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 enforcing CI/CD quality gates before release, running regression analysis, detecting flaky tests, or assessing deployment readiness in the QCSD Verification 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 CI/CD pipeline verification and release readiness.
The CI/CD Swarm takes code that passed Development quality checks and validates it is safe to release through the CI/CD pipeline. Where the Development Swarm asks "Is the code quality sufficient to ship?", the CI/CD Swarm asks "Is this change safe to release?"
This swarm operates at the pipeline level, analyzing test results, regression risk, flaky test impact, security pipeline status, and infrastructure changes to render a RELEASE / REMEDIATE / BLOCK decision.
| 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 |
PIPELINE_ARTIFACTS: Path to CI/CD artifacts, test results, and build output (required, e.g., ci/artifacts/)BASELINE_REF: Git ref for baseline comparison (optional, default: main)OUTPUT_FOLDER: Where to save reports (default: ${PROJECT_ROOT}/Agentic QCSD/cicd/)DEPLOY_TARGET: Target deployment environment (optional, e.g., staging, production)| Rule | Enforcement |
|---|---|
| E1 | You MUST spawn ALL THREE core agents (qe-quality-gate, qe-regression-analyzer, qe-flaky-hunter) 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 RELEASE/REMEDIATE/BLOCK 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-deployment-advisor analysis on ALL pipeline data 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 pipeline artifacts, detect all 6 flagssteps/02-core-agents.md -- Spawn qe-quality-gate, qe-regression-analyzer, qe-flaky-hunter 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 RELEASE/REMEDIATE/BLOCK logicsteps/06-report-generation.md -- Generate executive summary and full reportsteps/07-learning-persistence.md -- Store findings to memory, save persistence recordsteps/08-deployment-advisor.md -- Run qe-deployment-advisor analysis on all pipeline datasteps/09-final-output.md -- Display completion summary with all scoresRead({ file_path: ".claude/skills/qcsd-cicd-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-quality-gate | Core (always) | quality-assessment | 1 |
| qe-regression-analyzer | Core (always) | test-execution | 1 |
| qe-flaky-hunter | Core (always) | test-execution | 1 |
| qe-security-scanner | Conditional (HAS_SECURITY_PIPELINE) | security-compliance | 2 |
| qe-chaos-engineer | Conditional (HAS_INFRA_CHANGE) | chaos-resilience | 2 |
| qe-coverage-specialist | Conditional (HAS_PERFORMANCE_PIPELINE) | coverage-analysis | 2 |
| qe-middleware-validator | Conditional (HAS_MIDDLEWARE) | enterprise-integration | 2 |
| qe-soap-tester | Conditional (HAS_SAP_INTEGRATION) | enterprise-integration | 2 |
| qe-sod-analyzer | Conditional (HAS_AUTHORIZATION) | enterprise-integration | 2 |
| qe-deployment-advisor | Analysis (always) | quality-assessment | 3 |
Total: 10 agents (3 core + 6 conditional + 1 analysis)
| Metric | RELEASE | REMEDIATE | BLOCK |
|---|---|---|---|
| Test Pass Rate | >= 99% | 95 - 98.9% | < 95% |
| Regression Count | 0 | 1-3 (P2/P3) | Any P0/P1 |
| Flaky Test Rate | < 2% | 2 - 5% | > 5% |
| Quality Gate | ALL PASS | WARN gates only | ANY FAIL gate |
| Security Scan | No HIGH/CRITICAL | MEDIUM only | HIGH/CRITICAL found |
| Coverage Delta | >= 0% | -1% to -5% | < -5% |
| Agent | Report Filename | Step |
|---|---|---|
| qe-quality-gate | 02-quality-gates.md | 2 |
| qe-regression-analyzer | 03-regression-analysis.md | 2 |
| qe-flaky-hunter | 04-flaky-test-analysis.md | 2 |
| qe-security-scanner | 05-security-scan.md | 4 |
| qe-chaos-engineer | 06-chaos-resilience.md | 4 |
| qe-coverage-specialist | 07-coverage-analysis.md | 4 |
| qe-middleware-validator | 08-middleware-health.md | 4 |
| qe-soap-tester | 09-soap-contracts.md | 4 |
| qe-sod-analyzer | 10-sod-compliance.md | 4 |
| Learning Persistence | 11-learning-persistence.json | 7 |
| qe-deployment-advisor | 12-deployment-advisory.md | 8 |
| Synthesis | 01-executive-summary.md | 6 |
| Model | When to Use | Agent Spawn |
|---|---|---|
| Task Tool (PRIMARY) | Claude Code sessions | Task({ subagent_type, run_in_background: true }) |
| MCP Tools | MCP server available | fleet_init({}) / task_submit({}) |
| CLI | Terminal/scripts | swarm init / agent spawn |
Release safety is verified by evidence, not assumptions. This swarm provides pipeline-level quality assessment to ensure every release meets quality gates.
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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。