QA Resilience

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/).

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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

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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

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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)

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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

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Navigation: Operational Resources

• Resilience Checklists - Production hardening checklists

  • Dependency resilience
  • Health and readiness probes
  • Observability for resilience
  • Failure testing

• Chaos Engineering Guide - Safe reliability experiments

  • Planning chaos experiments
  • Common failure injection scenarios
  • Execution steps and debrief checklist

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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

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Quick Decision Matrix

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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

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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.

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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

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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

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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.