Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
中文简介正在准备中。当前显示原文说明。
Use when monitoring in-sprint code quality with TDD adherence checks, complexity analysis, coverage gap detection, or defect prediction in the QCSD Development phase.
中文简介正在准备中。当前显示原文说明。
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Shift-left quality engineering swarm for in-sprint code quality assurance.
The Development Swarm takes refined stories (that passed Refinement) and validates code quality during sprint execution. Where the Ideation Swarm asks "Should we build this?" and the Refinement Swarm asks "How should we test this?", the Development Swarm asks "Is the code quality sufficient to ship?"
| Phase | Swarm | Decision | When |
|---|---|---|---|
| Ideation | qcsd-ideation-swarm | GO / CONDITIONAL / NO-GO | PI/Sprint Planning |
| Refinement | qcsd-refinement-swarm | READY / CONDITIONAL / NOT-READY | Sprint Refinement |
| Development | qcsd-development-swarm | SHIP / CONDITIONAL / HOLD | During Sprint |
| Verification | qcsd-cicd-swarm | RELEASE / REMEDIATE / BLOCK | Pre-Release / CI-CD |
| Production | qcsd-production-swarm | HEALTHY / DEGRADED / CRITICAL | Post-Release |
SOURCE_PATH: Source code directory to analyze (required, e.g., src/auth/)TEST_PATH: Test directory for coverage analysis (optional, e.g., tests/auth/)OUTPUT_FOLDER: Where to save reports (default: ${PROJECT_ROOT}/Agentic QCSD/development/)| Rule | Enforcement |
|---|---|
| E1 | You MUST spawn ALL THREE core agents (qe-tdd-specialist, qe-code-complexity, qe-coverage-specialist) in Step 2. No exceptions. |
| E2 | You MUST put all parallel Task calls in a SINGLE message. |
| E3 | You MUST STOP and WAIT after each batch. No proceeding early. |
| E4 | You MUST spawn conditional agents if flags are TRUE. No skipping. |
| E5 | You MUST apply SHIP/CONDITIONAL/HOLD logic exactly as specified in Step 5. |
| E6 | You MUST generate the full report structure. No abbreviated versions. |
| E7 | Each agent MUST read its reference files before analysis. |
| E8 | You MUST apply qe-defect-predictor analysis on ALL code changes in Step 8. Always. |
| E9 | You MUST execute Step 7 learning persistence. No skipping. |
PROHIBITED BEHAVIORS:
This skill uses a micro-file step architecture. Each step is a self-contained file loaded one at a time to avoid "lost in the middle" context degradation.
Execute steps sequentially by reading each step file with the Read tool.
steps/01-flag-detection.md -- Scan source code and tests, detect all 6 flagssteps/02-core-agents.md -- Spawn qe-tdd-specialist, qe-code-complexity, qe-coverage-specialist in parallelsteps/03-batch1-results.md -- Wait for core agents, extract all metricssteps/04-conditional-agents.md -- Spawn flagged conditional agents in parallelsteps/05-decision-synthesis.md -- Apply SHIP/CONDITIONAL/HOLD logicsteps/06-report-generation.md -- Generate executive summary and full reportsteps/07-learning-persistence.md -- Store findings to memory, save persistence recordsteps/08-defect-predictor.md -- Run qe-defect-predictor analysis on all code changessteps/09-final-output.md -- Display completion summary with all scoresRead({ file_path: ".claude/skills/qcsd-development-swarm/steps/01-flag-detection.md" }))To resume from a specific step: specify --from-step N and the orchestrator will
skip to step N. Ensure you have the required prerequisite data from prior steps.
