team-quality-assurance
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ChineseAuto Mode
自动模式
When or : Auto-confirm task decomposition, skip interactive validation, use defaults.
--yes-y当使用 或 参数时:自动确认任务分解,跳过交互校验,使用默认配置。
--yes-yTeam Quality Assurance
团队质量保障
Usage
使用方法
bash
$team-quality-assurance "Full QA for the authentication module"
$team-quality-assurance --mode=discovery "Scan codebase for security and bug issues"
$team-quality-assurance --mode=testing "Test recent changes with progressive coverage"
$team-quality-assurance -c 4 --mode=full "Complete QA cycle with regression scanning"
$team-quality-assurance -y "QA all changed files since last commit"
$team-quality-assurance --continue "qa-auth-module-20260308"Flags:
- : Skip all confirmations (auto mode)
-y, --yes - : Max concurrent agents within each wave (default: 3)
-c, --concurrency N - : Resume existing session
--continue - : Force QA mode (default: auto-detect or full)
--mode=discovery|testing|full
Output Directory:
Core Output: (master state) + (final) + (shared exploration) + (human-readable report)
.workflow/.csv-wave/{session-id}/tasks.csvresults.csvdiscoveries.ndjsoncontext.mdbash
$team-quality-assurance "Full QA for the authentication module"
$team-quality-assurance --mode=discovery "Scan codebase for security and bug issues"
$team-quality-assurance --mode=testing "Test recent changes with progressive coverage"
$team-quality-assurance -c 4 --mode=full "Complete QA cycle with regression scanning"
$team-quality-assurance -y "QA all changed files since last commit"
$team-quality-assurance --continue "qa-auth-module-20260308"参数说明:
- : 跳过所有确认步骤(自动模式)
-y, --yes - : 每批次最多并发Agent数量(默认值:3)
-c, --concurrency N - : 恢复已存在的会话
--continue - : 强制指定QA模式(默认:自动检测或全量模式)
--mode=discovery|testing|full
输出目录:
核心输出: (总控状态表) + (最终结果) + (共享探索记录) + (可读性报告)
.workflow/.csv-wave/{session-id}/tasks.csvresults.csvdiscoveries.ndjsoncontext.mdOverview
概述
Orchestrate multi-agent QA pipeline: scout -> strategist -> generator -> executor -> analyst. Supports three modes: discovery (issue scanning), testing (progressive test coverage), and full (closed-loop QA with regression). Multi-perspective scanning from bug, security, test-coverage, code-quality, and UX viewpoints. Progressive layer coverage (L1/L2/L3) with Generator-Critic loops for coverage convergence.
Execution Model: Hybrid -- CSV wave pipeline (primary) + individual agent spawn (secondary)
+-------------------------------------------------------------------+
| TEAM QUALITY ASSURANCE WORKFLOW |
+-------------------------------------------------------------------+
| |
| Phase 0: Pre-Wave Interactive (Requirement Clarification) |
| +- Parse task description, detect QA mode |
| +- Mode selection (discovery/testing/full) |
| +- Output: refined requirements for decomposition |
| |
| Phase 1: Requirement -> CSV + Classification |
| +- Select pipeline based on QA mode |
| +- Build dependency chain with appropriate roles |
| +- Classify tasks: csv-wave | interactive (exec_mode) |
| +- Compute dependency waves (topological sort) |
| +- Generate tasks.csv with wave + exec_mode columns |
| +- User validates task breakdown (skip if -y) |
| |
| Phase 2: Wave Execution Engine (Extended) |
| +- For each wave (1..N): |
| | +- Execute pre-wave interactive tasks (if any) |
| | +- Build wave CSV (filter csv-wave tasks for this wave) |
| | +- Inject previous findings into prev_context column |
| | +- spawn_agents_on_csv(wave CSV) |
| | +- Execute post-wave interactive tasks (if any) |
| | +- Merge all results into master tasks.csv |
| | +- GC Loop Check: coverage < target? -> spawn fix tasks |
| | +- Check: any failed? -> skip dependents |
| +- discoveries.ndjson shared across all modes (append-only) |
| |
| Phase 3: Post-Wave Interactive (Completion Action) |
| +- Pipeline completion report with quality score |
| +- Interactive completion choice (Archive/Keep/Export) |
| +- Final aggregation / report |
| |
| Phase 4: Results Aggregation |
| +- Export final results.csv |
| +- Generate context.md with all findings |
| +- Display summary: completed/failed/skipped per wave |
| +- Offer: view results | retry failed | done |
| |
+-------------------------------------------------------------------+编排多Agent QA流水线:侦察员→策略师→生成器→执行器→分析师。支持三种模式:discovery(问题发现)(问题扫描)、testing(测试)(递进测试覆盖)、full(全量)(带回归的闭环QA)。从漏洞、安全、测试覆盖、代码质量、UX多视角开展扫描,支持L1/L2/L3分层覆盖,结合Generator-Critic循环实现覆盖度收敛。
执行模型: 混合模式 -- CSV批次流水线(主)+ 独立Agent调度(次)
+-------------------------------------------------------------------+
| TEAM QUALITY ASSURANCE WORKFLOW |
+-------------------------------------------------------------------+
| |
| 阶段0: 批次前交互(需求澄清) |
| +- 解析任务描述,检测QA模式 |
| +- 模式选择(discovery/testing/full) |
| +- 输出:供任务拆解的精细化需求 |
| |
| 阶段1: 需求拆解为CSV+分类 |
| +- 根据QA模式选择流水线 |
| +- 基于对应角色构建依赖链 |
| +- 任务分类:csv-wave | 交互式(exec_mode) |
| +- 计算依赖批次(拓扑排序) |
| +- 生成包含批次、exec_mode列的tasks.csv |
| +- 用户校验任务拆解(使用-y参数则跳过) |
| |
| 阶段2: 批次执行引擎(扩展版) |
| +- 遍历每个批次(1到N): |
| | +- 执行批次前交互式任务(如有) |
| | +- 构建批次CSV(过滤当前批次的csv-wave任务) |
| | +- 将历史发现注入prev_context列 |
| | +- spawn_agents_on_csv(当前批次CSV) |
| | +- 执行批次后交互式任务(如有) |
| | +- 合并所有结果到总控tasks.csv |
| | +- GC循环检查:覆盖度<目标?→ 生成修复任务 |
| | +- 检查:是否有任务失败?→ 跳过依赖该任务的后续任务 |
| +- discoveries.ndjson全模式共享(仅追加) |
| |
| 阶段3: 批次后交互(完成动作) |
| +- 输出带质量评分的流水线完成报告 |
| +- 交互式完成选择(归档/保留/导出) |
| +- 最终聚合/生成报告 |
| |
| 阶段4: 结果聚合 |
| +- 导出最终results.csv |
| +- 生成包含所有发现的context.md |
| +- 展示汇总:每批次完成/失败/跳过数量 |
| +- 提供选项:查看结果 | 重试失败任务 | 完成 |
| |
+-------------------------------------------------------------------+Task Classification Rules
任务分类规则
Each task is classified by :
exec_mode| exec_mode | Mechanism | Criteria |
|---|---|---|
| | One-shot, structured I/O, no multi-round interaction |
| | Multi-round, needs iterative fix-verify cycles |
Classification Decision:
| Task Property | Classification |
|---|---|
| Multi-perspective code scanning (scout) | |
| Strategy formulation (single-pass analysis) | |
| Test generation (single-pass code creation) | |
| Test execution with auto-fix cycle | |
| Quality analysis (single-pass report) | |
| GC loop fix-verify iteration | |
| Regression scanning (post-fix) | |
每个任务通过分类:
exec_mode| exec_mode | 机制 | 判断标准 |
|---|---|---|
| | 单次执行、结构化输入输出、无需多轮交互 |
| | 多轮执行、需要迭代修复-验证循环 |
分类判定:
| 任务属性 | 分类 |
|---|---|
| 多视角代码扫描(侦察员) | |
| 策略制定(单次分析) | |
| 测试用例生成(单次代码生成) | |
| 带自动修复循环的测试执行 | |
| 质量分析(单次报告生成) | |
| GC循环修复-验证迭代 | |
| 回归扫描(修复后) | |
CSV Schema
CSV Schema
tasks.csv (Master State)
tasks.csv(总控状态表)
csv
id,title,description,role,perspective,layer,coverage_target,deps,context_from,exec_mode,wave,status,findings,issues_found,pass_rate,coverage_achieved,test_files,quality_score,error
