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testing-assistant测试助理

Agent Skill

用于辅助测试设计、自动化测试、用例整理和回归验证。它适合让 Agent 编写单元测试、端到端测试、测试计划或根据失败日志定位问题。使用时需要确认项目测试框架、运行命令和夹具数据,避免为了通过测试而改坏真实逻辑;涉及浏览器或外部服务时,应区分本地模拟、测试环境和生产环境。

总安装

461

周安装

19

GitHub Stars

96

下载量

150
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:testing-assistant(测试助理)
来源仓库:https://github.com/robthepcguy/claude-patent-creator
仓库路径:skills/testing-assistant
安装命令:
npx skills add https://github.com/robthepcguy/claude-patent-creator --skill testing-assistant
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/robthepcguy/claude-patent-creator --skill testing-assistant

简介

用于协助完成各类测试任务的设计和执行。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

  • 适合生成测试用例、分析测试结果和优化测试流程。
  • 可基于项目特点定制测试策略和文档模板。
  • 涉及浏览器或外部服务时应区分模拟环境与真实调用。
  • testing-assistant 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Testing Assistant Skill

Expert system for testing and validating the Claude Patent Creator.

FOR CLAUDE: Test scripts in scripts/ directory.

  • Go directly to running appropriate test
  • Run from project root
  • Tests require active venv
  • Only run diagnostics if tests fail

When to Use

Running test suites, validating new features, testing after changes, debugging failures, creating tests, setting up CI/CD, performance testing, E2E validation, regression testing.

Testing Pyramid

         /\
        /  \       E2E (Manual + Automated)
       /----\
      / API  \     Integration Tests
     /--------\
    /  Unit   \    Unit Tests
   /----------\

Strategy: More unit tests (fast, isolated), fewer integration (moderate), minimal E2E (slow).

Test Suite Overview

scripts/
+-- test_install.py          # Complete installation validation
+-- test_gpu.py              # GPU detection and CUDA
+-- test_bigquery.py         # BigQuery connection
+-- test_analyzers.py        # Claims, spec, formalities
+-- test_embedding_speed.py  # Performance benchmarks
+-- test_checkpoint.py       # Index checkpoint system

Quick Test:

python scripts/test_install.py

Manual Testing via Claude

Test MCP tools through Claude Code interface.

Quick Test Examples

1. MPEP Search: "Search MPEP for claim definiteness requirements"
2. Patent Search: "Search for patents about neural networks filed in 2024"
3. Claims Review: "Review these claims: [paste test claims]"
4. Full Review: "/full-review" (with test application)
5. Diagrams: "Create a flowchart for this process: [describe]"

Validation Checklist

[OK] MPEP search returns relevant results
[OK] BigQuery search finds patents
[OK] Claims analyzer identifies issues
[OK] Specification analyzer checks support
[OK] Formalities checker validates format
[OK] Diagrams generate successfully
[OK] Full review workflow completes
[OK] All MCP tools accessible
[OK] Error messages clear and helpful
[OK] Performance acceptable (<2s most ops)

Creating New Tests

Quick Start

# Unit test template
def test_basic_functionality():
    from mcp_server.your_module import YourClass
    instance = YourClass()
    result = instance.method("test input")
    assert result is not None
    print("[OK] test_basic_functionality passed")

Test Categories:

  1. Basic functionality
  2. Edge cases
  3. Performance
  4. Error handling

Performance Testing

Quick Benchmark

from mcp_server.mpep_search import MPEPIndex
import time

index = MPEPIndex()
index.search("test", top_k=5)  # Warm up

start = time.time()
result = index.search("claim definiteness", top_k=5)
duration = time.time() - start
print(f"Search took: {duration:.3f}s")

Performance Thresholds

OperationThresholdNotes
MPEP search (first)<3sModel loading
MPEP search (subsequent)<500msCached models
BigQuery search<2sNetwork dependent
Claims analysis<3s20 claims
Spec analysis<10s10 pages
Diagram generation<1sSVG output

Troubleshooting Test Failures

ProblemSolution
Import errorsActivate venv, pip install -r requirements.txt
GPU tests failCheck nvidia-smi, reinstall PyTorch, or skip
BigQuery failsRe-auth: gcloud auth application-default login
Index not foundRebuild: patent-creator rebuild-index
Too slowCheck GPU usage, first run slower, check system load

Best Practices

  1. Test after every change
  2. Automated testing
  3. Test pyramid (more unit, fewer E2E)
  4. Fast tests (<5 min suite)
  5. Isolated tests (no dependencies)
  6. Clear assertions
  7. Document tests
  8. Version control tests
  9. Regular execution (weekly)
  10. Monitor performance

Quick Reference

Run All Tests

python scripts/test_install.py
python scripts/test_gpu.py
python scripts/test_bigquery.py
python scripts/test_analyzers.py
python scripts/test_embedding_speed.py

Regression Test

python scripts/test_install.py || exit 1
python scripts/test_bigquery.py || exit 1
python scripts/test_analyzers.py || exit 1
echo "[OK] All regression tests passed!"

Manual Checklist

□ Ask Claude to search MPEP
□ Ask Claude to search patents
□ Ask Claude to review claims
□ Run /full-review command
□ Generate a diagram
□ Verify all tools work
□ Check performance (<2s)

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

33.18%
按下载量换算50

Claude

31.64%
按下载量换算47

Cursor

17.95%
按下载量换算27

Gemini CLI

9.79%
按下载量换算15

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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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