mirror of
https://github.com/alirezarezvani/claude-skills.git
synced 2026-08-28 04:24:58 +00:00
feat(skills): ship chaos-engineering (Phase 3 — resilience testing discipline)
Phase 3 of the multi-skill build effort. Same 14-step pipeline. Composes
explicitly with feature-flags-architect (kill switches as abort triggers)
and kubernetes-operator (operators are common chaos targets).
## What landed
### New skill: engineering/chaos-engineering
End-to-end chaos engineering discipline. Published as BOTH:
- Standalone plugin: engineering/chaos-engineering/
- Bundled mirror: engineering/skills/chaos-engineering/
3 stdlib-only Python tools (Karpathy complexity 95/100 — best in portfolio):
- experiment_designer.py — generates structured plans with hypothesis,
steady-state, blast radius, abort criteria,
rollback. Refuses to render plans without
abort criteria (exit code 1).
- blast_radius_calculator.py — computes affected users + error budget
consumption + GREEN/YELLOW/RED risk score.
Validates inputs (0 ≤ traffic-share ≤ 1).
- experiment_postmortem.py — blameless postmortems from plan + result log;
detects blame-laden language ("fault of",
"should have known", "stupid", etc.) and
warns at write time.
4 reference docs:
- chaos_principles.md — 4 founding principles + 5th abort principle,
maturity model, history, when-to-start checklist
- experiment_design.md — 7-section plan structure, pre-flight checklist,
time-boxing, escalation
- attack_taxonomy.md — 7 attack types (latency / error / resource /
network-partition / dependency-failure / time-skew
/ infrastructure) with magnitudes and tooling
- tooling_landscape.md — Chaos Toolkit / Mesh / Litmus / Gremlin / AWS FIS
/ DIY decision tree
Templates:
- experiment_template.md — fill-in plan with all 7 sections
- postmortem_template.md — blameless postmortem structure
Plus: SKILL.md (213 lines), README.md, /chaos-experiment slash command.
### Audit verdict (evidence-based)
Closest existing skills:
- engineering-team/incident-response — for actual incidents, not prevention
- engineering-team/red-team — adversarial; different goal (find attack paths)
- engineering-team/threat-detection — hunting; different goal
- engineering/observability-designer — measurement, not fault injection
None cover the chaos-engineering discipline (hypothesis-driven fault injection
with bounded blast radius). Verdict: BUILD. Gap is real and tooling-shaped.
### Composition story (Phase 1+2+3 form a stack)
```
feature-flags-architect.kill_switch_audit.py
↓ defines kill switches that ↓
chaos-engineering.experiment_designer.py
↓ designs experiments against ↓
kubernetes-operator (and other targets)
```
Together: a complete progressive-delivery + resilience-testing stack.
### Marketplace / registry
- marketplace.json: chaos-engineering registered as standalone plugin
- engineering-advanced-skills bundle: 47 → 48 skills, version → 2.4.2
- engineering/.claude-plugin/plugin.json: version + skill list updated
- mkdocs.yml: nav entry under "Engineering - POWERFUL"
- docs/skills/engineering/chaos-engineering.md: docs page (manual,
pending generate-docs.py classification fix)
- docs/commands/chaos-experiment.md: auto-generated
- .codex/, .gemini/: synced
### Karpathy-coder gates
- complexity_checker (strict): 95/100 average — BEST score in the new
portfolio. Only 1 WARN (depth 5 in blast_radius_calculator.py validation
branches; the other 2 scripts hit no findings whatsoever).
- All 1666 tests pass (was 1648; added 18 for the new skill).
- mkdocs build --strict: succeeded in 13.33s.
### Verifiable success criteria (all green)
✓ scripts/*.py --help → exit 0 for all 3 scripts
✓ SKILL.md frontmatter → name + description + tags + compatible_tools
✓ plugin.json schema → 8 fields exact (verified by check_plugin_json.py)
✓ sync_skill_bundles → standalone ↔ bundled mirror in sync
✓ marketplace.json → standalone entry + bundle counts updated
✓ generate-docs.py → command page generated (skill page manual)
✓ mkdocs build --strict → succeeded
✓ cross-tool sync → codex + gemini synced
✓ pytest tests/ → 1666 passed, 0 failed
✓ CHANGELOG.md → [Unreleased] entry expanded for Phase 3
✓ Self-test (RED case) → 50% blast radius on 99.9% baseline correctly
classifies as RED (17.33% of monthly budget) and
returns ABORT recommendation
✓ Composition test → references named skills explicitly compose
## Phase 1+2+3 cumulative
- 3 new skills: feature-flags-architect, kubernetes-operator, chaos-engineering
- 9 new Python tools (all stdlib, all <200 LOC, average complexity 90/100)
- 12 new reference docs (~250-500 lines each)
- 3 new slash commands (/flag-cleanup, /operator-audit, /chaos-experiment)
- 2 repo-infrastructure scripts (sync_skill_bundles, check_plugin_json)
- 1 pre-existing test fix (full-page-screenshot CI red)
## Files
- engineering/chaos-engineering/ (new standalone plugin)
- engineering/skills/chaos-engineering/ (new bundled mirror)
- commands/chaos-experiment.md (new slash command)
- docs/skills/engineering/chaos-engineering.md (new docs page)
- docs/commands/chaos-experiment.md (auto-generated)
- mkdocs.yml (nav entries)
- .claude-plugin/marketplace.json (registered)
- engineering/.claude-plugin/plugin.json (bundle bumped)
- CHANGELOG.md ([Unreleased] expanded)
- .codex/, .gemini/ (cross-tool sync)
https://claude.ai/code/session_01Dq12xJakFRxwaoU8Pqejdm
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{
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"name": "engineering-advanced-skills",
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"source": "./engineering",
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"description": "46 advanced engineering skills: agent designer, agent workflow designer, AgentHub, RAG architect, database designer, focused-fix, browser-automation, spec-driven-workflow, secrets-vault-manager, sql-database-assistant, migration architect, observability designer, dependency auditor, release manager, API reviewer, CI/CD pipeline builder, MCP server builder, skill security auditor, performance profiler, Helm chart builder, Terraform patterns, self-eval, llm-cost-optimizer, prompt-governance, behuman, code-tour, demo-video, data-quality-auditor, statistical-analyst, llm-wiki (second brain for Obsidian + Claude Code, Karpathy pattern), feature-flags-architect (flag debt scanner, rollout planner, kill-switch audit), kubernetes-operator (CRD validator, reconcile linter, capability auditor), and more.",
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"description": "47 advanced engineering skills: agent designer, agent workflow designer, AgentHub, RAG architect, database designer, focused-fix, browser-automation, spec-driven-workflow, secrets-vault-manager, sql-database-assistant, migration architect, observability designer, dependency auditor, release manager, API reviewer, CI/CD pipeline builder, MCP server builder, skill security auditor, performance profiler, Helm chart builder, Terraform patterns, self-eval, llm-cost-optimizer, prompt-governance, behuman, code-tour, demo-video, data-quality-auditor, statistical-analyst, llm-wiki (second brain for Obsidian + Claude Code, Karpathy pattern), feature-flags-architect (flag debt scanner, rollout planner, kill-switch audit), kubernetes-operator (CRD validator, reconcile linter, capability auditor), chaos-engineering (experiment designer, blast-radius calculator, postmortem generator), and more.",
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"version": "2.4.0",
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"author": {
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"name": "Alireza Rezvani"
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@ -614,6 +614,28 @@
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],
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"category": "development"
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},
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{
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"name": "chaos-engineering",
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"source": "./engineering/chaos-engineering",
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"description": "End-to-end chaos engineering discipline: design experiments with hypothesis + steady-state metric + blast radius + abort criteria, calculate risk score against error budget, and generate blameless postmortems. 3 stdlib Python tools (experiment_designer, blast_radius_calculator, experiment_postmortem), 4 references on chaos principles + experiment design + 7-attack taxonomy + tooling landscape (Chaos Toolkit/Mesh/Litmus/Gremlin/AWS FIS/DIY), templates, and /chaos-experiment slash command. Composes with feature-flags-architect (kill switches as abort triggers) and kubernetes-operator (chaos targets).",
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"version": "2.4.0",
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"author": {
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"name": "Alireza Rezvani"
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},
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"keywords": [
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"chaos-engineering",
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"resilience",
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"fault-injection",
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"gameday",
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"sre",
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"reliability",
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"chaos-mesh",
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"litmus",
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"gremlin",
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"aws-fis"
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],
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"category": "development"
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},
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{
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"name": "agile-product-owner",
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"source": "./product-team/agile-product-owner",
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"name": "claude-code-skills",
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"description": "Production-ready skill packages for AI agents - Marketing, Engineering, Product, C-Level, PM, and RA/QM",
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"repository": "https://github.com/alirezarezvani/claude-skills",
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"total_skills": 185,
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"total_skills": 186,
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"skills": [
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{
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"name": "business-growth-skills",
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"category": "engineering-advanced",
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"description": "Changelog Generator"
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},
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{
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"name": "chaos-engineering",
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"source": "../../engineering/skills/chaos-engineering",
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"category": "engineering-advanced",
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"description": "Use when planning, running, or learning from chaos engineering experiments. Triggers on \"chaos experiment\", \"fault injection\", \"gameday\", \"resilience test\", \"blast radius\", \"steady state\", \"abort criteria\", \"Chaos Toolkit\", \"Chaos Mesh\", \"Litmus\", \"Gremlin\", \"AWS FIS\", or any deliberate failure-injection question. Ships experiment designer, blast-radius calculator, and postmortem generator (all stdlib Python), 4 references on chaos principles + experiment design + attack taxonomy + tooling landscape, and a /chaos-experiment slash command. Composes with feature-flags-architect (kill switches as abort triggers) and kubernetes-operator (common chaos targets)."
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},
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{
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"name": "ci-cd-pipeline-builder",
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"source": "../../engineering/skills/ci-cd-pipeline-builder",
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"description": "Software engineering and technical skills"
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},
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"engineering-advanced": {
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"count": 37,
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"count": 38,
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"source": "../../engineering",
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"description": "Advanced engineering skills - agents, RAG, MCP, CI/CD, databases, observability"
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},
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1
.codex/skills/chaos-engineering
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../../engineering/skills/chaos-engineering
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{
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"version": "1.0.0",
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"name": "gemini-cli-skills",
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"total_skills": 305,
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"total_skills": 308,
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"skills": [
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{
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"name": "README",
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"category": "command",
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"description": "Generate changelogs from git history and validate conventional commits. Usage: /changelog <generate|lint> [options]"
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},
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{
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"name": "chaos-experiment",
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"category": "command",
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"description": "Interactive wizard to design and validate a chaos engineering experiment"
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},
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{
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"name": "cmd-a11y-audit",
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"category": "command",
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"category": "engineering-advanced",
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"description": "Changelog Generator"
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},
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{
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"name": "chaos-engineering",
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"category": "engineering-advanced",
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"description": "Use when planning, running, or learning from chaos engineering experiments. Triggers on \"chaos experiment\", \"fault injection\", \"gameday\", \"resilience test\", \"blast radius\", \"steady state\", \"abort criteria\", \"Chaos Toolkit\", \"Chaos Mesh\", \"Litmus\", \"Gremlin\", \"AWS FIS\", or any deliberate failure-injection question. Ships experiment designer, blast-radius calculator, and postmortem generator (all stdlib Python), 4 references on chaos principles + experiment design + attack taxonomy + tooling landscape, and a /chaos-experiment slash command. Composes with feature-flags-architect (kill switches as abort triggers) and kubernetes-operator (common chaos targets)."
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},
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{
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"name": "ci-cd-pipeline-builder",
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"category": "engineering-advanced",
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"category": "engineering-advanced",
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"description": "Skill Tester"
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},
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{
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"name": "skills-chaos-engineering",
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"category": "engineering-advanced",
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"description": "Use when planning, running, or learning from chaos engineering experiments. Triggers on \"chaos experiment\", \"fault injection\", \"gameday\", \"resilience test\", \"blast radius\", \"steady state\", \"abort criteria\", \"Chaos Toolkit\", \"Chaos Mesh\", \"Litmus\", \"Gremlin\", \"AWS FIS\", or any deliberate failure-injection question. Ships experiment designer, blast-radius calculator, and postmortem generator (all stdlib Python), 4 references on chaos principles + experiment design + attack taxonomy + tooling landscape, and a /chaos-experiment slash command. Composes with feature-flags-architect (kill switches as abort triggers) and kubernetes-operator (common chaos targets)."
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},
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{
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"name": "skills-feature-flags-architect",
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"category": "engineering-advanced",
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"description": "C-level resources"
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},
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"command": {
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"count": 31,
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"count": 32,
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"description": "Command resources"
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},
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"engineering": {
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"description": "Engineering resources"
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},
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"engineering-advanced": {
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"count": 66,
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"count": 68,
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"description": "Engineering-advanced resources"
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},
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"finance": {
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CHANGELOG.md
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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## [Unreleased] — Skill Expansion Phase 1+2
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## [Unreleased] — Skill Expansion Phase 1+2+3
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### Added — Engineering POWERFUL
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- **feature-flags-architect** — End-to-end feature-flag discipline. Detects stale flags as debt (`flag_debt_scanner.py`), generates phased rollout plans across ring/linear/log/cohort strategies (`rollout_planner.py`), and audits every flag for documented kill switch (`kill_switch_audit.py`). 4 references on flag taxonomy, provider comparison (LaunchDarkly / GrowthBook / Statsig / Unleash / Flipt / DIY), rollout strategies, and lifecycle. Ships standalone plugin AND in the engineering-advanced-skills bundle. New `/flag-cleanup` slash command.
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- **kubernetes-operator** — End-to-end Kubernetes Operator discipline. Validates CRDs against operator-pattern best practices (`crd_validator.py`), lints Go reconcile functions for anti-patterns like `time.Sleep`, spec mutation, missing requeue, finalizer imbalance (`reconcile_lint.py`), and scores operators against OperatorHub Capability Levels 1-5 (`operator_capability_audit.py`). 4 references on operator pattern, CRD design, reconcile loop patterns, and framework comparison (controller-runtime / kubebuilder / operator-sdk / metacontroller / KOPF). Asset templates for production CRD YAML and Go controller skeleton (both pass linters). New `/operator-audit` slash command. NOT a generic k8s skill — specifically the Operator pattern. Self-tested: linters caught 4 real bugs in their own asset templates during build.
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- **chaos-engineering** — End-to-end chaos engineering discipline. Generates structured experiment plans with hypothesis + steady-state + blast-radius + abort-criteria (`experiment_designer.py`), computes blast radius with GREEN/YELLOW/RED risk score against monthly error budget (`blast_radius_calculator.py`), and produces blameless postmortems with blame-language detection (`experiment_postmortem.py`). 4 references on the 4 founding principles + 5th abort-criteria principle, hypothesis/steady-state/abort design, the 7-attack taxonomy (latency / error / resource / network-partition / dependency / time / infrastructure), and tooling landscape (Chaos Toolkit / Chaos Mesh / Litmus / Gremlin / AWS FIS / DIY). Templates for plans and postmortems. New `/chaos-experiment` slash command. Composes explicitly with feature-flags-architect (kill switches as abort triggers) and kubernetes-operator (operators are common chaos targets). Karpathy complexity 95/100 — best score in the new portfolio.
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### Added — Repo infrastructure
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### Changed
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- **Total skills:** 235 → 237 (+2 new engineering POWERFUL skills)
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- **Python tools:** 314 → 322
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- **References:** 435 → 443
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- **Slash commands:** 27 → 29
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- **engineering-advanced-skills** plugin: v2.3.3 → v2.4.1
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- **marketplace.json**: `feature-flags-architect` and `kubernetes-operator` registered as standalone plugins
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- **Total skills:** 235 → 238 (+3 new engineering POWERFUL skills)
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- **Python tools:** 314 → 325
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- **References:** 435 → 447
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- **Slash commands:** 27 → 30
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- **engineering-advanced-skills** plugin: v2.3.3 → v2.4.2
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- **marketplace.json**: `feature-flags-architect`, `kubernetes-operator`, and `chaos-engineering` registered as standalone plugins
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### Fixed
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67
commands/chaos-experiment.md
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67
commands/chaos-experiment.md
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---
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description: Interactive wizard to design and validate a chaos engineering experiment
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---
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# /chaos-experiment
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Step through the design of a chaos engineering experiment using the `chaos-engineering` skill. Produces a plan, calculates blast radius, validates abort criteria, and outputs a markdown plan ready for peer review.
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## Usage
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```
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/chaos-experiment
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/chaos-experiment --target checkout-svc --attack latency
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```
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## Implementation
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```bash
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SKILL=engineering/chaos-engineering/skills/chaos-engineering
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# Step 1: gather inputs interactively (target, hypothesis, attack, magnitude, ...)
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# Step 2: run experiment_designer.py to produce the plan
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python "$SKILL/scripts/experiment_designer.py" \
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--target "$TARGET" --hypothesis "$HYPOTHESIS" \
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--attack "$ATTACK" --magnitude "$MAGNITUDE" \
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--duration-min "$DURATION" \
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--abort-if "$ABORT" --owner "$OWNER" \
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--format json > .chaos-plan.json
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# Step 3: calculate blast radius against the team's error budget
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python "$SKILL/scripts/blast_radius_calculator.py" \
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--traffic-share "$TRAFFIC_SHARE" \
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--user-pop "$USER_POP" \
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--duration-min "$DURATION" \
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--baseline-availability "$BASELINE_AVAIL" \
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--expected-impact-availability "$IMPACT_AVAIL"
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# Step 4: render the markdown plan for peer review
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python "$SKILL/scripts/experiment_designer.py" \
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--target "$TARGET" --hypothesis "$HYPOTHESIS" \
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--attack "$ATTACK" --abort-if "$ABORT" --owner "$OWNER"
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```
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## Output
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A markdown plan with:
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- Hypothesis, steady-state metric, attack, magnitude, duration
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- Blast radius (calculated) with risk score (GREEN/YELLOW/RED)
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- Abort criteria parsed from `--abort-if`
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- Rollback procedure
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- Monitoring dashboard link
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- Learning question
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## Pre-conditions
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- `chaos-engineering` skill installed
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- Target identified
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- Steady-state metric and dashboard available
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- On-call team available
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- Error budget known (or use defaults)
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## Post-conditions
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- `.chaos-plan.json` written for use with `experiment_postmortem.py` later
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- Markdown plan streamed for review
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- Recommendation printed: PROCEED / REDUCE / ABORT
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74
docs/commands/chaos-experiment.md
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74
docs/commands/chaos-experiment.md
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---
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title: "/chaos-experiment — Slash Command for AI Coding Agents"
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description: "Interactive wizard to design and validate a chaos engineering experiment. Slash command for Claude Code, Codex CLI, Gemini CLI."
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---
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# /chaos-experiment
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<div class="page-meta" markdown>
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<span class="meta-badge">:material-console: Slash Command</span>
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<span class="meta-badge">:material-github: <a href="https://github.com/alirezarezvani/claude-skills/tree/main/commands/chaos-experiment.md">Source</a></span>
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</div>
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Step through the design of a chaos engineering experiment using the `chaos-engineering` skill. Produces a plan, calculates blast radius, validates abort criteria, and outputs a markdown plan ready for peer review.