"SCOUT-001","Multi-perspective code scan","Scan codebase from bug, security, test-coverage, code-quality perspectives. Produce severity-ranked findings with file:line references.","scout","bug;security;test-coverage;code-quality","","","","","csv-wave","1","pending","","","","","","",""
"QASTRAT-001","Test strategy formulation","Analyze scout findings and code changes. Determine test layers, define coverage targets, generate test strategy document.","strategist","","","","SCOUT-001","SCOUT-001","csv-wave","2","pending","","","","","","",""
"QAGEN-L1-001","Generate L1 unit tests","Generate L1 unit tests based on strategy. Cover priority files, include happy path, edge cases, error handling.","generator","","L1","80","QASTRAT-001","QASTRAT-001","csv-wave","3","pending","","","","","","",""Columns:
| Column | Phase | Description |
|---|---|---|
| Input | Unique task identifier (PREFIX-NNN format) |
| Input | Short task title |
| Input | Detailed task description (self-contained) |
| Input | Worker role: |
| Input | Scan perspectives (semicolon-separated, scout only) |
| Input | Test layer: |
| Input | Target coverage percentage for this layer (empty if N/A) |
| Input | Semicolon-separated dependency task IDs |
| Input | Semicolon-separated task IDs whose findings this task needs |
| Input | |
| Computed | Wave number (computed by topological sort, 1-based) |
| Output | |
| Output | Key discoveries or implementation notes (max 500 chars) |
| Output | Count of issues discovered (scout/analyst) |
| Output | Test pass rate as decimal (executor only) |
| Output | Actual coverage percentage achieved (executor only) |
| Output | Semicolon-separated paths of test files (generator only) |
| Output | Quality score 0-100 (analyst only) |
| Output | Error message if failed (empty if success) |
csv
id,title,description,role,perspective,layer,coverage_target,deps,context_from,exec_mode,wave,status,findings,issues_found,pass_rate,coverage_achieved,test_files,quality_score,error
"SCOUT-001","Multi-perspective code scan","Scan codebase from bug, security, test-coverage, code-quality perspectives. Produce severity-ranked findings with file:line references.","scout","bug;security;test-coverage;code-quality","","","","","csv-wave","1","pending","","","","","","",""
"QASTRAT-001","Test strategy formulation","Analyze scout findings and code changes. Determine test layers, define coverage targets, generate test strategy document.","strategist","","","","SCOUT-001","SCOUT-001","csv-wave","2","pending","","","","","","",""
"QAGEN-L1-001","Generate L1 unit tests","Generate L1 unit tests based on strategy. Cover priority files, include happy path, edge cases, error handling.","generator","","L1","80","QASTRAT-001","QASTRAT-001","csv-wave","3","pending","","","","","","",""字段说明:
| 字段 | 所属阶段 | 描述 |
|---|---|---|
| 输入 | 唯一任务标识(前缀-NNN格式) |
| 输入 | 短任务标题 |
| 输入 | 详细任务描述(自包含) |
| 输入 | 执行角色: |
| 输入 | 扫描视角(分号分隔,仅侦察员角色使用) |
| 输入 | 测试层级: |
| 输入 | 对应层级的目标覆盖度百分比(不适用则为空) |
| 输入 | 分号分隔的依赖任务ID |
| 输入 | 分号分隔的上下文来源任务ID,当前任务需要这些任务的发现结果 |
| 输入 | |
| 计算生成 | 批次号(拓扑排序计算,从1开始) |
| 输出 | |
| 输出 | 核心发现或实现说明(最多500字符) |
| 输出 | 发现的问题数量(侦察员/分析师角色) |
| 输出 | 测试通过率,小数格式(仅执行器角色) |
| 输出 | 实际达成的覆盖度百分比(仅执行器角色) |
| 输出 | 分号分隔的测试文件路径(仅生成器角色) |
| 输出 | 质量评分0-100(仅分析师角色) |
| 输出 | 失败时的错误信息(成功则为空) |
Per-Wave CSV (Temporary)
批次级CSV(临时文件)
Each wave generates a temporary with extra column (csv-wave tasks only).
wave-{N}.csvprev_context每个批次生成临时的,额外包含列(仅csv-wave任务)。
wave-{N}.csvprev_contextAgent Registry (Interactive Agents)
Agent注册表(交互式Agent)
| Agent | Role File | Pattern | Responsibility | Position |
|---|---|---|---|---|
| Test Executor | agents/executor.md | 2.3 (send_input cycle) | Execute tests with iterative fix cycle, report pass rate and coverage | per-wave |
| GC Loop Handler | agents/gc-loop-handler.md | 2.3 (send_input cycle) | Manage Generator-Critic loop: evaluate coverage, trigger fix rounds | post-wave |
COMPACT PROTECTION: Agent files are execution documents. When context compression occurs, you MUST immediatelythe corresponding agent.md to reload.Read
| Agent | 角色文件 | 模式 | 职责 | 执行位置 |
|---|---|---|---|---|
| 测试执行器 | agents/executor.md | 2.3 (send_input循环) | 执行测试并迭代修复,报告通过率和覆盖度 | 每批次内 |
| GC循环处理器 | agents/gc-loop-handler.md | 2.3 (send_input循环) | 管理Generator-Critic循环:评估覆盖度,触发修复轮次 | 批次后 |
精简保护提示: Agent文件是执行说明文档。当发生上下文压缩时,你必须立即对应的agent.md文件重新加载。Read
Output Artifacts
输出产物
| File | Purpose | Lifecycle |
|---|---|---|
| Master state -- all tasks with status/findings | Updated after each wave |
| Per-wave input (temporary, csv-wave tasks only) | Created before wave, deleted after |
| Final export of all task results | Created in Phase 4 |
| Shared exploration board (all agents, both modes) | Append-only, carries across waves |
| Human-readable execution report | Created in Phase 4 |
| Scout output: multi-perspective scan results | Created in scout wave |
| Strategist output: test strategy document | Created in strategy wave |
| Generator output: L1 unit test files | Created in L1 wave |
| Generator output: L2 integration test files | Created in L2 wave |
| Generator output: L3 E2E test files | Created in L3 wave |
| Executor output: per-layer test results | Created per execution |
| Analyst output: quality analysis report | Created in final wave |
| Results from interactive tasks | Created per interactive task |
| 文件 | 用途 | 生命周期 |
|---|---|---|
| 总控状态 -- 所有任务的状态/发现结果 | 每批次完成后更新 |
| 批次级输入(临时文件,仅csv-wave任务) | 批次执行前创建,执行后删除 |
| 所有任务结果的最终导出 | 阶段4生成 |
| 共享探索看板(所有Agent、所有模式共用) | 仅追加,跨批次保留 |
| 可读性执行报告 | 阶段4生成 |
| 侦察员输出:多视角扫描结果 | 侦察员批次生成 |
| 策略师输出:测试策略文档 | 策略批次生成 |
| 生成器输出:L1单元测试文件 | L1批次生成 |
| 生成器输出:L2集成测试文件 | L2批次生成 |
| 生成器输出:L3端到端测试文件 | L3批次生成 |
| 执行器输出:分层测试结果 | 每次执行生成 |
| 分析师输出:质量分析报告 | 最终批次生成 |
| 交互式任务结果 | 每个交互式任务生成 |
Session Structure
会话结构
.workflow/.csv-wave/{session-id}/
+-- tasks.csv # Master state (all tasks, both modes)
+-- results.csv # Final results export
+-- discoveries.ndjson # Shared discovery board (all agents)
+-- context.md # Human-readable report
+-- wave-{N}.csv # Temporary per-wave input (csv-wave only)
+-- scan/ # Scout output
| +-- scan-results.json
+-- strategy/ # Strategist output
| +-- test-strategy.md
+-- tests/ # Generator output
| +-- L1-unit/
| +-- L2-integration/
| +-- L3-e2e/
+-- results/ # Executor output
| +-- run-L1.json
| +-- run-L2.json
+-- analysis/ # Analyst output
| +-- quality-report.md
+-- wisdom/ # Cross-task knowledge
| +-- learnings.md
| +-- conventions.md
| +-- decisions.md
| +-- issues.md
+-- interactive/ # Interactive task artifacts
| +-- {id}-result.json
+-- gc-state.json # GC loop tracking state.workflow/.csv-wave/{session-id}/
+-- tasks.csv # 总控状态(所有任务、所有模式)
+-- results.csv # 最终结果导出
