| name | qa-resilience |
| description | Resilience engineering for QA: failure mode testing (timeouts/retries/dependency failures), chaos experiments with blast-radius controls, degraded-mode UX expectations, and reliability gates. |
QA Resilience (Dec 2025) — Failure Mode Testing & Production Hardening
This skill provides execution-ready patterns for building resilient, fault-tolerant systems that handle failures gracefully, and for validating those behaviors with tests.
Core references: Principles of Chaos Engineering (https://principlesofchaos.org/), AWS Well-Architected Reliability Pillar (https://docs.aws.amazon.com/wellarchitected/latest/reliability-pillar/welcome.html), and Kubernetes probes (https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/).
When to Use This Skill
Claude should invoke this skill when a user requests:
- Circuit breaker implementation
- Retry strategies and exponential backoff
- Bulkhead pattern for resource isolation
- Timeout policies for external dependencies
- Graceful degradation and fallback mechanisms
- Health check design (liveness vs readiness)
- Error handling best practices
- Chaos engineering setup
- Production hardening strategies
- Fault injection testing
Core QA (Default)
Failure Mode Testing (What to Validate)
- Timeouts: every network call and DB query has a bounded timeout; validate timeout budgets across chained calls.
- Retries: bounded retries with backoff + jitter; validate idempotency and “retry storm” safeguards.
- Dependency failure: partial outage, slow downstream, rate limiting, DNS failures, auth failures.
- Degraded-mode UX: what the user sees/gets when dependencies fail (cached/stale/partial responses).
- Health checks: validate liveness/readiness/startup probe behavior (Kubernetes probes: https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/).
Right-Sized Chaos Engineering (Safe by Construction)
- Define steady state and hypothesis (Principles of Chaos Engineering: https://principlesofchaos.org/).
- Start in non-prod; in prod, use minimal blast radius, timeboxed runs, and explicit abort criteria.
- REQUIRED: rollback plan, owners, and observability signals before running experiments.
Load/Perf + Production Guardrails
- Load tests validate capacity and tail latency; resilience tests validate behavior under failure.
- Guardrails [Inference]:
- Run heavy resilience/perf suites on schedule (nightly) and on canary deploys, not on every PR.
- Gate releases on regression budgets (p99 latency, error rate) rather than on raw CPU/memory.
Flake Control for Resilience Tests
- Chaos/fault injection can look “flaky” if the experiment is not deterministic.
- Stabilize the experiment first: fixed blast radius, controlled fault parameters, deterministic duration, strong observability.
Debugging Ergonomics
- Every resilience test run should capture: experiment parameters, target scope, timestamps, and trace/log links for failures.
- Prefer tracing/metrics to confirm the failure is the expected one (not collateral damage).
Do / Avoid
Do:
- Test degraded mode explicitly; document expected UX and API responses.
- Validate retries/timeouts in integration tests with fault injection.
Avoid:
- Unbounded retries and missing timeouts (amplifies incidents).
- “Happy-path only” testing that ignores downstream failure classes.
Quick Reference
| Pattern | Library/Tool | When to Use | Configuration |
|---|
| Circuit Breaker | Opossum (Node.js), pybreaker (Python) | External API calls, database connections | Threshold: 50%, timeout: 30s, volume: 10 |
| Retry with Backoff | p-retry (Node.js), tenacity (Python) | Transient failures, rate limits | Max retries: 5, exponential backoff with jitter |
| Bulkhead Isolation | Semaphore pattern, thread pools | Prevent resource exhaustion | Pool size based on workload (CPU cores + wait/service time) |
| Timeout Policies | AbortSignal, statement timeout | Slow dependencies, database queries | Connection: 5s, API: 10-30s, DB query: 5-10s |
| Graceful Degradation | Feature flags, cached fallback | Non-critical features, ML recommendations | Cache recent data, default values, reduced functionality |
| Health Checks | Kubernetes probes, /health endpoints | Service orchestration, load balancing | Liveness: simple, readiness: dependency checks, startup: slow apps |
| Chaos Engineering | Chaos Toolkit, Netflix Chaos Monkey | Proactive resilience testing | Start non-prod, define hypothesis, automate failure injection |
Decision Tree: Resilience Pattern Selection
Failure scenario: [System Dependency Type]
├─ External API/Service?
│ ├─ Transient errors? → Retry with exponential backoff + jitter
│ ├─ Cascading failures? → Circuit breaker + fallback
│ ├─ Rate limiting? → Retry with Retry-After header respect
│ └─ Slow response? → Timeout + circuit breaker
│
├─ Database Dependency?
│ ├─ Connection pool exhaustion? → Bulkhead isolation + timeout
│ ├─ Query timeout? → Statement timeout (5-10s)
│ ├─ Replica lag? → Read from primary fallback
│ └─ Connection failures? → Retry + circuit breaker
│
├─ Non-Critical Feature?
│ ├─ ML recommendations? → Feature flag + default values fallback
│ ├─ Search service? → Cached results or basic SQL fallback
│ ├─ Email/notifications? → Log error, don't block main flow
│ └─ Analytics? → Fire-and-forget, circuit breaker for protection
│
├─ Kubernetes/Orchestration?