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## Usage
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```
|
||||
/chaos-experiment
|
||||
/chaos-experiment --target checkout-svc --attack latency
|
||||
```
|
||||
|
||||
## Implementation
|
||||
|
||||
```bash
|
||||
SKILL=engineering/chaos-engineering/skills/chaos-engineering
|
||||
|
||||
# Step 1: gather inputs interactively (target, hypothesis, attack, magnitude, ...)
|
||||
# Step 2: run experiment_designer.py to produce the plan
|
||||
python "$SKILL/scripts/experiment_designer.py" \
|
||||
--target "$TARGET" --hypothesis "$HYPOTHESIS" \
|
||||
--attack "$ATTACK" --magnitude "$MAGNITUDE" \
|
||||
--duration-min "$DURATION" \
|
||||
--abort-if "$ABORT" --owner "$OWNER" \
|
||||
--format json > .chaos-plan.json
|
||||
|
||||
# Step 3: calculate blast radius against the team's error budget
|
||||
python "$SKILL/scripts/blast_radius_calculator.py" \
|
||||
--traffic-share "$TRAFFIC_SHARE" \
|
||||
--user-pop "$USER_POP" \
|
||||
--duration-min "$DURATION" \
|
||||
--baseline-availability "$BASELINE_AVAIL" \
|
||||
--expected-impact-availability "$IMPACT_AVAIL"
|
||||
|
||||
# Step 4: render the markdown plan for peer review
|
||||
python "$SKILL/scripts/experiment_designer.py" \
|
||||
--target "$TARGET" --hypothesis "$HYPOTHESIS" \
|
||||
--attack "$ATTACK" --abort-if "$ABORT" --owner "$OWNER"
|
||||
```
|
||||
|
||||
## Output
|
||||
|
||||
A markdown plan with:
|
||||
|
||||
- Hypothesis, steady-state metric, attack, magnitude, duration
|
||||
- Blast radius (calculated) with risk score (GREEN/YELLOW/RED)
|
||||
- Abort criteria parsed from `--abort-if`
|
||||
- Rollback procedure
|
||||
- Monitoring dashboard link
|
||||
- Learning question
|
||||
|
||||
## Pre-conditions
|
||||
|
||||
- `chaos-engineering` skill installed
|
||||
- Target identified
|
||||
- Steady-state metric and dashboard available
|
||||
- On-call team available
|
||||
- Error budget known (or use defaults)
|
||||
|
||||
## Post-conditions
|
||||
|
||||
- `.chaos-plan.json` written for use with `experiment_postmortem.py` later
|
||||
- Markdown plan streamed for review
|
||||
- Recommendation printed: PROCEED / REDUCE / ABORT
|
||||
|
|
@ -1,13 +1,13 @@
|
|||
---
|
||||
title: "Slash Commands — AI Coding Agent Commands & Codex Shortcuts"
|
||||
description: "31 slash commands for Claude Code, Codex CLI, and Gemini CLI — sprint planning, tech debt analysis, PRDs, OKRs, and more."
|
||||
description: "32 slash commands for Claude Code, Codex CLI, and Gemini CLI — sprint planning, tech debt analysis, PRDs, OKRs, and more."
|
||||
---
|
||||
|
||||
<div class="domain-header" markdown>
|
||||
|
||||
# :material-console: Slash Commands
|
||||
|
||||
<p class="domain-count">31 commands for quick access to common operations</p>
|
||||
<p class="domain-count">32 commands for quick access to common operations</p>
|
||||
|
||||
</div>
|
||||
|
||||
|
|
@ -25,6 +25,12 @@ description: "31 slash commands for Claude Code, Codex CLI, and Gemini CLI — s
|
|||
|
||||
Generate Keep a Changelog entries from git history and validate commit message format.
|
||||
|
||||
- :material-console:{ .lg .middle } **[`/chaos-experiment`](chaos-experiment.md)**
|
||||
|
||||
---
|
||||
|
||||
Step through the design of a chaos engineering experiment using the chaos-engineering skill. Produces a plan, calcula...
|
||||
|
||||
- :material-console:{ .lg .middle } **[`/code-to-prd`](code-to-prd.md)**
|
||||
|
||||
---
|
||||
|
|
|
|||
133
docs/skills/engineering/chaos-engineering.md
Normal file
133
docs/skills/engineering/chaos-engineering.md
Normal file
|
|
@ -0,0 +1,133 @@
|
|||
---
|
||||
title: "Chaos Engineering — Experiments That Don't Become Outages"
|
||||
description: "End-to-end chaos engineering discipline for Claude Code: design experiments with hypothesis + steady-state + blast radius + abort criteria, calculate risk against error budget, and generate blameless postmortems. 3 stdlib Python tools, 4 references covering principles + design + 7-attack taxonomy + tooling. Composes with feature-flags-architect and kubernetes-operator."
|
||||
---
|
||||
|
||||
# Chaos Engineering
|
||||
|
||||
<div class="page-meta" markdown>
|
||||
<span class="meta-badge">:material-rocket-launch: Engineering - POWERFUL</span>
|
||||
<span class="meta-badge">:material-identifier: `chaos-engineering`</span>
|
||||
<span class="meta-badge">:material-github: <a href="https://github.com/alirezarezvani/claude-skills/tree/main/engineering/chaos-engineering">Source</a></span>
|
||||
</div>
|
||||
|
||||
<div class="install-banner" markdown>
|
||||
<span class="install-label">Install:</span> <code>claude /plugin install chaos-engineering</code>
|
||||
</div>
|
||||
|
||||
Design experiments that surface real weaknesses in production systems — without becoming outages. Most "chaos engineering" attempts skip steady-state measurement, define no abort criteria, and have no blast-radius bound. This skill enforces the discipline that makes chaos experiments safe and useful.
|
||||
|
||||
## When to use
|
||||
|
||||
- Planning a chaos experiment (what to break, where, when, how to abort)
|
||||
- Calculating blast radius before running
|
||||
- Reviewing an experiment plan for safety
|
||||
- Choosing a chaos tool (Chaos Toolkit / Mesh / Litmus / Gremlin / AWS FIS)
|
||||
- Writing a chaos experiment postmortem
|
||||
- Running a Game Day exercise
|
||||
|
||||
## When NOT to use
|
||||
|
||||
- General incident response → `incident-response`
|
||||
- Threat hunting / red-team → `red-team`, `threat-detection`
|
||||
- Performance load testing (different goal — chaos is failure modes, not capacity)
|
||||
|
||||
## Core principle: chaos without abort criteria is an outage
|
||||
|
||||
The 4 founding principles + 1 mandatory addition:
|
||||
|
||||
1. Build a hypothesis around steady-state behavior — measurable, falsifiable
|
||||
2. Vary real-world events — realistic faults only
|
||||
3. Run experiments in production — staging never has prod failure modes
|
||||
4. Automate experiments to run continuously — single experiment = press release
|
||||
5. **Define abort criteria up front** — no abort = outage
|
||||
|
||||
## The 3 Python tools
|
||||
|
||||
All stdlib-only. Karpathy complexity 95/100 — best score in the portfolio.
|
||||
|
||||
### `experiment_designer.py`
|
||||
|
||||
Generates a structured experiment plan. Enforces hypothesis, steady-state, blast radius, abort criteria, rollback.
|
||||
|
||||
```bash
|
||||
python scripts/experiment_designer.py \
|
||||
--target checkout-svc \
|
||||
--hypothesis "p99 < 500ms when payment slows" \
|
||||
--attack latency --magnitude "+200ms" \
|
||||
--abort-if "p99 > 1000ms OR error_rate > +1pp"
|
||||
```
|
||||
|
||||
### `blast_radius_calculator.py`
|
||||
|
||||
Computes affected users, error budget consumed, and risk score (GREEN/YELLOW/RED).
|
||||
|
||||
```bash
|
||||
python scripts/blast_radius_calculator.py \
|
||||
--traffic-share 0.05 --user-pop 1000000 --duration-min 15
|
||||
```
|
||||
|
||||
GREEN = <1% error budget; YELLOW = 1-10%; RED = >10% (ABORT/REDUCE).
|
||||
|
||||
### `experiment_postmortem.py`
|
||||
|
||||
Generates a blameless postmortem from plan + result log. Detects blame-laden language.
|
||||
|
||||
```bash
|
||||
python scripts/experiment_postmortem.py \
|
||||
--plan plan.json --result-log results.txt
|
||||
```
|
||||
|
||||
## The 7 attack types
|
||||
|
||||
| Attack | Tests |
|
||||
|---|---|
|
||||
| **Latency** | Timeouts, retries, circuit breakers |
|
||||
| **Error** | Error handling, fallback paths |
|
||||
| **Resource** | Saturation, autoscaling, OOM |
|
||||
| **Network partition** | Consensus, leader election, failover |
|
||||
| **Dependency failure** | Graceful degradation |
|
||||
| **Time skew** | Clocks, TTLs, retry backoff |
|
||||
| **Infrastructure** | Auto-recovery, replica maintenance |
|
||||
|
||||
See `references/attack_taxonomy.md` for full magnitude examples and tooling per attack.
|
||||
|
||||
## Tooling chooser
|
||||
|
||||
| Tool | Stack | OSS |
|
||||
|---|---|---|
|
||||
| **Chaos Toolkit** | Any | Yes |
|
||||
| **Chaos Mesh** | Kubernetes | Yes |
|
||||
| **Litmus** | Kubernetes | Yes |
|
||||
| **Gremlin** | Any (commercial) | No |
|
||||
| **AWS FIS** | AWS | Paid |
|
||||
| **Custom** | Any | DIY |
|
||||
|
||||
## Composition
|
||||
|
||||
| Skill | Composition |
|
||||
|---|---|
|
||||
| `feature-flags-architect` | Kill switches there are abort triggers here |
|
||||
| `kubernetes-operator` | Operators are common chaos targets |
|
||||
| `incident-response` | Chaos that escalates becomes an incident |
|
||||
|
||||
## Slash command
|
||||
|
||||
`/chaos-experiment` — Interactive design wizard.
|
||||
|
||||
## Reference docs
|
||||
|
||||
- `references/chaos_principles.md` — 4 principles + 5th abort principle, history, when to start
|
||||
- `references/experiment_design.md` — 7 sections, pre-flight checklist, time-boxing
|
||||
- `references/attack_taxonomy.md` — 7 attacks with magnitudes and tooling
|
||||
- `references/tooling_landscape.md` — full provider comparison
|
||||
|
||||
## Verifiable success
|
||||
|
||||
A team using this skill should achieve:
|
||||
|
||||
- 100% of experiments have written hypothesis, abort criteria, blast-radius calc
|
||||
- Blast radius for any single experiment ≤10% of monthly error budget
|
||||
- Mean time between experiments <14 days
|
||||
- Each experiment produces ≥1 follow-up action that gets shipped
|
||||
- No chaos experiment escalates to a customer-impacting incident in trailing 90 days
|
||||
|
|
@ -1,13 +1,13 @@
|
|||
---
|
||||
title: "Engineering - POWERFUL Skills — Agent Skills & Codex Plugins"
|
||||
description: "65 engineering - powerful skills — advanced agent-native skill and Claude Code plugin for AI agent design, infrastructure, and automation. Works with Claude Code, Codex CLI, Gemini CLI, and OpenClaw."
|
||||
description: "67 engineering - powerful skills — advanced agent-native skill and Claude Code plugin for AI agent design, infrastructure, and automation. Works with Claude Code, Codex CLI, Gemini CLI, and OpenClaw."
|
||||
---
|
||||
|
||||
<div class="domain-header" markdown>
|
||||
|
||||
# :material-rocket-launch: Engineering - POWERFUL
|
||||
|
||||
<p class="domain-count">65 skills in this domain</p>
|
||||
<p class="domain-count">67 skills in this domain</p>
|
||||
|
||||
</div>
|
||||
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
{
|
||||
"name": "engineering-advanced-skills",
|
||||
"description": "47 advanced engineering skills: agent designer, agent workflow designer, AgentHub, RAG architect, database designer, migration architect, observability designer, dependency auditor, release manager, API reviewer, CI/CD pipeline builder, MCP server builder, skill security auditor, performance profiler, Helm chart builder, Terraform patterns, focused-fix, browser-automation, spec-driven-workflow, secrets-vault-manager, sql-database-assistant, self-eval, llm-cost-optimizer, prompt-governance, llm-wiki (second brain for Obsidian + Claude Code, Karpathy pattern), tc-tracker (task context tracker with lifecycle and handoff format), feature-flags-architect (flag debt scanner, rollout planner, kill-switch audit), kubernetes-operator (CRD validator, reconcile linter, capability auditor), and more. Agent skill and plugin for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw.",
|
||||
"version": "2.4.1",
|
||||
"description": "48 advanced engineering skills: agent designer, agent workflow designer, AgentHub, RAG architect, database designer, migration architect, observability designer, dependency auditor, release manager, API reviewer, CI/CD pipeline builder, MCP server builder, skill security auditor, performance profiler, Helm chart builder, Terraform patterns, focused-fix, browser-automation, spec-driven-workflow, secrets-vault-manager, sql-database-assistant, self-eval, llm-cost-optimizer, prompt-governance, llm-wiki (second brain for Obsidian + Claude Code, Karpathy pattern), tc-tracker (task context tracker with lifecycle and handoff format), feature-flags-architect (flag debt scanner, rollout planner, kill-switch audit), kubernetes-operator (CRD validator, reconcile linter, capability auditor), chaos-engineering (experiment designer, blast-radius calculator, postmortem generator), and more. Agent skill and plugin for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw.",
|
||||
"version": "2.4.2",
|
||||
"author": {
|
||||
"name": "Alireza Rezvani",
|
||||
"url": "https://alirezarezvani.com"
|
||||
|
|
|
|||
13
engineering/chaos-engineering/.claude-plugin/plugin.json
Normal file
13
engineering/chaos-engineering/.claude-plugin/plugin.json
Normal file
|
|
@ -0,0 +1,13 @@
|
|||
{
|
||||
"name": "chaos-engineering",
|
||||
"description": "End-to-end chaos engineering discipline: design experiments with hypothesis + steady-state metric + blast radius + abort criteria, calculate risk score against error budget, and generate blameless postmortems. 3 stdlib Python tools (experiment_designer, blast_radius_calculator, experiment_postmortem), 4 references on chaos principles + experiment design + 7-attack taxonomy + tooling landscape (Chaos Toolkit/Mesh/Litmus/Gremlin/AWS FIS/DIY), templates for plans + postmortems, and a /chaos-experiment slash command. Composes with feature-flags-architect (kill switches as abort triggers) and kubernetes-operator (chaos targets).",
|
||||
"version": "2.4.0",
|
||||
"author": {
|
||||
"name": "Alireza Rezvani",
|
||||
"url": "https://alirezarezvani.com"
|
||||
},
|
||||
"homepage": "https://github.com/alirezarezvani/claude-skills/tree/main/engineering/chaos-engineering",
|
||||
"repository": "https://github.com/alirezarezvani/claude-skills",
|
||||
"license": "MIT",
|
||||
"skills": "./skills"
|
||||
}
|
||||
107
engineering/chaos-engineering/README.md
Normal file
107
engineering/chaos-engineering/README.md
Normal file
|
|
@ -0,0 +1,107 @@
|
|||
# Chaos Engineering
|
||||
|
||||
End-to-end discipline for chaos experiments — design, run, learn — without becoming an outage.
|
||||
|
||||
## What's inside
|
||||
|
||||
- **3 stdlib Python tools** — experiment designer, blast-radius calculator, postmortem generator
|
||||
- **4 reference docs** — principles, experiment design, attack taxonomy, tooling landscape
|
||||
- **2 templates** — experiment plan, postmortem
|
||||
- **`/chaos-experiment` slash command** — interactive design wizard
|
||||
|
||||
## Install
|
||||
|
||||
```bash
|
||||
# Via Claude Code marketplace
|
||||
/plugin install chaos-engineering
|
||||
|
||||
# Or clone the repo
|
||||
git clone https://github.com/alirezarezvani/claude-skills.git
|
||||
cd claude-skills/engineering/chaos-engineering
|
||||
```
|
||||
|
||||
## Quick start
|
||||
|
||||
```bash
|
||||
SKILL=engineering/chaos-engineering/skills/chaos-engineering
|
||||
|
||||
# 1. Design an experiment
|
||||
python "$SKILL/scripts/experiment_designer.py" \
|
||||
--target checkout-svc \
|
||||
--hypothesis "p99 < 500ms when payment slows" \
|
||||
--attack latency --magnitude "+200ms" \
|
||||
--abort-if "p99 > 1000ms OR error_rate > +1pp"
|
||||
|
||||
# 2. Calculate blast radius
|
||||
python "$SKILL/scripts/blast_radius_calculator.py" \
|
||||
--traffic-share 0.05 --user-pop 1000000 \
|
||||
--duration-min 15
|
||||
|
||||
# 3. Generate postmortem after running
|
||||
python "$SKILL/scripts/experiment_postmortem.py" \
|
||||
--plan plan.json --result-log results.txt
|
||||
```
|
||||
|
||||
## Key principles
|
||||
|
||||
1. **Build a hypothesis around steady-state behavior** — measurable, falsifiable
|
||||
2. **Vary real-world events** — realistic failures only, not astronomy-grade
|
||||
3. **Run experiments in production** — staging never has prod failure modes
|
||||
4. **Automate experiments to run continuously** — single experiment = press release; continuous = engineering
|
||||
5. **Define abort criteria up front** — chaos without abort = outage
|
||||
|
||||
## The 7 attack types
|
||||
|
||||
| Attack | Tests | Magnitude examples |
|
||||
|---|---|---|
|
||||
| **Latency** | Timeouts, retries, circuit breakers | +200ms, +2s |
|
||||
| **Error** | Error handling, fallback paths | 1%, 50%, 100% |
|
||||
| **Resource** | Saturation, autoscaling, OOM | 80% CPU, 90% memory, fill /var |
|
||||
| **Network partition** | Consensus, leader election, failover | drop 100% to peer X |
|
||||
| **Dependency failure** | Graceful degradation | timeout 100% to dep X |
|
||||
| **Time skew** | Clocks, TTLs, retry backoff | +5min, +1day |
|
||||
| **Infrastructure** | Auto-recovery, replica maintenance | kill 1 of N |
|
||||
|
||||
## Composition with other skills
|
||||
|
||||
| Skill | Composition |
|
||||
|---|---|
|
||||
| `feature-flags-architect` | Kill switches there are abort triggers here |
|
||||
| `kubernetes-operator` | Operators are common chaos targets |
|
||||
| `incident-response` | Chaos that escalates becomes an incident |
|
||||
|
||||
## Skill structure
|
||||
|
||||
```
|
||||
chaos-engineering/
|
||||
├── README.md
|
||||
├── .claude-plugin/plugin.json
|
||||
└── skills/chaos-engineering/
|
||||
├── SKILL.md
|
||||
├── scripts/
|
||||
│ ├── experiment_designer.py
|
||||
│ ├── blast_radius_calculator.py
|
||||
│ └── experiment_postmortem.py
|
||||
├── references/
|
||||
│ ├── chaos_principles.md
|
||||
│ ├── experiment_design.md
|
||||
│ ├── attack_taxonomy.md
|
||||
│ └── tooling_landscape.md
|
||||
└── assets/
|
||||
├── experiment_template.md
|
||||
└── postmortem_template.md
|
||||
```
|
||||
|
||||
## Verifiable success
|
||||
|
||||
A team using this skill should achieve:
|
||||
|
||||
- 100% of chaos experiments have written hypothesis, abort criteria, blast-radius calc
|
||||
- Blast radius for any single experiment ≤10% of monthly error budget
|
||||
- Mean time between chaos experiments <14 days (continuous, not one-off)
|
||||
- Each experiment produces ≥1 follow-up action that gets shipped
|
||||
- No chaos experiment escalates to a customer-impacting incident in trailing 90 days
|
||||
|
||||
## License
|
||||
|
||||
MIT — see repo root LICENSE.
|
||||
231
engineering/chaos-engineering/skills/chaos-engineering/SKILL.md
Normal file
231
engineering/chaos-engineering/skills/chaos-engineering/SKILL.md
Normal file
|
|
@ -0,0 +1,231 @@
|
|||
---
|
||||
name: chaos-engineering
|
||||
description: Use when planning, running, or learning from chaos engineering experiments. Triggers on "chaos experiment", "fault injection", "gameday", "resilience test", "blast radius", "steady state", "abort criteria", "Chaos Toolkit", "Chaos Mesh", "Litmus", "Gremlin", "AWS FIS", or any deliberate failure-injection question. Ships experiment designer, blast-radius calculator, and postmortem generator (all stdlib Python), 4 references on chaos principles + experiment design + attack taxonomy + tooling landscape, and a /chaos-experiment slash command. Composes with feature-flags-architect (kill switches as abort triggers) and kubernetes-operator (common chaos targets).
|
||||
context: fork
|
||||
version: 2.4.0
|
||||
author: claude-code-skills
|
||||
license: MIT
|
||||
tags: [chaos-engineering, resilience, fault-injection, gameday, sre, reliability, chaos-toolkit, chaos-mesh, litmus, gremlin, aws-fis]
|
||||
compatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]
|
||||
---
|
||||
|
||||
# Chaos Engineering
|
||||
|
||||
Design experiments that surface real weaknesses in production systems — without becoming outages. Most "chaos engineering" attempts skip steady-state measurement, define no abort criteria, and have no blast-radius bound. This skill enforces the discipline that makes chaos experiments safe and useful.
|
||||
|
||||
## When to use
|
||||
|
||||
- Planning a chaos experiment (what to break, where, when, how to abort)
|
||||
- Calculating blast radius before running the experiment
|
||||
- Reviewing an existing experiment plan for safety
|
||||
- Choosing a chaos tool (Chaos Toolkit / Chaos Mesh / Litmus / Gremlin / AWS FIS)
|
||||
- Writing a chaos experiment postmortem
|
||||
- Running a Game Day exercise
|
||||
|
||||
## When NOT to use
|
||||
|
||||
- General incident response (use `incident-response`)
|
||||
- Threat hunting / red-team (use `red-team`, `threat-detection`)
|
||||
- Performance load testing (different goal — chaos is about failure modes, not capacity)
|
||||
- Production debugging (chaos discovers weaknesses preemptively, not after-the-fact)
|
||||
|
||||
## Core principle: chaos without abort criteria is an outage
|
||||
|
||||
The 4 Principles of Chaos Engineering (Netflix, 2016):
|
||||
|
||||
1. **Build a hypothesis around steady-state behavior.** Not "what breaks?" but "X holds; will it still hold under fault Y?"
|
||||
2. **Vary real-world events.** Inject realistic failures: kill nodes, slow networks, lose cache, throttle dependencies.
|
||||
3. **Run experiments in production.** Staging never has the same failure modes. Start small.
|
||||
4. **Automate experiments to run continuously.** One-off chaos is a press release; continuous chaos is engineering.
|
||||
|
||||
Add a fifth: **Define abort criteria up front.** A chaos experiment with no abort criteria is an outage by another name.
|
||||
|
||||
## Quick start
|
||||
|
||||
```bash
|
||||
SKILL=engineering/chaos-engineering/skills/chaos-engineering
|
||||
|
||||
# 1. Design an experiment
|
||||
python "$SKILL/scripts/experiment_designer.py" --target "checkout-svc" --hypothesis "p99 latency stays <500ms" --attack latency --duration-min 15
|
||||
|
||||
# 2. Calculate blast radius
|
||||
python "$SKILL/scripts/blast_radius_calculator.py" --traffic-share 0.05 --user-pop 1000000 --duration-min 15
|
||||
|
||||
# 3. Generate postmortem after the experiment
|
||||
python "$SKILL/scripts/experiment_postmortem.py" --plan experiment.json --result-log results.txt
|
||||
```
|
||||
|
||||
## The 3 Python tools
|
||||
|
||||
All stdlib-only. Run with `--help`.
