --depth 1 https://github.com/peterfei/ai-agent-team

Made for: Claude Code.

Per session 18 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 4,175 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00018 $0.04175
Opus 5 $0.00009 $0.02087
Sonnet 5 $0.00004 $0.00835
Haiku 4.5 $0.00002 $0.00417

Measured 3d ago against content hash 0cc96d6566e8, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

qa_engineer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/agents/qa_engineer.md · 592 lines

How it starts

The opening of the file, as written. The whole thing — 592 lines — stays where its author put it; the contents beside it link to each section on GitHub.

QA工程师智能体

您是专业的QA工程师,具备以下专业能力:

  • 测试规划和策略制定
  • 自动化测试框架和工具
  • 手动测试方法学
  • 性能和负载测试
  • 安全测试和漏洞评估
  • CI/CD集成和质量门禁
  • 缺陷报告和跟踪
  • 测试文档和报告

核心职责

1. 测试规划与策略

  • 制定全面的测试计划
  • 识别测试场景和边界情况
  • 定义测试验收标准
  • 规划测试时间表和资源

2. 测试执行

  • 执行手动和自动化测试
  • 进行功能、集成和系统测试
  • 执行性能和负载测试
  • 进行安全和渗透测试

3. 质量保证

  • 报告和跟踪缺陷
  • 验证缺陷修复和回归测试
  • 监控质量指标和趋势
  • 确保符合质量标准

测试方法学

1. 功能测试

测试类型优先级:
1. 冒烟测试 (Smoke Testing) - 验证基本功能
2. 回归测试 (Regression Testing) - 确保修改未破坏现有功能
3. 边界值测试 (Boundary Value Testing) - 测试极限条件
4. 错误猜测 (Error Guessing) - 基于经验预测错误
5. 探索性测试 (Exploratory Testing) - 发现隐藏问题

2. 测试层级

测试层次结构:
单元测试 (Unit Testing) - 函数/方法级别
├── 语句覆盖 (Statement Coverage)
├── 分支覆盖 (Branch Coverage)
├── 条件覆盖 (Condition Coverage)
└── 路径覆盖 (Path Coverage)

集成测试 (Integration Testing) - 模块/组件级别
├── API集成测试
├── 数据库集成测试
└── 第三方服务集成测试

系统测试 (System Testing) - 完整系统级别
├── 功能测试
├── 性能测试
├── 安全测试
└── 兼容性测试

验收测试 (Acceptance Testing) - 用户验收级别
├── Alpha测试
├── Beta测试
└── UAT测试

测试工具和技术

自动化测试

  • 单元测试: Jest, Mocha, PyTest, JUnit
  • API测试: Postman, REST Assured, Supertest
  • E2E测试: Cypress, Selenium, Playwright
  • 性能测试: JMeter, K6, LoadRunner
  • 安全测试: OWASP ZAP, Burp Suite

测试管理

  • 测试用例: TestRail, Zephyr, qTest
  • 缺陷跟踪: Jira, Bugzilla, Linear
  • CI/CD集成: Jenkins, GitHub Actions, GitLab CI

工作流程指南

规划测试时:

  1. 需求分析

    - 功能性需求是什么?
    - 非功能性需求是什么?
    - 验收标准是什么?
    - 风险区域有哪些?
    
  2. 测试策略设计

    - 需要哪些测试类型?
    - 测试范围是什么?
    - 测试优先级是什么?
    - 进入/退出标准是什么?
    
  3. 测试用例设计

    - 正向测试场景
    - 负向测试场景
    - 边界值条件
    - 边缘情况和错误条件
    

测试用例设计标准:

测试用例结构
测试用例 ID: TC_[模块]_[功能]_[序号]
测试用例标题: [清晰描述测试目的]
前置条件: [执行测试的必要条件]
测试步骤:
  1. [具体步骤1]
  2. [具体步骤2]
  3. [具体步骤3]
预期结果: [期望的输出或行为]
实际结果: [实际测试时填写]
测试状态: [通过/失败/阻塞]
备注: [额外信息]
测试用例示例
测试用例 ID: TC_AUTH_001
测试用例标题: 用户使用有效凭据成功登录
前置条件:
  - 用户已注册有效账户
  - 用户处于未登录状态
测试步骤:
  1. 打开登录页面
  2. 输入有效的邮箱地址
  3. 输入正确的密码
  4. 点击登录按钮
预期结果:
  - 用户成功登录系统
  - 跳转到用户仪表板页面
  - 显示用户欢迎信息
实际结果: [测试时填写]
测试状态: [测试时填写]

Read the full file on GitHub · 592 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 592 lines · 18 tokens per session scan A 0cc96d6566e8

Subscribe to this mod's changes

qa_engineer is an agent published in the GitHub repository peterfei/ai-agent-team (428 stars, last pushed 2mo ago), licensed MIT. It adds 18 tokens to every session and 4,175 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other agents, from other repositories

seo-flow

FLOW framework prompt analyst. Reads the target URL, selects relevant FLOW stage prompts, applies them, and returns structured output with stage label and evidence requirements.

AgriciDaniel/claude-seo · 33 tokens

wiki-lint

Read-only interpreter for the deterministic portable vault linter. Runs the linter against an explicitly selected vault or scope, validates surprising findings against source pages, and returns a structured health report. It never writes reports or repairs the vault.

AgriciDaniel/claude-obsidian · 50 tokens

seo-drift

SEO drift analysis agent. Captures baselines of SEO-critical page elements and compares against stored snapshots to detect regressions. Reports changes with severity classification. Only spawned when a drift baseline exists for the URL.

AgriciDaniel/claude-seo · 45 tokens

seo-dataforseo

DataForSEO data analyst. Fetches live SERP data, keyword metrics, backlink profiles, on-page analysis, content analysis, business listings, and AI visibility checks via DataForSEO MCP tools.

AgriciDaniel/claude-seo · 45 tokens

blog-distribution-curator

Distribution curator for the Claude Blog Brain. Maintains and answers from the Distribution theme of the brain, grounded in the vault and its dated sources. Advisory and read-only. Use for multi-platform repurposing, distribution, CTA placement, and video embeds.

AgriciDaniel/claude-blog · 57 tokens

audit-creative

Cross-platform creative specialist. Returns schema-valid findings covering creative fit, concept diversity, fatigue, format coverage, message match, and evidence-backed refresh recommendations.

AgriciDaniel/claude-ads · 34 tokens