mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
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Refactor code structure for improved readability and maintainability
This commit is contained in:
catlog22
@@ -0,0 +1,360 @@
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# Command: quality-report
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> 缺陷模式分析 + 覆盖率分析 + 综合质量报告。多维度分析 QA 数据，生成质量评分和改进建议。
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## When to Use
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- Phase 3 of Analyst
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- 测试执行完成，需要分析结果
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- 需要识别缺陷模式和覆盖率趋势
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**Trigger conditions**:
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- QAANA-* 任务进入执行阶段
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- 所有 QARUN 任务已完成
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- Coordinator 请求质量报告
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## Strategy
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### Delegation Mode
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**Mode**: CLI Fan-out（深度分析）/ Direct（基础分析）
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**CLI Tool**: `gemini` (primary)
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**CLI Mode**: `analysis`
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### Decision Logic
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```javascript
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const dataPoints = discoveredIssues.length + Object.keys(executionResults).length
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if (dataPoints <= 5) {
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// 基础内联分析
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mode = 'direct'
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} else {
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// CLI 辅助深度分析
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mode = 'cli-assisted'
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}
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```
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## Execution Steps
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### Step 1: Context Preparation
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```javascript
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// 从 shared memory 加载所有 QA 数据
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const discoveredIssues = sharedMemory.discovered_issues || []
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const strategy = sharedMemory.test_strategy || {}
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const generatedTests = sharedMemory.generated_tests || {}
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const executionResults = sharedMemory.execution_results || {}
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const historicalPatterns = sharedMemory.defect_patterns || []
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const coverageHistory = sharedMemory.coverage_history || []
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// 读取覆盖率详细数据
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let coverageData = null
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try {
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coverageData = JSON.parse(Read('coverage/coverage-summary.json'))
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} catch {}
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// 读取各层级执行结果
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const layerResults = {}
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try {
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const resultFiles = Glob(`${sessionFolder}/results/run-*.json`)
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for (const f of resultFiles) {
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const data = JSON.parse(Read(f))
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layerResults[data.layer] = data
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}
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} catch {}
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```
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### Step 2: Execute Strategy
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```javascript
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if (mode === 'direct') {
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// 基础内联分析
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analysis = performDirectAnalysis()
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} else {
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// CLI 辅助深度分析
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const analysisContext = JSON.stringify({
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issues: discoveredIssues.slice(0, 20),
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execution: layerResults,
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coverage: coverageData?.total || {},
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strategy: { layers: strategy.layers?.map(l => ({ level: l.level, target: l.target_coverage })) }
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}, null, 2)
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Bash(`ccw cli -p "PURPOSE: Perform deep quality analysis on QA results to identify defect patterns, coverage trends, and improvement opportunities
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TASK: • Classify defects by root cause pattern (logic errors, integration issues, missing validation, etc.) • Identify files with highest defect density • Analyze coverage gaps vs risk levels • Compare actual coverage to targets • Generate actionable improvement recommendations
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MODE: analysis
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CONTEXT: @${sessionFolder}/shared-memory.json @${sessionFolder}/results/**/*
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EXPECTED: Structured analysis with: defect pattern taxonomy, risk-coverage matrix, quality score rationale, top 5 improvement recommendations with expected impact
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CONSTRAINTS: Be data-driven, avoid speculation without evidence" --tool gemini --mode analysis --rule analysis-analyze-code-patterns`, {
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run_in_background: true
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})
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// 等待 CLI 完成
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}
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// ===== 分析维度 =====
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// 1. 缺陷模式分析
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function analyzeDefectPatterns(issues, results) {
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const byType = {}
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for (const issue of issues) {
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const type = issue.perspective || 'unknown'
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if (!byType[type]) byType[type] = []
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byType[type].push(issue)
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}
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// 识别重复模式
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const patterns = []
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for (const [type, typeIssues] of Object.entries(byType)) {
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if (typeIssues.length >= 2) {
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// 分析共同特征
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const commonFiles = findCommonPatterns(typeIssues.map(i => i.file))