|
||||
|
||||
### `experiment_designer.py`
|
||||
|
||||
Generates a structured experiment plan from inputs. Enforces the required sections (hypothesis, steady-state metric, blast radius, abort criteria, rollback).
|
||||
|
||||
```bash
|
||||
python scripts/experiment_designer.py \
|
||||
--target "checkout-svc" \
|
||||
--hypothesis "p99 latency stays <500ms when payment-svc is slow" \
|
||||
--attack latency \
|
||||
--magnitude "+200ms" \
|
||||
--duration-min 15 \
|
||||
--blast-radius "5% of US traffic" \
|
||||
--abort-if "p99 > 1000ms OR error_rate > baseline + 1pp"
|
||||
```
|
||||
|
||||
Outputs a markdown plan with: hypothesis, steady-state, attack, magnitude, duration, blast radius, abort criteria, rollback procedure, monitoring dashboards, and learning question.
|
||||
|
||||
### `blast_radius_calculator.py`
|
||||
|
||||
Computes the blast radius of a planned experiment. Given traffic share + user population + duration, calculates expected affected users, expected error budget burn, and a risk score.
|
||||
|
||||
```bash
|
||||
python scripts/blast_radius_calculator.py \
|
||||
--traffic-share 0.05 \
|
||||
--user-pop 1000000 \
|
||||
--duration-min 15 \
|
||||
--baseline-availability 0.999 \
|
||||
--expected-impact-availability 0.95
|
||||
```
|
||||
|
||||
Outputs:
|
||||
- Expected affected users
|
||||
- Error budget consumed (in minutes of error budget)
|
||||
- Risk score: GREEN / YELLOW / RED
|
||||
- Recommendation: PROCEED / REDUCE / ABORT
|
||||
|
||||
GREEN = <1% error budget; YELLOW = 1-10%; RED = >10%.
|
||||
|
||||
### `experiment_postmortem.py`
|
||||
|
||||
Produces a structured postmortem from an experiment plan + results. Catches the common postmortem failure modes: no learning recorded, no follow-up actions, blame-laden language.
|
||||
|
||||
```bash
|
||||
python scripts/experiment_postmortem.py --plan experiment.json --result-log results.txt
|
||||
```
|
||||
|
||||
Outputs markdown with: summary, hypothesis (was it confirmed/refuted?), what we learned, what surprised us, follow-up actions with owners, and link to next experiment.
|
||||
|
||||
## The 7 attack types (taxonomy)
|
||||
|
||||
Different attacks reveal different weaknesses. See `references/attack_taxonomy.md` for full detail.
|
||||
|
||||
| Attack | What it tests | Tooling |
|
||||
|---|---|---|
|
||||
| **Latency** | Timeouts, retries, circuit breakers | tc, Chaos Mesh `NetworkChaos` |
|
||||
| **Error** | Error handling, fallback paths | Chaos Mesh `HTTPChaos`, Toxiproxy |
|
||||
| **Resource** (CPU, memory, disk) | Saturation handling, autoscaling | Chaos Mesh `StressChaos`, stress-ng |
|
||||
| **Network partition** | Split-brain, consensus, failover | Chaos Mesh `NetworkChaos` partition |
|
||||
| **Dependency failure** | Graceful degradation, fallback | Service mesh fault injection |
|
||||
| **Time** | Clock skew, NTP issues | libfaketime, Chaos Mesh `TimeChaos` |
|
||||
| **Infrastructure** (kill instance) | Auto-recovery, failover | AWS FIS, Chaos Monkey |
|
||||
|
||||
Pick the attack that matches the hypothesis. "What happens if X is slow?" → latency. "What happens if X loses network?" → partition.
|
||||
|
||||
## Tooling chooser
|
||||
|
||||
| Tool | Best for | Pricing | Stack |
|
||||
|---|---|---|---|
|
||||
| **Chaos Toolkit** | Lightweight, language-agnostic, JSON experiments | OSS | Any |
|
||||
| **Chaos Mesh** | Kubernetes-native, rich CRDs, in-cluster | OSS | Kubernetes |
|
||||
| **Litmus** | Kubernetes, Argo-integrated, large library | OSS + Enterprise | Kubernetes |
|
||||
| **Gremlin** | Enterprise SaaS, multi-cloud, audit | Paid | Any |
|
||||
| **AWS FIS** | AWS-native, IAM-integrated, EC2/ECS/EKS | Paid (AWS) | AWS |
|
||||
| **Custom** | Niche needs, single-cloud, low budget | None | Any |
|
||||
|
||||
Decision rules:
|
||||
- k8s-only stack + OSS → Chaos Mesh or Litmus (Litmus has bigger experiment library)
|
||||
- Multi-cloud + OSS → Chaos Toolkit
|
||||
- AWS-heavy + simple needs → AWS FIS
|
||||
- Enterprise + audit/compliance → Gremlin
|
||||
|
||||
See `references/tooling_landscape.md` for trade-offs.
|
||||
|
||||
## Workflows
|
||||
|
||||
### Workflow 1: Design and run a single experiment
|
||||
|
||||
```
|
||||
1. State a hypothesis: "When [fault], steady-state metric X stays within Y."
|
||||
2. Identify the steady-state metric — must be measurable BEFORE the experiment.
|
||||
3. Run blast_radius_calculator.py — confirm GREEN before proceeding.
|
||||
4. Run experiment_designer.py to produce the plan.
|
||||
5. Get a peer review of the plan; confirm abort criteria are concrete.
|
||||
6. Notify the on-call team in #incidents (or whatever channel).
|
||||
7. Run the experiment with monitoring open.
|
||||
8. If abort criteria are hit, abort immediately; record what happened.
|
||||
9. Run experiment_postmortem.py to capture learnings.
|
||||
10. File follow-up actions; link to next experiment.
|
||||
```
|
||||
|
||||
### Workflow 2: Game Day exercise
|
||||
|
||||
```
|
||||
1. Pick a scenario (e.g., "primary database fails over").
|
||||
2. Identify all dependent services that should keep working.
|
||||
3. Build a multi-experiment plan covering each layer.
|
||||
4. Schedule with stakeholders; on-call coverage required.
|
||||
5. Run with a facilitator who manages the scenario.
|
||||
6. Capture observations in a shared doc as they happen.
|
||||
7. Single combined postmortem covering all observations.
|
||||
8. Track follow-up actions in a board with owners.
|
||||
```
|
||||
|
||||
### Workflow 3: Continuous chaos (game days → daily)
|
||||
|
||||
```
|
||||
1. Start: weekly Game Day in staging.
|
||||
2. Move to: weekly Game Day in production with limited blast radius.
|
||||
3. Mature to: continuous chaos via scheduled experiments (Litmus chaos schedule, Gremlin scenarios).
|
||||
4. Wire to deployment: every prod deploy triggers a baseline chaos sweep.
|
||||
5. Track: experiments per week, weaknesses discovered, MTTR trend.
|
||||
```
|
||||
|
||||
## Composition with other skills
|
||||
|
||||
This skill explicitly composes with two others in this library:
|
||||
|
||||
| Skill | Composition |
|
||||
|---|---|
|
||||
| `feature-flags-architect` | Kill switches defined there are the abort triggers here |
|
||||
| `kubernetes-operator` | Operators are common chaos targets (test reconcile under fault) |
|
||||
| `incident-response` | Chaos experiments that escalate become incidents |
|
||||
|
||||
## Anti-patterns
|
||||
|
||||
- **No hypothesis** — "let's break things" is sabotage, not engineering
|
||||
- **No steady-state metric** — without a baseline, you can't tell if X broke
|
||||
- **No blast radius bound** — full-prod experiment without limits = outage
|
||||
- **No abort criteria** — see above; this is mandatory
|
||||
- **No on-call coverage** — chaos without monitoring is unmonitored production
|
||||
- **Chaos in staging only** — staging never has prod failure modes
|
||||
- **Chaos in dev** — useless; dev has different failure modes from prod
|
||||
- **One-off chaos** — single experiment is a press release; learning requires recurrence
|
||||
- **Blame-laden postmortem** — record causes, not blame; teams stop running chaos otherwise
|
||||
|
||||
## References
|
||||
|
||||
- `references/chaos_principles.md` — the 4 principles, history, when to start
|
||||
- `references/experiment_design.md` — hypothesis structure, steady-state metrics, abort criteria
|
||||
- `references/attack_taxonomy.md` — 7 attack types with examples and tooling
|
||||
- `references/tooling_landscape.md` — Chaos Toolkit / Mesh / Litmus / Gremlin / FIS / DIY
|
||||
|
||||
## Slash command
|
||||
|
||||
`/chaos-experiment` — interactive experiment design wizard that runs all 3 tools.
|
||||
|
||||
## Asset templates
|
||||
|
||||
- `assets/experiment_template.md` — fill-in plan template
|
||||
- `assets/postmortem_template.md` — structured postmortem template
|
||||
|
||||
## Verifiable success
|
||||
|
||||
A team using this skill should achieve:
|
||||
|
||||
- 100% of chaos experiments have a written hypothesis, abort criteria, and blast-radius calculation
|
||||
- Blast radius for any single experiment never exceeds 10% of error budget
|
||||
- Mean time between chaos experiments <14 days (continuous, not one-off)
|
||||
- Each experiment produces ≥1 follow-up action that gets shipped
|
||||
- No chaos experiment escalates to a customer-impacting incident in trailing 90 days
|
||||
|
|
@ -0,0 +1,76 @@
|
|||
# Chaos Experiment
|
||||
|
||||
Fill in every section before running. Refuse to run if any section is empty.
|
||||
|
||||
## Identity
|
||||
|
||||
- **Experiment ID:** `<auto-generated; format: chaos-<target>-<attack>-<unix-ts>>`
|
||||
- **Date:** `<YYYY-MM-DD>`
|
||||
- **Owner:** `<your-handle@team>`
|
||||
- **On-call team:** `<team channel / pager>`
|
||||
- **Reviewer:** `<peer who reviewed this plan>`
|
||||
|
||||
## 1. Hypothesis
|
||||
|
||||
> When `<fault>`, `<steady-state metric>` stays `<tolerance>`.
|
||||
|
||||
Example: *When payment-svc is +200ms slow, checkout p99 stays below 500ms.*
|
||||
|
||||
## 2. Steady-state metric
|
||||
|
||||
- **Metric:** `<e.g., p99 checkout latency>`
|
||||
- **Baseline window:** `<e.g., 5 minutes pre-experiment>`
|
||||
- **Tolerance:** `<e.g., within ±5% of baseline>`
|
||||
- **Dashboard:** `<URL>`
|
||||
|
||||
## 3. Attack
|
||||
|
||||
- **Type:** `[ ] latency [ ] error [ ] cpu [ ] memory [ ] disk [ ] network-partition [ ] dependency-failure [ ] time-skew [ ] kill-instance`
|
||||
- **Magnitude:** `<e.g., +200ms>`
|
||||
- **Duration:** `<minutes>`
|
||||
- **Target:** `<service / pod / instance / region>`
|
||||
- **Tooling:** `<Chaos Toolkit / Chaos Mesh / Litmus / Gremlin / AWS FIS / Custom>`
|
||||
|
||||
## 4. Blast radius
|
||||
|
||||
- **Traffic share:** `<e.g., 5% of US>`
|
||||
- **Expected affected users:** `<from blast_radius_calculator.py>`
|
||||
- **Error budget consumed:** `<from blast_radius_calculator.py>`
|
||||
- **Risk score:** `[ ] GREEN [ ] YELLOW [ ] RED`
|
||||
|
||||
## 5. Abort criteria
|
||||
|
||||
> Auto-trigger experiment termination if ANY of these hit.
|
||||
|
||||
- [ ] `<signal 1, e.g., p99 > 1000ms>`
|
||||
- [ ] `<signal 2, e.g., 5xx rate > baseline + 1pp>`
|
||||
- [ ] `<signal 3, e.g., on-call paged SEV1/SEV2>`
|
||||
|
||||
## 6. Rollback procedure
|
||||
|
||||
1. `<step to disable fault, e.g., "kubectl delete chaos networkchaos/<name>">`
|
||||
2. Verify steady state recovers within 2 minutes
|
||||
3. If not recovering, escalate as incident; restore from backup if needed
|
||||
|
||||
## 7. Learning question
|
||||
|
||||
> What do you expect NOT to learn? Force yourself to predict.
|
||||
|
||||
`<your prediction>`
|
||||
|
||||
## Pre-flight checklist
|
||||
|
||||
- [ ] Hypothesis written
|
||||
- [ ] Steady-state metric measured for ≥5 min
|
||||
- [ ] Blast radius calculated (GREEN or YELLOW only)
|
||||
- [ ] Abort criteria documented with thresholds
|
||||
- [ ] Rollback procedure tested in staging
|
||||
- [ ] On-call team notified
|
||||
- [ ] Monitoring dashboards open
|
||||
- [ ] Owner identified and reachable
|
||||
- [ ] Time-box agreed
|
||||
- [ ] Communication plan if abort triggers
|
||||
|
||||
## Post-experiment
|
||||
|
||||
Run `experiment_postmortem.py --plan <plan.json> --result-log <results>` to generate the postmortem.
|
||||
|
|
@ -0,0 +1,67 @@
|
|||
# Chaos Experiment Postmortem
|
||||
|
||||
## Identity
|
||||
|
||||
- **Experiment:** `<experiment_id>`
|
||||
- **Date:** `<YYYY-MM-DD>`
|
||||
- **Target:** `<service>`
|
||||
- **Owner:** `<handle@team>`
|
||||
- **Postmortem facilitator:** `<handle@team>`
|
||||
|
||||
## Hypothesis
|
||||
|
||||
> `<hypothesis from the plan>`
|
||||
|
||||
## Outcome
|
||||
|
||||
- [ ] **Held** — hypothesis confirmed
|
||||
- [ ] **Refuted** — hypothesis disproven
|
||||
- [ ] **Inconclusive** — could not tell
|
||||
|
||||
## Timeline
|
||||
|
||||
| Time | Event |
|
||||
|---|---|
|
||||
| T-5min | Started baseline measurement |
|
||||
| T+0 | Attack injected |
|
||||
| T+? | `<observation>` |
|
||||
| T+? | `<observation>` |
|
||||
| T+N | Attack ended (or aborted) |
|
||||
| T+N+2 | Steady state recovered |
|
||||
|
||||
## What we learned
|
||||
|
||||
`<at least one concrete learning — required>`
|
||||
|
||||
## What surprised us
|
||||
|
||||
`<unexpected observations; "nothing surprised us" is a signal that you didn't push hard enough>`
|
||||
|
||||
## What failed
|
||||
|
||||
`<things that broke during the experiment that shouldn't have>`
|
||||
|
||||
## What held
|
||||
|
||||
`<things that worked as expected — confidence-building data points>`
|
||||
|
||||
## Root causes (if any failures)
|
||||
|
||||
`<technical analysis without blame>`
|
||||
|
||||
## Follow-up actions
|
||||
|
||||
| Action | Owner | Due | Status |
|
||||
|---|---|---|---|
|
||||
| `<concrete action>` | `<@owner>` | `<date>` | `[ ]` |
|
||||
| `<concrete action>` | `<@owner>` | `<date>` | `[ ]` |
|
||||
|
||||
> Every experiment should produce ≥1 follow-up. If none — re-examine whether you tested anything new.
|
||||
|
||||
## Next experiment
|
||||
|
||||
`<what's the next experiment that builds on this learning?>`
|
||||
|
||||
## Stakeholder summary (1-2 sentences)
|
||||
|
||||
`<for the team channel; describe outcome and biggest learning>`
|
||||
|
|
@ -0,0 +1,180 @@
|
|||
# Attack taxonomy
|
||||
|
||||
7 categories of fault injection. Each tests a different system property. Pick the one whose failure mode matches your hypothesis.
|
||||
|
||||
## 1. Latency
|
||||
|
||||
**What it tests:** timeouts, retries, circuit breakers, fallback paths.
|
||||
|
||||
**Inject:** add N ms of delay to network responses to a target.
|
||||
|
||||
**When to use:**
|
||||
- "What if dependency X is slow?"
|
||||
- "Are timeouts configured correctly upstream?"
|
||||
- "Does the retry budget kick in?"
|
||||
|
||||
**Tools:**
|
||||
- Linux `tc` (traffic control) — direct kernel-level shaping
|
||||
- Chaos Mesh `NetworkChaos` (delay)
|
||||
- Toxiproxy — proxy-based, language-agnostic
|
||||
- AWS FIS — `aws:network:traffic-control` action
|
||||
|
||||
**Example magnitude:** +200ms (90% of typical timeouts), +2000ms (test backoff), +30s (test giving-up logic).
|
||||
|
||||
## 2. Error injection
|
||||
|
||||
**What it tests:** error handling paths, fallback behavior, retry policies.
|
||||
|
||||
**Inject:** return errors (5xx, exceptions) for a fraction of requests.
|
||||
|
||||
**When to use:**
|
||||
- "What happens when X starts failing?"
|
||||
- "Does the fallback path actually work in prod?"
|
||||
- "Are we logging errors correctly?"
|
||||
|
||||
**Tools:**
|
||||
- Chaos Mesh `HTTPChaos`
|
||||
- Service mesh (Istio, Linkerd) fault injection
|
||||
- Toxiproxy with error toxic
|
||||
- Application-level feature flag for synthetic errors
|
||||
|
||||
**Example magnitude:** 1% errors (test handler), 50% errors (test retry), 100% errors (test fallback path).
|
||||
|
||||
## 3. Resource exhaustion
|
||||
|
||||
**What it tests:** saturation handling, autoscaling, OOM behavior, disk-full handling.
|
||||
|
||||
**Inject:** consume CPU, memory, or disk on the target.
|
||||
|
||||
**When to use:**
|
||||
- "What if memory leaks?"
|
||||
- "Does the autoscaler kick in?"
|
||||
- "What happens when disk fills?"
|
||||
|
||||
**Sub-types:**
|
||||
- **CPU pressure** — peg cores at N% usage
|
||||
- **Memory pressure** — allocate large blocks
|
||||
- **Disk fill** — write large files until partition fills
|
||||
- **I/O saturation** — high random read/write
|
||||
|
||||
**Tools:**
|
||||
- `stress-ng` — CPU/memory/IO/disk
|
||||
- Chaos Mesh `StressChaos` and `IOChaos`
|
||||
- AWS FIS `aws:ssm:send-command` with stress-ng
|
||||
|
||||
**Example magnitude:** 80% CPU sustained, 90% memory, fill /var to 95%.
|
||||
|
||||
## 4. Network partition
|
||||
|
||||
**What it tests:** consensus protocols, leader election, split-brain prevention, region failover.
|
||||
|
||||
**Inject:** drop all packets between a set of hosts.
|
||||
|
||||
**When to use:**
|
||||
- "What if AZ-A loses connectivity to AZ-B?"
|
||||
- "Does the database elect a new primary?"
|
||||
- "Does the cluster avoid split-brain?"
|
||||
|
||||
**Tools:**
|
||||
- Chaos Mesh `NetworkChaos` (partition mode)
|
||||
- `tc` with iptables drop rules
|
||||
- AWS FIS `aws:network:disrupt-connectivity`
|
||||
|
||||
**Example magnitude:** drop 100% to peer X (full partition), drop 50% (degraded link).
|
||||
|
||||
## 5. Dependency failure
|
||||
|
||||
**What it tests:** graceful degradation, fallback to cache, fallback to default values.
|
||||
|
||||
**Inject:** make a downstream dependency unavailable (timeout, refuse connections).
|
||||
|
||||
**When to use:**
|
||||
- "What if the rec engine goes down?"
|
||||
- "Does Search degrade gracefully when ML models are unreachable?"
|
||||
- "Is cache the fallback for the user-pref service?"
|
||||
|
||||
**Tools:**
|
||||
- Service mesh fault injection (most flexible)
|
||||
- Toxiproxy
|
||||
- iptables rules to refuse connections
|
||||
- Chaos Mesh `NetworkChaos` with `corrupt` or `drop`
|
||||
|
||||
**Example magnitude:** 100% requests to dep X timeout (full outage), 25% timeout (intermittent), 0% available for 5 min (sustained outage).
|
||||
|
||||
## 6. Time skew
|
||||
|
||||
**What it tests:** time-sensitive logic — token expiry, cron schedules, TTLs, retry backoff.
|
||||
|
||||
**Inject:** alter the wall clock seen by a process.
|
||||
|
||||
**When to use:**
|
||||
- "What if NTP fails?"
|
||||
- "What if a process clock drifts +5 minutes?"
|
||||
- "Do tokens correctly fail validation when expired?"
|
||||
- "Does cron skip or double-fire?"
