Feature Validation Skill

功能验证技能

Purpose

用途

This skill provides systematic validation of implemented features, ensuring code quality, test coverage, performance, security, and requirement fulfillment before marking work complete.

本技能可对已实现的功能进行系统性验证，确保在标记工作完成前，代码质量、测试覆盖率、性能、安全性及需求满足度均符合要求。

When to Use

使用时机

After implementation and testing are complete
Before creating pull request
Before marking feature as done
When verifying all acceptance criteria met
Final quality gate before deployment

实现与测试完成后
创建拉取请求（Pull Request）前
标记功能为已完成前
验证所有验收标准均已满足时
部署前的最终质量关卡

Validation Workflow

验证流程

1. Code Quality Validation

1. 代码质量验证

Run Quality Checks:

bash

undefined

运行质量检查：

bash

undefined

Format check (Black)

格式检查（Black）

black --check src/ tests/

Type checking (mypy)

类型检查（mypy）

mypy src/

Linting (flake8, if configured)

代码风格检查（flake8，若已配置）

flake8 src/ tests/

All checks together

一次性运行所有检查

make lint # If Makefile configured


**Quality Checklist:**
Refer to `quality-checklist.md` for comprehensive review

**Key Quality Metrics:**
- [ ] All functions have type hints
- [ ] All public functions have docstrings (Google style)
- [ ] No files exceed 500 lines
- [ ] No lint errors or warnings
- [ ] Code formatted with Black
- [ ] Type checking passes with mypy
- [ ] No code duplication (DRY principle)
- [ ] Single responsibility principle followed

**Automated Script:**
```bash

make lint # 若已配置Makefile


**质量检查清单：**
参考`quality-checklist.md`进行全面评审

**关键质量指标：**
- [ ] 所有函数均包含类型提示
- [ ] 所有公共函数均有文档字符串（Google风格）
- [ ] 无文件超过500行
- [ ] 无代码风格检查错误或警告
- [ ] 代码已使用Black格式化
- [ ] mypy类型检查通过
- [ ] 无代码重复（遵循DRY原则）
- [ ] 遵循单一职责原则

**自动化脚本：**
```bash

Use validation script

使用验证脚本

python scripts/run_checks.py --quality


**Deliverable:** Quality report with pass/fail

---

python scripts/run_checks.py --quality


**交付物：** 包含通过/未通过结果的质量报告

---

2. Test Coverage Validation

2. 测试覆盖率验证

Run Tests with Coverage:

bash

undefined

运行带覆盖率统计的测试：

bash

undefined

Run all tests with coverage

运行所有测试并统计覆盖率

pytest --cov=src --cov-report=html --cov-report=term-missing

Check coverage threshold

检查覆盖率阈值

pytest --cov=src --cov-fail-under=80

View HTML coverage report

查看HTML格式的覆盖率报告

open htmlcov/index.html


**Coverage Checklist:**
- [ ] Overall coverage ≥ 80%
- [ ] Core business logic ≥ 90%
- [ ] Utilities and helpers ≥ 85%
- [ ] No critical paths untested
- [ ] All branches covered
- [ ] Edge cases tested
- [ ] Error conditions tested

**Identify Coverage Gaps:**
```bash

open htmlcov/index.html


**覆盖率检查清单：**
- [ ] 整体覆盖率 ≥ 80%
- [ ] 核心业务逻辑覆盖率 ≥ 90%
- [ ] 工具与辅助函数覆盖率 ≥ 85%
- [ ] 无未测试的关键路径
- [ ] 所有分支均已覆盖
- [ ] 边界情况已测试
- [ ] 错误场景已测试

**识别覆盖率缺口：**
```bash

Show untested lines

显示未测试的代码行

pytest --cov=src --cov-report=term-missing

Generate detailed HTML report

生成详细的HTML报告

pytest --cov=src --cov-report=html


**Deliverable:** Coverage report with gaps identified

---

pytest --cov=src --cov-report=html


**交付物：** 标记出覆盖率缺口的覆盖率报告

---

3. Test Quality Validation

3. 测试质量验证

Review Test Suite:

All tests passing
No skipped tests (without justification)
No flaky tests (intermittent failures)
Tests run quickly (unit tests < 1 min)
Tests are independent (no order dependency)
Tests clean up after themselves
Mock external dependencies properly
Test names are clear and descriptive