+-- discoveries.ndjson # 共享探索看板(所有Agent)
+-- context.md # 可读性报告
+-- wave-{N}.csv # 临时批次输入(仅csv-wave任务)
+-- scan/ # 侦察员输出
| +-- scan-results.json
+-- strategy/ # 策略师输出
| +-- test-strategy.md
+-- tests/ # 生成器输出
| +-- L1-unit/
| +-- L2-integration/
| +-- L3-e2e/
+-- results/ # 执行器输出
| +-- run-L1.json
| +-- run-L2.json
+-- analysis/ # 分析师输出
| +-- quality-report.md
+-- wisdom/ # 跨任务知识
| +-- learnings.md
| +-- conventions.md
| +-- decisions.md
| +-- issues.md
+-- interactive/ # 交互式任务产物
| +-- {id}-result.json
+-- gc-state.json # GC循环追踪状态Implementation
实现说明
Session Initialization
会话初始化
javascript
const getUtc8ISOString = () => new Date(Date.now() + 8 * 60 * 60 * 1000).toISOString()
const AUTO_YES = $ARGUMENTS.includes('--yes') || $ARGUMENTS.includes('-y')
const continueMode = $ARGUMENTS.includes('--continue')
const concurrencyMatch = $ARGUMENTS.match(/(?:--concurrency|-c)\s+(\d+)/)
const maxConcurrency = concurrencyMatch ? parseInt(concurrencyMatch[1]) : 3
// Parse QA mode flag
const modeMatch = $ARGUMENTS.match(/--mode=(\w+)/)
const explicitMode = modeMatch ? modeMatch[1] : null
const requirement = $ARGUMENTS
.replace(/--yes|-y|--continue|--concurrency\s+\d+|-c\s+\d+|--mode=\w+/g, '')
.trim()
const slug = requirement.toLowerCase()
.replace(/[^a-z0-9\u4e00-\u9fa5]+/g, '-')
.substring(0, 40)
const dateStr = getUtc8ISOString().substring(0, 10).replace(/-/g, '')
const sessionId = `qa-${slug}-${dateStr}`
const sessionFolder = `.workflow/.csv-wave/${sessionId}`
Bash(`mkdir -p ${sessionFolder}/scan ${sessionFolder}/strategy ${sessionFolder}/tests/L1-unit ${sessionFolder}/tests/L2-integration ${sessionFolder}/tests/L3-e2e ${sessionFolder}/results ${sessionFolder}/analysis ${sessionFolder}/wisdom ${sessionFolder}/interactive`)
// Initialize discoveries.ndjson
Write(`${sessionFolder}/discoveries.ndjson`, '')
// Initialize wisdom files
Write(`${sessionFolder}/wisdom/learnings.md`, '# Learnings\n')
Write(`${sessionFolder}/wisdom/conventions.md`, '# Conventions\n')
Write(`${sessionFolder}/wisdom/decisions.md`, '# Decisions\n')
Write(`${sessionFolder}/wisdom/issues.md`, '# Issues\n')
// Initialize GC state
Write(`${sessionFolder}/gc-state.json`, JSON.stringify({
rounds: {}, coverage_history: [], max_rounds_per_layer: 3
}, null, 2))javascript
const getUtc8ISOString = () => new Date(Date.now() + 8 * 60 * 60 * 1000).toISOString()
const AUTO_YES = $ARGUMENTS.includes('--yes') || $ARGUMENTS.includes('-y')
const continueMode = $ARGUMENTS.includes('--continue')
const concurrencyMatch = $ARGUMENTS.match(/(?:--concurrency|-c)\s+(\d+)/)
const maxConcurrency = concurrencyMatch ? parseInt(concurrencyMatch[1]) : 3
// 解析QA模式参数
const modeMatch = $ARGUMENTS.match(/--mode=(\w+)/)
const explicitMode = modeMatch ? modeMatch[1] : null
const requirement = $ARGUMENTS
.replace(/--yes|-y|--continue|--concurrency\s+\d+|-c\s+\d+|--mode=\w+/g, '')
.trim()
const slug = requirement.toLowerCase()
.replace(/[^a-z0-9\u4e00-\u9fa5]+/g, '-')
.substring(0, 40)
const dateStr = getUtc8ISOString().substring(0, 10).replace(/-/g, '')
const sessionId = `qa-${slug}-${dateStr}`
const sessionFolder = `.workflow/.csv-wave/${sessionId}`
Bash(`mkdir -p ${sessionFolder}/scan ${sessionFolder}/strategy ${sessionFolder}/tests/L1-unit ${sessionFolder}/tests/L2-integration ${sessionFolder}/tests/L3-e2e ${sessionFolder}/results ${sessionFolder}/analysis ${sessionFolder}/wisdom ${sessionFolder}/interactive`)
// 初始化discoveries.ndjson
Write(`${sessionFolder}/discoveries.ndjson`, '')
// 初始化知识文件
Write(`${sessionFolder}/wisdom/learnings.md`, '# Learnings\n')
Write(`${sessionFolder}/wisdom/conventions.md`, '# Conventions\n')
Write(`${sessionFolder}/wisdom/decisions.md`, '# Decisions\n')
Write(`${sessionFolder}/wisdom/issues.md`, '# Issues\n')
// 初始化GC状态
Write(`${sessionFolder}/gc-state.json`, JSON.stringify({
rounds: {}, coverage_history: [], max_rounds_per_layer: 3
}, null, 2))Phase 0: Pre-Wave Interactive (Requirement Clarification)
阶段0: 批次前交互(需求澄清)
Objective: Parse task description, detect QA mode, prepare for decomposition.
Workflow:
-
Parse user task description from $ARGUMENTS
-
Check for existing sessions (continue mode):
- Scan for sessions with pending tasks
.workflow/.csv-wave/qa-*/tasks.csv - If : resume the specified or most recent session, skip to Phase 2
--continue - If active session found: ask user whether to resume or start new
- Scan
-
QA Mode Selection:
Condition Mode Description Explicit --mode=discoverydiscovery Scout-first: issue discovery then testing Explicit --mode=testingtesting Skip scout, direct test pipeline Explicit --mode=fullfull Complete QA closed loop + regression scan Keywords: discovery, scan, issue, audit discovery Auto-detected discovery mode Keywords: test, coverage, TDD, verify testing Auto-detected testing mode No explicit flag and no keyword match full Default to full QA -
Clarify if ambiguous (skip if AUTO_YES):javascript
AskUserQuestion({ questions: [{ question: "Detected QA mode: '" + qaMode + "'. Confirm?", header: "QA Mode Selection", multiSelect: false, options: [ { label: "Proceed with " + qaMode, description: "Detected mode is appropriate" }, { label: "Use discovery", description: "Scout-first: scan for issues, then test" }, { label: "Use testing", description: "Direct testing pipeline (skip scout)" }, { label: "Use full", description: "Complete QA closed loop with regression" } ] }] }) -
Output: Refined requirement, QA mode, scope
Success Criteria:
- QA mode selected
- Refined requirements available for Phase 1 decomposition
目标: 解析任务描述,检测QA模式,为任务拆解做准备。
工作流:
-
从$ARGUMENTS解析用户任务描述
-
检查已有会话(继续模式):
- 扫描查找包含待执行任务的会话
.workflow/.csv-wave/qa-*/tasks.csv - 如果指定:恢复指定或最新的会话,直接跳转到阶段2
--continue - 如果找到活跃会话:询问用户是否恢复或新建会话
- 扫描
-
QA模式选择:
触发条件 模式 描述 显式指定 --mode=discoverydiscovery 侦察优先:先发现问题再执行测试 显式指定 --mode=testingtesting 跳过侦察,直接进入测试流水线 显式指定 --mode=fullfull 完整QA闭环+回归扫描 关键词:discovery, scan, issue, audit discovery 自动识别为发现模式 关键词:test, coverage, TDD, verify testing 自动识别为测试模式 无显式参数且无关键词匹配 full 默认使用全量QA模式 -
歧义澄清(AUTO_YES模式下跳过):javascript
AskUserQuestion({ questions: [{ question: "Detected QA mode: '" + qaMode + "'. Confirm?", header: "QA Mode Selection", multiSelect: false, options: [ { label: "Proceed with " + qaMode, description: "Detected mode is appropriate" }, { label: "Use discovery", description: "Scout-first: scan for issues, then test" }, { label: "Use testing", description: "Direct testing pipeline (skip scout)" }, { label: "Use full", description: "Complete QA closed loop with regression" } ] }] }) -
输出: 精细化需求、QA模式、覆盖范围
成功判定标准:
- 已选定QA模式
- 已生成可供阶段1拆解的精细化需求
Phase 1: Requirement -> CSV + Classification
阶段1: 需求拆解为CSV+分类
Objective: Decompose QA task into dependency-ordered CSV tasks based on selected mode.
