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playwright-console-monitorPlaywright console monitor 测试

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

247

周安装

10

GitHub Stars

1

下载量

78
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:playwright-console-monitor(Playwright console monitor 测试)
来源仓库:https://github.com/dawiddutoit/custom-claude
仓库路径:skills/playwright-console-monitor
安装命令:
npx skills add https://github.com/dawiddutoit/custom-claude --skill playwright-console-monitor
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/dawiddutoit/custom-claude --skill playwright-console-monitor

简介

用于辅助测试设计、自动化测试、用例整理和回归验证。

  • 适合编写单元测试、端到端测试或根据失败日志定位问题。
  • 使用时需确认项目测试框架、运行命令和夹具数据,避免误改逻辑。
  • 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。
  • playwright-console-monitor 属于前端设计类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Playwright Console Monitor

Quick Start

Monitor console errors during a login workflow:

1. Navigate to application
2. Execute login steps
3. Check console for errors after each critical operation
4. Generate error report with screenshots
5. Provide debugging context

Immediate value: Catch JavaScript errors during automation that would otherwise go unnoticed.

Table of Contents

  1. When to Use This Skill
  2. What This Skill Does
  3. Console Error Monitoring Workflow 3.1 Setup and Navigation 3.2 Execute with Monitoring 3.3 Error Detection and Categorization 3.4 Report Generation
  4. Console Message Levels
  5. Supporting Files
  6. Expected Outcomes
  7. Integration Points
  8. Success Metrics
  9. Requirements
  10. Red Flags to Avoid
  11. Notes

When to Use This Skill

Explicit Triggers

  • User says "check for console errors"
  • User says "monitor JavaScript errors during [workflow]"
  • User says "validate error-free execution"
  • User says "debug this workflow"
  • User requests "console monitoring"

Implicit Triggers

  • Executing critical browser workflows (checkout, form submission, data operations)
  • Testing new features or deployments
  • Debugging intermittent failures
  • Validating application health
  • QA automation scenarios

Debugging Context

  • When workflows fail without obvious cause
  • When investigating "works for me" issues
  • When validating frontend error handling
  • When checking third-party script integration

What This Skill Does

This skill wraps browser automation workflows with console monitoring to:

  1. Proactive Error Detection - Catch JavaScript errors during workflow execution
  2. Error Categorization - Classify errors by severity (critical/warning/info/debug)
  3. Context Capture - Take screenshots and snapshots at error states
  4. Report Generation - Provide structured error reports with debugging context
  5. Continuous Monitoring - Check console after each critical operation

Console Error Monitoring Workflow

3.1 Setup and Navigation

Step 1: Navigate to application

Use: mcp__playwright__browser_navigate
- Navigate to target URL
- Wait for page load

Step 2: Clear initial console messages (optional)

Use: mcp__playwright__browser_console_messages
- Read and discard initial load messages
- Establishes clean baseline for workflow monitoring

3.2 Execute with Monitoring

Step 3: Execute workflow with checkpoints

For each critical operation:

  1. Execute the action (click, type, navigate)
  2. Wait for expected state
  3. Check console messages
  4. Log any new errors/warnings

Example pattern:

Action: Click "Submit Order" button
  → mcp__playwright__browser_click
Wait: Order confirmation appears
  → mcp__playwright__browser_wait_for
Check: Console errors
  → mcp__playwright__browser_console_messages({ level: "error" })

Critical operation checkpoints:

  • Form submissions
  • Navigation between pages
  • AJAX/fetch requests
  • Third-party script loads
  • Authentication flows
  • Payment processing
  • Data mutations

3.3 Error Detection and Categorization

Step 4: Categorize detected errors

Use console message levels to filter:

LevelIncludesWhen to Use
errorCritical errors onlyAfter critical operations (checkout, submit)
warningWarnings + errorsDuring form validation, feature usage
infoInfo + warnings + errorsGeneral workflow monitoring
debugAll messagesDetailed debugging scenarios

Step 5: Capture error context

For each error detected:

  1. Take screenshot of current state mcp__playwright__browser_take_screenshot
  2. Capture page snapshot mcp__playwright__browser_snapshot
  3. Record error message, timestamp, and operation

3.4 Report Generation

Step 6: Generate structured error report

Report format:

Console Error Report
====================

Workflow: [workflow name]
Executed: [timestamp]
Total Errors: [count]

Critical Errors (level: error):
  - [timestamp] [message]
    Context: [operation that triggered]
    Screenshot: [filename]

Warnings (level: warning):
  - [timestamp] [message]
    Context: [operation that triggered]

Info Messages:
  - [summary if relevant]

Debugging Context:
  - URL at time of error
  - Page snapshot
  - Network state (if available via browser_network_requests)

Recommendations:
  - [specific debugging suggestions]

Console Message Levels

Playwright console messages follow browser console API levels:

error - Critical errors that prevent functionality

  • Uncaught exceptions
  • Failed network requests (4xx/5xx from critical APIs)
  • Missing required resources
  • Use after: Checkout, payment, form submission, authentication

warning - Non-fatal issues that may cause problems

  • Deprecated API usage
  • Failed optional resources
  • Validation warnings
  • Use after: Form interactions, navigation, feature usage

info - Informational messages

  • Application lifecycle events
  • Successful operations
  • Debug information from application
  • Use for: General monitoring, understanding application flow

debug - Verbose debugging output

  • All console.log, console.debug calls
  • Framework/library debug messages
  • Use for: Deep debugging, understanding detailed behavior

Default recommendation: Use level: "error" after critical operations, level: "warning" for general monitoring.

