QCSD Development Swarm v1.0

Shift-left quality engineering swarm for in-sprint code quality assurance.

Overview

The Development Swarm takes refined stories (that passed Refinement) and validates code quality during sprint execution. Where the Ideation Swarm asks "Should we build this?" and the Refinement Swarm asks "How should we test this?", the Development Swarm asks "Is the code quality sufficient to ship?"

QCSD Phase Positioning

Parameters

• SOURCE_PATH

  : Source code directory to analyze (required, e.g.,

  src/auth/

  )

• TEST_PATH

  : Test directory for coverage analysis (optional, e.g.,

  tests/auth/

  )

• OUTPUT_FOLDER

  : Where to save reports (default:

  ${PROJECT_ROOT}/Agentic QCSD/development/

  )

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

   -- Scan source code and tests, detect all 6 flags
2. Core Agents --

   steps/02-core-agents.md

   -- Spawn qe-tdd-specialist, qe-code-complexity, qe-coverage-specialist 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 SHIP/CONDITIONAL/HOLD 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. Defect Predictor --

   steps/08-defect-predictor.md

   -- Run qe-defect-predictor analysis on all code changes
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-development-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: 10 agents (3 core + 6 conditional + 1 analysis)

Quality Gate Thresholds

Report Filename Mapping

02-tdd-analysis.md

03-complexity-analysis.md

04-coverage-analysis.md

05-security-scan.md

06-performance-analysis.md

07-mutation-testing.md

08-middleware-health.md

09-sap-integration.md

10-sod-compliance.md

11-learning-persistence.json

12-defect-prediction.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

Code quality is measured by evidence, not intentions. This swarm provides in-sprint quality assessment to ensure code meets engineering standards before entering the CI/CD pipeline.