Decomposition Rules:
-
Select pipeline based on QA mode:
Mode Pipeline discovery SCOUT-001 -> QASTRAT-001 -> QAGEN-001 -> QARUN-001 -> QAANA-001 testing QASTRAT-001 -> QAGEN-L1-001 -> QARUN-L1-001 -> QAGEN-L2-001 -> QARUN-L2-001 -> QAANA-001 full SCOUT-001 -> QASTRAT-001 -> [QAGEN-L1-001, QAGEN-L2-001] -> [QARUN-L1-001, QARUN-L2-001] -> QAANA-001 -> SCOUT-002 -
Assign roles, layers, perspectives, and coverage targets per task
-
Assign exec_mode:
- Scout, Strategist, Generator, Analyst tasks: (single-pass)
csv-wave - Executor tasks: (iterative fix cycle)
interactive
- Scout, Strategist, Generator, Analyst tasks:
Classification Rules:
| Task Property | exec_mode |
|---|---|
| Multi-perspective scanning (single-pass) | |
| Strategy analysis (single-pass read + write) | |
| Test code generation (single-pass write) | |
| Test execution with fix loop (multi-round) | |
| Quality analysis (single-pass read + write) | |
| Regression scanning (single-pass) | |
Wave Computation: Kahn's BFS topological sort with depth tracking.
User Validation: Display task breakdown with wave + exec_mode + role assignment (skip if AUTO_YES).
Success Criteria:
- tasks.csv created with valid schema, wave, and exec_mode assignments
- No circular dependencies
- User approved (or AUTO_YES)
目标: 基于选定的QA模式将QA任务拆解为按依赖排序的CSV任务。
拆解规则:
-
基于QA模式选择流水线:
模式 流水线 discovery SCOUT-001 -> QASTRAT-001 -> QAGEN-001 -> QARUN-001 -> QAANA-001 testing QASTRAT-001 -> QAGEN-L1-001 -> QARUN-L1-001 -> QAGEN-L2-001 -> QARUN-L2-001 -> QAANA-001 full SCOUT-001 -> QASTRAT-001 -> [QAGEN-L1-001, QAGEN-L2-001] -> [QARUN-L1-001, QARUN-L2-001] -> QAANA-001 -> SCOUT-002 -
为每个任务分配角色、层级、视角和覆盖度目标
-
分配exec_mode:
- 侦察员、策略师、生成器、分析师任务:(单次执行)
csv-wave - 执行器任务:(迭代修复循环)
interactive
- 侦察员、策略师、生成器、分析师任务:
分类规则:
| 任务属性 | exec_mode |
|---|---|
| 多视角扫描(单次执行) | |
| 策略分析(单次读写) | |
| 测试代码生成(单次写入) | |
| 带修复循环的测试执行(多轮) | |
| 质量分析(单次读写) | |
| 回归扫描(单次执行) | |
批次计算: 带深度追踪的Kahn BFS拓扑排序。
用户校验: 展示包含批次+exec_mode+角色分配的任务拆解(AUTO_YES模式下跳过)。
成功判定标准:
- 已生成符合Schema、批次和exec_mode分配正确的tasks.csv
- 无循环依赖
- 已获得用户确认(或AUTO_YES模式)
Phase 2: Wave Execution Engine (Extended)
阶段2: 批次执行引擎(扩展版)
Objective: Execute tasks wave-by-wave with hybrid mechanism support, GC loop handling, and cross-wave context propagation.
javascript
const masterCsv = Read(`${sessionFolder}/tasks.csv`)
let tasks = parseCsv(masterCsv)
const maxWave = Math.max(...tasks.map(t => t.wave))
for (let wave = 1; wave <= maxWave; wave++) {
console.log(`\nWave ${wave}/${maxWave}`)
// 1. Separate tasks by exec_mode
const waveTasks = tasks.filter(t => t.wave === wave && t.status === 'pending')
const csvTasks = waveTasks.filter(t => t.exec_mode === 'csv-wave')
const interactiveTasks = waveTasks.filter(t => t.exec_mode === 'interactive')
// 2. Check dependencies -- skip tasks whose deps failed
for (const task of waveTasks) {
const depIds = (task.deps || '').split(';').filter(Boolean)
const depStatuses = depIds.map(id => tasks.find(t => t.id === id)?.status)
if (depStatuses.some(s => s === 'failed' || s === 'skipped')) {
task.status = 'skipped'
task.error = `Dependency failed: ${depIds.filter((id, i) =>
['failed','skipped'].includes(depStatuses[i])).join(', ')}`
}
}
// 3. Execute csv-wave tasks
const pendingCsvTasks = csvTasks.filter(t => t.status === 'pending')
if (pendingCsvTasks.length > 0) {
for (const task of pendingCsvTasks) {
task.prev_context = buildPrevContext(task, tasks)
}
Write(`${sessionFolder}/wave-${wave}.csv`, toCsv(pendingCsvTasks))
// Read instruction template
Read(`instructions/agent-instruction.md`)
// Build instruction with session folder baked in
const instruction = buildQAInstruction(sessionFolder, wave)
spawn_agents_on_csv({
csv_path: `${sessionFolder}/wave-${wave}.csv`,
id_column: "id",
instruction: instruction,
max_concurrency: maxConcurrency,
max_runtime_seconds: 900,
output_csv_path: `${sessionFolder}/wave-${wave}-results.csv`,
output_schema: {
type: "object",
properties: {
id: { type: "string" },
status: { type: "string", enum: ["completed", "failed"] },
findings: { type: "string" },
issues_found: { type: "string" },
pass_rate: { type: "string" },
coverage_achieved: { type: "string" },
test_files: { type: "string" },
quality_score: { type: "string" },
error: { type: "string" }
}
}
})
// Merge results
const results = parseCsv(Read(`${sessionFolder}/wave-${wave}-results.csv`))
for (const r of results) {
const t = tasks.find(t => t.id === r.id)
if (t) Object.assign(t, r)
}
}
// 4. Execute interactive tasks (executor with fix cycle)
const pendingInteractive = interactiveTasks.filter(t => t.status === 'pending')
for (const task of pendingInteractive) {
Read(`agents/executor.md`)
const prevContext = buildPrevContext(task, tasks)
const agent = spawn_agent({
message: `## TASK ASSIGNMENT\n\n### MANDATORY FIRST STEPS\n1. Read: agents/executor.md\n2. Read: ${sessionFolder}/discoveries.ndjson\n3. Read: .workflow/project-tech.json (if exists)\n\n---\n\nGoal: ${task.description}\nLayer: ${task.layer}\nCoverage Target: ${task.coverage_target}%\nSession: ${sessionFolder}\n\n### Previous Context\n${prevContext}`
})
const result = wait({ ids: [agent], timeout_ms: 900000 })
if (result.timed_out) {
send_input({ id: agent, message: "Please finalize current test results and report." })
wait({ ids: [agent], timeout_ms: 120000 })
}
Write(`${sessionFolder}/interactive/${task.id}-result.json`, JSON.stringify({
task_id: task.id, status: "completed", findings: parseFindings(result),
timestamp: getUtc8ISOString()
}))
close_agent({ id: agent })
task.status = result.success ? 'completed' : 'failed'
task.findings = parseFindings(result)
}
// 5. GC Loop Check (after executor completes)
for (const task of pendingInteractive.filter(t => t.role === 'executor')) {
const gcState = JSON.parse(Read(`${sessionFolder}/gc-state.json`))
const layer = task.layer
const rounds = gcState.rounds[layer] || 0
const coverageAchieved = parseFloat(task.coverage_achieved || '0')
const coverageTarget = parseFloat(task.coverage_target || '80')
const passRate = parseFloat(task.pass_rate || '0')
if (coverageAchieved < coverageTarget && passRate < 0.95 && rounds < 3) {
gcState.rounds[layer] = rounds + 1
Write(`${sessionFolder}/gc-state.json`, JSON.stringify(gcState, null, 2))
Read(`agents/gc-loop-handler.md`)
const gcAgent = spawn_agent({