Run Tests Multiple Times:

bash

undefined

评审测试套件：

所有测试均通过
无无理由跳过的测试
无不稳定测试（间歇性失败）
测试运行速度快（单元测试总耗时 < 1分钟）
测试相互独立（无执行顺序依赖）
测试完成后会清理环境
外部依赖已正确模拟
测试名称清晰且具有描述性

多次运行测试：

bash

undefined

Run tests 10 times to check for flaky tests

运行测试10次以检查是否存在不稳定测试

for i in {1..10}; do pytest || break; done

Run in random order

随机顺序运行测试

pytest --random-order


**Test Markers:**
```bash

pytest --random-order


**测试标记：**
```bash

Verify no slow tests in unit tests

验证单元测试中无慢测试

pytest tests/unit/ -m "not slow"

Run integration tests separately

单独运行集成测试

pytest tests/integration/


**Deliverable:** Test quality assessment

---

pytest tests/integration/


**交付物：** 测试质量评估报告

---

4. Performance Validation

4. 性能验证

Performance Checklist: Refer to

performance-benchmarks.md

for target metrics

Key Performance Metrics:

Response time < target (e.g., < 200ms for p95)
Throughput meets requirements (e.g., 1000 req/s)
Memory usage within bounds (e.g., < 100MB)
CPU usage reasonable (e.g., < 50%)
No memory leaks detected
Database queries optimized (< 5 queries per operation)

Performance Testing:

bash

undefined

性能检查清单： 参考

performance-benchmarks.md

获取目标指标

关键性能指标：

响应时间 < 目标值（例如，p95 < 200ms）
吞吐量满足要求（例如，1000请求/秒）
内存使用在合理范围内（例如，< 100MB）
CPU使用率合理（例如，< 50%）
未检测到内存泄漏
数据库查询已优化（每次操作查询次数 < 5次）

性能测试：

bash

undefined

Run performance tests

运行性能测试

pytest tests/performance/ -v

Profile code

代码性能分析

python -m cProfile -o profile.stats script.py python -m pstats profile.stats

Memory profiling

内存分析

python -m memory_profiler script.py


**Benchmark Against Requirements:**
```python

python -m memory_profiler script.py


**与需求基准对比：**
```python

Example performance test

性能测试示例

def test_performance_requirement(): """Verify operation meets performance requirement.""" start = time.time() result = expensive_operation() duration = time.time() - start

assert duration < 1.0, f"Took {duration}s, required < 1.0s"


**Deliverable:** Performance report with metrics

---

def test_performance_requirement(): """验证操作是否满足性能要求。""" start = time.time() result = expensive_operation() duration = time.time() - start

assert duration < 1.0, f"耗时 {duration}秒，要求 < 1.0秒"


**交付物：** 包含指标的性能报告

---

5. Security Validation

5. 安全性验证

Security Checklist Review: Review

security-checklist.md

from analysis phase and verify:

Input Validation:

All user inputs validated and sanitized
SQL injection prevented (parameterized queries)
Command injection prevented (no shell=True with user input)
Path traversal prevented (sanitized file paths)
XSS prevented (escaped output)

Authentication & Authorization:

Authentication required for protected endpoints
Authorization checks at every access point
Session management secure
Credentials not hardcoded

Data Protection:

Sensitive data encrypted in transit
Sensitive data encrypted at rest (if applicable)
PII handling compliant
Secrets in environment variables (not code)
Error messages don't leak sensitive info

Dependency Security:

bash

undefined

安全检查清单评审： 回顾分析阶段的

security-checklist.md

并验证：

输入验证：

所有用户输入均已验证并净化
已防止SQL注入（使用参数化查询）
已防止命令注入（用户输入未搭配shell=True使用）
已防止路径遍历（文件路径已净化）
已防止XSS攻击（输出已转义）

认证与授权：

受保护的端点需要认证
每个访问点均有授权检查
会话管理安全
凭证未硬编码在代码中

数据保护：

敏感数据在传输过程中已加密
敏感数据在存储时已加密（如适用）
PII（个人可识别信息）处理合规
密钥存储在环境变量中（而非代码内）
错误信息未泄露敏感信息

依赖安全性：

bash

undefined

Check for vulnerable dependencies

检查存在漏洞的依赖

pip-audit

Or use safety

或使用safety工具

safety check --json

Check for outdated dependencies

检查过时的依赖

pip list --outdated


**Deliverable:** Security validation report

---

pip list --outdated


**交付物：** 安全性验证报告

---

6. Requirements Validation

6. 需求验证

Verify Acceptance Criteria: Review original requirements from analysis phase:

All functional requirements implemented
All acceptance criteria met
User stories fulfilled
Edge cases handled
Error scenarios handled

Manual Testing:

bash

undefined

验证验收标准： 回顾分析阶段的原始需求：

所有功能需求均已实现
所有验收标准均已满足
用户故事已完成
边界情况已处理
错误场景已处理

手动测试：

bash

undefined

Test CLI (if applicable)

测试CLI（如适用）

python -m src.tools.feature.main --help python -m src.tools.feature.main create --name test

Test with sample data

使用示例数据测试

python -m src.tools.feature.main --input samples/test.json

Test error cases

测试错误场景

python -m src.tools.feature.main --invalid-option


**Regression Testing:**
- [ ] Existing functionality not broken
- [ ] No breaking changes to public APIs
- [ ] Backward compatibility maintained (if required)

**Deliverable:** Requirements validation checklist

---

python -m src.tools.feature.main --invalid-option


**回归测试：**
- [ ] 现有功能未被破坏
- [ ] 公共API无破坏性变更
- [ ] 保持向后兼容性（如要求）

**交付物：** 需求验证检查清单

---

7. Documentation Validation

7. 文档验证

8. Integration Validation

8. 集成验证

Integration Testing:

bash

undefined

集成测试：

bash

undefined

Run integration tests

运行集成测试

pytest tests/integration/ -v

Test with real dependencies (in test environment)

使用真实依赖测试（在测试环境中）

pytest tests/integration/ --no-mock


**Integration Checklist:**
- [ ] Integrates correctly with existing code
- [ ] No circular dependencies
- [ ] Module imports work correctly
- [ ] Configuration loads correctly
- [ ] External services connect (if applicable)

**End-to-End Testing:**
```bash

pytest tests/integration/ --no-mock


**集成检查清单：**
- [ ] 与现有代码集成正确
- [ ] 无循环依赖
- [ ] 模块导入正常工作
- [ ] 配置加载正常
- [ ] 外部服务可连接（如适用）

**端到端测试：**
```bash

Test complete workflows

测试完整工作流

pytest tests/e2e/ -v

Manual E2E testing

手动端到端测试

./scripts/manual_test.sh


**Deliverable:** Integration test report

---

./scripts/manual_test.sh


**交付物：** 集成测试报告

---

9. Final Validation

9. 最终验证

Run Complete Validation Suite:

bash

undefined

运行完整验证套件：

bash

undefined

Use automated validation script

使用自动化验证脚本

python scripts/run_checks.py --all

Or run individual checks

或运行单独的检查项

python scripts/run_checks.py --quality python scripts/run_checks.py --tests python scripts/run_checks.py --coverage python scripts/run_checks.py --security


**Pre-PR Checklist:**
- [ ] All quality checks passing
- [ ] Test coverage ≥ 80%
- [ ] All tests passing
- [ ] Performance requirements met
- [ ] Security validated
- [ ] Requirements fulfilled
- [ ] Documentation complete
- [ ] Integration verified
- [ ] No known critical bugs

**Create Validation Report:**
```markdown

python scripts/run_checks.py --quality python scripts/run_checks.py --tests python scripts/run_checks.py --coverage python scripts/run_checks.py --security


**拉取请求前检查清单：**
- [ ] 所有质量检查均通过
- [ ] 测试覆盖率 ≥ 80%
- [ ] 所有测试均通过
- [ ] 性能需求已满足
- [ ] 安全性已验证
- [ ] 需求已满足
- [ ] 文档完整
- [ ] 集成已验证
- [ ] 无已知严重bug

**生成验证报告：**
```markdown

Validation Report: [Feature Name]

验证报告: [功能名称]

Quality ✅

质量 ✅

Black: PASS
mypy: PASS
flake8: PASS (0 errors, 0 warnings)

Black: 通过
mypy: 通过
flake8: 通过（0个错误，0个警告）

Testing ✅

测试 ✅

Unit tests: 45 passed
Integration tests: 12 passed
Coverage: 87% (target: 80%)

单元测试: 45个通过
集成测试: 12个通过
覆盖率: 87%（目标: 80%）

Performance ✅

性能 ✅

Response time (p95): 145ms (target: < 200ms)
Throughput: 1200 req/s (target: 1000 req/s)
Memory usage: 75MB (target: < 100MB)