|
||||
|
||||
**Tools:**
|
||||
- `libfaketime` — preload library
|
||||
- Chaos Mesh `TimeChaos`
|
||||
- Custom: change container's `/etc/localtime`
|
||||
|
||||
**Example magnitude:** +1 minute (subtle), +5 minutes (TLS / token failures), +1 day (catastrophic for some logic).
|
||||
|
||||
**Caution:** time skew can cause cluster-wide consensus failures. Test in isolation first.
|
||||
|
||||
## 7. Infrastructure (kill instance / pod / container)
|
||||
|
||||
**What it tests:** auto-recovery, failover, replica count maintenance.
|
||||
|
||||
**Inject:** terminate an instance, pod, or container.
|
||||
|
||||
**When to use:**
|
||||
- "Does Kubernetes restart the pod?"
|
||||
- "Does the load balancer remove the instance from rotation?"
|
||||
- "Is the replication factor maintained?"
|
||||
|
||||
**Tools:**
|
||||
- Chaos Monkey (the original)
|
||||
- Chaos Mesh `PodChaos` (kill, fail)
|
||||
- AWS FIS `aws:ec2:terminate-instances`
|
||||
- `kubectl delete pod` (manual, simplest)
|
||||
|
||||
**Example magnitude:** kill 1 of N pods (Chaos Monkey level), kill all pods of a deployment (test recreation), kill 1 of 3 replica DB nodes (test failover).
|
||||
|
||||
## Choosing an attack
|
||||
|
||||
| Hypothesis pattern | Attack type |
|
||||
|---|---|
|
||||
| "What if X is slow?" | Latency |
|
||||
| "What if X is failing?" | Error |
|
||||
| "What if we run hot?" | Resource |
|
||||
| "What if regions partition?" | Network partition |
|
||||
| "What if dep X is down?" | Dependency failure |
|
||||
| "What if clocks drift?" | Time skew |
|
||||
| "What if a node dies?" | Infrastructure |
|
||||
|
||||
## Combining attacks
|
||||
|
||||
Real outages often combine attacks (e.g., latency + saturation). Once basic experiments are stable, run combinations:
|
||||
|
||||
- Latency on dependency + CPU pressure on app → tests timeout + retry budget interaction
|
||||
- Pod kill + network partition → tests recovery during a partition
|
||||
- Disk fill + dependency failure → tests fallback path while disk is constrained
|
||||
|
||||
Combinations have higher risk; reduce blast radius accordingly.
|
||||
|
||||
## Severity ladder
|
||||
|
||||
```
|
||||
S1 — Latency (small) ← start here
|
||||
S2 — Error injection (low %)
|
||||
S3 — Resource pressure (CPU/mem)
|
||||
S4 — Latency (large) / errors (high %)
|
||||
S5 — Single instance kill
|
||||
S6 — Network partition (single peer)
|
||||
S7 — Multiple instance kill
|
||||
S8 — Region partition / time skew
|
||||
S9 — Combinations of S5-S8 ← here be dragons
|
||||
```
|
||||
|
||||
Don't skip levels. Earn confidence at S1-S3 before attempting S5+.
|
||||
|
|
@ -0,0 +1,136 @@
|
|||
# The principles of chaos engineering
|
||||
|
||||
Chaos engineering is the discipline of experimenting on a system in order to build confidence in its capability to withstand turbulent conditions in production. The phrase comes from Netflix's 2014-2016 work productizing what started as Chaos Monkey.
|
||||
|
||||
## The 4 founding principles (Netflix, 2016)
|
||||
|
||||
### 1. Build a hypothesis around steady-state behavior
|
||||
|
||||
Steady state = a measurable, normal-operations metric (latency, throughput, conversion rate, error rate).
|
||||
|
||||
Bad: *"What happens if the database goes down?"*
|
||||
Good: *"When the primary database fails over, p99 checkout latency stays below 800ms and conversion rate stays within 2% of baseline."*
|
||||
|
||||
The hypothesis must be **falsifiable** — there must be a measurement that can disprove it.
|
||||
|
||||
### 2. Vary real-world events
|
||||
|
||||
Inject realistic failure modes:
|
||||
- Servers crash
|
||||
- Networks partition or slow
|
||||
- Disks fill
|
||||
- Dependencies time out or return errors
|
||||
- Caches lose data
|
||||
- Time skews
|
||||
|
||||
Don't inject implausible events (e.g., "what if all 50 zones in 5 regions go down simultaneously"). That's not chaos engineering, that's astronomy.
|
||||
|
||||
### 3. Run experiments in production
|
||||
|
||||
Staging never reproduces:
|
||||
- Real traffic patterns
|
||||
- Real cache hit rates
|
||||
- Real cross-service dependencies
|
||||
- Real data volumes
|
||||
- Real user behavior
|
||||
|
||||
The only system that has prod failure modes is prod. Start with tiny blast radius (1%), grow as confidence grows.
|
||||
|
||||
### 4. Automate experiments to run continuously
|
||||
|
||||
A single chaos experiment is a press release. Continuous chaos is engineering.
|
||||
|
||||
Maturity progression:
|
||||
1. Manual one-offs → 2. Weekly Game Days → 3. Scheduled experiments → 4. Continuous chaos in CI/CD
|
||||
|
||||
The 5th principle this skill adds:
|
||||
|
||||
### 5. Define abort criteria up front
|
||||
|
||||
A chaos experiment with no abort criteria is an outage. Every plan must include:
|
||||
|
||||
- A specific signal (metric, threshold)
|
||||
- A specific action (auto-abort, manual abort, escalate)
|
||||
- A timeline (within N seconds of breach)
|
||||
|
||||
If the threshold is hit, abort immediately. Investigate later.
|
||||
|
||||
## When to start
|
||||
|
||||
You're ready for chaos engineering when:
|
||||
|
||||
- [ ] You have basic monitoring (you can detect a steady-state breach)
|
||||
- [ ] You have on-call rotations (someone is watching when chaos runs)
|
||||
- [ ] You have at least one tool to inject the desired fault
|
||||
- [ ] You have an SLO/SLI defined (so you know what "good" looks like)
|
||||
- [ ] You have postmortem culture that's blameless
|
||||
- [ ] You have a leadership champion who'll defend the practice
|
||||
|
||||
If any of these are missing, fix them first. Premature chaos = outages with no learning.
|
||||
|
||||
## When NOT to do chaos engineering
|
||||
|
||||
- During a release freeze
|
||||
- During a known incident
|
||||
- During peak traffic events without explicit approval
|
||||
- On systems that don't have steady-state metrics
|
||||
- On systems where you can't bound the blast radius
|
||||
- On the day of a security disclosure
|
||||
- When the team is already firefighting
|
||||
|
||||
## Maturity model
|
||||
|
||||
| Level | Description | Cadence | Tooling |
|
||||
|---|---|---|---|
|
||||
| L0 | None | n/a | none |
|
||||
| L1 | Manual one-offs in staging | quarterly | tc, manual scripts |
|
||||
| L2 | Weekly Game Days in staging | weekly | Chaos Toolkit, internal scripts |
|
||||
| L3 | Limited prod experiments | weekly | Chaos Toolkit / Mesh / Litmus / FIS |
|
||||
| L4 | Continuous prod chaos with bounded blast radius | daily | Chaos Mesh / Gremlin scenarios |
|
||||
| L5 | Chaos in CI/CD pipeline; deploys auto-trigger sweeps | per-deploy | Custom + tooling stack |
|
||||
|
||||
Most teams should target L3 within 6-12 months of starting. L5 is rare and only justified for the largest distributed systems.
|
||||
|
||||
## Common objections (and counters)
|
||||
|
||||
| Objection | Counter |
|
||||
|---|---|
|
||||
| "We can't break production!" | You already do, just unintentionally. Chaos is intentional, bounded, observed breaks. |
|
||||
| "This is a customer-facing system." | Start at 1% blast radius. The 99% are unaffected. |
|
||||
| "We don't have time." | Chaos finds bugs that would otherwise become 4am pages. Time spent on chaos saves time on incidents. |
|
||||
| "Our system is too critical." | Critical systems have the most to gain from learning their failure modes. |
|
||||
| "We have HA already." | HA without chaos is HA in theory. Chaos finds gaps in actual HA. |
|
||||
|
||||
## What a steady-state metric looks like
|
||||
|
||||
Good steady-state metrics:
|
||||
- p99 request latency (objective, measurable per second)
|
||||
- Error rate (objective, measurable)
|
||||
- Conversion rate (business metric, slow but real)
|
||||
- Successful logins per minute (business + tech signal)
|
||||
- Queue depth (system health)
|
||||
|
||||
Bad metrics:
|
||||
- "Things feel slow" (not measurable)
|
||||
- CPU usage (a means, not an end)
|
||||
- Number of pods running (not customer-facing)
|
||||
|
||||
Pick metrics that customers feel. CPU can spike without customer impact; latency and errors can't.
|
||||
|
||||
## History
|
||||
|
||||
- 2010: Netflix launches Chaos Monkey (kills random EC2 instances)
|
||||
- 2011: Simian Army expands (Latency Monkey, Conformity Monkey, etc.)
|
||||
- 2014: Chaos engineering term coined; principles drafted
|
||||
- 2016: principlesofchaos.org published
|
||||
- 2018: Chaos Toolkit released as OSS
|
||||
- 2019: Chaos Mesh and Litmus mature for Kubernetes
|
||||
- 2020: AWS launches Fault Injection Simulator (FIS)
|
||||
- 2023+: Chaos engineering becomes mainstream practice in SRE-heavy orgs
|
||||
|
||||
## Further reading
|
||||
|
||||
- principlesofchaos.org — the foundational document
|
||||
- *Chaos Engineering* (Casey Rosenthal, Nora Jones) — O'Reilly, 2020
|
||||
- *Learning Chaos Engineering* (Russ Miles) — O'Reilly, 2019
|
||||
- Netflix Tech Blog on Chaos Engineering posts (2016-2020)
|
||||
|
|
@ -0,0 +1,158 @@
|
|||
# Experiment design
|
||||
|
||||
A well-designed chaos experiment has 7 sections. Skip any of them and the experiment becomes either useless (no learning) or dangerous (no bounds).
|
||||
|
||||
## The 7 sections
|
||||
|
||||
```
|
||||
1. Hypothesis
|
||||
2. Steady-state metric
|
||||
3. Attack
|
||||
4. Blast radius
|
||||
5. Abort criteria
|
||||
6. Rollback procedure
|
||||
7. Learning question
|
||||
```
|
||||
|
||||
## 1. Hypothesis
|
||||
|
||||
**Format:** *When [fault], [steady-state metric] stays [tolerance].*
|
||||
|
||||
Examples:
|
||||
- *"When the primary Postgres replica fails, checkout p99 latency stays below 500ms."*
|
||||
- *"When 50% of payment-service requests are throttled to 1 RPS, conversion rate drops by less than 5% within 60 seconds of return-to-normal."*
|
||||
- *"When us-east-1 is partitioned from us-west-2, Search continues to return results from us-west-2 within 200ms p99."*
|
||||
|
||||
A good hypothesis:
|
||||
- Names a specific fault (not "things break")
|
||||
- Names a specific metric (not "everything")
|
||||
- States a specific tolerance (not "good enough")
|
||||
- Is measurable and falsifiable
|
||||
|
||||
## 2. Steady-state metric
|
||||
|
||||
The metric you'll measure before, during, and after the experiment.
|
||||
|
||||
Required properties:
|
||||
- **Quantitative** — a number, not a feeling
|
||||
- **Customer-relevant** — something users feel (latency, error rate, conversion)
|
||||
- **Measurable in <60s** — slow metrics give you no time to abort
|
||||
- **Stable in normal operation** — you need a baseline
|
||||
|
||||
| Good | Bad |
|
||||
|---|---|
|
||||
| p99 checkout latency | "the system is healthy" |
|
||||
| 4xx + 5xx rate | "errors are low" |
|
||||
| Successful login rate | CPU usage |
|
||||
| Items added to cart per minute | replica count |
|
||||
|
||||
## 3. Attack
|
||||
|
||||
The fault you're injecting. Must specify:
|
||||
|
||||
- **Type** — latency, error, resource, partition, dependency, time, infrastructure
|
||||
- **Magnitude** — *how* much (e.g., "+200ms", "10% errors", "100% timeout to peer X")
|
||||
- **Duration** — how long the attack runs (typically 5-30 minutes)
|
||||
- **Target** — which subset of the system gets the attack
|
||||
|
||||
See `attack_taxonomy.md` for the 7 attack types.
|
||||
|
||||
## 4. Blast radius
|
||||
|
||||
The maximum scope of customer impact. Use `blast_radius_calculator.py` to compute:
|
||||
|
||||
- **Affected users** — `traffic_share × user_population`
|
||||
- **Error budget consumed** — `duration × traffic_share × availability_delta`
|
||||
- **Risk score** — GREEN (<1% budget) / YELLOW (1-10%) / RED (>10%)
|
||||
|
||||
Rule of thumb:
|
||||
- Start at 1% traffic share
|
||||
- Grow only after 3 successful experiments at the previous level
|
||||
- Never exceed 10% of monthly error budget in a single experiment
|
||||
|
||||
## 5. Abort criteria
|
||||
|
||||
The signals that auto-trigger experiment termination. Each must be:
|
||||
|
||||
- **Concrete** — specific metric and threshold ("p99 > 1000ms" not "performance degrades")
|
||||
- **Detectable in <60s** — latency, error rate, throughput
|
||||
- **Wired to action** — manual abort link in the dashboard, automatic via alert webhook
|
||||
|
||||
Standard abort criteria:
|
||||
|
||||
| Signal | Threshold | Action |
|
||||
|---|---|---|
|
||||
| p99 latency | > 2× baseline | abort |
|
||||
| 5xx rate | > baseline + 1pp | abort |
|
||||
| 4xx rate (excl. 401/404) | > baseline + 5pp | abort |
|
||||
| Conversion rate | < baseline × 0.95 | abort |
|
||||
| Customer ticket spike | > 3× baseline | escalate |
|
||||
| On-call paged | any SEV1/SEV2 | abort |
|
||||
|
||||
## 6. Rollback procedure
|
||||
|
||||
How you'll revert the fault. Required because:
|
||||
- Sometimes the chaos tool itself fails to revert
|
||||
- Sometimes the fault has lingering effects (caches, connections)
|
||||
|
||||
Standard rollback:
|
||||
1. Disable fault injection in tool
|
||||
2. Verify steady-state recovers within 2 minutes
|
||||
3. If not recovering, escalate as incident; restore from backup if needed
|
||||
|
||||
## 7. Learning question
|
||||
|
||||
What do you expect NOT to learn? Force yourself to predict the outcome.
|
||||
|
||||
Examples:
|
||||
- *"We expect the cache to absorb the latency. We'll learn whether the timeout configuration on the upstream is correct."*
|
||||
- *"We expect failover to take 30s. We'll learn whether retry backoff is configured."*
|
||||
|
||||
If you predicted the outcome correctly: confidence increased.
|
||||
If you didn't: there's an unknown — file a follow-up.
|
||||
|
||||
## Pre-flight checklist
|
||||
|
||||
Before running the experiment, verify:
|
||||
|
||||
- [ ] Hypothesis written
|
||||
- [ ] Steady-state metric measured for ≥5 minutes
|
||||
- [ ] Blast radius calculated (GREEN or YELLOW)
|
||||
- [ ] Abort criteria documented with thresholds
|
||||
- [ ] Rollback procedure tested in staging
|
||||
- [ ] On-call team notified in the team channel
|
||||
- [ ] Monitoring dashboards open
|
||||
- [ ] Owner identified and reachable
|
||||
- [ ] Time-box agreed (max experiment duration)
|
||||
- [ ] Communication plan if abort triggers
|
||||
|
||||
## Time-boxing
|
||||
|
||||
| Experiment type | Typical duration | Max recommended |
|
||||
|---|---|---|
|
||||
| First-time chaos | 5 minutes | 10 minutes |
|
||||
| Familiar attack, new target | 15 minutes | 30 minutes |
|
||||
| Continuous (automated) | per scheduler | 10 min per attack |
|
||||
| Game Day (human-led) | 1-2 hours | 4 hours |
|
||||
|
||||
## Escalation
|
||||
|
||||
If abort criteria are hit:
|
||||
|
||||
1. **Stop the experiment immediately** (the obvious step many teams forget to script)
|
||||
2. Verify steady-state recovery
|
||||
3. If recovery doesn't happen in 5 min → declare an incident
|
||||
4. Open a postmortem doc using `experiment_postmortem.py`
|
||||
5. Notify stakeholders (whoever was promised "this won't impact anything")
|
||||
6. Capture timeline while memory is fresh
|
||||
|
||||
## Anti-patterns
|
||||
|
||||
- **Hypothesis written after running** — that's a postmortem, not chaos engineering
|
||||
- **Steady-state metric chosen during experiment** — pick before
|
||||
- **Magnitude "small"** — quantify; "small" varies by reader
|
||||
- **No abort criteria** — never run without them
|
||||
- **Single owner of all chaos** — culture problem; spread the practice
|
||||
- **Chaos that always succeeds** — increase magnitude; you're not learning if everything passes
|
||||
- **Chaos that always fails** — reduce magnitude; you can't learn if everything breaks
|
||||
- **Chaos with no follow-up actions** — what was the point?
|
||||
|
|
@ -0,0 +1,197 @@
|
|||
# Tooling landscape
|
||||
|
||||
Six options. Pick by stack, license preference, and required attack types.
|
||||
|
||||
## At-a-glance
|
||||
|
||||
| Tool | License | Stack | Attack coverage | Best for |
|
||||
|---|---|---|---|---|
|
||||
| **Chaos Toolkit** | OSS (Apache 2) | Any (Python) | Broad via plugins | Lightweight, multi-cloud, JSON experiments |
|
||||
| **Chaos Mesh** | OSS (Apache 2) | Kubernetes | Very broad (network, pod, IO, time, stress) | k8s-native, rich CRDs |
|
||||
| **Litmus** | OSS (Apache 2) | Kubernetes | Very broad (300+ experiments) | k8s, Argo-integrated |
|
||||
| **Gremlin** | Commercial | Any (agents) | Broad, polished | Enterprise, audit, multi-cloud |
|
||||
| **AWS FIS** | Paid (AWS) | AWS | AWS services + EC2/ECS/EKS | AWS-heavy, IAM-integrated |
|
||||
| **Custom** | Your code | Any | What you build | Niche, single-cloud, low budget |
|
||||
|
||||
## Decision tree
|
||||
|
||||
```
|
||||
Stack constraint?
|
||||
├── Kubernetes-only ──┬── OSS preferred → Chaos Mesh OR Litmus
|
||||
│ │ (Litmus has the bigger experiment library;
|
||||
│ │ Chaos Mesh has cleaner CRD model)
|
||||
│ └── Enterprise budget → Gremlin
|
||||
│
|
||||
├── AWS-heavy ────────┬── Simple needs → AWS FIS
|
||||
│ ├── Multi-cloud + AWS → Chaos Toolkit + AWS plugin
|
||||
│ └── Enterprise → Gremlin
|
||||
│
|
||||
├── Multi-cloud ──────┬── OSS → Chaos Toolkit
|
||||
│ └── Enterprise → Gremlin
|
||||
│
|
||||
└── No infra constraint
|
||||
└── Just need fault injection → Toxiproxy (a single-purpose tool, not full chaos framework)
|
||||
```
|
||||
|
||||
## Chaos Toolkit
|
||||
|
||||
**What it is:** Python-based framework. You write experiments as JSON or YAML files; the CLI runs them.
|
||||
|
||||
**Strengths:**
|
||||
- Lightweight; runs anywhere Python runs
|
||||
- Plugin ecosystem for AWS, Azure, GCP, Kubernetes, etc.
|
||||
- JSON experiments are version-controllable
|
||||
- Apache 2 license
|
||||
|
||||
**Weaknesses:**
|
||||
- No built-in scheduling (you bring cron / CI)
|
||||
- Smaller experiment library than Litmus
|
||||
- Plugin quality varies
|
||||
|
||||
**Example experiment (JSON):**
|
||||
```json
|
||||
{
|
||||
"title": "Latency on payment-svc",
|
||||
"description": "p99 latency stays <500ms when payment is +200ms slow",
|
||||
"steady-state-hypothesis": {
|
||||
"title": "p99 < 500ms",
|
||||
"probes": [{ "type": "probe", "tolerance": [0, 500],
|
||||
"provider": { "type": "http", "url": "https://my.dashboards/p99" } }]
|
||||
},
|
||||
"method": [{ "type": "action", "name": "add-latency",
|
||||
"provider": { "type": "process", "path": "tc", "arguments": [...] } }]
|
||||
}
|
||||
```
|
||||
|
||||
## Chaos Mesh
|
||||
|
||||
**What it is:** Kubernetes operator + CRDs for chaos. Install in-cluster; `kubectl apply` an experiment.
|
||||
|
||||
**Strengths:**
|
||||
- True k8s-native (no external orchestrator)
|
||||
- Comprehensive coverage: network, pod, IO, stress, time, DNS, HTTP, kernel
|
||||
- UI dashboard for running experiments
|
||||
- CNCF Incubating project
|
||||
|
||||
**Weaknesses:**
|
||||
- k8s-only
|
||||
- CRD layout is opinionated; some types feel similar but aren't
|
||||
- Setup requires cluster admin
|
||||
|
||||
**Example experiment (CRD):**
|
||||
```yaml
|
||||
apiVersion: chaos-mesh.org/v1alpha1
|
||||
kind: NetworkChaos
|
||||
metadata:
|
||||
name: payment-latency
|
||||
spec:
|
||||
action: delay
|
||||
mode: one
|
||||
selector:
|
||||
namespaces: [default]
|
||||
labelSelectors:
|
||||
app: payment-svc
|
||||
delay:
|
||||
latency: 200ms
|
||||
duration: 5m
|
||||
```
|
||||
|
||||
## Litmus
|
||||
|
||||
**What it is:** Kubernetes chaos framework with a large experiment library. Argo-CD integration.
|
||||
|
||||
**Strengths:**
|
||||
- 300+ pre-built experiments
|
||||
- Strong Argo / GitOps integration
|
||||
- ChaosHub community library
|
||||
- Workflow capability for multi-step experiments
|
||||
|
||||
**Weaknesses:**
|
||||
- More moving parts than Chaos Mesh
|
||||
- Some pre-built experiments are thin wrappers; quality varies
|
||||
- k8s-only
|
||||
|
||||
## Gremlin
|
||||
|
||||
**What it is:** Commercial SaaS. Agents on hosts; central control plane.
|
||||
|
||||
**Strengths:**
|
||||
- Polished UX
|
||||
- Comprehensive attack library
|
||||
- Audit logs (compliance)
|
||||
- Multi-cloud, multi-OS
|
||||
- Customer support
|
||||
|
||||
**Weaknesses:**
|
||||
- Paid (per-host or per-MAU)
|
||||
- Vendor lock-in
|
||||
- Less control than OSS
|
||||
|
||||
**When to choose:** large enterprise, compliance/audit requirements, dedicated chaos team, budget exists.
|
||||
|
||||
## AWS FIS (Fault Injection Simulator)
|
||||
|
||||
**What it is:** AWS-managed chaos service. Templates of "actions" (stop instance, throttle API) chained into experiments.