message: `## GC LOOP ROUND ${rounds + 1}\n\n### MANDATORY FIRST STEPS\n1. Read: agents/gc-loop-handler.md\n2. Read: ${sessionFolder}/discoveries.ndjson\n\nLayer: ${layer}\nRound: ${rounds + 1}/3\nCurrent Coverage: ${coverageAchieved}%\nTarget: ${coverageTarget}%\nPass Rate: ${passRate}\nSession: ${sessionFolder}\nPrevious Results: ${sessionFolder}/results/run-${layer}.json\nTest Directory: ${sessionFolder}/tests/${layer === 'L1' ? 'L1-unit' : layer === 'L2' ? 'L2-integration' : 'L3-e2e'}/`
})
const gcResult = wait({ ids: [gcAgent], timeout_ms: 900000 })
close_agent({ id: gcAgent })
}
}
// 6. Update master CSV
Write(`${sessionFolder}/tasks.csv`, toCsv(tasks))
// 7. Cleanup temp files
Bash(`rm -f ${sessionFolder}/wave-${wave}.csv ${sessionFolder}/wave-${wave}-results.csv`)
// 8. Display wave summary
const completed = waveTasks.filter(t => t.status === 'completed').length
const failed = waveTasks.filter(t => t.status === 'failed').length
const skipped = waveTasks.filter(t => t.status === 'skipped').length
console.log(`Wave ${wave} Complete: ${completed} completed, ${failed} failed, ${skipped} skipped`)
}Success Criteria:
- All waves executed in order
- Both csv-wave and interactive tasks handled per wave
- Each wave's results merged into master CSV before next wave starts
- GC loops triggered when coverage below target (max 3 rounds per layer)
- Dependent tasks skipped when predecessor failed
- discoveries.ndjson accumulated across all waves and mechanisms
目标: 逐批次执行任务,支持混合执行机制、GC循环处理和跨批次上下文传递。
javascript
const masterCsv = Read(`${sessionFolder}/tasks.csv`)
let tasks = parseCsv(masterCsv)
const maxWave = Math.max(...tasks.map(t => t.wave))
for (let wave = 1; wave <= maxWave; wave++) {
console.log(`\nWave ${wave}/${maxWave}`)
// 1. 按exec_mode拆分任务
const waveTasks = tasks.filter(t => t.wave === wave && t.status === 'pending')
const csvTasks = waveTasks.filter(t => t.exec_mode === 'csv-wave')
const interactiveTasks = waveTasks.filter(t => t.exec_mode === 'interactive')
// 2. 检查依赖 -- 跳过依赖失败的任务
for (const task of waveTasks) {
const depIds = (task.deps || '').split(';').filter(Boolean)
const depStatuses = depIds.map(id => tasks.find(t => t.id === id)?.status)
if (depStatuses.some(s => s === 'failed' || s === 'skipped')) {
task.status = 'skipped'
task.error = `Dependency failed: ${depIds.filter((id, i) =>
['failed','skipped'].includes(depStatuses[i])).join(', ')}`
}
}
// 3. 执行csv-wave任务
const pendingCsvTasks = csvTasks.filter(t => t.status === 'pending')
if (pendingCsvTasks.length > 0) {
for (const task of pendingCsvTasks) {
task.prev_context = buildPrevContext(task, tasks)
}
Write(`${sessionFolder}/wave-${wave}.csv`, toCsv(pendingCsvTasks))
// 读取指令模板
Read(`instructions/agent-instruction.md`)
// 构建嵌入会话目录的指令
const instruction = buildQAInstruction(sessionFolder, wave)
spawn_agents_on_csv({
csv_path: `${sessionFolder}/wave-${wave}.csv`,
id_column: "id",
instruction: instruction,
max_concurrency: maxConcurrency,
max_runtime_seconds: 900,
output_csv_path: `${sessionFolder}/wave-${wave}-results.csv`,
output_schema: {
type: "object",
properties: {
id: { type: "string" },
status: { type: "string", enum: ["completed", "failed"] },
findings: { type: "string" },
issues_found: { type: "string" },
pass_rate: { type: "string" },
coverage_achieved: { type: "string" },
test_files: { type: "string" },
quality_score: { type: "string" },
error: { type: "string" }
}
}
})
// 合并结果
const results = parseCsv(Read(`${sessionFolder}/wave-${wave}-results.csv`))
for (const r of results) {
const t = tasks.find(t => t.id === r.id)
if (t) Object.assign(t, r)
}
}
// 4. 执行交互式任务(带修复循环的执行器)
const pendingInteractive = interactiveTasks.filter(t => t.status === 'pending')
for (const task of pendingInteractive) {
Read(`agents/executor.md`)
const prevContext = buildPrevContext(task, tasks)
const agent = spawn_agent({
message: `## TASK ASSIGNMENT\n\n### MANDATORY FIRST STEPS\n1. Read: agents/executor.md\n2. Read: ${sessionFolder}/discoveries.ndjson\n3. Read: .workflow/project-tech.json (if exists)\n\n---\n\nGoal: ${task.description}\nLayer: ${task.layer}\nCoverage Target: ${task.coverage_target}%\nSession: ${sessionFolder}\n\n### Previous Context\n${prevContext}`
})
const result = wait({ ids: [agent], timeout_ms: 900000 })
if (result.timed_out) {
send_input({ id: agent, message: "Please finalize current test results and report." })
wait({ ids: [agent], timeout_ms: 120000 })
}
Write(`${sessionFolder}/interactive/${task.id}-result.json`, JSON.stringify({
task_id: task.id, status: "completed", findings: parseFindings(result),
timestamp: getUtc8ISOString()
}))
close_agent({ id: agent })
task.status = result.success ? 'completed' : 'failed'
task.findings = parseFindings(result)
}
// 5. GC循环检查(执行器完成后)
for (const task of pendingInteractive.filter(t => t.role === 'executor')) {
const gcState = JSON.parse(Read(`${sessionFolder}/gc-state.json`))
const layer = task.layer
const rounds = gcState.rounds[layer] || 0
const coverageAchieved = parseFloat(task.coverage_achieved || '0')
const coverageTarget = parseFloat(task.coverage_target || '80')
const passRate = parseFloat(task.pass_rate || '0')
if (coverageAchieved < coverageTarget && passRate < 0.95 && rounds < 3) {
gcState.rounds[layer] = rounds + 1
Write(`${sessionFolder}/gc-state.json`, JSON.stringify(gcState, null, 2))
Read(`agents/gc-loop-handler.md`)
const gcAgent = spawn_agent({
message: `## GC LOOP ROUND ${rounds + 1}\n\n### MANDATORY FIRST STEPS\n1. Read: agents/gc-loop-handler.md\n2. Read: ${sessionFolder}/discoveries.ndjson\n\nLayer: ${layer}\nRound: ${rounds + 1}/3\nCurrent Coverage: ${coverageAchieved}%\nTarget: ${coverageTarget}%\nPass Rate: ${passRate}\nSession: ${sessionFolder}\nPrevious Results: ${sessionFolder}/results/run-${layer}.json\nTest Directory: ${sessionFolder}/tests/${layer === 'L1' ? 'L1-unit' : layer === 'L2' ? 'L2-integration' : 'L3-e2e'}/`
})
const gcResult = wait({ ids: [gcAgent], timeout_ms: 900000 })
close_agent({ id: gcAgent })
}
}
// 6. 更新总控CSV
Write(`${sessionFolder}/tasks.csv`, toCsv(tasks))
// 7. 清理临时文件
Bash(`rm -f ${sessionFolder}/wave-${wave}.csv ${sessionFolder}/wave-${wave}-results.csv`)
// 8. 展示批次汇总
const completed = waveTasks.filter(t => t.status === 'completed').length
const failed = waveTasks.filter(t => t.status === 'failed').length
const skipped = waveTasks.filter(t => t.status === 'skipped').length
console.log(`Wave ${wave} Complete: ${completed} completed, ${failed} failed, ${skipped} skipped`)
}成功判定标准:
- 所有批次按顺序执行
- 每个批次的csv-wave和交互式任务都已处理
- 下一批次开始前当前批次结果已合并到总控CSV
- 覆盖度低于目标时触发GC循环(每层最多3轮)
- 前置任务失败时跳过依赖任务
- discoveries.ndjson已累计所有批次和执行机制的发现结果
Phase 3: Post-Wave Interactive (Completion Action)
阶段3: 批次后交互(完成动作)
Objective: Pipeline completion report with quality score and interactive completion choice.
