Agent skill · product strategy · borghei
chaos-engineering
Chaos engineering: hypothesis-driven fault injection to surface weakness before users do. Use when designing a chaos experiment, planning a gameday, choosing what to inject, computing blast radius, or building a chaos maturity model.
Why this skill is useful
Provides a detailed framework for chaos engineering experiments, including specific scripts for experiment design and blast radius calculation that the AI wouldn't reliably generate on its own.
What it needs
Requires python installed locally. About 3k tokens when loaded. Last updated 2026-07-21. 462 stars on the source repository.
What this skill does
Chaos Engineering End-to-end chaos engineering: experiment design, fault injection catalog, gameday execution, and the maturity model that turns one-off "let's break stuff" exercises into a reliable discipline. Provider-agnostic — works whether you use Litmus, Chaos Mesh, AWS FIS, Gremlin, ChaosToolkit, or hand-rolled scripts. This skill answers four questions: what to inject, where to inject it, how to size the blast, and how to extract durable learning from each run. Core Capabilities Principles & maturity — the five Principles of Chaos and a four-level maturity model (L0 none → L4 always-on production chaos) with level-up criteria. Experiment design loop — a nine-step loop (steady state → hypothesis → variables → blast radius → abort → run → analyze → act → document) with worked good/bad examples. Fault catalog — what to inject per layer: pod/host, network, dependency, resource, state, and traffic, with tool mappings. Blast-radius sizing — quantify worst-case affected users and recommended caps; experiments start tiny (1 pod / 1% / 1 min) and grow only after passing. Gameday execution — scheduled multi-scenario exercises with roles, agendas, scenario selection, and debrief templates. Discipline — anti-patterns to avoid, the "first five experiments" for new teams, and end-to-end workflows (single experiment, gameday, kill-switch verification, post-incident verification). When to Use Situation Skill applies ----------- --------------- Spinning up a chaos program from scratch Yes — start with maturity model + first 5 experiments Designing a single experiment for a known concern Yes — use the experiment design loop Planning a gameday for a team or service Yes — use scripts/gamedayplanner.py Validating a kill switch or fallback path actually works Yes — chaos is the way to test these in prod-like conditions Post-incident verification: "did the fix really fix it?" Yes — re-inject the original fault, confirm the new behavior Compliance evidence (SOC 2 A1 / DORA Art. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills borghei/chaos-engineering