mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
synced 2026-03-05 16:13:08 +08:00
feat: Add coordinator commands and role specifications for UI design team
- Implemented the 'monitor' command for coordinator role to handle monitoring events, task completion, and pipeline management. - Created role specifications for the coordinator, detailing responsibilities, command execution protocols, and session management. - Added role specifications for the analyst, discussant, explorer, and synthesizer in the ultra-analyze skill, defining their context loading, analysis, and synthesis processes.
This commit is contained in:
catlog22
80
.claude/skills/team-quality-assurance/role-specs/analyst.md
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80
.claude/skills/team-quality-assurance/role-specs/analyst.md
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---
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prefix: QAANA
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inner_loop: false
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subagents: []
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message_types:
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success: analysis_ready
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report: quality_report
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error: error
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---
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# Quality Analyst
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Analyze defect patterns, coverage gaps, test effectiveness, and generate comprehensive quality reports. Maintain defect pattern database and provide quality scoring.
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## Phase 2: Context Loading
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| Input | Source | Required |
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|-------|--------|----------|
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| Task description | From task subject/description | Yes |
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| Session path | Extracted from task description | Yes |
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| .msg/meta.json | <session>/wisdom/.msg/meta.json | Yes |
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| Discovered issues | meta.json -> discovered_issues | No |
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| Test strategy | meta.json -> test_strategy | No |
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| Generated tests | meta.json -> generated_tests | No |
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| Execution results | meta.json -> execution_results | No |
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| Historical patterns | meta.json -> defect_patterns | No |
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1. Extract session path from task description
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2. Read .msg/meta.json for all accumulated QA data
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3. Read coverage data from `coverage/coverage-summary.json` if available
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4. Read layer execution results from `<session>/results/run-*.json`
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5. Select analysis mode:
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| Data Points | Mode |
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|-------------|------|
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| <= 5 issues + results | Direct inline analysis |
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| > 5 | CLI-assisted deep analysis via gemini |
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## Phase 3: Multi-Dimensional Analysis
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**Five analysis dimensions**:
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1. **Defect Pattern Analysis**: Group issues by type/perspective, identify patterns with >= 2 occurrences, record type/count/files/description
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2. **Coverage Gap Analysis**: Compare actual coverage vs layer targets, identify per-file gaps (< 50% coverage), severity: critical (< 20%) / high (< 50%)
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3. **Test Effectiveness**: Per layer -- files generated, pass rate, iterations needed, coverage achieved. Effective = pass_rate >= 95% AND iterations <= 2
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4. **Quality Trend**: Compare against coverage_history. Trend: improving (delta > 5%), declining (delta < -5%), stable
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5. **Quality Score** (0-100 starting from 100):
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| Factor | Impact |
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|--------|--------|
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| Security issues | -10 per issue |
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| Bug issues | -5 per issue |
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| Coverage gap | -0.5 per gap percentage |
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| Test failures | -(100 - pass_rate) * 0.3 per layer |
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| Effective test layers | +5 per layer |
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| Improving trend | +3 |
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For CLI-assisted mode:
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```
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PURPOSE: Deep quality analysis on QA results to identify defect patterns and improvement opportunities
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TASK: Classify defects by root cause, identify high-density files, analyze coverage gaps vs risk, generate recommendations
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MODE: analysis
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```
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## Phase 4: Report Generation & Output
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1. Generate quality report markdown with: score, defect patterns, coverage analysis, test effectiveness, quality trend, recommendations
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2. Write report to `<session>/analysis/quality-report.md`
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3. Update `<session>/wisdom/.msg/meta.json`:
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- `defect_patterns`: identified patterns array
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- `quality_score`: calculated score
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- `coverage_history`: append new data point (date, coverage, quality_score, issues)
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**Score-based recommendations**:
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| Score | Recommendation |
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|-------|----------------|
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| >= 80 | Quality is GOOD. Maintain current testing practices. |
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| 60-79 | Quality needs IMPROVEMENT. Focus on coverage gaps and recurring patterns. |
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| < 60 | Quality is CONCERNING. Recommend comprehensive review and testing effort. |
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65
.claude/skills/team-quality-assurance/role-specs/executor.md
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65
.claude/skills/team-quality-assurance/role-specs/executor.md
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---
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prefix: QARUN
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inner_loop: true
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additional_prefixes: [QARUN-gc]
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subagents: []
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message_types:
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success: tests_passed
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failure: tests_failed
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coverage: coverage_report
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error: error
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---
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# Test Executor
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Run test suites, collect coverage data, and perform automatic fix cycles when tests fail. Implements the execution side of the Generator-Executor (GC) loop.
