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test-reporting-analytics测试报告分析

Agent Skill

用于辅助数据整理、表格处理、CSV/Excel 分析、指标计算和图表准备。它适合让 Agent 清洗字段、汇总数据、发现异常、生成统计口径或把分析结果转成可读说明。使用时需要确认数据来源、字段含义和时间范围,避免把样本数据当全量事实;涉及敏感数据、导出文件或批量写回时,应先确认权限和脱敏边界。

总安装

1,922

周安装

77

GitHub Stars

331

下载量

622
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

3

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:test-reporting-analytics(测试报告分析)
来源仓库:https://github.com/proffesor-for-testing/agentic-qe
仓库路径:skills/test-reporting-analytics
安装命令:
npx skills add https://github.com/proffesor-for-testing/agentic-qe --skill test-reporting-analytics
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/proffesor-for-testing/agentic-qe --skill test-reporting-analytics

简介

用于辅助数据整理、表格处理、CSV/Excel 分析、指标计算和图表准备。

  • 适合清洗字段、汇总数据、发现异常、生成统计口径或转成可读说明。
  • 需确认数据来源、字段含义和时间范围,避免把样本数据当全量事实。
  • 涉及敏感数据、导出文件或批量写回时,应先确认权限和脱敏边界。
  • 安装方式:通过 npx skills add 命令从指定 GitHub 仓库添加。

SKILL.md

Test Reporting & Analytics

<default_to_action> When building test reports:

  1. DEFINE audience (dev team vs executives)
  2. CHOOSE key metrics (max 5-7)
  3. SHOW trends (not just snapshots)
  4. HIGHLIGHT actions (what to do about it)
  5. AUTOMATE generation

Dashboard Quick Setup:

+------------------+------------------+------------------+
| Tests Passed     | Code Coverage    | Flaky Tests      |
| 1,247/1,250 ✅   | 82.3% ⬆️ +2.1%  | 1.2% ⬇️ -0.3%   |
+------------------+------------------+------------------+
| Critical Bugs    | Deploy Freq      | MTTR             |
| 0 open ✅        | 12x/day ⬆️       | 2.3h ⬇️          |
+------------------+------------------+------------------+

Key Metrics by Audience:

  • Dev Team: Pass rate, flaky %, execution time, coverage gaps
  • QE Team: Defect detection rate, test velocity, automation ROI
  • Leadership: Escaped defects, deployment frequency, quality cost </default_to_action>

Quick Reference Card

Essential Metrics

CategoryMetricTarget
ExecutionPass Rate>98%
ExecutionFlaky Test %<2%
ExecutionSuite Duration<10 min
CoverageLine Coverage>80%
CoverageBranch Coverage>70%
QualityEscaped Defects<5/release
QualityMTTR<4 hours
EfficiencyAutomation Rate>90%

Trend Indicators

SymbolMeaningAction
⬆️ImprovingContinue current approach
⬇️DecliningInvestigate root cause
➡️StableMaintain or improve
⚠️Threshold breachImmediate attention

Report Types

Real-Time Dashboard

Live quality status for CI/CD
- Build status (green/red)
- Test results (pass/fail counts)
- Coverage delta
- Flaky test alerts

Sprint Summary

## Sprint 47 Quality Summary

### Metrics
| Metric | Value | Trend |
|--------|-------|-------|
| Tests Added | +47 | ⬆️ |
| Coverage | 82.3% | ⬆️ +2.1% |
| Bugs Found | 12 | ➡️ |
| Escaped | 0 | ✅ |

### Highlights
- ✅ Zero escaped defects
- ⚠️ E2E suite now 45min (target: 30min)

### Actions
1. Optimize slow E2E tests
2. Add coverage for payment module

Executive Report

## Monthly Quality Report - Oct 2025

### Executive Summary
✅ Production uptime: 99.97% (target: 99.95%)
✅ Deploy frequency: 12x/day (up from 8x)
⚠️ Coverage: 82.3% (target: 85%)

### Business Impact
- Automation saves 120 hrs/month
- Bug cost: $150/bug found vs $5,000 escaped
- Estimated annual savings: $450K

### Recommendations
1. Invest in performance testing tooling
2. Hire senior QE for mobile coverage

Predictive Analytics

// Predict test failures
const prediction = await Task("Predict Failures", {
  codeChanges: prDiff,
  historicalData: last90Days,
  model: 'gradient-boosting'
}, "qe-quality-analyzer");

// Returns:
// {
//   failureProbability: 0.73,
//   likelyFailingTests: ['payment.test.ts'],
//   suggestedAction: 'Review payment module carefully',
//   confidence: 0.89
// }

// Trend analysis with anomaly detection
const trends = await Task("Analyze Trends", {
  metrics: ['passRate', 'coverage', 'flakyRate'],
  period: '30d',
  detectAnomalies: true
}, "qe-quality-analyzer");

Agent Integration

// Generate comprehensive quality report
const report = await Task("Generate Quality Report", {
  period: 'sprint',
  audience: 'executive',
  includeROI: true,
  includeTrends: true
}, "qe-quality-analyzer");

// Real-time quality gate check
const gateResult = await Task("Quality Gate Check", {
  metrics: currentMetrics,
  thresholds: qualityPolicy,
  environment: 'production'
}, "qe-quality-gate");

Agent Coordination Hints

Memory Namespace

aqe/reporting/
├── dashboards/*      - Dashboard configurations
├── reports/*         - Generated reports
├── trends/*          - Trend analysis data
└── predictions/*     - Predictive model outputs

Fleet Coordination

const reportingFleet = await FleetManager.coordinate({
  strategy: 'quality-reporting',
  agents: [
    'qe-quality-analyzer',      // Metrics aggregation
    'qe-quality-gate',          // Threshold validation
    'qe-deployment-readiness'   // Release readiness
  ],
  topology: 'parallel'
});

Related Skills


Remember

Measure to improve. Report to communicate.

Good reports:

  • Answer "so what?" (actionable insights)
  • Show trends (not just snapshots)
  • Match audience needs
  • Automate where possible

Data without action is noise. Action without data is guessing.

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

能力 5

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

Claude Code

29.58%
按下载量换算184

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20.61%
按下载量换算128

windsurf

17.58%
按下载量换算109

github-copilot

11.04%
按下载量换算69

Codex

6.88%
按下载量换算43

trae

3.53%
按下载量换算22

安全审计

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权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。

来源信息

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