javascript
const tasks = parseCsv(Read(`${sessionFolder}/tasks.csv`))
const completed = tasks.filter(t => t.status === 'completed')
const failed = tasks.filter(t => t.status === 'failed')
// Quality score from analyst
const analystTask = tasks.find(t => t.role === 'analyst' && t.status === 'completed')
const qualityScore = analystTask?.quality_score || 'N/A'
// Scout issues count
const scoutTasks = tasks.filter(t => t.role === 'scout' && t.status === 'completed')
const totalIssues = scoutTasks.reduce((sum, t) => sum + parseInt(t.issues_found || '0'), 0)
// Coverage summary per layer
const layerSummary = ['L1', 'L2', 'L3'].map(layer => {
const execTask = tasks.find(t => t.role === 'executor' && t.layer === layer && t.status === 'completed')
return execTask ? ` ${layer}: ${execTask.coverage_achieved}% coverage, ${execTask.pass_rate} pass rate` : null
}).filter(Boolean).join('\n')
console.log(`
============================================
QA PIPELINE COMPLETE
Quality Score: ${qualityScore}/100
Issues Discovered: ${totalIssues}
Deliverables:
${completed.map(t => ` - ${t.id}: ${t.title} (${t.role})`).join('\n')}
Coverage:
${layerSummary}
Pipeline: ${completed.length}/${tasks.length} tasks
Session: ${sessionFolder}
============================================
`)
if (!AUTO_YES) {
AskUserQuestion({
questions: [{
question: "Quality Assurance pipeline complete. What would you like to do?",
header: "Completion",
multiSelect: false,
options: [
{ label: "Archive & Clean (Recommended)", description: "Archive session, output final summary" },
{ label: "Keep Active", description: "Keep session for follow-up work" },
{ label: "Export Results", description: "Export deliverables to target directory" }
]
}]
})
}Success Criteria:
- Post-wave interactive processing complete
- Quality score and coverage metrics displayed
- User informed of results
目标: 输出带质量评分的流水线完成报告和交互式完成选项。
javascript
const tasks = parseCsv(Read(`${sessionFolder}/tasks.csv`))
const completed = tasks.filter(t => t.status === 'completed')
const failed = tasks.filter(t => t.status === 'failed')
// 分析师输出的质量评分
const analystTask = tasks.find(t => t.role === 'analyst' && t.status === 'completed')
const qualityScore = analystTask?.quality_score || 'N/A'
// 侦察员发现的问题总数
const scoutTasks = tasks.filter(t => t.role === 'scout' && t.status === 'completed')
const totalIssues = scoutTasks.reduce((sum, t) => sum + parseInt(t.issues_found || '0'), 0)
// 分层覆盖度汇总
const layerSummary = ['L1', 'L2', 'L3'].map(layer => {
const execTask = tasks.find(t => t.role === 'executor' && t.layer === layer && t.status === 'completed')
return execTask ? ` ${layer}: ${execTask.coverage_achieved}% coverage, ${execTask.pass_rate} pass rate` : null
}).filter(Boolean).join('\n')
console.log(`
============================================
QA PIPELINE COMPLETE
Quality Score: ${qualityScore}/100
Issues Discovered: ${totalIssues}
Deliverables:
${completed.map(t => ` - ${t.id}: ${t.title} (${t.role})`).join('\n')}
Coverage:
${layerSummary}
Pipeline: ${completed.length}/${tasks.length} tasks
Session: ${sessionFolder}
============================================
`)
if (!AUTO_YES) {
AskUserQuestion({
questions: [{
question: "Quality Assurance pipeline complete. What would you like to do?",
header: "Completion",
multiSelect: false,
options: [
{ label: "Archive & Clean (Recommended)", description: "Archive session, output final summary" },
{ label: "Keep Active", description: "Keep session for follow-up work" },
{ label: "Export Results", description: "Export deliverables to target directory" }
]
}]
})
}成功判定标准:
- 批次后交互处理完成
- 已展示质量评分和覆盖度指标
- 用户已获知结果
Phase 4: Results Aggregation
阶段4: 结果聚合
Objective: Generate final results and human-readable report.
javascript
// 1. Export results.csv
Bash(`cp ${sessionFolder}/tasks.csv ${sessionFolder}/results.csv`)
// 2. Generate context.md
const tasks = parseCsv(Read(`${sessionFolder}/tasks.csv`))
const gcState = JSON.parse(Read(`${sessionFolder}/gc-state.json`))
const analystTask = tasks.find(t => t.role === 'analyst' && t.status === 'completed')
let contextMd = `# Team Quality Assurance Report\n\n`
contextMd += `**Session**: ${sessionId}\n`
contextMd += `**Date**: ${getUtc8ISOString().substring(0, 10)}\n`
contextMd += `**QA Mode**: ${explicitMode || 'full'}\n`
contextMd += `**Quality Score**: ${analystTask?.quality_score || 'N/A'}/100\n\n`
contextMd += `## Summary\n`
contextMd += `| Status | Count |\n|--------|-------|\n`
contextMd += `| Completed | ${tasks.filter(t => t.status === 'completed').length} |\n`
contextMd += `| Failed | ${tasks.filter(t => t.status === 'failed').length} |\n`
contextMd += `| Skipped | ${tasks.filter(t => t.status === 'skipped').length} |\n\n`
// Scout findings
const scoutTasks = tasks.filter(t => t.role === 'scout' && t.status === 'completed')
if (scoutTasks.length > 0) {
contextMd += `## Scout Findings\n\n`
for (const t of scoutTasks) {
contextMd += `**${t.title}**: ${t.issues_found || 0} issues found\n${t.findings || ''}\n\n`
}
}
// Coverage results
contextMd += `## Coverage Results\n\n`
contextMd += `| Layer | Coverage | Target | Pass Rate | GC Rounds |\n`
contextMd += `|-------|----------|--------|-----------|----------|\n`
for (const layer of ['L1', 'L2', 'L3']) {
const execTask = tasks.find(t => t.role === 'executor' && t.layer === layer)
if (execTask) {
contextMd += `| ${layer} | ${execTask.coverage_achieved || 'N/A'}% | ${execTask.coverage_target}% | ${execTask.pass_rate || 'N/A'} | ${gcState.rounds[layer] || 0} |\n`
}
}
contextMd += '\n'
// Wave execution details
const maxWave = Math.max(...tasks.map(t => t.wave))
contextMd += `## Wave Execution\n\n`
for (let w = 1; w <= maxWave; w++) {
const waveTasks = tasks.filter(t => t.wave === w)
contextMd += `### Wave ${w}\n\n`
for (const t of waveTasks) {
const icon = t.status === 'completed' ? '[DONE]' : t.status === 'failed' ? '[FAIL]' : '[SKIP]'
contextMd += `${icon} **${t.title}** [${t.role}/${t.layer || '-'}] ${t.findings || ''}\n\n`
}
}
Write(`${sessionFolder}/context.md`, contextMd)
console.log(`Results exported to: ${sessionFolder}/results.csv`)
console.log(`Report generated at: ${sessionFolder}/context.md`)Success Criteria:
- results.csv exported (all tasks, both modes)
- context.md generated with quality score, scout findings, and coverage breakdown
- Summary displayed to user
目标: 生成最终结果和可读性报告。
javascript
// 1. 导出results.csv
Bash(`cp ${sessionFolder}/tasks.csv ${sessionFolder}/results.csv`)
// 2. 生成context.md
const tasks = parseCsv(Read(`${sessionFolder}/tasks.csv`))
const gcState = JSON.parse(Read(`${sessionFolder}/gc-state.json`))
const analystTask = tasks.find(t => t.role === 'analyst' && t.status === 'completed')
let contextMd = `# Team Quality Assurance Report\n\n`
contextMd += `**Session**: ${sessionId}\n`
contextMd += `**Date**: ${getUtc8ISOString().substring(0, 10)}\n`
contextMd += `**QA Mode**: ${explicitMode || 'full'}\n`
contextMd += `**Quality Score**: ${analystTask?.quality_score || 'N/A'}/100\n\n`
contextMd += `## Summary\n`
contextMd += `| Status | Count |\n|--------|-------|\n`
contextMd += `| Completed | ${tasks.filter(t => t.status === 'completed').length} |\n`
contextMd += `| Failed | ${tasks.filter(t => t.status === 'failed').length} |\n`
contextMd += `| Skipped | ${tasks.filter(t => t.status === 'skipped').length} |\n\n`
// 侦察员发现结果
const scoutTasks = tasks.filter(t => t.role === 'scout' && t.status === 'completed')
if (scoutTasks.length > 0) {
contextMd += `## Scout Findings\n\n`
for (const t of scoutTasks) {
contextMd += `**${t.title}**: ${t.issues_found || 0} issues found\n${t.findings || ''}\n\n`
}
}
// 覆盖度结果
contextMd += `## Coverage Results\n\n`
contextMd += `| Layer | Coverage | Target | Pass Rate | GC Rounds |\n`
contextMd += `|-------|----------|--------|-----------|----------|\n`
for (const layer of ['L1', 'L2', 'L3']) {
const execTask = tasks.find(t => t.role === 'executor' && t.layer === layer)
if (execTask) {
contextMd += `| ${layer} | ${execTask.coverage_achieved || 'N/A'}% | ${execTask.coverage_target}% | ${execTask.pass_rate || 'N/A'} | ${gcState.rounds[layer] || 0} |\n`
}
}
contextMd += '\n'
// 批次执行详情
const maxWave = Math.max(...tasks.map(t => t.wave))
contextMd += `## Wave Execution\n\n`
for (let w = 1; w <= maxWave; w++) {
const waveTasks = tasks.filter(t => t.wave === w)
contextMd += `### Wave ${w}\n\n`
for (const t of waveTasks) {
const icon = t.status === 'completed' ? '[DONE]' : t.status === 'failed' ? '[FAIL]' : '[SKIP]'
contextMd += `${icon} **${t.title}** [${t.role}/${t.layer || '-'}] ${t.findings || ''}\n\n`
}
}
Write(`${sessionFolder}/context.md`, contextMd)
console.log(`Results exported to: ${sessionFolder}/results.csv`)
console.log(`Report generated at: ${sessionFolder}/context.md`)成功判定标准:
- 已导出results.csv(包含所有模式的所有任务)
- 已生成包含质量评分、侦察员发现、覆盖度拆解的context.md
- 已向用户展示汇总信息
Shared Discovery Board Protocol
共享探索看板协议
All agents (csv-wave and interactive) share a single file for cross-task knowledge exchange.