| Agent | Type | Domain | Batch |
|---|---|---|---|
| qe-tdd-specialist | Core (always) | test-generation | 1 |
| qe-code-complexity | Core (always) | code-intelligence | 1 |
| qe-coverage-specialist | Core (always) | coverage-analysis | 1 |
| qe-security-scanner | Conditional (HAS_SECURITY_CODE) | security-compliance | 2 |
| qe-performance-tester | Conditional (HAS_PERFORMANCE_CODE) | chaos-resilience | 2 |
| qe-mutation-tester | Conditional (HAS_CRITICAL_CODE) | test-generation | 2 |
| qe-message-broker-tester | Conditional (HAS_MIDDLEWARE) | enterprise-integration | 2 |
| qe-sap-idoc-tester | Conditional (HAS_SAP_INTEGRATION) | enterprise-integration | 2 |
| qe-sod-analyzer | Conditional (HAS_AUTHORIZATION) | enterprise-integration | 2 |
| qe-defect-predictor | Analysis (always) | defect-intelligence | 3 |
Total: 10 agents (3 core + 6 conditional + 1 analysis)
| Metric | SHIP | CONDITIONAL | HOLD |
|---|---|---|---|
| TDD Adherence | >= 80% | 60 - 79% | < 60% |
| Code Complexity | Avg <= 10 | Avg 11-15 | Avg > 15 |
| Test Coverage | >= 80% | 60 - 79% | < 60% |
| Mutation Score | >= 70% | 50 - 69% | < 50% |
| Security Issues | No HIGH/CRITICAL | MEDIUM only | HIGH/CRITICAL found |
| Agent | Report Filename | Step |
|---|---|---|
| qe-tdd-specialist | 02-tdd-analysis.md | 2 |
| qe-code-complexity | 03-complexity-analysis.md | 2 |
| qe-coverage-specialist | 04-coverage-analysis.md | 2 |
| qe-security-scanner | 05-security-scan.md | 4 |
| qe-performance-tester | 06-performance-analysis.md | 4 |
| qe-mutation-tester | 07-mutation-testing.md | 4 |
| qe-message-broker-tester | 08-middleware-health.md | 4 |
| qe-sap-idoc-tester | 09-sap-integration.md | 4 |
| qe-sod-analyzer | 10-sod-compliance.md | 4 |
| Learning Persistence | 11-learning-persistence.json | 7 |
| qe-defect-predictor | 12-defect-prediction.md | 8 |
| Synthesis | 01-executive-summary.md | 6 |
| Model | When to Use | Agent Spawn |
|---|---|---|
| Workflow (PRIMARY, ADR-102) | Harness with the Workflow tool | Workflow({ name: "qcsd-development-review", args: { sourcePath, testPath } }) |
| Task Tool (fallback) | Claude Code sessions without Workflow support | Task({ subagent_type, run_in_background: true }) |
| MCP Tools | MCP server available | fleet_init({}) / task_submit({}) |
| CLI | Terminal/scripts | swarm init / agent spawn |
.claude/workflows/qcsd-development-review.js runs the review as a deterministic
pipeline: one finder per quality dimension (TDD adherence, complexity, coverage
gaps — args.dimensions selects a subset) → 3 blind adversarial refuters per
finding (Loki-mode, ADR-074: refuters see only the bare claim + evidence,
never the finder's confidence or each other; uncertainty defaults to refuted)
→ deterministic synthesis into finding-verdict@1 envelopes (ADR-103,
schemas/finding-verdict.schema.json). A finding survives only if fewer than
⌈N/2⌉ refuters kill it. The final report contains ONLY confirmed findings;
killed findings are retained under killed with their refutations for audit.
Args: sourcePath (required), testPath, dimensions (subset of
tdd-adherence|complexity|coverage-gaps), maxFindings per dimension (default 5).
When the Workflow tool is unavailable, fall back to the Task-tool protocol
below — the report format and gates are identical, minus the adversarial
verification stage (note this in the report header as verification: none).
Code quality is measured by evidence, not intentions. This swarm provides in-sprint quality assessment to ensure code meets engineering standards before entering the CI/CD pipeline.
还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
中文简介正在准备中。当前显示原文说明。
WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
中文简介正在准备中。当前显示原文说明。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
中文简介正在准备中。当前显示原文说明。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
中文简介正在准备中。当前显示原文说明。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
中文简介正在准备中。当前显示原文说明。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
中文简介正在准备中。当前显示原文说明。