响应时间（p95）: 145ms（目标: < 200ms）
吞吐量: 1200请求/秒（目标: 1000请求/秒）
内存使用: 75MB（目标: < 100MB）

Security ✅

安全性 ✅

No vulnerable dependencies
Input validation: Complete
Secrets management: Secure

无脆弱依赖
输入验证: 已完成
密钥管理: 安全

Requirements ✅

需求 ✅

All acceptance criteria met
No regressions detected

所有验收标准均已满足
未检测到回归问题

Documentation ✅

文档 ✅

Code documentation: Complete
Technical docs: Complete
CHANGELOG: Updated

代码文档: 完整
技术文档: 完整
CHANGELOG: 已更新

Status: READY FOR PR ✅

状态: 可提交拉取请求 ✅


**Deliverable:** Final validation report

---


**交付物：** 最终验证报告

---

Quality Standards

质量标准

Code Quality Metrics

代码质量指标

Complexity:

Cyclomatic complexity < 10 per function
Max nesting depth: 4 levels

Maintainability:

Files < 500 lines
Functions < 50 lines
Classes < 300 lines

Documentation:

100% public API documented
Docstring coverage ≥ 90%

复杂度：

每个函数的圈复杂度 < 10
最大嵌套深度: 4层

可维护性：

文件行数 < 500行
函数行数 < 50行
类行数 < 300行

文档：

100%的公共API已文档化
文档字符串覆盖率 ≥ 90%

Test Quality Metrics

测试质量指标

Coverage:

Overall: ≥ 80%
Critical paths: 100%
Core logic: ≥ 90%

Test Quality:

No flaky tests
Unit tests < 1 minute total
Integration tests < 5 minutes total

覆盖率：

整体: ≥ 80%
关键路径: 100%
核心逻辑: ≥ 90%

测试质量：

无不稳定测试
单元测试总耗时 < 1分钟
集成测试总耗时 < 5分钟

Performance Benchmarks

性能基准

Refer to

performance-benchmarks.md

for detailed criteria

Response Time:

p50: < 50ms
p95: < 200ms
p99: < 500ms

Resource Usage:

Memory: < 100MB
CPU: < 50% single core

参考

performance-benchmarks.md

获取详细标准

响应时间：

p50: < 50ms
p95: < 200ms
p99: < 500ms

资源使用：

内存: < 100MB
CPU: 单核使用率 < 50%

Automated Validation Script

自动化验证脚本

The

scripts/run_checks.py

script automates validation:

bash

undefined

scripts/run_checks.py

脚本可实现自动化验证：

bash

undefined

Run all checks

运行所有检查

python scripts/run_checks.py --all

Run specific checks

运行特定检查

python scripts/run_checks.py --quality python scripts/run_checks.py --tests python scripts/run_checks.py --coverage python scripts/run_checks.py --security python scripts/run_checks.py --performance

Generate report

生成报告

python scripts/run_checks.py --all --report validation-report.md

---

python scripts/run_checks.py --all --report validation-report.md

---

Supporting Resources

支持资源

quality-checklist.md: Comprehensive code quality standards
performance-benchmarks.md: Performance criteria and targets
scripts/run_checks.py: Automated validation runner

quality-checklist.md: 全面的代码质量标准
performance-benchmarks.md: 性能标准与目标
scripts/run_checks.py: 自动化验证运行器

Integration with Feature Implementation Flow

与功能实现流程的集成

Input: Completed implementation with tests Process: Systematic validation against all criteria Output: Validation report + approval for PR Next Step: Create pull request or deploy

输入： 已完成测试的功能实现 流程： 基于所有标准进行系统性验证 输出： 验证报告 + 拉取请求批准 下一步： 创建拉取请求或部署

Validation Checklist Summary

验证检查清单摘要

Quality ✓

质量 ✓

Testing ✓

测试 ✓

Performance ✓

性能 ✓

Response time < target
Throughput meets requirements
Memory usage reasonable
No performance regressions

响应时间 < 目标值
吞吐量满足要求
内存使用合理
无性能回归

Security ✓

安全性 ✓

Input validation complete
No hardcoded secrets
Dependencies scanned
Security checklist complete

输入验证已完成
无硬编码密钥
依赖已扫描
安全检查清单已完成

Requirements ✓

需求 ✓

Acceptance criteria met
User stories fulfilled
Edge cases handled
No regressions

验收标准已满足
用户故事已完成
边界情况已处理
无回归问题

Documentation ✓

文档 ✓

Code documented
Technical docs complete
User docs (if applicable)
CHANGELOG updated