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patterns.push({
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type,
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count: typeIssues.length,
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files: [...new Set(typeIssues.map(i => i.file))],
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common_pattern: commonFiles,
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description: `${type} 类问题在 ${typeIssues.length} 处重复出现`,
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recommendation: generateRecommendation(type, typeIssues)
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})
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}
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}
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return { by_type: byType, patterns, total: issues.length }
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}
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// 2. 覆盖率差距分析
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function analyzeCoverageGaps(coverage, strategy) {
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if (!coverage) return { status: 'no_data', gaps: [] }
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const totalCoverage = coverage.total?.lines?.pct || 0
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const gaps = []
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for (const layer of (strategy.layers || [])) {
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if (totalCoverage < layer.target_coverage) {
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gaps.push({
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layer: layer.level,
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target: layer.target_coverage,
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actual: totalCoverage,
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gap: Math.round(layer.target_coverage - totalCoverage),
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severity: (layer.target_coverage - totalCoverage) > 20 ? 'high' : 'medium'
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})
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}
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}
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// 按文件分析覆盖率
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const fileGaps = []
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if (coverage && typeof coverage === 'object') {
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for (const [file, data] of Object.entries(coverage)) {
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if (file === 'total') continue
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const linePct = data?.lines?.pct || 0
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if (linePct < 50) {
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fileGaps.push({ file, coverage: linePct, severity: linePct < 20 ? 'critical' : 'high' })
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}
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}
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}
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return { total_coverage: totalCoverage, gaps, file_gaps: fileGaps.slice(0, 10) }
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}
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// 3. 测试有效性分析
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function analyzeTestEffectiveness(generated, results) {
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const effectiveness = {}
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for (const [layer, data] of Object.entries(generated)) {
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const result = results[layer] || {}
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effectiveness[layer] = {
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files_generated: data.files?.length || 0,
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pass_rate: result.pass_rate || 0,
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iterations_needed: result.iterations || 0,
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coverage_achieved: result.coverage || 0,
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effective: (result.pass_rate || 0) >= 95 && (result.iterations || 0) <= 2
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}
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}
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return effectiveness
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}
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// 4. 质量趋势分析
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function analyzeQualityTrend(history) {
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if (history.length < 2) return { trend: 'insufficient_data', confidence: 'low' }
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const latest = history[history.length - 1]
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const previous = history[history.length - 2]
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const delta = (latest?.coverage || 0) - (previous?.coverage || 0)
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return {
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trend: delta > 5 ? 'improving' : delta < -5 ? 'declining' : 'stable',
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delta: Math.round(delta * 10) / 10,
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data_points: history.length,
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confidence: history.length >= 5 ? 'high' : history.length >= 3 ? 'medium' : 'low'
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}
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}
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// 5. 综合质量评分
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function calculateQualityScore(analysis) {
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let score = 100
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// 扣分: 安全问题
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const securityIssues = (analysis.defect_patterns.by_type?.security || []).length
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score -= securityIssues * 10
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// 扣分: Bug
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const bugIssues = (analysis.defect_patterns.by_type?.bug || []).length
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score -= bugIssues * 5
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// 扣分: 覆盖率差距
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for (const gap of (analysis.coverage_gaps.gaps || [])) {
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score -= gap.gap * 0.5
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}
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// 扣分: 测试失败
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for (const [layer, eff] of Object.entries(analysis.test_effectiveness)) {
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if (eff.pass_rate < 100) score -= (100 - eff.pass_rate) * 0.3
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}
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// 加分: 有效测试层
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const effectiveLayers = Object.values(analysis.test_effectiveness)
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.filter(e => e.effective).length
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score += effectiveLayers * 5
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// 加分: 改善趋势
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if (analysis.quality_trend.trend === 'improving') score += 3
