Use when running Sprint Refinement sessions with SFDIPOT product factors, generating BDD scenarios, or validating requirements in the QCSD Refinement phase.
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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 Sprint Refinement sessions.
The Refinement Swarm takes user stories that passed Ideation and prepares them for Sprint commitment using SFDIPOT product factors, BDD scenarios, and INVEST validation. It renders a READY / CONDITIONAL / NOT-READY 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 |
STORY_CONTENT: User story with acceptance criteria (required)OUTPUT_FOLDER: Where to save reports (default: ${PROJECT_ROOT}/Agentic QCSD/refinement/)| Rule | Enforcement |
|---|---|
| E1 | MUST spawn ALL THREE core agents in Step 2. |
| E2 | MUST put all parallel Task calls in a SINGLE message. |
| E3 | MUST STOP and WAIT after each batch. |
| E4 | MUST spawn conditional agents if flags are TRUE. |
| E5 | MUST apply READY/CONDITIONAL/NOT-READY logic exactly. |
| E6 | MUST generate the full report structure. |
| E7 | Each agent MUST read its reference files before analysis. |
| E8 | MUST apply qe-test-idea-rewriter transformation in Step 8. |
| E9 | MUST execute Step 7 learning persistence. |
Execute steps sequentially by reading each step file with the Read tool.
steps/01-flag-detection.md -- Analyze story content, evaluate all 7 flagssteps/02-core-agents.md -- Spawn qe-product-factors-assessor, qe-bdd-generator, qe-requirements-validatorsteps/03-batch1-results.md -- Wait and extract metricssteps/04-conditional-agents.md -- Spawn flagged agentssteps/05-decision-synthesis.md -- Apply READY/CONDITIONAL/NOT-READY logicsteps/06-report-generation.md -- Generate refinement reportsteps/07-learning-persistence.md -- Store findings to memorysteps/08-transformation.md -- Run test idea rewriter on all test ideassteps/09-final-output.md -- Display completion summaryTo resume from a specific step: specify --from-step N.
| Agent | Type | Domain | Batch |
|---|---|---|---|
| qe-product-factors-assessor | Core | requirements-validation | 1 |
| qe-bdd-generator | Core | requirements-validation | 1 |
| qe-requirements-validator | Core | requirements-validation | 1 |
| qe-contract-validator | Conditional (HAS_API) | contract-testing | 2 |
| qe-impact-analyzer | Conditional (HAS_REFACTORING) | code-intelligence | 2 |
| qe-dependency-mapper | Conditional (HAS_DEPENDENCIES) | code-intelligence | 2 |
| qe-middleware-validator | Conditional (HAS_MIDDLEWARE) | enterprise-integration | 2 |
| qe-odata-contract-tester | Conditional (HAS_SAP_INTEGRATION) | enterprise-integration | 2 |
| qe-sod-analyzer | Conditional (HAS_AUTHORIZATION) | enterprise-integration | 2 |
| qe-test-idea-rewriter | Transformation (always) | test-generation | 3 |
Total: 10 agents (3 core + 6 conditional + 1 transformation)
Refinement quality determines sprint success. This swarm ensures stories are testable, complete, and ready for development commitment.
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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.
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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.
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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.
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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.
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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.
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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.
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