Supporting Files

This skill includes:

  • references/playwright-console-api.md - Detailed Playwright console API reference, message formats, and advanced patterns
  • examples/examples.md - 5+ real-world monitoring scenarios (e-commerce checkout, form submission, SPA navigation, authentication, third-party integrations)
  • assets/error-report-template.md - Template for structured error reports

Expected Outcomes

Successful Monitoring (No Errors)

✅ Console Monitoring Complete

Workflow: User Login Flow
Operations Monitored: 5
Console Errors: 0
Warnings: 0

All operations completed without console errors.

Error Detection

❌ Console Errors Detected

Workflow: Checkout Process
Operations Monitored: 8
Console Errors: 2 critical, 1 warning

Critical Errors:
  1. [14:32:15] Uncaught TypeError: Cannot read property 'total' of undefined
     Context: After "Apply Discount Code" click
     Screenshot: checkout-error-1.png

  2. [14:32:18] Failed to load resource: POST /api/payment 500
     Context: After "Submit Payment" click
     Screenshot: checkout-error-2.png

Warnings:
  1. [14:32:10] [Deprecation] Synchronous XMLHttpRequest on the main thread
     Context: During address validation

Debugging Recommendations:
  - Investigate cart.total property initialization
  - Check /api/payment endpoint for server errors
  - Review discount code application logic

Integration Points

With Browser Automation Workflows

  • Wrap existing automation with console monitoring
  • Add checkpoints after critical operations
  • Enhance test suites with error detection

With Debugging Workflows

  • Console monitoring identifies error sources
  • Screenshots provide visual debugging context
  • Network requests correlate with console errors

With QA Processes

  • Automated error detection in test runs
  • Regression testing for console errors
  • Performance monitoring (console.time/timeEnd messages)

With CI/CD Pipelines

  • Fail builds on critical console errors
  • Track error trends across deployments
  • Validate error-free user flows

Success Metrics

MetricBefore (Manual)After (Skill)Improvement
Error DetectionReactive (user reports)Proactive (during automation)+95% early detection
Debugging Time30-60 min (reproduce + debug)5-10 min (context provided)80% reduction
False NegativesHigh (errors missed)Low (systematic checking)90% reduction
Context QualityMinimal (user description)Rich (screenshots, state, messages)10x improvement

Requirements

Required Tools

  • Playwright MCP browser automation

- mcp__playwright__browser_navigate - mcp__playwright__browser_console_messages - mcp__playwright__browser_snapshot - mcp__playwright__browser_take_screenshot - mcp__playwright__browser_click (for workflows) - mcp__playwright__browser_wait_for

Environment

  • Playwright browser configured and accessible
  • Target application URL

Knowledge

  • Basic understanding of JavaScript console API
  • Familiarity with browser automation workflows
  • Understanding of error severity levels

Red Flags to Avoid

  • Checking console only at end - Check after each critical operation
  • Ignoring warnings - Warnings often predict future critical errors
  • Not capturing context - Always take screenshots at error states
  • Wrong severity level - Use error level for critical operations
  • Skipping wait states - Ensure operations complete before checking console
  • Not clearing initial messages - Page load messages contaminate workflow monitoring
  • Missing operation context - Always note which operation triggered errors
  • Overly verbose monitoring - Don't check console after every single action
  • Ignoring network correlation - Console errors often correlate with network failures
  • Not providing recommendations - Error reports should suggest debugging steps

Notes

Key Principles:

  1. Monitor critical operations - Focus on checkout, submit, auth, payment
  2. Categorize by severity - Use appropriate console levels
  3. Capture rich context - Screenshots + snapshots + messages
  4. Provide actionable reports - Include debugging recommendations
  5. Balance coverage vs noise - Strategic checkpoints, not every action

Console Message Persistence:

  • Messages accumulate during page lifetime
  • Page navigation clears console (new document)
  • Consider clearing messages between checkpoints for clarity

Performance Considerations:

  • Console checks add minimal overhead (~100-200ms)
  • Screenshot capture is slower (~500ms-1s)
  • Balance monitoring thoroughness with workflow speed

Common Patterns:

  • Critical operations: Check level: "error" immediately after
  • Form workflows: Check level: "warning" after validation
  • Debugging: Use level: "info" or level: "debug" for full visibility
  • Regression testing: Establish baseline, detect new errors

Integration with Network Monitoring:

  • Combine with browser_network_requests for complete debugging context
  • Network failures often manifest as console errors
  • 500-series responses typically generate console errors
  • CORS issues appear in console, not network tab

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

38.89%
按下载量换算30

Claude

28.95%
按下载量换算23

Cursor

17.46%
按下载量换算14

Gemini CLI

9.25%
按下载量换算7

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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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