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return Math.max(0, Math.min(100, Math.round(score)))
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}
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// 辅助函数
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function findCommonPatterns(files) {
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const dirs = files.map(f => f.split('/').slice(0, -1).join('/'))
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const commonDir = dirs.reduce((a, b) => {
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const partsA = a.split('/')
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const partsB = b.split('/')
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const common = []
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for (let i = 0; i < Math.min(partsA.length, partsB.length); i++) {
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if (partsA[i] === partsB[i]) common.push(partsA[i])
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else break
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}
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return common.join('/')
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})
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return commonDir || 'scattered'
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}
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function generateRecommendation(type, issues) {
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const recommendations = {
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'security': '加强输入验证和安全审计，考虑引入 SAST 工具',
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'bug': '改进错误处理和边界检查，增加防御性编程',
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'test-coverage': '补充缺失的测试用例，聚焦未覆盖的分支',
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'code-quality': '重构复杂函数，消除代码重复',
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'ux': '统一错误提示和加载状态处理'
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}
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return recommendations[type] || '进一步分析并制定改进计划'
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}
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```
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### Step 3: Result Processing
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```javascript
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// 组装分析结果
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const analysis = {
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defect_patterns: analyzeDefectPatterns(discoveredIssues, layerResults),
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coverage_gaps: analyzeCoverageGaps(coverageData, strategy),
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test_effectiveness: analyzeTestEffectiveness(generatedTests, layerResults),
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quality_trend: analyzeQualityTrend(coverageHistory),
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quality_score: 0
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}
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analysis.quality_score = calculateQualityScore(analysis)
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// 生成报告文件
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const reportContent = generateReportMarkdown(analysis)
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Bash(`mkdir -p "${sessionFolder}/analysis"`)
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Write(`${sessionFolder}/analysis/quality-report.md`, reportContent)
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// 更新 shared memory
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sharedMemory.defect_patterns = analysis.defect_patterns.patterns
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sharedMemory.quality_score = analysis.quality_score
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sharedMemory.coverage_history = sharedMemory.coverage_history || []
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sharedMemory.coverage_history.push({
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date: new Date().toISOString(),
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coverage: analysis.coverage_gaps.total_coverage || 0,
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quality_score: analysis.quality_score,
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issues: analysis.defect_patterns.total
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})
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Write(`${sessionFolder}/shared-memory.json`, JSON.stringify(sharedMemory, null, 2))
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function generateReportMarkdown(analysis) {
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return `# Quality Assurance Report
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## Quality Score: ${analysis.quality_score}/100
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---
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## 1. Defect Pattern Analysis
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- Total issues found: ${analysis.defect_patterns.total}
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- Recurring patterns: ${analysis.defect_patterns.patterns.length}
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${analysis.defect_patterns.patterns.map(p =>
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`### Pattern: ${p.type} (${p.count} occurrences)
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- Files: ${p.files.join(', ')}
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- Common location: ${p.common_pattern}
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- Recommendation: ${p.recommendation}`
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).join('\n\n')}
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## 2. Coverage Analysis
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- Overall coverage: ${analysis.coverage_gaps.total_coverage || 'N/A'}%
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- Coverage gaps: ${(analysis.coverage_gaps.gaps || []).length}
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${(analysis.coverage_gaps.gaps || []).map(g =>
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`- **${g.layer}**: target ${g.target}% vs actual ${g.actual}% (gap: ${g.gap}%, severity: ${g.severity})`
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).join('\n')}
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### Low Coverage Files
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${(analysis.coverage_gaps.file_gaps || []).map(f =>
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`- ${f.file}: ${f.coverage}% [${f.severity}]`
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).join('\n')}
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## 3. Test Effectiveness
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${Object.entries(analysis.test_effectiveness).map(([layer, data]) =>
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`- **${layer}**: ${data.files_generated} files, pass rate ${data.pass_rate}%, ${data.iterations_needed} fix iterations, ${data.effective ? 'EFFECTIVE' : 'NEEDS IMPROVEMENT'}`
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).join('\n')}
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## 4. Quality Trend
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- Trend: ${analysis.quality_trend.trend}
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${analysis.quality_trend.delta !== undefined ? `- Coverage delta: ${analysis.quality_trend.delta > 0 ? '+' : ''}${analysis.quality_trend.delta}%` : ''}
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- Confidence: ${analysis.quality_trend.confidence}
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## 5. Recommendations
