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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.
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Ruflo is a multi-agent orchestration platform for AI coding agents (Claude Code, Cursor, Codex, Copilot, Gemini, Amp, +12 more). Use this skill when the user wants to (1) install/init ruflo in a project, (2) run multi-agent swarms with hierarchical coordination, (3) use ruflo's 314+ MCP tools for memory, routing, hooks, sub-agents, or workflows, (4) check ruflo status/version/doctor health, or (5) discover which of ruflo's 30+ plugins fits their task.
Build dependency-aware execution plans for complex Agentic QE, Ruflo, integration, migration, or multi-stream engineering programs. Use when Codex must turn research or requirements into phased work, select a small AQE fleet, map critical paths and parallel streams, define acceptance gates, or sequence risky changes. Use aqe-plan-quality instead when the primary output is only a test or quality plan.
Conduct evidence-first technical research for Agentic QE, Ruflo, related ruvnet projects, or external tools. Use when Codex must investigate a repository, compare current upstream changes, trace dependencies and history, distinguish verified facts from inference, or synthesize findings into actionable engineering recommendations. Do not use for a simple known-answer lookup or an implementation-only task.
基于 SOC 职业分类
正在显示 SKILL.md
| 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 |
| 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"]}]} |
| 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 | {"primary":"task-tool","alternatives":["mcp-tools","cli"]} |
| swarm_pattern | true |
| parallel_batches | 3 |
| last_updated | "2026-02-17T00:00:00.000Z" |
| 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"} |
Post-release production health assessment and QCSD feedback loop closure.
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.
| 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 |
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)| 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:
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 -- Retrieve CI/CD signals, detect telemetry source, evaluate all 7 flagssteps/02-core-agents.md -- Spawn qe-metrics-optimizer, qe-defect-predictor, qe-root-cause-analyzer 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 HEALTHY/DEGRADED/CRITICAL logicsteps/06-report-generation.md -- Generate executive summary and full reportsteps/07-learning-persistence.md -- Store findings to memory, save persistence recordsteps/08-feedback-loop.md -- Run learning coordinator then transfer specialist (sequential)steps/09-final-output.md -- Display completion summary with all scoresRead({ file_path: ".claude/skills/qcsd-production-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-metrics-optimizer | Core (always) | learning-optimization | 1 |
| qe-defect-predictor | Core (always) | defect-intelligence | 1 |
| qe-root-cause-analyzer | Core (always) | defect-intelligence | 1 |
| qe-chaos-engineer | Conditional (HAS_INFRASTRUCTURE_CHANGE) | chaos-resilience | 2 |
| qe-performance-tester | Conditional (HAS_PERFORMANCE_SLA) | chaos-resilience | 2 |
| qe-regression-analyzer | Conditional (HAS_REGRESSION_RISK) | defect-intelligence | 2 |
| qe-pattern-learner | Conditional (HAS_RECURRING_INCIDENTS) | defect-intelligence | 2 |
| qe-middleware-validator | Conditional (HAS_MIDDLEWARE) | enterprise-integration | 2 |
| qe-sap-rfc-tester | Conditional (HAS_SAP_INTEGRATION) | enterprise-integration | 2 |
| qe-sod-analyzer | Conditional (HAS_AUTHORIZATION) | enterprise-integration | 2 |
| qe-learning-coordinator | Feedback (always, sequential) | learning-optimization | 3 |
| qe-transfer-specialist | Feedback (always, sequential) | learning-optimization | 3 |
Total: 12 agents (3 core + 7 conditional + 2 feedback)
| Metric | HEALTHY | DEGRADED | CRITICAL |
|---|---|---|---|
| DORA Score | >= 0.7 | 0.4 - 0.69 | < 0.4 |
| SLA Compliance | >= 99% | 95 - 98.9% | < 95% |
| Incident Severity | P3/P4/NONE | P2 | P0/P1 |
| Defect Trend | declining/stable | stable (density > 2) | increasing + density > 5 |
| RCA Completeness | >= 80% | 50 - 79% | < 50% |
| Agent | Report Filename | Step |
|---|---|---|
| qe-metrics-optimizer | 02-dora-metrics.md | 2 |
| qe-defect-predictor | 03-defect-prediction.md | 2 |
| qe-root-cause-analyzer | 04-root-cause-analysis.md | 2 |
| qe-chaos-engineer | 05-chaos-resilience.md | 4 |
| qe-performance-tester | 06-performance-sla.md | 4 |
| qe-regression-analyzer | 07-regression-analysis.md | 4 |
| qe-pattern-learner | 08-pattern-analysis.md | 4 |
| Learning Persistence | 09-learning-persistence.json | 7 |
| qe-middleware-validator | 10-middleware-health.md | 4 |
| qe-sap-rfc-tester | 11-sap-health.md | 4 |
| qe-sod-analyzer | 12-sod-compliance.md | 4 |
| Feedback agents | 13-feedback-loops.md | 8 |
| Synthesis | 01-executive-summary.md | 6 |
| Model | When to Use | Agent Spawn |
|---|---|---|
| Task Tool (PRIMARY) | Claude Code sessions | Task({ subagent_type, run_in_background: true }) |
| MCP Tools | MCP server available | fleet_init({}) / task_submit({}) |
| CLI | Terminal/scripts | swarm init / agent spawn |
Production health is measured by outcomes, not intentions. This swarm provides evidence-based production assessment and closes the QCSD feedback loop.