/qcsd-production-swarm

name: qcsd-production-swarm
description: "Use when assessing post-release production health with DORA metrics, root cause analysis, defect prediction, or cross-phase feedback loops in the QCSD Production phase."
category: qcsd-phases
priority: critical
version: 1.0.0
tokenEstimate: 32000
# DDD Domain Mapping (from QCSD-AGENTIC-QE-MAPPING-FRAMEWORK.md)
domains:
  primary:
    - domain: learning-optimization
      agents: [qe-metrics-optimizer]
    - domain: defect-intelligence
      agents: [qe-defect-predictor, qe-root-cause-analyzer]
  conditional:
    - domain: chaos-resilience
      agents: [qe-chaos-engineer, qe-performance-tester]
    - domain: defect-intelligence
      agents: [qe-regression-analyzer, qe-pattern-learner]
    - domain: enterprise-integration
      agents: [qe-middleware-validator, qe-sap-rfc-tester, qe-sod-analyzer]
  feedback:
    - domain: learning-optimization
      agents: [qe-learning-coordinator, qe-transfer-specialist]
# Agent Inventory
agents:
  core: [qe-metrics-optimizer, qe-defect-predictor, qe-root-cause-analyzer]
  conditional: [qe-chaos-engineer, qe-performance-tester, qe-regression-analyzer, qe-pattern-learner, qe-middleware-validator, qe-sap-rfc-tester, qe-sod-analyzer]
  feedback: [qe-learning-coordinator, qe-transfer-specialist]
  total: 12
  sub_agents: 0
skills: [shift-right-testing, chaos-engineering-resilience, quality-metrics, performance-testing, holistic-testing-pact]
# Execution Models (Task Tool is PRIMARY)
execution:
  primary: task-tool
  alternatives: [mcp-tools, cli]
swarm_pattern: true
parallel_batches: 3
last_updated: 2026-02-17
enforcement_level: strict
tags: [qcsd, production, telemetry, dora, rca, defect-prediction, feedback-loop, learning, swarm, parallel, ddd]
trust_tier: 3
validation:
  schema_path: schemas/output.json
  validator_path: scripts/validate-config.json
  eval_path: evals/qcsd-production-swarm.yaml

QCSD Production Swarm v1.0

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

---

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)

---

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

---

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