|
||||
|
||||
**Strengths:**
|
||||
- IAM-integrated (proper auth/audit)
|
||||
- Native to AWS services (RDS failover, ECS/EKS, Network Manager)
|
||||
- Pay-per-experiment (no agents to maintain)
|
||||
|
||||
**Weaknesses:**
|
||||
- AWS-only
|
||||
- Smaller attack library than Chaos Mesh / Gremlin
|
||||
- Multi-account is awkward
|
||||
|
||||
**When to choose:** AWS-heavy team that wants chaos without managing the chaos infra.
|
||||
|
||||
## Custom (DIY)
|
||||
|
||||
**When to choose:**
|
||||
- Single-cloud, single-stack, low complexity
|
||||
- Budget = $0
|
||||
- Have engineering capacity to maintain the tool
|
||||
- Need a niche attack type that no tool covers
|
||||
|
||||
**Implementation patterns:**
|
||||
- Bash scripts that wrap `tc` / iptables / kill / stress-ng
|
||||
- Application-level chaos via feature flags + middleware
|
||||
- Service mesh fault injection (Istio / Linkerd) — covers many cases without a chaos framework
|
||||
|
||||
**Trade-offs:**
|
||||
- You build all the safety rails (abort, timeout, blast-radius)
|
||||
- You build the scheduler
|
||||
- You debug your own bugs
|
||||
|
||||
For most teams, this is a starter path; once chaos becomes regular, switch to a real tool.
|
||||
|
||||
## Pricing rule of thumb
|
||||
|
||||
| Tool | Typical cost (annual) |
|
||||
|---|---|
|
||||
| Chaos Toolkit | $0 |
|
||||
| Chaos Mesh | $0 |
|
||||
| Litmus OSS | $0 |
|
||||
| Litmus Enterprise | $5-30k |
|
||||
| Gremlin | $20-100k+ |
|
||||
| AWS FIS | pay-per-action, ~$100-2000/mo for active use |
|
||||
| Custom | engineering time only |
|
||||
|
||||
## Migration paths
|
||||
|
||||
| From | To | Effort |
|
||||
|---|---|---|
|
||||
| Custom scripts | Chaos Toolkit | Low (wrap scripts as actions) |
|
||||
| Chaos Toolkit | Chaos Mesh | Medium (k8s-only; rewrite for CRDs) |
|
||||
| Chaos Mesh | Litmus | Medium (similar shape, different CRDs) |
|
||||
| Anything | Gremlin | Easy (Gremlin imports many formats) |
|
||||
|
||||
## Selection checklist
|
||||
|
||||
Before committing:
|
||||
- [ ] Stack matches (k8s vs multi-cloud vs AWS-only)
|
||||
- [ ] Required attack types covered (cross-reference `attack_taxonomy.md`)
|
||||
- [ ] Audit logging requirement met (Gremlin / AWS FIS only have full audit)
|
||||
- [ ] Self-hosting requirement met (OSS only)
|
||||
- [ ] Budget approved
|
||||
- [ ] Run a 30-day proof-of-concept; verify abort path works
|
||||
|
|
@ -0,0 +1,101 @@
|
|||
#!/usr/bin/env python3
|
||||
"""Compute blast radius and risk score for a chaos experiment.
|
||||
|
||||
Inputs: traffic share affected, user population, duration, baseline availability,
|
||||
expected impacted availability. Outputs expected affected users, error budget
|
||||
consumed, and a GREEN / YELLOW / RED risk score with PROCEED / REDUCE / ABORT
|
||||
recommendation.
|
||||
"""
|
||||
import argparse
|
||||
import json
|
||||
import sys
|
||||
|
||||
|
||||
def calculate(traffic_share, user_pop, duration_min, baseline_avail, impacted_avail, monthly_budget_min):
|
||||
if not 0 <= traffic_share <= 1:
|
||||
raise ValueError("traffic-share must be between 0 and 1")
|
||||
if not 0 < impacted_avail <= 1:
|
||||
raise ValueError("impacted-availability must be between 0 (exclusive) and 1")
|
||||
if not 0 < baseline_avail <= 1:
|
||||
raise ValueError("baseline-availability must be between 0 (exclusive) and 1")
|
||||
affected_users = int(user_pop * traffic_share)
|
||||
delta_avail = max(baseline_avail - impacted_avail, 0.0)
|
||||
error_budget_consumed_min = round(duration_min * traffic_share * delta_avail, 4)
|
||||
pct_of_monthly_budget = round(100 * error_budget_consumed_min / monthly_budget_min, 2) if monthly_budget_min > 0 else 0
|
||||
if pct_of_monthly_budget < 1:
|
||||
risk = "GREEN"
|
||||
recommendation = "PROCEED"
|
||||
elif pct_of_monthly_budget < 10:
|
||||
risk = "YELLOW"
|
||||
recommendation = "PROCEED with explicit owner sign-off; consider reducing traffic share"
|
||||
else:
|
||||
risk = "RED"
|
||||
recommendation = "ABORT or REDUCE — blast radius exceeds 10% of monthly error budget"
|
||||
return {
|
||||
"inputs": {
|
||||
"traffic_share": traffic_share,
|
||||
"user_pop": user_pop,
|
||||
"duration_min": duration_min,
|
||||
"baseline_availability": baseline_avail,
|
||||
"impacted_availability": impacted_avail,
|
||||
"monthly_budget_min": monthly_budget_min,
|
||||
},
|
||||
"expected_affected_users": affected_users,
|
||||
"expected_availability_delta": round(delta_avail, 4),
|
||||
"error_budget_consumed_min": error_budget_consumed_min,
|
||||
"pct_of_monthly_budget": pct_of_monthly_budget,
|
||||
"risk": risk,
|
||||
"recommendation": recommendation,
|
||||
}
|
||||
|
||||
|
||||
def render_text(result):
|
||||
print("Blast Radius Calculator")
|
||||
print("=" * 40)
|
||||
i = result["inputs"]
|
||||
print(f"Traffic share affected: {i['traffic_share'] * 100:.2f}%")
|
||||
print(f"User population: {i['user_pop']:,}")
|
||||
print(f"Duration: {i['duration_min']} min")
|
||||
print(f"Baseline availability: {i['baseline_availability']}")
|
||||
print(f"Impacted availability: {i['impacted_availability']}")
|
||||
print(f"Monthly error budget: {i['monthly_budget_min']} min")
|
||||
print("")
|
||||
print(f"Expected affected users: {result['expected_affected_users']:,}")
|
||||
print(f"Availability delta: {result['expected_availability_delta']}")
|
||||
print(f"Error budget consumed: {result['error_budget_consumed_min']} min ({result['pct_of_monthly_budget']}% of monthly)")
|
||||
print("")
|
||||
print(f"Risk: {result['risk']}")
|
||||
print(f"Recommendation: {result['recommendation']}")
|
||||
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
|
||||
ap.add_argument("--traffic-share", type=float, required=True, help="Fraction (0-1) of traffic affected")
|
||||
ap.add_argument("--user-pop", type=int, required=True, help="Total user population")
|
||||
ap.add_argument("--duration-min", type=int, required=True, help="Experiment duration in minutes")
|
||||
ap.add_argument("--baseline-availability", type=float, default=0.999, help="Baseline availability (default: 0.999)")
|
||||
ap.add_argument("--expected-impact-availability", type=float, default=0.95, dest="impact_avail",
|
||||
help="Availability under fault (default: 0.95)")
|
||||
ap.add_argument("--monthly-budget-min", type=float, default=43.2,
|
||||
help="Monthly error budget in minutes (default: 43.2 for 99.9%% on 30 days)")
|
||||
ap.add_argument("--format", choices=["text", "json"], default="text")
|
||||
args = ap.parse_args()
|
||||
|
||||
try:
|
||||
result = calculate(
|
||||
args.traffic_share, args.user_pop, args.duration_min,
|
||||
args.baseline_availability, args.impact_avail, args.monthly_budget_min,
|
||||
)
|
||||
except ValueError as e:
|
||||
print(f"ERROR: {e}", file=sys.stderr)
|
||||
return 2
|
||||
|
||||
if args.format == "json":
|
||||
print(json.dumps(result, indent=2))
|
||||
else:
|
||||
render_text(result)
|
||||
return 0 if result["risk"] != "RED" else 1
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
|
|
@ -0,0 +1,139 @@
|
|||
#!/usr/bin/env python3
|
||||
"""Generate a structured chaos engineering experiment plan.
|
||||
|
||||
Enforces the required sections (hypothesis, steady-state metric, blast radius,
|
||||
abort criteria, rollback). Output is markdown by default; JSON available for
|
||||
piping into experiment_postmortem.py.
|
||||
"""
|
||||
import argparse
|
||||
import json
|
||||
import sys
|
||||
from datetime import datetime, timezone
|
||||
|
||||
ATTACK_DEFAULTS = {
|
||||
"latency": {"magnitude_hint": "+200ms", "tooling_hint": "tc / Chaos Mesh NetworkChaos"},
|
||||
"error": {"magnitude_hint": "10% of requests return 5xx", "tooling_hint": "Toxiproxy / Chaos Mesh HTTPChaos"},
|
||||
"cpu": {"magnitude_hint": "80% sustained", "tooling_hint": "stress-ng / Chaos Mesh StressChaos"},
|
||||
"memory": {"magnitude_hint": "+1GiB pressure", "tooling_hint": "stress-ng / Chaos Mesh StressChaos"},
|
||||
"disk": {"magnitude_hint": "fill /var to 95%", "tooling_hint": "stress-ng / Chaos Mesh IOChaos"},
|
||||
"network-partition": {"magnitude_hint": "drop 100% to peer X", "tooling_hint": "Chaos Mesh NetworkChaos partition"},
|
||||
"dependency-failure": {"magnitude_hint": "100% timeout to dependency", "tooling_hint": "service mesh fault injection"},
|
||||
"time-skew": {"magnitude_hint": "+5 minutes", "tooling_hint": "libfaketime / Chaos Mesh TimeChaos"},
|
||||
"kill-instance": {"magnitude_hint": "1 of N instances", "tooling_hint": "AWS FIS / Chaos Monkey"},
|
||||
}
|
||||
|
||||
|
||||
def build_plan(args):
|
||||
attack_meta = ATTACK_DEFAULTS.get(args.attack, {})
|
||||
magnitude = args.magnitude or attack_meta.get("magnitude_hint", "<set magnitude>")
|
||||
tooling = args.tooling or attack_meta.get("tooling_hint", "<set tooling>")
|
||||
plan = {
|
||||
"experiment_id": f"chaos-{args.target}-{args.attack}-{int(datetime.now(timezone.utc).timestamp())}",
|
||||
"created": datetime.now(timezone.utc).isoformat(),
|
||||
"target": args.target,
|
||||
"hypothesis": args.hypothesis,
|
||||
"steady_state": {
|
||||
"metric": args.steady_metric or "<must define before experiment>",
|
||||
"baseline_window": "5 minutes pre-experiment",
|
||||
"tolerance": args.tolerance or "within ±5% of baseline",
|
||||
},
|
||||
"attack": {
|
||||
"type": args.attack,
|
||||
"magnitude": magnitude,
|
||||
"duration_min": args.duration_min,
|
||||
"tooling": tooling,
|
||||
},
|
||||
"blast_radius": {
|
||||
"scope": args.blast_radius or "<must define before experiment>",
|
||||
"rollback_immediately_if": args.abort_if or "<must define abort criteria>",
|
||||
},
|
||||
"abort_criteria": _parse_abort_criteria(args.abort_if),
|
||||
"rollback_procedure": args.rollback or "Disable fault injection; verify steady state recovers within 2 minutes.",
|
||||
"monitoring_dashboard": args.dashboard or "<paste dashboard URL>",
|
||||
"owner": args.owner or "<assign owner>",
|
||||
"on_call_acknowledged": False,
|
||||
"learning_question": args.learning or "What did we learn that we did not know before?",
|
||||
}
|
||||
return plan
|
||||
|
||||
|
||||
def _parse_abort_criteria(raw):
|
||||
if not raw:
|
||||
return []
|
||||
parts = [p.strip() for p in raw.split(" OR ")]
|
||||
return [{"signal": p, "action": "abort"} for p in parts if p]
|
||||
|
||||
|
||||
def render_markdown(plan):
|
||||
lines = []
|
||||
lines.append(f"# Chaos Experiment: {plan['experiment_id']}")
|
||||
lines.append("")
|
||||
lines.append(f"- **Target:** `{plan['target']}`")
|
||||
lines.append(f"- **Created:** {plan['created']}")
|
||||
lines.append(f"- **Owner:** {plan['owner']}")
|
||||
lines.append("")
|
||||
lines.append("## Hypothesis")
|
||||
lines.append(f"> {plan['hypothesis']}")
|
||||
lines.append("")
|
||||
lines.append("## Steady-state metric")
|
||||
lines.append(f"- **Metric:** {plan['steady_state']['metric']}")
|
||||
lines.append(f"- **Baseline window:** {plan['steady_state']['baseline_window']}")
|
||||
lines.append(f"- **Tolerance:** {plan['steady_state']['tolerance']}")
|
||||
lines.append("")
|
||||
lines.append("## Attack")
|
||||
a = plan["attack"]
|
||||
lines.append(f"- **Type:** {a['type']}")
|
||||
lines.append(f"- **Magnitude:** {a['magnitude']}")
|
||||
lines.append(f"- **Duration:** {a['duration_min']} minutes")
|
||||
lines.append(f"- **Tooling:** {a['tooling']}")
|
||||
lines.append("")
|
||||
lines.append("## Blast radius")
|
||||
lines.append(f"- **Scope:** {plan['blast_radius']['scope']}")
|
||||
lines.append("")
|
||||
lines.append("## Abort criteria")
|
||||
if plan["abort_criteria"]:
|
||||
for c in plan["abort_criteria"]:
|
||||
lines.append(f"- {c['signal']}")
|
||||
else:
|
||||
lines.append("- **WARNING: no abort criteria defined — DO NOT RUN**")
|
||||
lines.append("")
|
||||
lines.append("## Rollback procedure")
|
||||
lines.append(plan["rollback_procedure"])
|
||||
lines.append("")
|
||||
lines.append("## Monitoring")
|
||||
lines.append(f"- Dashboard: {plan['monitoring_dashboard']}")
|
||||
lines.append("")
|
||||
lines.append("## Learning question")
|
||||
lines.append(f"> {plan['learning_question']}")
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
|
||||
ap.add_argument("--target", required=True, help="Target system or service")
|
||||
ap.add_argument("--hypothesis", required=True, help='Hypothesis: "When X, metric Y stays Z"')
|
||||
ap.add_argument("--attack", required=True, choices=list(ATTACK_DEFAULTS.keys()))
|
||||
ap.add_argument("--magnitude", help="Attack magnitude (default: per-attack hint)")
|
||||
ap.add_argument("--duration-min", type=int, default=15)
|
||||
ap.add_argument("--steady-metric", help="Steady-state metric name (e.g., 'p99 latency')")
|
||||
ap.add_argument("--tolerance", help="Tolerance vs baseline (e.g., 'within ±5%%')")
|
||||
ap.add_argument("--blast-radius", help="Blast radius (e.g., '5%% of US traffic')")
|
||||
ap.add_argument("--abort-if", dest="abort_if", help='Abort criteria, OR-separated (e.g., "p99 > 1000ms OR error_rate > +1pp")')
|
||||
ap.add_argument("--rollback", help="Rollback procedure")
|
||||
ap.add_argument("--tooling", help="Chaos tool to use (default: per-attack hint)")
|
||||
ap.add_argument("--dashboard", help="Monitoring dashboard URL")
|
||||
ap.add_argument("--owner", help="Experiment owner")
|
||||
ap.add_argument("--learning", help="Learning question")
|
||||
ap.add_argument("--format", choices=["markdown", "json"], default="markdown")
|
||||
args = ap.parse_args()
|
||||
|
||||
plan = build_plan(args)
|
||||
if args.format == "json":
|
||||
print(json.dumps(plan, indent=2))
|
||||
else:
|
||||
print(render_markdown(plan))
|
||||
return 0 if plan["abort_criteria"] else 1
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
|
|
@ -0,0 +1,144 @@
|
|||
#!/usr/bin/env python3
|
||||
"""Generate a structured chaos experiment postmortem.
|
||||
|
||||
Takes an experiment plan (JSON from experiment_designer.py) plus a results
|
||||
file (free-form text or structured key=value lines), and produces a markdown
|
||||
postmortem with hypothesis verdict, learning, surprises, and follow-up actions.
|
||||
Catches common postmortem failure modes: no learning, no follow-up, blame-laden
|
||||
language.
|
||||
"""
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import sys
|
||||
from datetime import datetime, timezone
|
||||
|
||||
BLAME_PHRASES = [
|
||||
"fault of",
|
||||
"should have known",
|
||||
"stupid",
|
||||
"incompetent",
|
||||
"obvious",
|
||||
"lazy",
|
||||
"didn't bother",
|
||||
]
|
||||
|
||||
REQUIRED_RESULT_FIELDS = {
|
||||
"outcome": "Did the hypothesis hold? (held|refuted|inconclusive)",
|
||||
"duration_actual_min": "Actual experiment duration in minutes",
|
||||
"aborted": "Was the experiment aborted? (true|false)",
|
||||
}
|
||||
|
||||
|
||||
def _parse_results(path):
|
||||
"""Parse a results file. Lines like 'key=value' OR free text. Returns dict."""
|
||||
if not os.path.isfile(path):
|
||||
return {"_raw_text": ""}
|
||||
with open(path, "r", encoding="utf-8", errors="replace") as f:
|
||||
text = f.read()
|
||||
parsed = {}
|
||||
for line in text.splitlines():
|
||||
m = re.match(r"^\s*([\w_.\-]+)\s*=\s*(.+?)\s*$", line)
|
||||
if m:
|
||||
parsed[m.group(1)] = m.group(2)
|
||||
parsed["_raw_text"] = text
|
||||
return parsed
|
||||
|
||||
|
||||
def _check_blame(text):
|
||||
found = []
|
||||
low = text.lower()
|
||||
for phrase in BLAME_PHRASES:
|
||||
if phrase in low:
|
||||
found.append(phrase)
|
||||
return found
|
||||
|
||||
|
||||
def build_postmortem(plan, results, follow_ups):
|
||||
raw_text = results.get("_raw_text", "")
|
||||
blame = _check_blame(raw_text)
|
||||
pm = {
|
||||
"experiment_id": plan.get("experiment_id", "?"),
|
||||
"target": plan.get("target", "?"),
|
||||
"created": datetime.now(timezone.utc).isoformat(),
|
||||
"hypothesis": plan.get("hypothesis", "?"),
|
||||
"outcome": results.get("outcome", "<UNRECORDED — must record>"),
|
||||
"aborted": results.get("aborted", "<unrecorded>"),
|
||||
"duration_actual_min": results.get("duration_actual_min", "<unrecorded>"),
|
||||
"duration_planned_min": plan.get("attack", {}).get("duration_min", "?"),
|
||||
"what_we_learned": results.get("learned", "<UNRECORDED — must record at least one learning>"),
|
||||
"what_surprised_us": results.get("surprised", "<unrecorded>"),
|
||||
"what_failed": results.get("failed", "<none recorded>"),
|
||||
"what_held": results.get("held", "<none recorded>"),
|
||||
"follow_ups": follow_ups,
|
||||
"blame_warnings": blame,
|
||||
"raw_results_excerpt": raw_text[:500],
|
||||
}
|
||||
return pm
|
||||
|
||||
|
||||
def render_markdown(pm):
|
||||
lines = []
|
||||
lines.append(f"# Postmortem: {pm['experiment_id']}")
|
||||
lines.append("")
|
||||
lines.append(f"- **Target:** `{pm['target']}`")
|
||||
lines.append(f"- **Postmortem date:** {pm['created']}")
|
||||
lines.append(f"- **Outcome:** {pm['outcome']}")
|
||||
lines.append(f"- **Aborted:** {pm['aborted']}")
|
||||
lines.append(f"- **Duration:** planned={pm['duration_planned_min']}min, actual={pm['duration_actual_min']}min")
|
||||
lines.append("")
|
||||
lines.append("## Hypothesis")
|
||||
lines.append(f"> {pm['hypothesis']}")
|
||||
lines.append("")
|
||||
lines.append("## What we learned")
|
||||
lines.append(pm["what_we_learned"])
|
||||
lines.append("")
|
||||
lines.append("## What surprised us")
|
||||
lines.append(pm["what_surprised_us"])
|
||||
lines.append("")
|
||||
lines.append("## What failed")
|
||||
lines.append(pm["what_failed"])
|
||||
lines.append("")
|
||||
lines.append("## What held")
|
||||
lines.append(pm["what_held"])
|
||||
lines.append("")
|
||||
lines.append("## Follow-up actions")
|
||||
if pm["follow_ups"]:
|
||||
for f in pm["follow_ups"]:
|
||||
lines.append(f"- [ ] {f}")
|
||||
else:
|
||||
lines.append("- _none recorded — every experiment should produce ≥1 follow-up_")
|
||||
if pm["blame_warnings"]:
|
||||
lines.append("")
|
||||
lines.append("## ⚠️ Blame warning")
|
||||
lines.append("Blame-laden language detected — postmortems should be blameless.")
