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backend-tester后端测试员

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

216

周安装

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下载量

72
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

MIT

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:backend-tester(后端测试员)
来源仓库:https://github.com/edanstarfire/claudecode_webui
仓库路径:skills/backend-tester
安装命令:
npx skills add edanstarfire/claudecode_webui --skill "backend-tester"
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。

AgentSkills.tonpx skills
npx skills add edanstarfire/claudecode_webui --skill "backend-tester"

简介

辅助设计后端接口的测试用例,支持单元测试与集成测试生成。

  • 适合在 API 开发阶段提升代码质量与覆盖率检查效率。
  • 可基于 OpenAPI 规范自动生成请求模板与预期响应断言。
  • 测试脚本需配合项目实际框架运行,避免硬编码导致兼容性问题。
  • 涉及数据库或外部服务时应区分测试环境与生产环境配置。backend-tester 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Backend Tester

Instructions

When to Invoke This Skill

  • Testing changes to Python backend code
  • Verifying API endpoint functionality
  • Testing session/project management logic
  • Debugging SDK integration or message processing
  • Testing bug fixes that need verification
  • Any backend business logic changes

Testing Environment

CRITICAL: Always use isolated test environment to avoid conflicts with user's production instance.

Test Configuration:

  • Port: 8001 (production uses 8000)
  • Data Directory: test_data/ (production uses data/)
  • Debug Flags: --debug-all for full logging

Standard Workflows

Automated API Testing (Preferred)

Use for testing API endpoints without UI interaction.

1. Start Test Server

uv run python main.py --debug-all --data-dir test_data --port 8001

2. Run Test Commands Use curl or Python requests to test endpoints:

# Create project
curl -X POST http://localhost:8001/api/projects \
  -H "Content-Type: application/json" \
  -d '{"name": "Test Project", "working_directory": "/tmp/test"}'

# Create session
curl -X POST http://localhost:8001/api/sessions \
  -H "Content-Type: application/json" \
  -d '{"name": "Test Session", "project_id": "<project_id>"}'

# Get sessions
curl http://localhost:8001/api/sessions

# Start session
curl -X POST http://localhost:8001/api/sessions/<session_id>/start

3. Verify Responses

  • Check HTTP status codes
  • Validate response JSON structure
  • Verify expected data returned
  • Check error messages for error cases

4. Stop Server

# Press Ctrl+C to stop server

5. Review Logs Check test_data/logs/ for detailed debugging:

  • error.log - All errors
  • coordinator.log - Session coordination
  • storage.log - File operations
  • sdk_debug.log - SDK integration
  • websocket_debug.log - WebSocket lifecycle

6. Clean Up

# Remove test data (optional)
rm -rf test_data/

Manual Testing with UI

Use when user needs to interact with frontend.

1. Start Test Server in Background

Invoke the process-manager skill to safely manage the test server:

  • Start server: uv run python main.py --debug-all --data-dir test_data --port 8001
  • Track process by PID
  • Inform user: "Test server running on http://localhost:8001 - please test the changes"

2. Wait for User Confirmation User tests functionality in browser at http://localhost:8001

3. Stop Test Server

Invoke the process-manager skill to safely terminate:

  • Stop server by PID (never by name/pattern)
  • Verify cleanup
  • Confirm port 8001 is free

CRITICAL: Always delegate process management to the skill to avoid killing production servers.

Testing Strategies

Testing API Endpoints

Create Operation:

# Test creation
curl -X POST http://localhost:8001/api/<resource> -H "Content-Type: application/json" -d '{...}'

# Verify creation
curl http://localhost:8001/api/<resource>

Read Operation:

# Get list
curl http://localhost:8001/api/<resources>

# Get specific item
curl http://localhost:8001/api/<resources>/<id>

Update Operation:

# Update item
curl -X PUT http://localhost:8001/api/<resources>/<id> -H "Content-Type: application/json" -d '{...}'

# Verify update
curl http://localhost:8001/api/<resources>/<id>

Delete Operation:

# Delete item
curl -X DELETE http://localhost:8001/api/<resources>/<id>

# Verify deletion
curl http://localhost:8001/api/<resources>/<id>
# Should return 404

Testing Error Cases

Test validation:

# Missing required field
curl -X POST http://localhost:8001/api/<resource> -H "Content-Type: application/json" -d '{}'
# Should return 400

# Invalid data type
curl -X POST http://localhost:8001/api/<resource> -H "Content-Type: application/json" -d '{"id": "not-a-uuid"}'
# Should return 400

# Non-existent resource
curl http://localhost:8001/api/<resources>/nonexistent-id
# Should return 404

Testing Session Lifecycle

Complete session workflow:

# 1. Create project
PROJECT=$(curl -X POST http://localhost:8001/api/projects -H "Content-Type: application/json" -d '{"name":"Test","working_directory":"/tmp"}' | jq -r '.project_id')

# 2. Create session
SESSION=$(curl -X POST http://localhost:8001/api/sessions -H "Content-Type: application/json" -d "{\"project_id\":\"$PROJECT\",\"name\":\"Test Session\"}" | jq -r '.session_id')

# 3. Start session
curl -X POST http://localhost:8001/api/sessions/$SESSION/start

# 4. Send message
curl -X POST http://localhost:8001/api/sessions/$SESSION/messages -H "Content-Type: application/json" -d '{"content":"Hello"}'

# 5. Get messages
curl "http://localhost:8001/api/sessions/$SESSION/messages?limit=50&offset=0"

# 6. Pause session
curl -X POST http://localhost:8001/api/sessions/$SESSION/pause

# 7. Terminate session
curl -X POST http://localhost:8001/api/sessions/$SESSION/terminate

# 8. Clean up
curl -X DELETE http://localhost:8001/api/sessions/$SESSION
curl -X DELETE http://localhost:8001/api/projects/$PROJECT

When to Use Each Approach

Automated Testing (curl/requests):

  • Testing API logic and responses
  • Regression testing after changes
  • Testing error handling
  • Quick verification of endpoints
  • CI/CD integration (future)

Manual Testing (browser):

  • Testing UI interactions
  • WebSocket functionality
  • Visual verification
  • User flow testing
  • Complex multi-step scenarios

Unit Tests (pytest):

  • Testing individual functions
  • Testing business logic
  • Testing data models
  • Mocking external dependencies

Test Data Management

Test Data Location:

test_data/
├── logs/                    # Test run logs
├── projects/                # Test projects
└── sessions/                # Test sessions

Cleaning Up:

# Remove all test data
rm -rf test_data/

# Remove just logs
rm -rf test_data/logs/

# Remove specific session
rm -rf test_data/sessions/<session-id>/

Persistent Test Data: Sometimes useful to keep test data for debugging:

  • Comment out cleanup step
  • Rerun tests against same data
  • Inspect files directly

Examples

Example 1: Test new API endpoint

Context: Added new endpoint POST /api/sessions/<id>/reset

Test:
1. Start server: uv run python main.py --debug-all --data-dir test_data --port 8001
2. Create test session
3. Test endpoint: curl -X POST http://localhost:8001/api/sessions/<id>/reset
4. Verify: curl http://localhost:8001/api/sessions/<id>/messages (should be empty)
5. Stop server: Ctrl+C
6. Clean up: rm -rf test_data/

Example 2: Test bug fix with UI

Context: Fixed WebSocket reconnection issue

Test:
1. Invoke process-manager skill to start server in background
2. Server runs on port 8001 with test_data/
3. Inform user: "Test at http://localhost:8001"
4. User tests reconnection scenario
5. User confirms: "Works now"
6. Invoke process-manager skill to stop server by PID
7. Verify cleanup successful

Example 3: Automated regression test

Context: Need to verify session CRUD operations still work

Test Script:
1. Start server
2. Create project, verify response
3. Create session, verify response
4. Update session name, verify
5. Delete session, verify 404 on next get
6. Delete project, verify 404 on next get
7. Stop server
8. Check logs for errors
9. Clean up test_data/

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

04

需要参考平台分布和安装热度时

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

补充不同宿主或平台的使用分布数据

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

windsurf

29.01%
按下载量换算21

OpenCode

21.36%
按下载量换算15

Codex

20.47%
按下载量换算15

Claude Code

14.26%
按下载量换算10

Antigravity

8.5%
按下载量换算6

Gemini CLI

3.28%
按下载量换算2

安全审计

暂无安全审计结果可展示。

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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