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${analysis.quality_score >= 80 ? '- Quality is **GOOD**. Maintain current testing practices.' : ''}
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${analysis.quality_score >= 60 && analysis.quality_score < 80 ? '- Quality needs **IMPROVEMENT**. Focus on coverage gaps and recurring patterns.' : ''}
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${analysis.quality_score < 60 ? '- Quality is **CONCERNING**. Recommend comprehensive review and testing effort.' : ''}
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${analysis.defect_patterns.patterns.map(p => `- [${p.type}] ${p.recommendation}`).join('\n')}
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${(analysis.coverage_gaps.gaps || []).map(g => `- Close ${g.layer} coverage gap: +${g.gap}% needed`).join('\n')}
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`
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}
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```
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## Output Format
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```
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## Quality Analysis Results
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### Quality Score: [score]/100
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### Dimensions
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1. Defect Patterns: [count] recurring
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2. Coverage Gaps: [count] layers below target
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3. Test Effectiveness: [effective_count]/[total_layers] effective
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4. Quality Trend: [improving|stable|declining]
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### Report Location
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[session]/analysis/quality-report.md
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```
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## Error Handling
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| Scenario | Resolution |
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|----------|------------|
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| No coverage data available | Score based on other dimensions only |
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| No execution results | Analyze only scout findings and strategy |
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| Shared memory empty/corrupt | Generate minimal report with available data |
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| CLI analysis fails | Fall back to direct inline analysis |
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| Insufficient history for trend | Report 'insufficient_data', skip trend scoring |
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| Agent/CLI failure | Retry once, then fallback to inline execution |
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| Timeout (>5 min) | Report partial results, notify coordinator |
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326
.claude/skills/team-quality-assurance/roles/analyst/role.md
Normal file
326
.claude/skills/team-quality-assurance/roles/analyst/role.md
Normal file
@@ -0,0 +1,326 @@
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# Role: analyst
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质量分析师。分析缺陷模式、覆盖率差距、测试有效性，生成综合质量报告。维护缺陷模式数据库，为 scout 和 strategist 提供反馈数据。
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## Role Identity
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- **Name**: `analyst`
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- **Task Prefix**: `QAANA-*`
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- **Responsibility**: Read-only analysis（质量分析）
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- **Communication**: SendMessage to coordinator only
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- **Output Tag**: `[analyst]`
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## Role Boundaries
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### MUST
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- 仅处理 `QAANA-*` 前缀的任务
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- 所有输出必须带 `[analyst]` 标识
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- 基于数据生成分析报告
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- 更新 shared memory 中的缺陷模式和质量分数
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### MUST NOT
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- ❌ 修改源代码或测试代码
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- ❌ 执行测试
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- ❌ 为其他角色创建任务
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- ❌ 直接与其他 worker 通信
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## Message Types
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||||
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||||
| Type | Direction | Trigger | Description |
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|------|-----------|---------|-------------|
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| `analysis_ready` | analyst → coordinator | 分析完成 | 包含质量评分 |
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| `quality_report` | analyst → coordinator | 报告生成 | 包含详细分析 |
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| `error` | analyst → coordinator | 分析失败 | 阻塞性错误 |
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## Toolbox
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||||
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### Available Commands
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||||
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||||
| Command | File | Phase | Description |
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||||
|---------|------|-------|-------------|
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||||
| `quality-report` | [commands/quality-report.md](commands/quality-report.md) | Phase 3 | 缺陷模式分析 + 覆盖率分析 |
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### CLI Capabilities
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||||
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||||
| CLI Tool | Mode | Used By | Purpose |
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||||
|----------|------|---------|---------|
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||||
| `gemini` | analysis | quality-report.md | 缺陷模式识别和趋势分析 |
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## Execution (5-Phase)
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### Phase 1: Task Discovery
|
||||
|
||||
```javascript
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const tasks = TaskList()
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const myTasks = tasks.filter(t =>
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||||
t.subject.startsWith('QAANA-') &&
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||||
t.owner === 'analyst' &&
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||||
t.status === 'pending' &&
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||||
t.blockedBy.length === 0
|
||||
)
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||||
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||||
if (myTasks.length === 0) return
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||||
const task = TaskGet({ taskId: myTasks[0].id })
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||||
TaskUpdate({ taskId: task.id, status: 'in_progress' })
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||||
```
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||||
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||||
### Phase 2: Context Loading
|
||||
|
||||
```javascript
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||||
// 读取 shared memory 获取所有数据
|
||||
const sessionFolder = task.description.match(/session:\s*(.+)/)?.[1] || '.'