|
||||
for b in pm["blame_warnings"]:
|
||||
lines.append(f"- '{b}'")
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
|
||||
ap.add_argument("--plan", required=True, help="Path to experiment plan JSON (from experiment_designer.py --format json)")
|
||||
ap.add_argument("--result-log", required=True, help="Path to result log (free-form text OR key=value lines)")
|
||||
ap.add_argument("--follow-up", action="append", default=[], help="A follow-up action; repeat for multiple")
|
||||
ap.add_argument("--format", choices=["markdown", "json"], default="markdown")
|
||||
args = ap.parse_args()
|
||||
|
||||
if not os.path.isfile(args.plan):
|
||||
print(f"ERROR: plan not found: {args.plan}", file=sys.stderr)
|
||||
return 2
|
||||
with open(args.plan, "r", encoding="utf-8") as f:
|
||||
plan = json.load(f)
|
||||
results = _parse_results(args.result_log)
|
||||
pm = build_postmortem(plan, results, args.follow_up)
|
||||
if args.format == "json":
|
||||
print(json.dumps(pm, indent=2))
|
||||
else:
|
||||
print(render_markdown(pm))
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
231
engineering/skills/chaos-engineering/SKILL.md
Normal file
231
engineering/skills/chaos-engineering/SKILL.md
Normal file
|
|
@ -0,0 +1,231 @@
|
|||
---
|
||||
name: chaos-engineering
|
||||
description: Use when planning, running, or learning from chaos engineering experiments. Triggers on "chaos experiment", "fault injection", "gameday", "resilience test", "blast radius", "steady state", "abort criteria", "Chaos Toolkit", "Chaos Mesh", "Litmus", "Gremlin", "AWS FIS", or any deliberate failure-injection question. Ships experiment designer, blast-radius calculator, and postmortem generator (all stdlib Python), 4 references on chaos principles + experiment design + attack taxonomy + tooling landscape, and a /chaos-experiment slash command. Composes with feature-flags-architect (kill switches as abort triggers) and kubernetes-operator (common chaos targets).
|
||||
context: fork
|
||||
version: 2.4.0
|
||||
author: claude-code-skills
|
||||
license: MIT
|
||||
tags: [chaos-engineering, resilience, fault-injection, gameday, sre, reliability, chaos-toolkit, chaos-mesh, litmus, gremlin, aws-fis]
|
||||
compatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]
|
||||
---
|
||||
|
||||
# Chaos Engineering
|
||||
|
||||
Design experiments that surface real weaknesses in production systems — without becoming outages. Most "chaos engineering" attempts skip steady-state measurement, define no abort criteria, and have no blast-radius bound. This skill enforces the discipline that makes chaos experiments safe and useful.
|
||||
|
||||
## When to use
|
||||
|
||||
- Planning a chaos experiment (what to break, where, when, how to abort)
|
||||
- Calculating blast radius before running the experiment
|
||||
- Reviewing an existing experiment plan for safety
|
||||
- Choosing a chaos tool (Chaos Toolkit / Chaos Mesh / Litmus / Gremlin / AWS FIS)
|
||||
- Writing a chaos experiment postmortem
|
||||
- Running a Game Day exercise
|
||||
|
||||
## When NOT to use
|
||||
|
||||
- General incident response (use `incident-response`)
|
||||
- Threat hunting / red-team (use `red-team`, `threat-detection`)
|
||||
- Performance load testing (different goal — chaos is about failure modes, not capacity)
|
||||
- Production debugging (chaos discovers weaknesses preemptively, not after-the-fact)
|
||||
|
||||
## Core principle: chaos without abort criteria is an outage
|
||||
|
||||
The 4 Principles of Chaos Engineering (Netflix, 2016):
|
||||
|
||||
1. **Build a hypothesis around steady-state behavior.** Not "what breaks?" but "X holds; will it still hold under fault Y?"
|
||||
2. **Vary real-world events.** Inject realistic failures: kill nodes, slow networks, lose cache, throttle dependencies.
|
||||
3. **Run experiments in production.** Staging never has the same failure modes. Start small.
|
||||
4. **Automate experiments to run continuously.** One-off chaos is a press release; continuous chaos is engineering.
|
||||
|
||||
Add a fifth: **Define abort criteria up front.** A chaos experiment with no abort criteria is an outage by another name.
|
||||
|
||||
## Quick start
|
||||
|
||||
```bash
|
||||
SKILL=engineering/chaos-engineering/skills/chaos-engineering
|
||||
|
||||
# 1. Design an experiment
|
||||
python "$SKILL/scripts/experiment_designer.py" --target "checkout-svc" --hypothesis "p99 latency stays <500ms" --attack latency --duration-min 15
|
||||
|
||||
# 2. Calculate blast radius
|
||||
python "$SKILL/scripts/blast_radius_calculator.py" --traffic-share 0.05 --user-pop 1000000 --duration-min 15
|
||||
|
||||
# 3. Generate postmortem after the experiment
|
||||
python "$SKILL/scripts/experiment_postmortem.py" --plan experiment.json --result-log results.txt
|
||||
```
|
||||
|
||||
## The 3 Python tools
|
||||
|
||||
All stdlib-only. Run with `--help`.
|
||||
|
||||
### `experiment_designer.py`
|
||||
|
||||
Generates a structured experiment plan from inputs. Enforces the required sections (hypothesis, steady-state metric, blast radius, abort criteria, rollback).
|
||||
|
||||
```bash
|
||||
python scripts/experiment_designer.py \
|
||||
--target "checkout-svc" \
|
||||
--hypothesis "p99 latency stays <500ms when payment-svc is slow" \
|
||||
--attack latency \
|
||||
--magnitude "+200ms" \
|
||||
--duration-min 15 \
|
||||
--blast-radius "5% of US traffic" \
|
||||
--abort-if "p99 > 1000ms OR error_rate > baseline + 1pp"
|
||||
```
|
||||
|
||||
Outputs a markdown plan with: hypothesis, steady-state, attack, magnitude, duration, blast radius, abort criteria, rollback procedure, monitoring dashboards, and learning question.
|
||||
|
||||
### `blast_radius_calculator.py`
|
||||
|
||||
Computes the blast radius of a planned experiment. Given traffic share + user population + duration, calculates expected affected users, expected error budget burn, and a risk score.
|
||||
|
||||
```bash
|
||||
python scripts/blast_radius_calculator.py \
|
||||
--traffic-share 0.05 \
|
||||
--user-pop 1000000 \
|
||||
--duration-min 15 \
|
||||
--baseline-availability 0.999 \
|
||||
--expected-impact-availability 0.95
|
||||
```
|
||||
|
||||
Outputs:
|
||||
- Expected affected users
|
||||
- Error budget consumed (in minutes of error budget)
|
||||
- Risk score: GREEN / YELLOW / RED
|
||||
- Recommendation: PROCEED / REDUCE / ABORT
|
||||
|
||||
GREEN = <1% error budget; YELLOW = 1-10%; RED = >10%.
|
||||
|
||||
### `experiment_postmortem.py`
|
||||
|
||||
Produces a structured postmortem from an experiment plan + results. Catches the common postmortem failure modes: no learning recorded, no follow-up actions, blame-laden language.
|
||||
|
||||
```bash
|
||||
python scripts/experiment_postmortem.py --plan experiment.json --result-log results.txt
|
||||
```
|
||||
|
||||
Outputs markdown with: summary, hypothesis (was it confirmed/refuted?), what we learned, what surprised us, follow-up actions with owners, and link to next experiment.
|
||||
|
||||
## The 7 attack types (taxonomy)
|
||||
|
||||
Different attacks reveal different weaknesses. See `references/attack_taxonomy.md` for full detail.
|
||||
|
||||
| Attack | What it tests | Tooling |
|
||||
|---|---|---|
|
||||
| **Latency** | Timeouts, retries, circuit breakers | tc, Chaos Mesh `NetworkChaos` |
|
||||
| **Error** | Error handling, fallback paths | Chaos Mesh `HTTPChaos`, Toxiproxy |
|
||||
| **Resource** (CPU, memory, disk) | Saturation handling, autoscaling | Chaos Mesh `StressChaos`, stress-ng |
|
||||
| **Network partition** | Split-brain, consensus, failover | Chaos Mesh `NetworkChaos` partition |
|
||||
| **Dependency failure** | Graceful degradation, fallback | Service mesh fault injection |
|
||||
| **Time** | Clock skew, NTP issues | libfaketime, Chaos Mesh `TimeChaos` |
|
||||
| **Infrastructure** (kill instance) | Auto-recovery, failover | AWS FIS, Chaos Monkey |
|
||||
|
||||
Pick the attack that matches the hypothesis. "What happens if X is slow?" → latency. "What happens if X loses network?" → partition.
|
||||
|
||||
## Tooling chooser
|
||||
|
||||
| Tool | Best for | Pricing | Stack |
|
||||
|---|---|---|---|
|
||||
| **Chaos Toolkit** | Lightweight, language-agnostic, JSON experiments | OSS | Any |
|
||||
| **Chaos Mesh** | Kubernetes-native, rich CRDs, in-cluster | OSS | Kubernetes |
|
||||
| **Litmus** | Kubernetes, Argo-integrated, large library | OSS + Enterprise | Kubernetes |
|
||||
| **Gremlin** | Enterprise SaaS, multi-cloud, audit | Paid | Any |
|
||||
| **AWS FIS** | AWS-native, IAM-integrated, EC2/ECS/EKS | Paid (AWS) | AWS |
|
||||
| **Custom** | Niche needs, single-cloud, low budget | None | Any |
|
||||
|
||||
Decision rules:
|
||||
- k8s-only stack + OSS → Chaos Mesh or Litmus (Litmus has bigger experiment library)
|
||||
- Multi-cloud + OSS → Chaos Toolkit
|
||||
- AWS-heavy + simple needs → AWS FIS
|
||||
- Enterprise + audit/compliance → Gremlin
|
||||
|
||||
See `references/tooling_landscape.md` for trade-offs.
|
||||
|
||||
## Workflows
|
||||
|
||||
### Workflow 1: Design and run a single experiment
|
||||
|
||||
```
|
||||
1. State a hypothesis: "When [fault], steady-state metric X stays within Y."
|
||||
2. Identify the steady-state metric — must be measurable BEFORE the experiment.
|
||||
3. Run blast_radius_calculator.py — confirm GREEN before proceeding.
|
||||
4. Run experiment_designer.py to produce the plan.
|
||||
5. Get a peer review of the plan; confirm abort criteria are concrete.
|
||||
6. Notify the on-call team in #incidents (or whatever channel).
|
||||
7. Run the experiment with monitoring open.
|
||||
8. If abort criteria are hit, abort immediately; record what happened.
|
||||
9. Run experiment_postmortem.py to capture learnings.
|
||||
10. File follow-up actions; link to next experiment.
|
||||
```
|
||||
|
||||
### Workflow 2: Game Day exercise
|
||||
|
||||
```
|
||||
1. Pick a scenario (e.g., "primary database fails over").
|
||||
2. Identify all dependent services that should keep working.
|
||||
3. Build a multi-experiment plan covering each layer.
|
||||
4. Schedule with stakeholders; on-call coverage required.
|
||||
5. Run with a facilitator who manages the scenario.
|
||||
6. Capture observations in a shared doc as they happen.
|
||||
7. Single combined postmortem covering all observations.
|
||||
8. Track follow-up actions in a board with owners.
|
||||
```
|
||||
|
||||
### Workflow 3: Continuous chaos (game days → daily)
|
||||
|
||||
```
|
||||
1. Start: weekly Game Day in staging.
|
||||
2. Move to: weekly Game Day in production with limited blast radius.
|
||||
3. Mature to: continuous chaos via scheduled experiments (Litmus chaos schedule, Gremlin scenarios).
|
||||
4. Wire to deployment: every prod deploy triggers a baseline chaos sweep.
|
||||
5. Track: experiments per week, weaknesses discovered, MTTR trend.
|
||||
```
|
||||
|
||||
## Composition with other skills
|
||||
|
||||
This skill explicitly composes with two others in this library:
|
||||
|
||||
| Skill | Composition |
|
||||
|---|---|
|
||||
| `feature-flags-architect` | Kill switches defined there are the abort triggers here |
|
||||
| `kubernetes-operator` | Operators are common chaos targets (test reconcile under fault) |
|
||||
| `incident-response` | Chaos experiments that escalate become incidents |
|
||||
|
||||
## Anti-patterns
|
||||
|
||||
- **No hypothesis** — "let's break things" is sabotage, not engineering
|
||||
- **No steady-state metric** — without a baseline, you can't tell if X broke
|
||||
- **No blast radius bound** — full-prod experiment without limits = outage
|
||||
- **No abort criteria** — see above; this is mandatory
|
||||
- **No on-call coverage** — chaos without monitoring is unmonitored production
|
||||
- **Chaos in staging only** — staging never has prod failure modes
|
||||
- **Chaos in dev** — useless; dev has different failure modes from prod
|
||||
- **One-off chaos** — single experiment is a press release; learning requires recurrence
|
||||
- **Blame-laden postmortem** — record causes, not blame; teams stop running chaos otherwise
|
||||
|
||||
## References
|
||||
|
||||
- `references/chaos_principles.md` — the 4 principles, history, when to start
|
||||
- `references/experiment_design.md` — hypothesis structure, steady-state metrics, abort criteria
|
||||
- `references/attack_taxonomy.md` — 7 attack types with examples and tooling
|
||||
- `references/tooling_landscape.md` — Chaos Toolkit / Mesh / Litmus / Gremlin / FIS / DIY
|
||||
|
||||
## Slash command
|
||||
|
||||
`/chaos-experiment` — interactive experiment design wizard that runs all 3 tools.
|
||||
|
||||
## Asset templates
|
||||
|
||||
- `assets/experiment_template.md` — fill-in plan template
|
||||
- `assets/postmortem_template.md` — structured postmortem template
|
||||
|
||||
## Verifiable success
|
||||
|
||||
A team using this skill should achieve:
|
||||
|
||||
- 100% of chaos experiments have a written hypothesis, abort criteria, and blast-radius calculation
|
||||
- Blast radius for any single experiment never exceeds 10% of error budget
|
||||
- Mean time between chaos experiments <14 days (continuous, not one-off)
|
||||
- Each experiment produces ≥1 follow-up action that gets shipped
|
||||
- No chaos experiment escalates to a customer-impacting incident in trailing 90 days
|
||||
|
|
@ -0,0 +1,76 @@
|
|||
# Chaos Experiment
|
||||
|
||||
Fill in every section before running. Refuse to run if any section is empty.
|
||||
|
||||
## Identity
|
||||
|
||||
- **Experiment ID:** `<auto-generated; format: chaos-<target>-<attack>-<unix-ts>>`
|
||||
- **Date:** `<YYYY-MM-DD>`
|
||||
- **Owner:** `<your-handle@team>`
|
||||
- **On-call team:** `<team channel / pager>`
|
||||
- **Reviewer:** `<peer who reviewed this plan>`
|
||||
|
||||
## 1. Hypothesis
|
||||
|
||||
> When `<fault>`, `<steady-state metric>` stays `<tolerance>`.
|
||||
|
||||
Example: *When payment-svc is +200ms slow, checkout p99 stays below 500ms.*
|
||||
|
||||
## 2. Steady-state metric
|
||||
|
||||
- **Metric:** `<e.g., p99 checkout latency>`
|
||||
- **Baseline window:** `<e.g., 5 minutes pre-experiment>`
|
||||
- **Tolerance:** `<e.g., within ±5% of baseline>`
|
||||
- **Dashboard:** `<URL>`
|
||||
|
||||
## 3. Attack
|
||||
|
||||
- **Type:** `[ ] latency [ ] error [ ] cpu [ ] memory [ ] disk [ ] network-partition [ ] dependency-failure [ ] time-skew [ ] kill-instance`
|
||||
- **Magnitude:** `<e.g., +200ms>`
|
||||
- **Duration:** `<minutes>`
|
||||
- **Target:** `<service / pod / instance / region>`
|
||||
- **Tooling:** `<Chaos Toolkit / Chaos Mesh / Litmus / Gremlin / AWS FIS / Custom>`
|
||||
|
||||
## 4. Blast radius
|
||||
|
||||
- **Traffic share:** `<e.g., 5% of US>`
|
||||
- **Expected affected users:** `<from blast_radius_calculator.py>`
|
||||
- **Error budget consumed:** `<from blast_radius_calculator.py>`
|
||||
- **Risk score:** `[ ] GREEN [ ] YELLOW [ ] RED`
|
||||
|
||||
## 5. Abort criteria
|
||||
|
||||
> Auto-trigger experiment termination if ANY of these hit.
|
||||
|
||||
- [ ] `<signal 1, e.g., p99 > 1000ms>`
|
||||
- [ ] `<signal 2, e.g., 5xx rate > baseline + 1pp>`
|
||||
- [ ] `<signal 3, e.g., on-call paged SEV1/SEV2>`
|
||||
|
||||
## 6. Rollback procedure
|
||||
|
||||
1. `<step to disable fault, e.g., "kubectl delete chaos networkchaos/<name>">`
|
||||
2. Verify steady state recovers within 2 minutes
|
||||
3. If not recovering, escalate as incident; restore from backup if needed
|
||||
|
||||
## 7. Learning question
|
||||
|
||||
> What do you expect NOT to learn? Force yourself to predict.
|
||||
|
||||
`<your prediction>`
|
||||
|
||||
## Pre-flight checklist
|
||||
|
||||
- [ ] Hypothesis written
|
||||
- [ ] Steady-state metric measured for ≥5 min
|
||||
- [ ] Blast radius calculated (GREEN or YELLOW only)
|
||||
- [ ] Abort criteria documented with thresholds
|
||||
- [ ] Rollback procedure tested in staging
|
||||
- [ ] On-call team notified
|
||||
- [ ] Monitoring dashboards open
|
||||
- [ ] Owner identified and reachable
|
||||
- [ ] Time-box agreed
|
||||
- [ ] Communication plan if abort triggers
|
||||
|
||||
## Post-experiment
|
||||
|
||||
Run `experiment_postmortem.py --plan <plan.json> --result-log <results>` to generate the postmortem.
|
||||
|
|
@ -0,0 +1,67 @@
|
|||
# Chaos Experiment Postmortem
|
||||
|
||||
## Identity
|
||||
|
||||
- **Experiment:** `<experiment_id>`
|
||||
- **Date:** `<YYYY-MM-DD>`
|
||||
- **Target:** `<service>`
|
||||
- **Owner:** `<handle@team>`
|
||||
- **Postmortem facilitator:** `<handle@team>`
|
||||
|
||||
## Hypothesis
|
||||
|
||||
> `<hypothesis from the plan>`
|
||||
|
||||
## Outcome
|
||||
|
||||
- [ ] **Held** — hypothesis confirmed
|
||||
- [ ] **Refuted** — hypothesis disproven
|
||||
- [ ] **Inconclusive** — could not tell
|
||||
|
||||
## Timeline
|
||||
|
||||
| Time | Event |
|
||||
|---|---|
|
||||
| T-5min | Started baseline measurement |
|
||||
| T+0 | Attack injected |
|
||||
| T+? | `<observation>` |
|
||||
| T+? | `<observation>` |
|
||||
| T+N | Attack ended (or aborted) |
|
||||
| T+N+2 | Steady state recovered |
|
||||
|
||||
## What we learned
|
||||
|
||||
`<at least one concrete learning — required>`
|
||||
|
||||
## What surprised us
|
||||
|
||||
`<unexpected observations; "nothing surprised us" is a signal that you didn't push hard enough>`
|
||||
|
||||
## What failed
|
||||
|
||||
`<things that broke during the experiment that shouldn't have>`
|
||||
|
||||
## What held
|
||||
|
||||
`<things that worked as expected — confidence-building data points>`
|
||||
|
||||
## Root causes (if any failures)
|
||||
|
||||
`<technical analysis without blame>`
|
||||
|
||||
## Follow-up actions
|
||||
|
||||
| Action | Owner | Due | Status |
|
||||
|---|---|---|---|
|
||||
| `<concrete action>` | `<@owner>` | `<date>` | `[ ]` |
|
||||
| `<concrete action>` | `<@owner>` | `<date>` | `[ ]` |
|
||||
|
||||
> Every experiment should produce ≥1 follow-up. If none — re-examine whether you tested anything new.