discoveries.ndjsonFormat: One JSON object per line (NDJSON):
jsonl
{"ts":"2026-03-08T10:00:00Z","worker":"SCOUT-001","type":"issue_found","data":{"file":"src/auth.ts","line":42,"severity":"high","perspective":"security","description":"Hardcoded secret key in auth module"}}
{"ts":"2026-03-08T10:05:00Z","worker":"QASTRAT-001","type":"framework_detected","data":{"framework":"vitest","config_file":"vitest.config.ts","test_pattern":"**/*.test.ts"}}
{"ts":"2026-03-08T10:10:00Z","worker":"QAGEN-L1-001","type":"test_generated","data":{"file":"tests/L1-unit/auth.test.ts","source_file":"src/auth.ts","test_count":8}}
{"ts":"2026-03-08T10:15:00Z","worker":"QARUN-L1-001","type":"defect_found","data":{"file":"src/auth.ts","line":42,"pattern":"null_reference","description":"Missing null check on token payload"}}Discovery Types:
| Type | Data Schema | Description |
|---|---|---|
| | Issue discovered by scout |
| | Test framework identified |
| | Test file created |
| | Defect pattern discovered during testing |
| | Coverage gap identified |
| | Test convention detected |
| | Test fix during GC loop |
| | Quality dimension score |
Protocol:
- Agents MUST read discoveries.ndjson at start of execution
- Agents MUST append relevant discoveries during execution
- Agents MUST NOT modify or delete existing entries
- Deduplication by key (where applicable)
{type, data.file, data.line}
所有Agent(csv-wave和交互式)共用单个文件实现跨任务知识交换。
discoveries.ndjson格式: 每行一个JSON对象(NDJSON):
jsonl
{"ts":"2026-03-08T10:00:00Z","worker":"SCOUT-001","type":"issue_found","data":{"file":"src/auth.ts","line":42,"severity":"high","perspective":"security","description":"Hardcoded secret key in auth module"}}
{"ts":"2026-03-08T10:05:00Z","worker":"QASTRAT-001","type":"framework_detected","data":{"framework":"vitest","config_file":"vitest.config.ts","test_pattern":"**/*.test.ts"}}
{"ts":"2026-03-08T10:10:00Z","worker":"QAGEN-L1-001","type":"test_generated","data":{"file":"tests/L1-unit/auth.test.ts","source_file":"src/auth.ts","test_count":8}}
{"ts":"2026-03-08T10:15:00Z","worker":"QARUN-L1-001","type":"defect_found","data":{"file":"src/auth.ts","line":42,"pattern":"null_reference","description":"Missing null check on token payload"}}发现类型:
| 类型 | 数据Schema | 描述 |
|---|---|---|
| | 侦察员发现的问题 |
| | 识别到的测试框架 |
| | 生成的测试文件 |
| | 测试过程中发现的缺陷模式 |
| | 识别到的覆盖度缺口 |
| | 检测到的测试规范 |
| | GC循环中应用的测试修复 |
| | 质量维度评分 |
协议规则:
- Agent执行开始时必须读取discoveries.ndjson
- Agent执行过程中必须追加相关发现结果
- Agent不得修改或删除已有条目
- 按键去重(适用场景下)
{type, data.file, data.line}
Pipeline Definitions
流水线定义
Discovery Mode (5 tasks, serial)
发现模式(5个任务,串行)
SCOUT-001 -> QASTRAT-001 -> QAGEN-001 -> QARUN-001 -> QAANA-001| Task ID | Role | Layer | Wave | exec_mode |
|---|---|---|---|---|
| SCOUT-001 | scout | - | 1 | csv-wave |
| QASTRAT-001 | strategist | - | 2 | csv-wave |
| QAGEN-001 | generator | L1 | 3 | csv-wave |
| QARUN-001 | executor | L1 | 4 | interactive |
| QAANA-001 | analyst | - | 5 | csv-wave |
SCOUT-001 -> QASTRAT-001 -> QAGEN-001 -> QARUN-001 -> QAANA-001| 任务ID | 角色 | 层级 | 批次 | exec_mode |
|---|---|---|---|---|
| SCOUT-001 | scout | - | 1 | csv-wave |
| QASTRAT-001 | strategist | - | 2 | csv-wave |
| QAGEN-001 | generator | L1 | 3 | csv-wave |
| QARUN-001 | executor | L1 | 4 | interactive |
| QAANA-001 | analyst | - | 5 | csv-wave |
Testing Mode (6 tasks, progressive layers)
测试模式(6个任务,分层递进)
QASTRAT-001 -> QAGEN-L1-001 -> QARUN-L1-001 -> QAGEN-L2-001 -> QARUN-L2-001 -> QAANA-001| Task ID | Role | Layer | Wave | exec_mode |
|---|---|---|---|---|
| QASTRAT-001 | strategist | - | 1 | csv-wave |
| QAGEN-L1-001 | generator | L1 | 2 | csv-wave |
| QARUN-L1-001 | executor | L1 | 3 | interactive |
| QAGEN-L2-001 | generator | L2 | 4 | csv-wave |
| QARUN-L2-001 | executor | L2 | 5 | interactive |
| QAANA-001 | analyst | - | 6 | csv-wave |
QASTRAT-001 -> QAGEN-L1-001 -> QARUN-L1-001 -> QAGEN-L2-001 -> QARUN-L2-001 -> QAANA-001| 任务ID | 角色 | 层级 | 批次 | exec_mode |
|---|---|---|---|---|
| QASTRAT-001 | strategist | - | 1 | csv-wave |
| QAGEN-L1-001 | generator | L1 | 2 | csv-wave |
| QARUN-L1-001 | executor | L1 | 3 | interactive |
| QAGEN-L2-001 | generator | L2 | 4 | csv-wave |
| QARUN-L2-001 | executor | L2 | 5 | interactive |
| QAANA-001 | analyst | - | 6 | csv-wave |
Full Mode (8 tasks, parallel windows + regression)
全量模式(8个任务,并行窗口+回归)
SCOUT-001 -> QASTRAT-001 -> [QAGEN-L1-001 // QAGEN-L2-001] -> [QARUN-L1-001 // QARUN-L2-001] -> QAANA-001 -> SCOUT-002| Task ID | Role | Layer | Wave | exec_mode |
|---|---|---|---|---|
| SCOUT-001 | scout | - | 1 | csv-wave |
| QASTRAT-001 | strategist | - | 2 | csv-wave |
| QAGEN-L1-001 | generator | L1 | 3 | csv-wave |
| QAGEN-L2-001 | generator | L2 | 3 | csv-wave |
| QARUN-L1-001 | executor | L1 | 4 | interactive |
| QARUN-L2-001 | executor | L2 | 4 | interactive |
| QAANA-001 | analyst | - | 5 | csv-wave |
| SCOUT-002 | scout | - | 6 | csv-wave |
SCOUT-001 -> QASTRAT-001 -> [QAGEN-L1-001 // QAGEN-L2-001] -> [QARUN-L1-001 // QARUN-L2-001] -> QAANA-001 -> SCOUT-002| 任务ID | 角色 | 层级 | 批次 | exec_mode |
|---|---|---|---|---|
| SCOUT-001 | scout | - | 1 | csv-wave |
| QASTRAT-001 | strategist | - | 2 | csv-wave |
| QAGEN-L1-001 | generator | L1 | 3 | csv-wave |
| QAGEN-L2-001 | generator | L2 | 3 | csv-wave |
| QARUN-L1-001 | executor | L1 | 4 | interactive |
| QARUN-L2-001 | executor | L2 | 4 | interactive |
| QAANA-001 | analyst | - | 5 | csv-wave |
| SCOUT-002 | scout | - | 6 | csv-wave |
GC Loop (Generator-Critic)
GC循环(Generator-Critic)
Generator and executor iterate per test layer until coverage converges:
QAGEN -> QARUN -> (if coverage < target) -> GC Loop Handler
(if coverage >= target) -> next wave- Max iterations: 3 per layer
- After 3 iterations: accept current coverage with warning
- GC loop runs as interactive agent (gc-loop-handler.md) which internally generates fixes and re-runs tests
生成器和执行器按测试层迭代直到覆盖度收敛:
QAGEN -> QARUN -> (如果覆盖度<目标) -> GC Loop Handler
(如果覆盖度>=目标) -> 下一批次- 最大迭代次数:每层3次
- 3次迭代后:接受当前覆盖度并给出警告
- GC循环作为交互式Agent运行(gc-loop-handler.md),内部生成修复并重新运行测试
Scan Perspectives (Scout)
扫描视角(侦察员)
| Perspective | Focus |
|---|---|
| bug | Logic errors, crash paths, null references |