|
||||
let sharedMemory = {}
|
||||
try { sharedMemory = JSON.parse(Read(`${sessionFolder}/shared-memory.json`)) } catch {}
|
||||
|
||||
const discoveredIssues = sharedMemory.discovered_issues || []
|
||||
const strategy = sharedMemory.test_strategy || {}
|
||||
const generatedTests = sharedMemory.generated_tests || {}
|
||||
const executionResults = sharedMemory.execution_results || {}
|
||||
const historicalPatterns = sharedMemory.defect_patterns || []
|
||||
|
||||
// 读取覆盖率数据
|
||||
let coverageData = null
|
||||
try {
|
||||
coverageData = JSON.parse(Read('coverage/coverage-summary.json'))
|
||||
} catch {}
|
||||
|
||||
// 读取测试执行日志
|
||||
const runResults = {}
|
||||
try {
|
||||
const resultFiles = Glob(`${sessionFolder}/results/run-*.json`)
|
||||
for (const f of resultFiles) {
|
||||
const data = JSON.parse(Read(f))
|
||||
runResults[data.layer] = data
|
||||
}
|
||||
} catch {}
|
||||
```
|
||||
|
||||
### Phase 3: Multi-Dimensional Analysis
|
||||
|
||||
```javascript
|
||||
// Read commands/quality-report.md for full implementation
|
||||
Read("commands/quality-report.md")
|
||||
```
|
||||
|
||||
**分析维度**:
|
||||
|
||||
```javascript
|
||||
const analysis = {
|
||||
// 1. 缺陷模式分析
|
||||
defect_patterns: analyzeDefectPatterns(discoveredIssues, executionResults),
|
||||
|
||||
// 2. 覆盖率差距分析
|
||||
coverage_gaps: analyzeCoverageGaps(coverageData, strategy),
|
||||
|
||||
// 3. 测试有效性分析
|
||||
test_effectiveness: analyzeTestEffectiveness(generatedTests, executionResults),
|
||||
|
||||
// 4. 质量趋势
|
||||
quality_trend: analyzeQualityTrend(sharedMemory.coverage_history || []),
|
||||
|
||||
// 5. 综合质量评分
|
||||
quality_score: 0
|
||||
}
|
||||
|
||||
function analyzeDefectPatterns(issues, results) {
|
||||
// 按类型分组
|
||||
const byType = {}
|
||||
for (const issue of issues) {
|
||||
const type = issue.perspective || 'unknown'
|
||||
if (!byType[type]) byType[type] = []
|
||||
byType[type].push(issue)
|
||||
}
|
||||
|
||||
// 识别重复模式
|
||||
const patterns = []
|
||||
for (const [type, typeIssues] of Object.entries(byType)) {
|
||||
if (typeIssues.length >= 2) {
|
||||
patterns.push({
|
||||
type,
|
||||
count: typeIssues.length,
|
||||
files: [...new Set(typeIssues.map(i => i.file))],
|
||||
description: `${type} 类问题在 ${typeIssues.length} 处重复出现`
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
return { by_type: byType, patterns, total: issues.length }
|
||||
}
|
||||
|
||||
function analyzeCoverageGaps(coverage, strategy) {
|
||||
if (!coverage) return { status: 'no_data' }
|
||||
|
||||
const gaps = []
|
||||
const totalCoverage = coverage.total?.lines?.pct || 0
|
||||
|
||||
// 对比策略目标
|
||||
for (const layer of (strategy.layers || [])) {
|
||||
const actual = totalCoverage
|
||||
if (actual < layer.target_coverage) {
|
||||
gaps.push({
|
||||
layer: layer.level,
|
||||
target: layer.target_coverage,
|
||||
actual,
|
||||
gap: layer.target_coverage - actual,
|
||||
files_below_target: [] // 可以进一步分析
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
return { total_coverage: totalCoverage, gaps }
|
||||
}
|
||||
|
||||
function analyzeTestEffectiveness(generated, results) {
|
||||
const effectiveness = {}
|
||||
for (const [layer, data] of Object.entries(generated)) {