|
||||
|
||||
## Next experiment
|
||||
|
||||
`<what's the next experiment that builds on this learning?>`
|
||||
|
||||
## Stakeholder summary (1-2 sentences)
|
||||
|
||||
`<for the team channel; describe outcome and biggest learning>`
|
||||
|
|
@ -0,0 +1,180 @@
|
|||
# Attack taxonomy
|
||||
|
||||
7 categories of fault injection. Each tests a different system property. Pick the one whose failure mode matches your hypothesis.
|
||||
|
||||
## 1. Latency
|
||||
|
||||
**What it tests:** timeouts, retries, circuit breakers, fallback paths.
|
||||
|
||||
**Inject:** add N ms of delay to network responses to a target.
|
||||
|
||||
**When to use:**
|
||||
- "What if dependency X is slow?"
|
||||
- "Are timeouts configured correctly upstream?"
|
||||
- "Does the retry budget kick in?"
|
||||
|
||||
**Tools:**
|
||||
- Linux `tc` (traffic control) — direct kernel-level shaping
|
||||
- Chaos Mesh `NetworkChaos` (delay)
|
||||
- Toxiproxy — proxy-based, language-agnostic
|
||||
- AWS FIS — `aws:network:traffic-control` action
|
||||
|
||||
**Example magnitude:** +200ms (90% of typical timeouts), +2000ms (test backoff), +30s (test giving-up logic).
|
||||
|
||||
## 2. Error injection
|
||||
|
||||
**What it tests:** error handling paths, fallback behavior, retry policies.
|
||||
|
||||
**Inject:** return errors (5xx, exceptions) for a fraction of requests.
|
||||
|
||||
**When to use:**
|
||||
- "What happens when X starts failing?"
|
||||
- "Does the fallback path actually work in prod?"
|
||||
- "Are we logging errors correctly?"
|
||||
|
||||
**Tools:**
|
||||
- Chaos Mesh `HTTPChaos`
|
||||
- Service mesh (Istio, Linkerd) fault injection
|
||||
- Toxiproxy with error toxic
|
||||
- Application-level feature flag for synthetic errors
|
||||
|
||||
**Example magnitude:** 1% errors (test handler), 50% errors (test retry), 100% errors (test fallback path).
|
||||
|
||||
## 3. Resource exhaustion
|
||||
|
||||
**What it tests:** saturation handling, autoscaling, OOM behavior, disk-full handling.
|
||||
|
||||
**Inject:** consume CPU, memory, or disk on the target.
|
||||
|
||||
**When to use:**
|
||||
- "What if memory leaks?"
|
||||
- "Does the autoscaler kick in?"
|
||||
- "What happens when disk fills?"
|
||||
|
||||
**Sub-types:**
|
||||
- **CPU pressure** — peg cores at N% usage
|
||||
- **Memory pressure** — allocate large blocks
|
||||
- **Disk fill** — write large files until partition fills
|
||||
- **I/O saturation** — high random read/write
|
||||
|
||||
**Tools:**
|
||||
- `stress-ng` — CPU/memory/IO/disk
|
||||
- Chaos Mesh `StressChaos` and `IOChaos`
|
||||
- AWS FIS `aws:ssm:send-command` with stress-ng
|
||||
|
||||
**Example magnitude:** 80% CPU sustained, 90% memory, fill /var to 95%.
|
||||
|
||||
## 4. Network partition
|
||||
|
||||
**What it tests:** consensus protocols, leader election, split-brain prevention, region failover.
|
||||
|
||||
**Inject:** drop all packets between a set of hosts.
|
||||
|
||||
**When to use:**
|
||||
- "What if AZ-A loses connectivity to AZ-B?"
|
||||
- "Does the database elect a new primary?"
|
||||
- "Does the cluster avoid split-brain?"
|
||||
|
||||
**Tools:**
|
||||
- Chaos Mesh `NetworkChaos` (partition mode)
|
||||
- `tc` with iptables drop rules
|
||||
- AWS FIS `aws:network:disrupt-connectivity`
|
||||
|
||||
**Example magnitude:** drop 100% to peer X (full partition), drop 50% (degraded link).
|
||||
|
||||
## 5. Dependency failure
|
||||
|
||||
**What it tests:** graceful degradation, fallback to cache, fallback to default values.
|
||||
|
||||
**Inject:** make a downstream dependency unavailable (timeout, refuse connections).
|
||||
|
||||
**When to use:**
|
||||
- "What if the rec engine goes down?"
|
||||
- "Does Search degrade gracefully when ML models are unreachable?"
|
||||
- "Is cache the fallback for the user-pref service?"
|
||||
|
||||
**Tools:**
|
||||
- Service mesh fault injection (most flexible)
|
||||
- Toxiproxy
|
||||
- iptables rules to refuse connections
|
||||
- Chaos Mesh `NetworkChaos` with `corrupt` or `drop`
|
||||
|
||||
**Example magnitude:** 100% requests to dep X timeout (full outage), 25% timeout (intermittent), 0% available for 5 min (sustained outage).
|
||||
|
||||
## 6. Time skew
|
||||
|
||||
**What it tests:** time-sensitive logic — token expiry, cron schedules, TTLs, retry backoff.
|
||||
|
||||
**Inject:** alter the wall clock seen by a process.
|
||||
|
||||
**When to use:**
|
||||
- "What if NTP fails?"
|
||||
- "What if a process clock drifts +5 minutes?"
|
||||
- "Do tokens correctly fail validation when expired?"
|
||||
- "Does cron skip or double-fire?"
|
||||
|
||||
**Tools:**
|
||||
- `libfaketime` — preload library
|
||||
- Chaos Mesh `TimeChaos`
|
||||
- Custom: change container's `/etc/localtime`
|
||||
|
||||
**Example magnitude:** +1 minute (subtle), +5 minutes (TLS / token failures), +1 day (catastrophic for some logic).
|
||||
|
||||
**Caution:** time skew can cause cluster-wide consensus failures. Test in isolation first.
|
||||
|
||||
## 7. Infrastructure (kill instance / pod / container)
|
||||
|
||||
**What it tests:** auto-recovery, failover, replica count maintenance.
|
||||
|
||||
**Inject:** terminate an instance, pod, or container.
|
||||
|
||||
**When to use:**
|
||||
- "Does Kubernetes restart the pod?"
|
||||
- "Does the load balancer remove the instance from rotation?"
|
||||
- "Is the replication factor maintained?"
|
||||
|
||||
**Tools:**
|
||||
- Chaos Monkey (the original)
|
||||
- Chaos Mesh `PodChaos` (kill, fail)
|
||||
- AWS FIS `aws:ec2:terminate-instances`
|
||||
- `kubectl delete pod` (manual, simplest)
|
||||
|
||||
**Example magnitude:** kill 1 of N pods (Chaos Monkey level), kill all pods of a deployment (test recreation), kill 1 of 3 replica DB nodes (test failover).
|
||||
|
||||
## Choosing an attack
|
||||
|
||||
| Hypothesis pattern | Attack type |
|
||||
|---|---|
|
||||
| "What if X is slow?" | Latency |
|
||||
| "What if X is failing?" | Error |
|
||||
| "What if we run hot?" | Resource |
|
||||
| "What if regions partition?" | Network partition |
|
||||
| "What if dep X is down?" | Dependency failure |
|
||||
| "What if clocks drift?" | Time skew |
|
||||
| "What if a node dies?" | Infrastructure |
|
||||
|
||||
## Combining attacks
|
||||
|
||||
Real outages often combine attacks (e.g., latency + saturation). Once basic experiments are stable, run combinations:
|
||||
|
||||
- Latency on dependency + CPU pressure on app → tests timeout + retry budget interaction
|
||||
- Pod kill + network partition → tests recovery during a partition
|
||||
- Disk fill + dependency failure → tests fallback path while disk is constrained
|
||||
|
||||
Combinations have higher risk; reduce blast radius accordingly.
|
||||
|
||||
## Severity ladder
|
||||
|
||||
```
|
||||
S1 — Latency (small) ← start here
|
||||
S2 — Error injection (low %)
|
||||
S3 — Resource pressure (CPU/mem)
|
||||
S4 — Latency (large) / errors (high %)
|
||||
S5 — Single instance kill
|
||||
S6 — Network partition (single peer)
|
||||
S7 — Multiple instance kill
|
||||
S8 — Region partition / time skew
|
||||
S9 — Combinations of S5-S8 ← here be dragons
|
||||
```
|
||||
|
||||
Don't skip levels. Earn confidence at S1-S3 before attempting S5+.
|
||||
|
|
@ -0,0 +1,136 @@
|
|||
# The principles of chaos engineering
|
||||
|
||||
Chaos engineering is the discipline of experimenting on a system in order to build confidence in its capability to withstand turbulent conditions in production. The phrase comes from Netflix's 2014-2016 work productizing what started as Chaos Monkey.
|
||||
|
||||
## The 4 founding principles (Netflix, 2016)
|
||||
|
||||
### 1. Build a hypothesis around steady-state behavior
|
||||
|
||||
Steady state = a measurable, normal-operations metric (latency, throughput, conversion rate, error rate).
|
||||
|
||||
Bad: *"What happens if the database goes down?"*
|
||||
Good: *"When the primary database fails over, p99 checkout latency stays below 800ms and conversion rate stays within 2% of baseline."*
|
||||
|
||||
The hypothesis must be **falsifiable** — there must be a measurement that can disprove it.
|
||||
|
||||
### 2. Vary real-world events
|
||||
|
||||
Inject realistic failure modes:
|
||||
- Servers crash
|
||||
- Networks partition or slow
|
||||
- Disks fill
|
||||
- Dependencies time out or return errors
|
||||
- Caches lose data
|
||||
- Time skews
|
||||
|
||||
Don't inject implausible events (e.g., "what if all 50 zones in 5 regions go down simultaneously"). That's not chaos engineering, that's astronomy.
|
||||
|
||||
### 3. Run experiments in production
|
||||
|
||||
Staging never reproduces:
|
||||
- Real traffic patterns
|
||||
- Real cache hit rates
|
||||
- Real cross-service dependencies
|
||||
- Real data volumes
|
||||
- Real user behavior
|
||||
|
||||
The only system that has prod failure modes is prod. Start with tiny blast radius (1%), grow as confidence grows.
|
||||
|
||||
### 4. Automate experiments to run continuously
|
||||
|
||||
A single chaos experiment is a press release. Continuous chaos is engineering.
|
||||
|
||||
Maturity progression:
|
||||
1. Manual one-offs → 2. Weekly Game Days → 3. Scheduled experiments → 4. Continuous chaos in CI/CD
|
||||
|
||||
The 5th principle this skill adds:
|
||||
|
||||
### 5. Define abort criteria up front
|
||||
|
||||
A chaos experiment with no abort criteria is an outage. Every plan must include:
|
||||
|
||||
- A specific signal (metric, threshold)
|
||||
- A specific action (auto-abort, manual abort, escalate)
|
||||
- A timeline (within N seconds of breach)
|
||||
|
||||
If the threshold is hit, abort immediately. Investigate later.
|
||||
|
||||
## When to start
|
||||
|
||||
You're ready for chaos engineering when:
|
||||
|
||||
- [ ] You have basic monitoring (you can detect a steady-state breach)
|
||||
- [ ] You have on-call rotations (someone is watching when chaos runs)
|
||||
- [ ] You have at least one tool to inject the desired fault
|
||||
- [ ] You have an SLO/SLI defined (so you know what "good" looks like)
|
||||
- [ ] You have postmortem culture that's blameless
|
||||
- [ ] You have a leadership champion who'll defend the practice
|
||||
|
||||
If any of these are missing, fix them first. Premature chaos = outages with no learning.
|
||||
|
||||
## When NOT to do chaos engineering
|
||||
|
||||
- During a release freeze
|
||||
- During a known incident
|
||||
- During peak traffic events without explicit approval
|
||||
- On systems that don't have steady-state metrics
|
||||
- On systems where you can't bound the blast radius
|
||||
- On the day of a security disclosure
|
||||
- When the team is already firefighting
|
||||
|
||||
## Maturity model
|
||||
|
||||
| Level | Description | Cadence | Tooling |
|
||||
|---|---|---|---|
|
||||
| L0 | None | n/a | none |
|
||||
| L1 | Manual one-offs in staging | quarterly | tc, manual scripts |
|
||||
| L2 | Weekly Game Days in staging | weekly | Chaos Toolkit, internal scripts |
|
||||
| L3 | Limited prod experiments | weekly | Chaos Toolkit / Mesh / Litmus / FIS |
|
||||
| L4 | Continuous prod chaos with bounded blast radius | daily | Chaos Mesh / Gremlin scenarios |
|
||||
| L5 | Chaos in CI/CD pipeline; deploys auto-trigger sweeps | per-deploy | Custom + tooling stack |
|
||||
|
||||
Most teams should target L3 within 6-12 months of starting. L5 is rare and only justified for the largest distributed systems.
|
||||
|
||||
## Common objections (and counters)
|
||||
|
||||
| Objection | Counter |
|
||||
|---|---|
|
||||
| "We can't break production!" | You already do, just unintentionally. Chaos is intentional, bounded, observed breaks. |
|
||||
| "This is a customer-facing system." | Start at 1% blast radius. The 99% are unaffected. |
|
||||
| "We don't have time." | Chaos finds bugs that would otherwise become 4am pages. Time spent on chaos saves time on incidents. |
|
||||
| "Our system is too critical." | Critical systems have the most to gain from learning their failure modes. |
|
||||
| "We have HA already." | HA without chaos is HA in theory. Chaos finds gaps in actual HA. |
|
||||
|
||||
## What a steady-state metric looks like
|
||||
|
||||
Good steady-state metrics:
|
||||
- p99 request latency (objective, measurable per second)
|
||||
- Error rate (objective, measurable)
|
||||
- Conversion rate (business metric, slow but real)
|
||||
- Successful logins per minute (business + tech signal)
|
||||
- Queue depth (system health)
|
||||
|
||||
Bad metrics:
|
||||
- "Things feel slow" (not measurable)
|
||||
- CPU usage (a means, not an end)
|
||||
- Number of pods running (not customer-facing)
|
||||
|
||||
Pick metrics that customers feel. CPU can spike without customer impact; latency and errors can't.
|
||||
|
||||
## History
|
||||
|
||||
- 2010: Netflix launches Chaos Monkey (kills random EC2 instances)
|
||||
- 2011: Simian Army expands (Latency Monkey, Conformity Monkey, etc.)
|
||||
- 2014: Chaos engineering term coined; principles drafted
|
||||
- 2016: principlesofchaos.org published
|
||||
- 2018: Chaos Toolkit released as OSS
|
||||
- 2019: Chaos Mesh and Litmus mature for Kubernetes
|
||||
- 2020: AWS launches Fault Injection Simulator (FIS)
|
||||
- 2023+: Chaos engineering becomes mainstream practice in SRE-heavy orgs
|
||||
|
||||
## Further reading
|
||||
|
||||
- principlesofchaos.org — the foundational document
|
||||
- *Chaos Engineering* (Casey Rosenthal, Nora Jones) — O'Reilly, 2020
|
||||
- *Learning Chaos Engineering* (Russ Miles) — O'Reilly, 2019
|
||||
- Netflix Tech Blog on Chaos Engineering posts (2016-2020)
|
||||
|
|
@ -0,0 +1,158 @@
|
|||
# Experiment design
|
||||
|
||||
A well-designed chaos experiment has 7 sections. Skip any of them and the experiment becomes either useless (no learning) or dangerous (no bounds).
|
||||
|
||||
## The 7 sections
|
||||
|
||||
```
|
||||
1. Hypothesis
|
||||
2. Steady-state metric
|
||||
3. Attack
|
||||
4. Blast radius
|
||||
5. Abort criteria
|
||||
6. Rollback procedure
|
||||
7. Learning question
|
||||
```
|
||||
|
||||
## 1. Hypothesis
|
||||
|
||||
**Format:** *When [fault], [steady-state metric] stays [tolerance].*
|
||||
|
||||
Examples:
|
||||
- *"When the primary Postgres replica fails, checkout p99 latency stays below 500ms."*
|
||||
- *"When 50% of payment-service requests are throttled to 1 RPS, conversion rate drops by less than 5% within 60 seconds of return-to-normal."*
|
||||
- *"When us-east-1 is partitioned from us-west-2, Search continues to return results from us-west-2 within 200ms p99."*
|
||||
|
||||
A good hypothesis:
|
||||
- Names a specific fault (not "things break")
|
||||
- Names a specific metric (not "everything")
|
||||
- States a specific tolerance (not "good enough")
|
||||
- Is measurable and falsifiable
|
||||
|
||||
## 2. Steady-state metric
|
||||
|
||||
The metric you'll measure before, during, and after the experiment.
|
||||
|
||||
Required properties:
|
||||
- **Quantitative** — a number, not a feeling
|
||||
- **Customer-relevant** — something users feel (latency, error rate, conversion)
|
||||
- **Measurable in <60s** — slow metrics give you no time to abort
|
||||
- **Stable in normal operation** — you need a baseline
|
||||
|
||||
| Good | Bad |
|
||||
|---|---|
|
||||
| p99 checkout latency | "the system is healthy" |
|
||||
| 4xx + 5xx rate | "errors are low" |
|
||||
| Successful login rate | CPU usage |
|
||||
| Items added to cart per minute | replica count |
|
||||
|
||||
## 3. Attack
|
||||
|
||||
The fault you're injecting. Must specify:
|
||||
|
||||
- **Type** — latency, error, resource, partition, dependency, time, infrastructure
|
||||
- **Magnitude** — *how* much (e.g., "+200ms", "10% errors", "100% timeout to peer X")
|
||||
- **Duration** — how long the attack runs (typically 5-30 minutes)
|
||||
- **Target** — which subset of the system gets the attack
|
||||
|
||||
See `attack_taxonomy.md` for the 7 attack types.
|
||||
|
||||
## 4. Blast radius
|
||||
|
||||
The maximum scope of customer impact. Use `blast_radius_calculator.py` to compute:
|
||||
|
||||
- **Affected users** — `traffic_share × user_population`
|
||||
- **Error budget consumed** — `duration × traffic_share × availability_delta`
|
||||
- **Risk score** — GREEN (<1% budget) / YELLOW (1-10%) / RED (>10%)
|
||||
|
||||
Rule of thumb:
|
||||
- Start at 1% traffic share
|
||||
- Grow only after 3 successful experiments at the previous level
|
||||
- Never exceed 10% of monthly error budget in a single experiment
|
||||
|
||||
## 5. Abort criteria
|
||||
|
||||
The signals that auto-trigger experiment termination. Each must be:
|
||||
|
||||
- **Concrete** — specific metric and threshold ("p99 > 1000ms" not "performance degrades")
|
||||
- **Detectable in <60s** — latency, error rate, throughput
|
||||
- **Wired to action** — manual abort link in the dashboard, automatic via alert webhook
|
||||
|
||||
Standard abort criteria:
|
||||
|
||||
| Signal | Threshold | Action |
|
||||
|---|---|---|
|
||||
| p99 latency | > 2× baseline | abort |
|
||||
| 5xx rate | > baseline + 1pp | abort |
|
||||
| 4xx rate (excl. 401/404) | > baseline + 5pp | abort |
|
||||
| Conversion rate | < baseline × 0.95 | abort |
|
||||
| Customer ticket spike | > 3× baseline | escalate |
|
||||
| On-call paged | any SEV1/SEV2 | abort |
|
||||
|
||||
## 6. Rollback procedure
|
||||
|
||||
How you'll revert the fault. Required because:
|
||||
- Sometimes the chaos tool itself fails to revert
|
||||
- Sometimes the fault has lingering effects (caches, connections)
|
||||
|
||||
Standard rollback:
|
||||
1. Disable fault injection in tool
|
||||
2. Verify steady-state recovers within 2 minutes
|
||||
3. If not recovering, escalate as incident; restore from backup if needed
|
||||
|
||||
## 7. Learning question
|
||||
|
||||
What do you expect NOT to learn? Force yourself to predict the outcome.
|
||||
|
||||
Examples:
|
||||
- *"We expect the cache to absorb the latency. We'll learn whether the timeout configuration on the upstream is correct."*
|
||||
- *"We expect failover to take 30s. We'll learn whether retry backoff is configured."*
|
||||
|
||||
If you predicted the outcome correctly: confidence increased.
|
||||
If you didn't: there's an unknown — file a follow-up.
