qcsd-development-swarm

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Use when monitoring in-sprint code quality with TDD adherence checks, complexity analysis, coverage gap detection, or defect prediction in the QCSD Development phase.

proffesor-for-testing By proffesor-for-testing schedule Updated 6/12/2026

name: qcsd-development-swarm description: "Use when monitoring in-sprint code quality with TDD adherence checks, complexity analysis, coverage gap detection, or defect prediction in the QCSD Development phase." category: qcsd-phases priority: critical version: 1.1.0 tokenEstimate: 3400

DDD Domain Mapping (from QCSD-AGENTIC-QE-MAPPING-FRAMEWORK.md)

domains: primary: - domain: test-generation agents: [qe-tdd-specialist] - domain: coverage-analysis agents: [qe-coverage-specialist] - domain: code-intelligence agents: [qe-code-complexity] conditional: - domain: security-compliance agents: [qe-security-scanner] - domain: chaos-resilience agents: [qe-performance-tester] - domain: test-generation agents: [qe-mutation-tester] - domain: enterprise-integration agents: [qe-message-broker-tester, qe-sap-idoc-tester, qe-sod-analyzer] analysis: - domain: defect-intelligence agents: [qe-defect-predictor]

Agent Inventory

agents: core: [qe-tdd-specialist, qe-code-complexity, qe-coverage-specialist] conditional: [qe-security-scanner, qe-performance-tester, qe-mutation-tester, qe-message-broker-tester, qe-sap-idoc-tester, qe-sod-analyzer] analysis: [qe-defect-predictor] total: 10 sub_agents: 0 skills: [tdd-london-chicago, mutation-testing, performance-testing, security-testing]

Execution Models (Workflow is PRIMARY where the harness supports it — ADR-102)

execution: primary: workflow workflow_name: qcsd-development-review alternatives: [task-tool, mcp-tools, cli] swarm_pattern: true parallel_batches: 3 last_updated: 2026-06-10 enforcement_level: strict tags: [qcsd, development, tdd, complexity, coverage, security, performance, mutation, defect-prediction, swarm, parallel, ddd] trust_tier: 3 validation: schema_path: schemas/output.json validator_path: scripts/validate-config.json eval_path: evals/qcsd-development-swarm.yaml

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

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

  • 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

Rule Enforcement
E1 You MUST spawn ALL THREE core agents (qe-tdd-specialist, qe-code-complexity, qe-coverage-specialist) 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 SHIP/CONDITIONAL/HOLD 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 apply qe-defect-predictor analysis on ALL code changes in Step 8. Always.
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 -- 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 and the orchestrator will skip to step N. Ensure you have the required prerequisite data from prior steps.


Agent Inventory

Agent Type Domain Batch
qe-tdd-specialist Core (always) test-generation 1
qe-code-complexity Core (always) code-intelligence 1
qe-coverage-specialist Core (always) coverage-analysis 1
qe-security-scanner Conditional (HAS_SECURITY_CODE) security-compliance 2
qe-performance-tester Conditional (HAS_PERFORMANCE_CODE) chaos-resilience 2
qe-mutation-tester Conditional (HAS_CRITICAL_CODE) test-generation 2
qe-message-broker-tester Conditional (HAS_MIDDLEWARE) enterprise-integration 2
qe-sap-idoc-tester Conditional (HAS_SAP_INTEGRATION) enterprise-integration 2
qe-sod-analyzer Conditional (HAS_AUTHORIZATION) enterprise-integration 2
qe-defect-predictor Analysis (always) defect-intelligence 3

Total: 10 agents (3 core + 6 conditional + 1 analysis)


Quality Gate Thresholds

Metric SHIP CONDITIONAL HOLD
TDD Adherence >= 80% 60 - 79% < 60%
Code Complexity Avg <= 10 Avg 11-15 Avg > 15
Test Coverage >= 80% 60 - 79% < 60%
Mutation Score >= 70% 50 - 69% < 50%
Security Issues No HIGH/CRITICAL MEDIUM only HIGH/CRITICAL found

Report Filename Mapping

Agent Report Filename Step
qe-tdd-specialist 02-tdd-analysis.md 2
qe-code-complexity 03-complexity-analysis.md 2
qe-coverage-specialist 04-coverage-analysis.md 2
qe-security-scanner 05-security-scan.md 4
qe-performance-tester 06-performance-analysis.md 4
qe-mutation-tester 07-mutation-testing.md 4
qe-message-broker-tester 08-middleware-health.md 4
qe-sap-idoc-tester 09-sap-integration.md 4
qe-sod-analyzer 10-sod-compliance.md 4
Learning Persistence 11-learning-persistence.json 7
qe-defect-predictor 12-defect-prediction.md 8
Synthesis 01-executive-summary.md 6

Execution Model Options

Model When to Use Agent Spawn
Workflow (PRIMARY, ADR-102) Harness with the Workflow tool Workflow({ name: "qcsd-development-review", args: { sourcePath, testPath } })
Task Tool (fallback) Claude Code sessions without Workflow support Task({ subagent_type, run_in_background: true })
MCP Tools MCP server available fleet_init({}) / task_submit({})
CLI Terminal/scripts swarm init / agent spawn

Workflow execution (ADR-102)

.claude/workflows/qcsd-development-review.js runs the review as a deterministic pipeline: one finder per quality dimension (TDD adherence, complexity, coverage gaps — args.dimensions selects a subset) → 3 blind adversarial refuters per finding (Loki-mode, ADR-074: refuters see only the bare claim + evidence, never the finder's confidence or each other; uncertainty defaults to refuted) → deterministic synthesis into finding-verdict@1 envelopes (ADR-103, schemas/finding-verdict.schema.json). A finding survives only if fewer than ⌈N/2⌉ refuters kill it. The final report contains ONLY confirmed findings; killed findings are retained under killed with their refutations for audit.

Args: sourcePath (required), testPath, dimensions (subset of tdd-adherence|complexity|coverage-gaps), maxFindings per dimension (default 5).

When the Workflow tool is unavailable, fall back to the Task-tool protocol below — the report format and gates are identical, minus the adversarial verification stage (note this in the report header as verification: none).


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.

Install via CLI
npx skills add https://github.com/proffesor-for-testing/agentic-qe --skill qcsd-development-swarm
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