|
||||
const result = results[layer] || {}
|
||||
effectiveness[layer] = {
|
||||
files_generated: data.files?.length || 0,
|
||||
pass_rate: result.pass_rate || 0,
|
||||
iterations_needed: result.iterations || 0,
|
||||
effective: (result.pass_rate || 0) >= 95
|
||||
}
|
||||
}
|
||||
return effectiveness
|
||||
}
|
||||
|
||||
function analyzeQualityTrend(history) {
|
||||
if (history.length < 2) return { trend: 'insufficient_data' }
|
||||
const latest = history[history.length - 1]
|
||||
const previous = history[history.length - 2]
|
||||
const delta = (latest?.coverage || 0) - (previous?.coverage || 0)
|
||||
return {
|
||||
trend: delta > 0 ? 'improving' : delta < 0 ? 'declining' : 'stable',
|
||||
delta,
|
||||
data_points: history.length
|
||||
}
|
||||
}
|
||||
|
||||
// 综合质量评分 (0-100)
|
||||
function calculateQualityScore(analysis) {
|
||||
let score = 100
|
||||
|
||||
// 扣分项
|
||||
const criticalIssues = (analysis.defect_patterns.by_type?.security || []).length
|
||||
score -= criticalIssues * 10
|
||||
|
||||
const highIssues = (analysis.defect_patterns.by_type?.bug || []).length
|
||||
score -= highIssues * 5
|
||||
|
||||
// 覆盖率不达标扣分
|
||||
for (const gap of (analysis.coverage_gaps.gaps || [])) {
|
||||
score -= gap.gap * 0.5
|
||||
}
|
||||
|
||||
// 测试有效性加分
|
||||
const effectiveLayers = Object.values(analysis.test_effectiveness)
|
||||
.filter(e => e.effective).length
|
||||
score += effectiveLayers * 5
|
||||
|
||||
return Math.max(0, Math.min(100, Math.round(score)))
|
||||
}
|
||||
|
||||
analysis.quality_score = calculateQualityScore(analysis)
|
||||
```
|
||||
|
||||
### Phase 4: Report Generation
|
||||
|
||||
```javascript
|
||||
// 生成质量报告
|
||||
const reportContent = `# Quality Assurance Report
|
||||
|
||||
## Quality Score: ${analysis.quality_score}/100
|
||||
|
||||
## 1. Defect Pattern Analysis
|
||||
- Total issues found: ${analysis.defect_patterns.total}
|
||||
- Recurring patterns: ${analysis.defect_patterns.patterns.length}
|
||||
${analysis.defect_patterns.patterns.map(p => ` - **${p.type}**: ${p.count} occurrences across ${p.files.length} files`).join('\n')}
|
||||
|
||||
## 2. Coverage Analysis
|
||||
- Overall coverage: ${analysis.coverage_gaps.total_coverage || 'N/A'}%
|
||||
- Coverage gaps: ${(analysis.coverage_gaps.gaps || []).length}
|
||||
${(analysis.coverage_gaps.gaps || []).map(g => ` - **${g.layer}**: target ${g.target}% vs actual ${g.actual}% (gap: ${g.gap}%)`).join('\n')}
|
||||
|
||||
## 3. Test Effectiveness
|
||||
${Object.entries(analysis.test_effectiveness).map(([layer, data]) =>
|
||||
`- **${layer}**: ${data.files_generated} files, pass rate ${data.pass_rate}%, ${data.iterations_needed} fix iterations`
|
||||
).join('\n')}
|
||||
|
||||
## 4. Quality Trend
|
||||
- Trend: ${analysis.quality_trend.trend}
|
||||
${analysis.quality_trend.delta !== undefined ? `- Coverage change: ${analysis.quality_trend.delta > 0 ? '+' : ''}${analysis.quality_trend.delta}%` : ''}
|
||||
|
||||
## 5. Recommendations
|
||||
${analysis.quality_score >= 80 ? '- Quality is GOOD. Continue with current testing strategy.' : ''}
|
||||
${analysis.quality_score >= 60 && analysis.quality_score < 80 ? '- Quality needs IMPROVEMENT. Focus on coverage gaps and recurring patterns.' : ''}
