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

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

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, halt and report the failure point -- do not skip ahead

Resume Support

To resume from a specific step: specify

--from-step N

Agent Inventory

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

Quality Gate Thresholds

Report Filename Mapping

02-dora-metrics.md

03-defect-prediction.md

04-root-cause-analysis.md

05-chaos-resilience.md

06-performance-sla.md

07-regression-analysis.md

08-pattern-analysis.md

09-learning-persistence.json

10-middleware-health.md

11-sap-health.md

12-sod-compliance.md

13-feedback-loops.md

01-executive-summary.md

Execution Model Options

Task({ subagent_type, run_in_background: true })

fleet_init({})

task_submit({})

swarm init

agent spawn

Key Principle

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