代码已文档化
技术文档完整
用户文档（如适用）已完成
CHANGELOG已更新

Integration ✓

集成 ✓

Integration tests passing
No breaking changes
Backward compatible

集成测试已通过
无破坏性变更
向后兼容

Final Approval ✓

最终批准 ✓

All checklists complete
Validation report generated
Ready for pull request
Stakeholder approval (if required)

所有检查清单已完成
已生成验证报告
可提交拉取请求
相关方已批准（如需要）

Sign-off

签署确认

Feature: [Feature Name] Validated By: [Your Name] Date: [YYYY-MM-DD]

Status: ☐ Approved ☐ Needs Work

Notes: [Any additional notes or concerns]

功能： [功能名称] 验证人： [你的姓名] 日期： [YYYY-MM-DD]

状态： ☐ 批准 ☐ 需要改进

备注： [任何额外备注或问题]

What to Do If Validation Fails

验证失败时的处理步骤

Quality Issues:

Fix formatting:
```
black src/ tests/
```
Fix type errors: Review mypy output
Fix lint errors: Review flake8 output
Refactor large files/functions

Coverage Issues:

Identify untested code:
```
pytest --cov-report=html
```
Add missing tests
Review edge cases
Add error condition tests

Performance Issues:

Profile code:
```
python -m cProfile
```
Optimize hot paths
Add caching where appropriate
Optimize database queries

Security Issues:

Address vulnerabilities:
```
pip-audit
```
Review input validation
Check secrets management
Run security checklist again

Requirement Issues:

Review acceptance criteria
Implement missing functionality
Test edge cases
Verify with stakeholders

After Fixes:

Re-run validation
Update validation report
Verify all checks pass
Proceed to PR

质量问题：

修复格式：
```
black src/ tests/
```
修复类型错误：查看mypy输出
修复代码风格错误：查看flake8输出
重构大文件/函数

覆盖率问题：

识别未测试代码：
```
pytest --cov-report=html
```
添加缺失的测试
评审边界情况
添加错误场景测试

性能问题：

代码性能分析：
```
python -m cProfile
```
优化热点路径
合理添加缓存
优化数据库查询

安全性问题：

处理漏洞：
```
pip-audit
```
评审输入验证
检查密钥管理
重新运行安全检查清单

需求问题：

评审验收标准
实现缺失的功能
测试边界情况
与相关方确认

修复后：

重新运行验证
更新验证报告
确认所有检查均通过
提交拉取请求

validation

Original

Translation

Feature Validation Skill

功能验证技能

Purpose

用途

When to Use

使用时机

Validation Workflow

验证流程

1. Code Quality Validation

1. 代码质量验证

Format check (Black)

格式检查（Black）

Type checking (mypy)

类型检查（mypy）

Linting (flake8, if configured)

代码风格检查（flake8，若已配置）

All checks together

一次性运行所有检查

Use validation script

使用验证脚本

2. Test Coverage Validation

2. 测试覆盖率验证

Run all tests with coverage

运行所有测试并统计覆盖率

Check coverage threshold

检查覆盖率阈值

View HTML coverage report

查看HTML格式的覆盖率报告

Show untested lines

显示未测试的代码行

Generate detailed HTML report

生成详细的HTML报告

3. Test Quality Validation

3. 测试质量验证

Run tests 10 times to check for flaky tests

运行测试10次以检查是否存在不稳定测试

Run in random order

随机顺序运行测试

Verify no slow tests in unit tests

验证单元测试中无慢测试

Run integration tests separately

单独运行集成测试

4. Performance Validation

4. 性能验证

Run performance tests

运行性能测试

Profile code

代码性能分析

Memory profiling

内存分析

Example performance test

性能测试示例

5. Security Validation

5. 安全性验证

Check for vulnerable dependencies

检查存在漏洞的依赖

Or use safety

或使用safety工具

Check for outdated dependencies

检查过时的依赖

6. Requirements Validation

6. 需求验证

Test CLI (if applicable)

测试CLI（如适用）

Test with sample data

使用示例数据测试

Test error cases

测试错误场景

7. Documentation Validation

7. 文档验证

8. Integration Validation

8. 集成验证

Run integration tests

运行集成测试

Test with real dependencies (in test environment)

使用真实依赖测试（在测试环境中）

Test complete workflows