Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
日本語の概要は準備中です。原文の説明を表示しています。
Use when monitoring in-sprint code quality with TDD adherence checks, complexity analysis, coverage gap detection, or defect prediction in the QCSD Development phase.
日本語の概要は準備中です。原文の説明を表示しています。
実行スコア 未検証
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
Shift-left quality engineering swarm for in-sprint code quality assurance.
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?"
| 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 |
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/)| 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:
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/01-flag-detection.md -- Scan source code and tests, detect all 6 flagssteps/02-core-agents.md -- Spawn qe-tdd-specialist, qe-code-complexity, qe-coverage-specialist in parallelsteps/03-batch1-results.md -- Wait for core agents, extract all metricssteps/04-conditional-agents.md -- Spawn flagged conditional agents in parallelsteps/05-decision-synthesis.md -- Apply SHIP/CONDITIONAL/HOLD logicsteps/06-report-generation.md -- Generate executive summary and full reportsteps/07-learning-persistence.md -- Store findings to memory, save persistence recordsteps/08-defect-predictor.md -- Run qe-defect-predictor analysis on all code changessteps/09-final-output.md -- Display completion summary with all scoresRead({ file_path: ".claude/skills/qcsd-development-swarm/steps/01-flag-detection.md" }))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 | 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)
| 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 |
| 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 |
| 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 |
.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).
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.
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Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
日本語の概要は準備中です。原文の説明を表示しています。
WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
日本語の概要は準備中です。原文の説明を表示しています。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
日本語の概要は準備中です。原文の説明を表示しています。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
日本語の概要は準備中です。原文の説明を表示しています。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
日本語の概要は準備中です。原文の説明を表示しています。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
日本語の概要は準備中です。原文の説明を表示しています。