proffesor-for-testing/qcsd-production-swarm

QCSD Production Swarm v1.0

Post-release production health assessment and QCSD feedback loop closure.

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Overview

The Production Swarm assesses release health in the live production environment using DORA metrics, incident RCA, defect prediction, and cross-phase feedback loops. It renders a HEALTHY / DEGRADED / CRITICAL decision and is the only QCSD phase with dual responsibility: assessing current production health AND closing the feedback loop back to Ideation and Refinement phases.

QCSD Phase Positioning

| 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 |

Parameters

• TELEMETRY_DATA: Path to production telemetry, incident reports, and DORA metrics (required)
• RELEASE_ID: Release identifier for tracking (optional)
• OUTPUT_FOLDER: Where to save reports (default: ${PROJECT_ROOT}/Agentic QCSD/production/)
• SLA_DEFINITIONS: Path to SLA/SLO target definitions (optional)

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ENFORCEMENT RULES - READ FIRST

| Rule | Enforcement | |------|-------------| | E1 | You MUST spawn ALL THREE core agents 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 HEALTHY/DEGRADED/CRITICAL 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 run BOTH feedback agents in Step 8 SEQUENTIALLY. Always. Both agents. | | E9 | You MUST execute Step 7 learning persistence. No skipping. |

PROHIBITED BEHAVIORS:

• Summarizing instead of spawning agents
• Skipping agents "for brevity"
• Proceeding before background tasks complete
• Providing your own analysis instead of spawning specialists
• Omitting report sections or using placeholder text

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Step Execution Protocol

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

1. Flag Detection -- steps/01-flag-detection.md -- Retrieve CI/CD signals, detect telemetry source, evaluate all 7 flags
2. Core Agents -- steps/02-core-agents.md -- Spawn qe-metrics-optimizer, qe-defect-predictor, qe-root-cause-analyzer in parallel
3. Batch 1 Results -- steps/03-batch1-results.md -- Wait for core agents, extract all metrics
4. Conditional Agents -- steps/04-conditional-agents.md -- Spawn flagged conditional agents in parallel
5. Decision Synthesis -- steps/05-decision-synthesis.md -- Apply HEALTHY/DEGRADED/CRITICAL logic
6. Report Generation -- steps/06-report-generation.md -- Generate executive summary and full report
7. Learning Persistence -- steps/07-learning-persistence.md -- Store findings to memory, save persistence record
8. Feedback Loop -- steps/08-feedback-loop.md -- Run learning coordinator then transfer specialist (sequential)
9. Final Output -- steps/09-final-output.md -- Display completion summary with all scores

Execution Instructions

1. Use the Read tool to load the current step file (e.g., Read({ file_path: ".claude/skills/qcsd-production-swarm/steps/01-flag-detection.md" }))
2. Execute the step's instructions completely
3. Verify all success criteria are met before proceeding
4. Pass the step's output as context to the next step
5. If a step fails, halt and report the failure point -- do not skip ahead

Resume Support

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.

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Agent Inventory

| Agent | Type | Domain | Batch | |-------|------|--------|-------| | qe-metrics-optimizer | Core (always) | learning-optimization | 1 | | qe-defect-predictor | Core (always) | defect-intelligence | 1 | | qe-root-cause-analyzer | Core (always) | defect-intelligence | 1 | | qe-chaos-engineer | Conditional (HAS_INFRASTRUCTURE_CHANGE) | chaos-resilience | 2 | | qe-performance-tester | Conditional (HAS_PERFORMANCE_SLA) | chaos-resilience | 2 | | qe-regression-analyzer | Conditional (HAS_REGRESSION_RISK) | defect-intelligence | 2 | | qe-pattern-learner | Conditional (HAS_RECURRING_INCIDENTS) | defect-intelligence | 2 | | qe-middleware-validator | Conditional (HAS_MIDDLEWARE) | enterprise-integration | 2 | | qe-sap-rfc-tester | Conditional (HAS_SAP_INTEGRATION) | enterprise-integration | 2 | | qe-sod-analyzer | Conditional (HAS_AUTHORIZATION) | enterprise-integration | 2 | | qe-learning-coordinator | Feedback (always, sequential) | learning-optimization | 3 | | qe-transfer-specialist | Feedback (always, sequential) | learning-optimization | 3 |

Total: 12 agents (3 core + 7 conditional + 2 feedback)

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Quality Gate Thresholds

| Metric | HEALTHY | DEGRADED | CRITICAL | |--------|---------|----------|----------| | DORA Score | >= 0.7 | 0.4 - 0.69 | < 0.4 | | SLA Compliance | >= 99% | 95 - 98.9% | < 95% | | Incident Severity | P3/P4/NONE | P2 | P0/P1 | | Defect Trend | declining/stable | stable (density > 2) | increasing + density > 5 | | RCA Completeness | >= 80% | 50 - 79% | < 50% |

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Report Filename Mapping

| Agent | Report Filename | Step | |-------|----------------|------| | qe-metrics-optimizer | 02-dora-metrics.md | 2 | | qe-defect-predictor | 03-defect-prediction.md | 2 | | qe-root-cause-analyzer | 04-root-cause-analysis.md | 2 | | qe-chaos-engineer | 05-chaos-resilience.md | 4 | | qe-performance-tester | 06-performance-sla.md | 4 | | qe-regression-analyzer | 07-regression-analysis.md | 4 | | qe-pattern-learner | 08-pattern-analysis.md | 4 | | Learning Persistence | 09-learning-persistence.json | 7 | | qe-middleware-validator | 10-middleware-health.md | 4 | | qe-sap-rfc-tester | 11-sap-health.md | 4 | | qe-sod-analyzer | 12-sod-compliance.md | 4 | | Feedback agents | 13-feedback-loops.md | 8 | | Synthesis | 01-executive-summary.md | 6 |

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Execution Model Options

| 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 |

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Key Principle

Production health is measured by outcomes, not intentions. This swarm provides evidence-based production assessment and closes the QCSD feedback loop.