| security | Vulnerabilities, auth bypass, data exposure |
| test-coverage | Untested code paths, missing assertions |
| code-quality | Anti-patterns, complexity, maintainability |
| ux | User-facing issues, accessibility (optional, when task mentions UX/UI) |
| 视角 | 关注方向 |
|---|---|
| bug | 逻辑错误、崩溃路径、空引用 |
| security | 漏洞、认证绕过、数据泄露 |
| test-coverage | 未测试代码路径、缺失断言 |
| code-quality | 反模式、复杂度、可维护性 |
| ux | 用户侧问题、可访问性(可选,仅任务提到UX/UI时启用) |
Error Handling
错误处理
| Error | Resolution |
|---|---|
| Circular dependency | Detect in wave computation, abort with error message |
| CSV agent timeout | Mark as failed in results, continue with wave |
| CSV agent failed | Mark as failed, skip dependent tasks in later waves |
| Interactive agent timeout | Urge convergence via send_input, then close if still timed out |
| Interactive agent failed | Mark as failed, skip dependents |
| All agents in wave failed | Log error, offer retry or abort |
| CSV parse error | Validate CSV format before execution, show line number |
| discoveries.ndjson corrupt | Ignore malformed lines, continue with valid entries |
| Scout finds no issues | Report clean scan, proceed to testing (skip discovery-specific tasks) |
| GC loop exceeded (3 rounds) | Accept current coverage with warning, proceed to next layer |
| Test framework not detected | Default to Jest patterns |
| Coverage tool unavailable | Degrade to pass rate judgment |
| quality_score < 60 | Report with WARNING, suggest re-run with deeper coverage |
| Continue mode: no session found | List available sessions, prompt user to select |
| 错误 | 解决方案 |
|---|---|
| 循环依赖 | 批次计算时检测,输出错误信息并终止 |
| CSV Agent超时 | 标记为失败,继续执行当前批次 |
| CSV Agent执行失败 | 标记为失败,跳过后续批次的依赖任务 |
| 交互式Agent超时 | 通过send_input催促收敛,仍超时则关闭 |
| 交互式Agent执行失败 | 标记为失败,跳过依赖任务 |
| 批次内所有Agent失败 | 记录错误,提供重试或终止选项 |
| CSV解析错误 | 执行前校验CSV格式,展示行号 |
| discoveries.ndjson损坏 | 忽略格式错误行,继续处理有效条目 |
| 侦察员未发现问题 | 报告干净扫描结果,进入测试阶段(跳过发现模式专属任务) |
| GC循环超出上限(3轮) | 接受当前覆盖度并给出警告,进入下一层级 |
| 未检测到测试框架 | 默认使用Jest模式 |
| 覆盖度工具不可用 | 降级为通过率判断 |
| 质量评分<60 | 输出WARNING,建议使用更深覆盖度重新运行 |
| 继续模式:未找到会话 | 列出可用会话,提示用户选择 |
Core Rules
核心规则
- Start Immediately: First action is session initialization, then Phase 0/1
- Wave Order is Sacred: Never execute wave N before wave N-1 completes and results are merged
- CSV is Source of Truth: Master tasks.csv holds all state (both csv-wave and interactive)
- CSV First: Default to csv-wave for tasks; only use interactive when multi-round interaction is required
- Context Propagation: prev_context built from master CSV, not from memory
- Discovery Board is Append-Only: Never clear, modify, or recreate discoveries.ndjson
- Skip on Failure: If a dependency failed, skip the dependent task
- GC Loop Discipline: Max 3 rounds per layer; never infinite-loop on coverage
- Scout Feeds Strategy: Scout findings flow into strategist via prev_context and discoveries.ndjson
- Cleanup Temp Files: Remove wave-{N}.csv after results are merged
- DO NOT STOP: Continuous execution until all waves complete or all remaining tasks are skipped
- 立即启动: 第一步执行会话初始化,然后进入阶段0/1
- 批次顺序不可更改: 批次N-1执行完成并合并结果前,不得执行批次N
- CSV是唯一真值来源: 总控tasks.csv保存所有状态(包含csv-wave和交互式任务)
- CSV优先: 任务默认使用csv-wave模式,仅需要多轮交互时使用交互式
- 上下文传递: prev_context从总控CSV构建,而非内存
- 探索看板仅追加: 不得清空、修改或重建discoveries.ndjson
- 失败即跳过: 依赖任务失败时,跳过当前任务
- GC循环约束: 每层最多3轮,禁止无限循环追求覆盖度
- 侦察结果供给策略: 侦察员发现通过prev_context和discoveries.ndjson传递给策略师
- 临时文件清理: 结果合并后删除wave-{N}.csv
- 不得终止执行: 持续执行直到所有批次完成或所有剩余任务被跳过
Coordinator Role Constraints (Main Agent)
协调者角色约束(主Agent)
CRITICAL: The coordinator (main agent executing this skill) is responsible for orchestration only, NOT implementation.
-
Coordinator Does NOT Execute Code: The main agent MUST NOT write, modify, or implement any code directly. All implementation work is delegated to spawned team agents. The coordinator only:
- Spawns agents with task assignments
- Waits for agent callbacks
- Merges results and coordinates workflow
- Manages workflow transitions between phases
-
Patient Waiting is Mandatory: Agent execution takes significant time (typically 10-30 minutes per phase, sometimes longer). The coordinator MUST:
- Wait patiently for calls to complete
wait() - NOT skip workflow steps due to perceived delays
- NOT assume agents have failed just because they're taking time
- Trust the timeout mechanisms defined in the skill
- Wait patiently for
-
Use send_input for Clarification: When agents need guidance or appear stuck, the coordinator MUST:
- Use to ask questions or provide clarification
send_input() - NOT skip the agent or move to next phase prematurely
- Give agents opportunity to respond before escalating
- Example:
send_input({ id: agent_id, message: "Please provide status update or clarify blockers" })
- Use
-
No Workflow Shortcuts: The coordinator MUST NOT:
- Skip phases or stages defined in the workflow
- Bypass required approval or review steps
- Execute dependent tasks before prerequisites complete
- Assume task completion without explicit agent callback
- Make up or fabricate agent results
-
Respect Long-Running Processes: This is a complex multi-agent workflow that requires patience:
- Total execution time may range from 30-90 minutes or longer
- Each phase may take 10-30 minutes depending on complexity
- The coordinator must remain active and attentive throughout the entire process
- Do not terminate or skip steps due to time concerns
关键: 执行本工具的协调者(主Agent)仅负责编排,不负责具体实现。
-
协调者不执行代码: 主Agent不得直接编写、修改或实现任何代码。所有实现工作都委托给调度的团队Agent。协调者仅负责:
- 为Agent分配任务并调度
- 等待Agent回调
- 合并结果并协调工作流
- 管理阶段间的工作流跳转
-
必须耐心等待: Agent执行需要大量时间(通常每个阶段10-30分钟,有时更长)。协调者必须:
- 耐心等待调用完成
wait() - 不得因感知到延迟跳过工作流步骤
- 不得仅因Agent执行时间长就判定为失败
- 信任工具中定义的超时机制
- 耐心等待
-
使用send_input澄清问题: 当Agent需要指导或看起来卡住时,协调者必须:
- 使用询问问题或提供澄清
send_input() - 不得提前跳过Agent或进入下一个阶段
- 升级问题前给Agent响应机会
- 示例:
send_input({ id: agent_id, message: "Please provide status update or clarify blockers" })
- 使用
-
不得精简工作流: 协调者不得:
- 跳过工作流中定义的阶段或步骤
- 绕过要求的审批或审核步骤
- 前置条件完成前执行依赖任务
- 无明确Agent回调就假设任务完成
- 编造或虚构Agent结果
-
尊重长运行流程: 这是复杂的多Agent工作流,需要耐心:
- 总执行时间可能在30-90分钟或更长
- 根据复杂度不同每个阶段可能需要10-30分钟
- 协调者必须在整个过程中保持活跃和专注
- 不得因时间问题终止或跳过步骤