|
||||
${analysis.quality_score < 60 ? '- Quality is CONCERNING. Recommend deep scan and comprehensive test generation.' : ''}
|
||||
${analysis.defect_patterns.patterns.length > 0 ? `- Address ${analysis.defect_patterns.patterns.length} recurring defect patterns` : ''}
|
||||
${(analysis.coverage_gaps.gaps || []).length > 0 ? `- Close ${analysis.coverage_gaps.gaps.length} coverage gaps` : ''}
|
||||
`
|
||||
|
||||
Bash(`mkdir -p "${sessionFolder}/analysis"`)
|
||||
Write(`${sessionFolder}/analysis/quality-report.md`, reportContent)
|
||||
|
||||
// 更新 shared memory
|
||||
sharedMemory.defect_patterns = analysis.defect_patterns.patterns
|
||||
sharedMemory.quality_score = analysis.quality_score
|
||||
sharedMemory.coverage_history = sharedMemory.coverage_history || []
|
||||
sharedMemory.coverage_history.push({
|
||||
date: new Date().toISOString(),
|
||||
coverage: analysis.coverage_gaps.total_coverage || 0,
|
||||
quality_score: analysis.quality_score,
|
||||
issues: analysis.defect_patterns.total
|
||||
})
|
||||
Write(`${sessionFolder}/shared-memory.json`, JSON.stringify(sharedMemory, null, 2))
|
||||
```
|
||||
|
||||
### Phase 5: Report to Coordinator
|
||||
|
||||
```javascript
|
||||
mcp__ccw-tools__team_msg({
|
||||
operation: "log",
|
||||
team: teamName,
|
||||
from: "analyst",
|
||||
to: "coordinator",
|
||||
type: "quality_report",
|
||||
summary: `[analyst] 质量评分: ${analysis.quality_score}/100, 缺陷模式: ${analysis.defect_patterns.patterns.length}, 覆盖率: ${analysis.coverage_gaps.total_coverage || 'N/A'}%`,
|
||||
ref: `${sessionFolder}/analysis/quality-report.md`
|
||||
})
|
||||
|
||||
SendMessage({
|
||||
type: "message",
|
||||
recipient: "coordinator",
|
||||
content: `## [analyst] Quality Analysis Results
|
||||
|
||||
**Task**: ${task.subject}
|
||||
**Quality Score**: ${analysis.quality_score}/100
|
||||
**Defect Patterns**: ${analysis.defect_patterns.patterns.length} recurring
|
||||
**Coverage**: ${analysis.coverage_gaps.total_coverage || 'N/A'}%
|
||||
**Trend**: ${analysis.quality_trend.trend}
|
||||
|
||||
### Report
|
||||
${sessionFolder}/analysis/quality-report.md`,
|
||||
summary: `[analyst] QAANA complete: score ${analysis.quality_score}/100`
|
||||
})
|
||||
|
||||
TaskUpdate({ taskId: task.id, status: 'completed' })
|
||||
|
||||
const nextTasks = TaskList().filter(t =>
|
||||
t.subject.startsWith('QAANA-') && t.owner === 'analyst' &&
|
||||
t.status === 'pending' && t.blockedBy.length === 0
|
||||
)
|
||||
if (nextTasks.length > 0) { /* back to Phase 1 */ }
|
||||
```
|
||||
|
||||
## Error Handling
|
||||
|
||||
| Scenario | Resolution |
|
||||
|----------|------------|
|
||||
| No QAANA-* tasks available | Idle, wait for coordinator |
|
||||
| Coverage data not found | Report quality score based on other dimensions |
|
||||
| Shared memory empty | Generate minimal report with available data |
|
||||
| No execution results | Analyze only scout findings and strategy coverage |
|
||||
| CLI analysis fails | Fall back to inline pattern analysis |
|
||||
| Critical issue beyond scope | SendMessage error to coordinator |
|
||||
Reference in New Issue
Block a user