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## Phase 2: Environment Detection
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| Input | Source | Required |
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|-------|--------|----------|
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| Task description | From task subject/description | Yes |
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| Session path | Extracted from task description | Yes |
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| .msg/meta.json | <session>/wisdom/.msg/meta.json | Yes |
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| Test strategy | meta.json -> test_strategy | Yes |
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| Generated tests | meta.json -> generated_tests | Yes |
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| Target layer | task description `layer: L1/L2/L3` | Yes |
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1. Extract session path and target layer from task description
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2. Read .msg/meta.json for strategy and generated test file list
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3. Detect test command by framework:
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| Framework | Command |
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|-----------|---------|
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| vitest | `npx vitest run --coverage --reporter=json --outputFile=test-results.json` |
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| jest | `npx jest --coverage --json --outputFile=test-results.json` |
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| pytest | `python -m pytest --cov --cov-report=json -v` |
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| mocha | `npx mocha --reporter json > test-results.json` |
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| unknown | `npm test -- --coverage` |
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4. Get test files from `generated_tests[targetLayer].files`
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## Phase 3: Iterative Test-Fix Cycle
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**Max iterations**: 5. **Pass threshold**: 95% or all tests pass.
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Per iteration:
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1. Run test command, capture output
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2. Parse results: extract passed/failed counts, parse coverage from output or `coverage/coverage-summary.json`
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3. If all pass (0 failures) -> exit loop (success)
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4. If pass rate >= 95% and iteration >= 2 -> exit loop (good enough)
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5. If iteration >= MAX -> exit loop (report current state)
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6. Extract failure details (error lines, assertion failures)
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7. Delegate fix to code-developer subagent with constraints:
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- ONLY modify test files, NEVER modify source code
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- Fix: incorrect assertions, missing imports, wrong mocks, setup issues
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- Do NOT: skip tests, add `@ts-ignore`, use `as any`
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8. Increment iteration, repeat
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## Phase 4: Result Analysis & Output
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1. Build result data: layer, framework, iterations, pass_rate, coverage, tests_passed, tests_failed, all_passed
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2. Save results to `<session>/results/run-<layer>.json`
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3. Save last test output to `<session>/results/output-<layer>.txt`
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4. Update `<session>/wisdom/.msg/meta.json` under `execution_results[layer]` and top-level `execution_results.pass_rate`, `execution_results.coverage`
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5. Message type: `tests_passed` if all_passed, else `tests_failed`
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---
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prefix: QAGEN
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inner_loop: false
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additional_prefixes: [QAGEN-fix]
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subagents: []
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message_types:
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success: tests_generated
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revised: tests_revised
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error: error
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---
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# Test Generator
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Generate test code according to strategist's strategy and layers. Support L1 unit tests, L2 integration tests, L3 E2E tests. Follow project's existing test patterns and framework conventions.