│ ├─ Service discovery? → Liveness + readiness + startup probes
│ ├─ Slow startup? → Startup probe (failureThreshold: 30)
│ ├─ Load balancing? → Readiness probe (check dependencies)
│ └─ Auto-restart? → Liveness probe (simple check)
│
└─ Testing Resilience?
├─ Pre-production? → Chaos Toolkit experiments
├─ Production (low risk)? → Feature flags + canary deployments
├─ Scheduled testing? → Game days (quarterly)
└─ Continuous chaos? → Netflix Chaos Monkey (1% failure injection)
Navigation: Core Resilience Patterns
-
Circuit Breaker Patterns - Prevent cascading failures
- Classic circuit breaker implementation (Node.js, Python)
- Adaptive circuit breakers with ML-based thresholds (2024-2025)
- Fallback strategies and event monitoring
-
Retry Patterns - Handle transient failures
- Exponential backoff with jitter
- Retry decision table (which errors to retry)
- Idempotency patterns and Retry-After headers
-
Bulkhead Isolation - Resource compartmentalization
- Semaphore pattern for thread/connection pools
- Database connection pooling strategies
- Queue-based bulkheads with load shedding
-
Timeout Policies - Prevent resource exhaustion
- Connection, request, and idle timeouts
- Database query timeouts (PostgreSQL, MySQL)
- Nested timeout budgets for chained operations
-
Graceful Degradation - Maintain partial functionality
- Cached fallback strategies
- Default values and feature toggles
- Partial responses with Promise.allSettled
-
Health Check Patterns - Service availability monitoring
- Liveness, readiness, and startup probes
- Kubernetes probe configuration
- Shallow vs deep health checks
Navigation: Operational Resources
Navigation: Templates
-
Resilience Runbook Template - Service hardening profile
- Dependencies and SLOs
- Fallback strategies
- Rollback procedures
-
Fault Injection Playbook - Chaos testing script
- Success signals
- Rollback criteria
- Post-experiment debrief
-
Resilience Test Plan Template - Failure mode test plan (timeouts/retries/degraded mode)
- Scope and dependencies
- Fault matrix and expected behavior
- Observability signals and pass/fail criteria
Quick Decision Matrix
| Scenario | Recommendation |
|---|
| External API calls | Circuit breaker + retry with exponential backoff |
| Database queries | Timeout + connection pooling + circuit breaker |
| Slow dependency | Bulkhead isolation + timeout |
| Non-critical feature | Feature flag + graceful degradation |
| Kubernetes deployment | Liveness + readiness + startup probes |
| Testing resilience | Chaos engineering experiments |
| Transient failures | Retry with exponential backoff + jitter |
| Cascading failures | Circuit breaker + bulkhead |
Anti-Patterns to Avoid
- No timeouts - Infinite waits exhaust resources
- Infinite retries - Amplifies problems (thundering herd)
- No circuit breakers - Cascading failures
- Tight coupling - One failure breaks everything
- Silent failures - No observability into degraded state
- No bulkheads - Shared thread pools exhaust all resources
- Testing only happy path - Production reveals failures
Optional: AI / Automation
Do:
- Use AI to propose failure-mode scenarios from an explicit risk register; keep only scenarios that map to known dependencies and business journeys.
- Use AI to summarize experiment results (metrics deltas, error clusters) and draft postmortem timelines; verify with telemetry.
Avoid:
- “Scenario generation” without a risk map (creates noise and wasted load).
- Letting AI relax timeouts/retries or remove guardrails.
Related Skills
- ../ops-devops-platform/SKILL.md — Incident response, SLOs, and platform runbooks
- ../software-backend/SKILL.md — API error handling, retries, and database reliability patterns
- ../software-architecture-design/SKILL.md — System decomposition and dependency design for reliability
- ../qa-testing-strategy/SKILL.md — Regression, load, and fault-injection testing strategies
- ../software-security-appsec/SKILL.md — Security failure modes and guardrails
- ../qa-observability/SKILL.md — Metrics, tracing, logging, and performance monitoring
- ../qa-debugging/SKILL.md — Production debugging and incident investigation
- ../data-sql-optimization/SKILL.md — Database resilience, connection pooling, and query timeouts
- ../dev-api-design/SKILL.md — API design patterns including error handling and retry semantics
Usage Notes
Pattern Selection:
- Start with circuit breakers for external dependencies
- Add retries for transient failures (network, rate limits)
- Use bulkheads to prevent resource exhaustion
- Combine patterns for defense-in-depth
Observability:
- Track circuit breaker state changes
- Monitor retry attempts and success rates
- Alert on degraded mode duration
- Measure recovery time after failures
Testing:
- Start chaos experiments in non-production
- Define hypothesis before failure injection
- Set blast radius limits and auto-revert
- Document learnings and action items
Success Criteria: Systems gracefully handle failures, recover automatically, maintain partial functionality during outages, and fail fast to prevent cascading failures. Resilience is tested proactively through chaos engineering.
