Design and audit chaos experiments

Design and execute chaos experiments with hypothesis, blast radius, and error-budget risk scoring

When improving system resilience requires controlled failure injection with documented hypothesis and error-budget impact, not guesswork.

• · system architecture diagram
• · steady-state metric (SLO, latency, error rate, etc.)
• · blast radius boundaries + abort criteria

• · experiment design: hypothesis + injection plan + risk score
• · blast radius calculation
• · blameless postmortem template

• · 3 stdlib Python tools (experiment_designer, blast_radius_calculator, experiment_postmortem)
• · 4 references: chaos principles + experiment design + 7-attack taxonomy + tool landscape (Chaos Toolkit, Mesh, Litmus, Gremlin, AWS FIS, DIY)
• · /chaos-experiment slash command
• · templates for plans + postmortems

• · system has defined SLO or steady-state metric
• · error budget available (not depleted)
• · abort switch (feature flag or kill switch) documented

• · Blast radius estimation too narrow, failure cascades beyond prediction
• · Error budget depletion from previous incidents leaves no margin for chaos
• · Experiment hypothesis too loose, results don't inform actionable improvements

• · 3 stdlib Python tools (designer, calculator, postmortem generator)
• · 4 references on chaos discipline + 7-attack taxonomy
• · Risk scoring against error budget (principled abort criteria)
• · Composes with feature-flags-architect (kill switches) + kubernetes-operator (chaos targets)