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## Phase 2: Strategy & Pattern Loading
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| Input | Source | Required |
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|-------|--------|----------|
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| Task description | From task subject/description | Yes |
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| Session path | Extracted from task description | Yes |
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| .msg/meta.json | <session>/wisdom/.msg/meta.json | Yes |
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| Test strategy | meta.json -> test_strategy | Yes |
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| Target layer | task description `layer: L1/L2/L3` | Yes |
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1. Extract session path and target layer from task description
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2. Read .msg/meta.json for test strategy (layers, coverage targets)
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3. Determine if this is a GC fix task (subject contains "fix")
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4. Load layer config from strategy: level, name, target_coverage, focus_files
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5. Learn existing test patterns -- find 3 similar test files via Glob(`**/*.{test,spec}.{ts,tsx,js,jsx}`)
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6. Detect test conventions: file location (colocated vs __tests__), import style, describe/it nesting, framework (vitest/jest/pytest)
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## Phase 3: Test Code Generation
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**Mode selection**:
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| Condition | Mode |
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|-----------|------|
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| GC fix task | Read failure info from `<session>/results/run-<layer>.json`, fix failing tests only |
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| <= 3 focus files | Direct: inline Read source -> Write test file |
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| > 3 focus files | Batch by module, delegate to code-developer subagent |
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**Direct generation flow** (per source file):
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1. Read source file content, extract exports
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2. Determine test file path following project conventions
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3. If test exists -> analyze missing cases -> append new tests via Edit
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4. If no test -> generate full test file via Write
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5. Include: happy path, edge cases, error cases per export
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**GC fix flow**:
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1. Read execution results and failure output from results directory
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2. Read each failing test file
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3. Fix assertions, imports, mocks, or test setup
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4. Do NOT modify source code, do NOT skip/ignore tests
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**General rules**:
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- Follow existing test patterns exactly (imports, naming, structure)
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- Target coverage per layer config
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- Do NOT use `any` type assertions or `@ts-ignore`
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## Phase 4: Self-Validation & Output
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1. Collect generated/modified test files
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2. Run syntax check (TypeScript: `tsc --noEmit`, or framework-specific)
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3. Auto-fix syntax errors (max 3 attempts)
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4. Write test metadata to `<session>/wisdom/.msg/meta.json` under `generated_tests[layer]`:
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- layer, files list, count, syntax_clean, mode, gc_fix flag
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5. Message type: `tests_generated` for new, `tests_revised` for GC fix iterations
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67
.claude/skills/team-quality-assurance/role-specs/scout.md
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67
.claude/skills/team-quality-assurance/role-specs/scout.md
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---
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prefix: SCOUT
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inner_loop: false
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subagents: []
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message_types:
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success: scan_ready
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error: error
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issues: issues_found
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---
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# Multi-Perspective Scout
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Scan codebase from multiple perspectives (bug, security, test-coverage, code-quality, UX) to discover potential issues. Produce structured scan results with severity-ranked findings.
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## Phase 2: Context & Scope Assessment
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| Input | Source | Required |
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|-------|--------|----------|
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| Task description | From task subject/description | Yes |
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| Session path | Extracted from task description | Yes |
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| .msg/meta.json | <session>/wisdom/.msg/meta.json | No |
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1. Extract session path and target scope from task description
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2. Determine scan scope: explicit scope from task or `**/*` default
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3. Get recent changed files: `git diff --name-only HEAD~5 2>/dev/null || echo ""`
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4. Read .msg/meta.json for historical defect patterns (`defect_patterns`)
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5. Select scan perspectives based on task description:
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- Default: `["bug", "security", "test-coverage", "code-quality"]`
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- Add `"ux"` if task mentions UX/UI
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6. Assess complexity to determine scan strategy:
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| Complexity | Condition | Strategy |
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|------------|-----------|----------|
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| Low | < 5 changed files, no specific keywords | ACE search + Grep inline |
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| Medium | 5-15 files or specific perspective requested | CLI fan-out (3 core perspectives) |
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| High | > 15 files or full-project scan | CLI fan-out (all perspectives) |
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## Phase 3: Multi-Perspective Scan
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**Low complexity**: Use `mcp__ace-tool__search_context` for quick pattern-based scan.
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**Medium/High complexity**: CLI fan-out -- one `ccw cli --mode analysis` per perspective:
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For each active perspective, build prompt:
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```
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PURPOSE: Scan code from <perspective> perspective to discover potential issues
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TASK: Analyze code patterns for <perspective> problems, identify anti-patterns, check for common issues
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MODE: analysis
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CONTEXT: @<scan-scope>
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EXPECTED: List of findings with severity (critical/high/medium/low), file:line references, description
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CONSTRAINTS: Focus on actionable findings only
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```
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Execute via: `ccw cli -p "<prompt>" --tool gemini --mode analysis`
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After all perspectives complete:
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- Parse CLI outputs into structured findings
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- Deduplicate by file:line (merge perspectives for same location)
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- Compare against known defect patterns from .msg/meta.json
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- Rank by severity: critical > high > medium > low
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## Phase 4: Result Aggregation
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1. Build `discoveredIssues` array from critical + high findings (with id, severity, perspective, file, line, description)
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2. Write scan results to `<session>/scan/scan-results.json`:
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- scan_date, perspectives scanned, total findings, by_severity counts, findings detail, issues_created count
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3. Update `<session>/wisdom/.msg/meta.json`: merge `discovered_issues` field
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4. Contribute to wisdom/issues.md if new patterns found
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---
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prefix: QASTRAT
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inner_loop: false
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subagents: []
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message_types:
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success: strategy_ready
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error: error
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---
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# Test Strategist
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Analyze change scope, determine test layers (L1-L3), define coverage targets, and generate test strategy document. Create targeted test plans based on scout discoveries and code changes.
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## Phase 2: Context & Change Analysis
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| Input | Source | Required |
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|-------|--------|----------|
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| Task description | From task subject/description | Yes |
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| Session path | Extracted from task description | Yes |
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| .msg/meta.json | <session>/wisdom/.msg/meta.json | Yes |
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| Discovered issues | meta.json -> discovered_issues | No |
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| Defect patterns | meta.json -> defect_patterns | No |
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1. Extract session path from task description
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2. Read .msg/meta.json for scout discoveries and historical patterns
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3. Analyze change scope: `git diff --name-only HEAD~5`
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4. Categorize changed files:
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| Category | Pattern |
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|----------|---------|
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| Source | `\.(ts|tsx|js|jsx|py|java|go|rs)$` |
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| Test | `\.(test|spec)\.(ts|tsx|js|jsx)$` or `test_` |
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| Config | `\.(json|yaml|yml|toml|env)$` |
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5. Detect test framework from package.json / project files
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6. Check existing coverage baseline from `coverage/coverage-summary.json`
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7. Select analysis mode:
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| Total Scope | Mode |
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|-------------|------|
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| <= 5 files + issues | Direct inline analysis |
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| 6-15 | Single CLI analysis |
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| > 15 | Multi-dimension CLI analysis |
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## Phase 3: Strategy Generation
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**Layer Selection Logic**:
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| Condition | Layer | Target |
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|-----------|-------|--------|
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| Has source file changes | L1: Unit Tests | 80% |
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| >= 3 source files OR critical issues | L2: Integration Tests | 60% |
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| >= 3 critical/high severity issues | L3: E2E Tests | 40% |
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| No changes but has scout issues | L1 focused on issue files | 80% |
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For CLI-assisted analysis, use:
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```
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PURPOSE: Analyze code changes and scout findings to determine optimal test strategy
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TASK: Classify changed files by risk, map issues to test requirements, identify integration points, recommend test layers with coverage targets
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MODE: analysis
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```
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Build strategy document with: scope analysis, layer configs (level, name, target_coverage, focus_files, rationale), priority issues list.
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**Validation**: Verify strategy has layers, targets > 0, covers discovered issues, and framework detected.
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## Phase 4: Output & Persistence
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1. Write strategy to `<session>/strategy/test-strategy.md`
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2. Update `<session>/wisdom/.msg/meta.json`: merge `test_strategy` field with scope, layers, coverage_targets, test_framework
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3. Contribute to wisdom/decisions.md with layer selection rationale
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