|
||||
|
||||
## Pre-flight checklist
|
||||
|
||||
Before running the experiment, verify:
|
||||
|
||||
- [ ] Hypothesis written
|
||||
- [ ] Steady-state metric measured for ≥5 minutes
|
||||
- [ ] Blast radius calculated (GREEN or YELLOW)
|
||||
- [ ] Abort criteria documented with thresholds
|
||||
- [ ] Rollback procedure tested in staging
|
||||
- [ ] On-call team notified in the team channel
|
||||
- [ ] Monitoring dashboards open
|
||||
- [ ] Owner identified and reachable
|
||||
- [ ] Time-box agreed (max experiment duration)
|
||||
- [ ] Communication plan if abort triggers
|
||||
|
||||
## Time-boxing
|
||||
|
||||
| Experiment type | Typical duration | Max recommended |
|
||||
|---|---|---|
|
||||
| First-time chaos | 5 minutes | 10 minutes |
|
||||
| Familiar attack, new target | 15 minutes | 30 minutes |
|
||||
| Continuous (automated) | per scheduler | 10 min per attack |
|
||||
| Game Day (human-led) | 1-2 hours | 4 hours |
|
||||
|
||||
## Escalation
|
||||
|
||||
If abort criteria are hit:
|
||||
|
||||
1. **Stop the experiment immediately** (the obvious step many teams forget to script)
|
||||
2. Verify steady-state recovery
|
||||
3. If recovery doesn't happen in 5 min → declare an incident
|
||||
4. Open a postmortem doc using `experiment_postmortem.py`
|
||||
5. Notify stakeholders (whoever was promised "this won't impact anything")
|
||||
6. Capture timeline while memory is fresh
|
||||
|
||||
## Anti-patterns
|
||||
|
||||
- **Hypothesis written after running** — that's a postmortem, not chaos engineering
|
||||
- **Steady-state metric chosen during experiment** — pick before
|
||||
- **Magnitude "small"** — quantify; "small" varies by reader
|
||||
- **No abort criteria** — never run without them
|
||||
- **Single owner of all chaos** — culture problem; spread the practice
|
||||
- **Chaos that always succeeds** — increase magnitude; you're not learning if everything passes
|
||||
- **Chaos that always fails** — reduce magnitude; you can't learn if everything breaks
|
||||
- **Chaos with no follow-up actions** — what was the point?
|
||||
|
|
@ -0,0 +1,197 @@
|
|||
# Tooling landscape
|
||||
|
||||
Six options. Pick by stack, license preference, and required attack types.
|
||||
|
||||
## At-a-glance
|
||||
|
||||
| Tool | License | Stack | Attack coverage | Best for |
|
||||
|---|---|---|---|---|
|
||||
| **Chaos Toolkit** | OSS (Apache 2) | Any (Python) | Broad via plugins | Lightweight, multi-cloud, JSON experiments |
|
||||
| **Chaos Mesh** | OSS (Apache 2) | Kubernetes | Very broad (network, pod, IO, time, stress) | k8s-native, rich CRDs |
|
||||
| **Litmus** | OSS (Apache 2) | Kubernetes | Very broad (300+ experiments) | k8s, Argo-integrated |
|
||||
| **Gremlin** | Commercial | Any (agents) | Broad, polished | Enterprise, audit, multi-cloud |
|
||||
| **AWS FIS** | Paid (AWS) | AWS | AWS services + EC2/ECS/EKS | AWS-heavy, IAM-integrated |
|
||||
| **Custom** | Your code | Any | What you build | Niche, single-cloud, low budget |
|
||||
|
||||
## Decision tree
|
||||
|
||||
```
|
||||
Stack constraint?
|
||||
├── Kubernetes-only ──┬── OSS preferred → Chaos Mesh OR Litmus
|
||||
│ │ (Litmus has the bigger experiment library;
|
||||
│ │ Chaos Mesh has cleaner CRD model)
|
||||
│ └── Enterprise budget → Gremlin
|
||||
│
|
||||
├── AWS-heavy ────────┬── Simple needs → AWS FIS
|
||||
│ ├── Multi-cloud + AWS → Chaos Toolkit + AWS plugin
|
||||
│ └── Enterprise → Gremlin
|
||||
│
|
||||
├── Multi-cloud ──────┬── OSS → Chaos Toolkit
|
||||
│ └── Enterprise → Gremlin
|
||||
│
|
||||
└── No infra constraint
|
||||
└── Just need fault injection → Toxiproxy (a single-purpose tool, not full chaos framework)
|
||||
```
|
||||
|
||||
## Chaos Toolkit
|
||||
|
||||
**What it is:** Python-based framework. You write experiments as JSON or YAML files; the CLI runs them.
|
||||
|
||||
**Strengths:**
|
||||
- Lightweight; runs anywhere Python runs
|
||||
- Plugin ecosystem for AWS, Azure, GCP, Kubernetes, etc.
|
||||
- JSON experiments are version-controllable
|
||||
- Apache 2 license
|
||||
|
||||
**Weaknesses:**
|
||||
- No built-in scheduling (you bring cron / CI)
|
||||
- Smaller experiment library than Litmus
|
||||
- Plugin quality varies
|
||||
|
||||
**Example experiment (JSON):**
|
||||
```json
|
||||
{
|
||||
"title": "Latency on payment-svc",
|
||||
"description": "p99 latency stays <500ms when payment is +200ms slow",
|
||||
"steady-state-hypothesis": {
|
||||
"title": "p99 < 500ms",
|
||||
"probes": [{ "type": "probe", "tolerance": [0, 500],
|
||||
"provider": { "type": "http", "url": "https://my.dashboards/p99" } }]
|
||||
},
|
||||
"method": [{ "type": "action", "name": "add-latency",
|
||||
"provider": { "type": "process", "path": "tc", "arguments": [...] } }]
|
||||
}
|
||||
```
|
||||
|
||||
## Chaos Mesh
|
||||
|
||||
**What it is:** Kubernetes operator + CRDs for chaos. Install in-cluster; `kubectl apply` an experiment.
|
||||
|
||||
**Strengths:**
|
||||
- True k8s-native (no external orchestrator)
|
||||
- Comprehensive coverage: network, pod, IO, stress, time, DNS, HTTP, kernel
|
||||
- UI dashboard for running experiments
|
||||
- CNCF Incubating project
|
||||
|
||||
**Weaknesses:**
|
||||
- k8s-only
|
||||
- CRD layout is opinionated; some types feel similar but aren't
|
||||
- Setup requires cluster admin
|
||||
|
||||
**Example experiment (CRD):**
|
||||
```yaml
|
||||
apiVersion: chaos-mesh.org/v1alpha1
|
||||
kind: NetworkChaos
|
||||
metadata:
|
||||
name: payment-latency
|
||||
spec:
|
||||
action: delay
|
||||
mode: one
|
||||
selector:
|
||||
namespaces: [default]
|
||||
labelSelectors:
|
||||
app: payment-svc
|
||||
delay:
|
||||
latency: 200ms
|
||||
duration: 5m
|
||||
```
|
||||
|
||||
## Litmus
|
||||
|
||||
**What it is:** Kubernetes chaos framework with a large experiment library. Argo-CD integration.
|
||||
|
||||
**Strengths:**
|
||||
- 300+ pre-built experiments
|
||||
- Strong Argo / GitOps integration
|
||||
- ChaosHub community library
|
||||
- Workflow capability for multi-step experiments
|
||||
|
||||
**Weaknesses:**
|
||||
- More moving parts than Chaos Mesh
|
||||
- Some pre-built experiments are thin wrappers; quality varies
|
||||
- k8s-only
|
||||
|
||||
## Gremlin
|
||||
|
||||
**What it is:** Commercial SaaS. Agents on hosts; central control plane.
|
||||
|
||||
**Strengths:**
|
||||
- Polished UX
|
||||
- Comprehensive attack library
|
||||
- Audit logs (compliance)
|
||||
- Multi-cloud, multi-OS
|
||||
- Customer support
|
||||
|
||||
**Weaknesses:**
|
||||
- Paid (per-host or per-MAU)
|
||||
- Vendor lock-in
|
||||
- Less control than OSS
|
||||
|
||||
**When to choose:** large enterprise, compliance/audit requirements, dedicated chaos team, budget exists.
|
||||
|
||||
## AWS FIS (Fault Injection Simulator)
|
||||
|
||||
**What it is:** AWS-managed chaos service. Templates of "actions" (stop instance, throttle API) chained into experiments.
|
||||
|
||||
**Strengths:**
|
||||
- IAM-integrated (proper auth/audit)
|
||||
- Native to AWS services (RDS failover, ECS/EKS, Network Manager)
|
||||
- Pay-per-experiment (no agents to maintain)
|
||||
|
||||
**Weaknesses:**
|
||||
- AWS-only
|
||||
- Smaller attack library than Chaos Mesh / Gremlin
|
||||
- Multi-account is awkward
|
||||
|
||||
**When to choose:** AWS-heavy team that wants chaos without managing the chaos infra.
|
||||
|
||||
## Custom (DIY)
|
||||
|
||||
**When to choose:**
|
||||
- Single-cloud, single-stack, low complexity
|
||||
- Budget = $0
|
||||
- Have engineering capacity to maintain the tool
|
||||
- Need a niche attack type that no tool covers
|
||||
|
||||
**Implementation patterns:**
|
||||
- Bash scripts that wrap `tc` / iptables / kill / stress-ng
|
||||
- Application-level chaos via feature flags + middleware
|
||||
- Service mesh fault injection (Istio / Linkerd) — covers many cases without a chaos framework
|
||||
|
||||
**Trade-offs:**
|
||||
- You build all the safety rails (abort, timeout, blast-radius)
|
||||
- You build the scheduler
|
||||
- You debug your own bugs
|
||||
|
||||
For most teams, this is a starter path; once chaos becomes regular, switch to a real tool.
|
||||
|
||||
## Pricing rule of thumb
|
||||
|
||||
| Tool | Typical cost (annual) |
|
||||
|---|---|
|
||||
| Chaos Toolkit | $0 |
|
||||
| Chaos Mesh | $0 |
|
||||
| Litmus OSS | $0 |
|
||||
| Litmus Enterprise | $5-30k |
|
||||
| Gremlin | $20-100k+ |
|
||||
| AWS FIS | pay-per-action, ~$100-2000/mo for active use |
|
||||
| Custom | engineering time only |
|
||||
|
||||
## Migration paths
|
||||
|
||||
| From | To | Effort |
|
||||
|---|---|---|
|
||||
| Custom scripts | Chaos Toolkit | Low (wrap scripts as actions) |
|
||||
| Chaos Toolkit | Chaos Mesh | Medium (k8s-only; rewrite for CRDs) |
|
||||
| Chaos Mesh | Litmus | Medium (similar shape, different CRDs) |
|
||||
| Anything | Gremlin | Easy (Gremlin imports many formats) |
|
||||
|
||||
## Selection checklist
|
||||
|
||||
Before committing:
|
||||
- [ ] Stack matches (k8s vs multi-cloud vs AWS-only)
|
||||
- [ ] Required attack types covered (cross-reference `attack_taxonomy.md`)
|
||||
- [ ] Audit logging requirement met (Gremlin / AWS FIS only have full audit)
|
||||
- [ ] Self-hosting requirement met (OSS only)
|
||||
- [ ] Budget approved
|
||||
- [ ] Run a 30-day proof-of-concept; verify abort path works
|
||||
101
engineering/skills/chaos-engineering/scripts/blast_radius_calculator.py
Executable file
101
engineering/skills/chaos-engineering/scripts/blast_radius_calculator.py
Executable file
|
|
@ -0,0 +1,101 @@
|
|||
#!/usr/bin/env python3
|
||||
"""Compute blast radius and risk score for a chaos experiment.
|
||||
|
||||
Inputs: traffic share affected, user population, duration, baseline availability,
|
||||
expected impacted availability. Outputs expected affected users, error budget
|
||||
consumed, and a GREEN / YELLOW / RED risk score with PROCEED / REDUCE / ABORT
|
||||
recommendation.
|
||||
"""
|
||||
import argparse
|
||||
import json
|
||||
import sys
|
||||
|
||||
|
||||
def calculate(traffic_share, user_pop, duration_min, baseline_avail, impacted_avail, monthly_budget_min):
|
||||
if not 0 <= traffic_share <= 1:
|
||||
raise ValueError("traffic-share must be between 0 and 1")
|
||||
if not 0 < impacted_avail <= 1:
|
||||
raise ValueError("impacted-availability must be between 0 (exclusive) and 1")
|
||||
if not 0 < baseline_avail <= 1:
|
||||
raise ValueError("baseline-availability must be between 0 (exclusive) and 1")
|
||||
affected_users = int(user_pop * traffic_share)
|
||||
delta_avail = max(baseline_avail - impacted_avail, 0.0)
|
||||
error_budget_consumed_min = round(duration_min * traffic_share * delta_avail, 4)
|
||||
pct_of_monthly_budget = round(100 * error_budget_consumed_min / monthly_budget_min, 2) if monthly_budget_min > 0 else 0
|
||||
if pct_of_monthly_budget < 1:
|
||||
risk = "GREEN"
|
||||
recommendation = "PROCEED"
|
||||
elif pct_of_monthly_budget < 10:
|
||||
risk = "YELLOW"
|
||||
recommendation = "PROCEED with explicit owner sign-off; consider reducing traffic share"
|
||||
else:
|
||||
risk = "RED"
|
||||
recommendation = "ABORT or REDUCE — blast radius exceeds 10% of monthly error budget"
|
||||
return {
|
||||
"inputs": {
|
||||
"traffic_share": traffic_share,
|
||||
"user_pop": user_pop,
|
||||
"duration_min": duration_min,
|
||||
"baseline_availability": baseline_avail,
|
||||
"impacted_availability": impacted_avail,
|
||||
"monthly_budget_min": monthly_budget_min,
|
||||
},
|
||||
"expected_affected_users": affected_users,
|
||||
"expected_availability_delta": round(delta_avail, 4),
|
||||
"error_budget_consumed_min": error_budget_consumed_min,
|
||||
"pct_of_monthly_budget": pct_of_monthly_budget,
|
||||
"risk": risk,
|
||||
"recommendation": recommendation,
|
||||
}
|
||||
|
||||
|
||||
def render_text(result):
|
||||
print("Blast Radius Calculator")
|
||||
print("=" * 40)
|
||||
i = result["inputs"]
|
||||
print(f"Traffic share affected: {i['traffic_share'] * 100:.2f}%")
|
||||
print(f"User population: {i['user_pop']:,}")
|
||||
print(f"Duration: {i['duration_min']} min")
|
||||
print(f"Baseline availability: {i['baseline_availability']}")
|
||||
print(f"Impacted availability: {i['impacted_availability']}")
|
||||
print(f"Monthly error budget: {i['monthly_budget_min']} min")
|
||||
print("")
|
||||
print(f"Expected affected users: {result['expected_affected_users']:,}")
|
||||
print(f"Availability delta: {result['expected_availability_delta']}")
|
||||
print(f"Error budget consumed: {result['error_budget_consumed_min']} min ({result['pct_of_monthly_budget']}% of monthly)")
|
||||
print("")
|
||||
print(f"Risk: {result['risk']}")
|
||||
print(f"Recommendation: {result['recommendation']}")
|
||||
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
|
||||
ap.add_argument("--traffic-share", type=float, required=True, help="Fraction (0-1) of traffic affected")
|
||||
ap.add_argument("--user-pop", type=int, required=True, help="Total user population")
|
||||
ap.add_argument("--duration-min", type=int, required=True, help="Experiment duration in minutes")
|
||||
ap.add_argument("--baseline-availability", type=float, default=0.999, help="Baseline availability (default: 0.999)")
|
||||
ap.add_argument("--expected-impact-availability", type=float, default=0.95, dest="impact_avail",
|
||||
help="Availability under fault (default: 0.95)")
|
||||
ap.add_argument("--monthly-budget-min", type=float, default=43.2,
|
||||
help="Monthly error budget in minutes (default: 43.2 for 99.9%% on 30 days)")
|
||||
ap.add_argument("--format", choices=["text", "json"], default="text")
|
||||
args = ap.parse_args()
|
||||
|
||||
try:
|
||||
result = calculate(
|
||||
args.traffic_share, args.user_pop, args.duration_min,
|
||||
args.baseline_availability, args.impact_avail, args.monthly_budget_min,
|
||||
)
|
||||
except ValueError as e:
|
||||
print(f"ERROR: {e}", file=sys.stderr)
|
||||
return 2
|
||||
|
||||
if args.format == "json":
|
||||
print(json.dumps(result, indent=2))
|
||||
else:
|
||||
render_text(result)
|
||||
return 0 if result["risk"] != "RED" else 1
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
139
engineering/skills/chaos-engineering/scripts/experiment_designer.py
Executable file
139
engineering/skills/chaos-engineering/scripts/experiment_designer.py
Executable file
|
|
@ -0,0 +1,139 @@
|
|||
#!/usr/bin/env python3
|
||||
"""Generate a structured chaos engineering experiment plan.
|
||||
|
||||
Enforces the required sections (hypothesis, steady-state metric, blast radius,
|
||||
abort criteria, rollback). Output is markdown by default; JSON available for
|
||||
piping into experiment_postmortem.py.
|
||||
"""
|
||||
import argparse
|
||||
import json
|
||||
import sys
|
||||
from datetime import datetime, timezone
|
||||
|
||||
ATTACK_DEFAULTS = {
|
||||
"latency": {"magnitude_hint": "+200ms", "tooling_hint": "tc / Chaos Mesh NetworkChaos"},
|
||||
"error": {"magnitude_hint": "10% of requests return 5xx", "tooling_hint": "Toxiproxy / Chaos Mesh HTTPChaos"},
|
||||
"cpu": {"magnitude_hint": "80% sustained", "tooling_hint": "stress-ng / Chaos Mesh StressChaos"},
|
||||
"memory": {"magnitude_hint": "+1GiB pressure", "tooling_hint": "stress-ng / Chaos Mesh StressChaos"},
|
||||
"disk": {"magnitude_hint": "fill /var to 95%", "tooling_hint": "stress-ng / Chaos Mesh IOChaos"},
|
||||
"network-partition": {"magnitude_hint": "drop 100% to peer X", "tooling_hint": "Chaos Mesh NetworkChaos partition"},
|
||||
"dependency-failure": {"magnitude_hint": "100% timeout to dependency", "tooling_hint": "service mesh fault injection"},
|
||||
"time-skew": {"magnitude_hint": "+5 minutes", "tooling_hint": "libfaketime / Chaos Mesh TimeChaos"},
|
||||
"kill-instance": {"magnitude_hint": "1 of N instances", "tooling_hint": "AWS FIS / Chaos Monkey"},
|
||||
}
|
||||
|
||||
|
||||
def build_plan(args):
|
||||
attack_meta = ATTACK_DEFAULTS.get(args.attack, {})
|
||||
magnitude = args.magnitude or attack_meta.get("magnitude_hint", "<set magnitude>")
|
||||
tooling = args.tooling or attack_meta.get("tooling_hint", "<set tooling>")
|
||||
plan = {
|
||||
"experiment_id": f"chaos-{args.target}-{args.attack}-{int(datetime.now(timezone.utc).timestamp())}",
|
||||
"created": datetime.now(timezone.utc).isoformat(),
|
||||
"target": args.target,
|
||||
"hypothesis": args.hypothesis,
|
||||
"steady_state": {
|
||||
"metric": args.steady_metric or "<must define before experiment>",
|
||||
"baseline_window": "5 minutes pre-experiment",
|
||||
"tolerance": args.tolerance or "within ±5% of baseline",
|
||||
},
|
||||
"attack": {
|
||||
"type": args.attack,
|
||||
"magnitude": magnitude,
|
||||
"duration_min": args.duration_min,
|
||||
"tooling": tooling,
|
||||
},
|
||||
"blast_radius": {
|
||||
"scope": args.blast_radius or "<must define before experiment>",
|
||||
"rollback_immediately_if": args.abort_if or "<must define abort criteria>",
|
||||
},
|
||||
"abort_criteria": _parse_abort_criteria(args.abort_if),
|
||||
"rollback_procedure": args.rollback or "Disable fault injection; verify steady state recovers within 2 minutes.",
|
||||
"monitoring_dashboard": args.dashboard or "<paste dashboard URL>",
|
||||
"owner": args.owner or "<assign owner>",
|
||||
"on_call_acknowledged": False,
|
||||
"learning_question": args.learning or "What did we learn that we did not know before?",
|
||||
}
|
||||
return plan
|
||||
|
||||
|
||||
def _parse_abort_criteria(raw):
|
||||
if not raw:
|
||||
return []
|
||||
parts = [p.strip() for p in raw.split(" OR ")]
|
||||
return [{"signal": p, "action": "abort"} for p in parts if p]
|
||||
|
||||
|
||||
def render_markdown(plan):
|
||||
lines = []
|
||||
lines.append(f"# Chaos Experiment: {plan['experiment_id']}")
|
||||
lines.append("")
|
||||
lines.append(f"- **Target:** `{plan['target']}`")
|
||||
lines.append(f"- **Created:** {plan['created']}")
|
||||
lines.append(f"- **Owner:** {plan['owner']}")
|
||||
lines.append("")
|
||||
lines.append("## Hypothesis")
|
||||
lines.append(f"> {plan['hypothesis']}")
|
||||
lines.append("")
|
||||
lines.append("## Steady-state metric")
|
||||
lines.append(f"- **Metric:** {plan['steady_state']['metric']}")
|
||||
lines.append(f"- **Baseline window:** {plan['steady_state']['baseline_window']}")
|
||||
lines.append(f"- **Tolerance:** {plan['steady_state']['tolerance']}")
|
||||
lines.append("")
|
||||
lines.append("## Attack")
|
||||
a = plan["attack"]
|
||||
lines.append(f"- **Type:** {a['type']}")
|
||||
lines.append(f"- **Magnitude:** {a['magnitude']}")
|
||||
lines.append(f"- **Duration:** {a['duration_min']} minutes")
|
||||
lines.append(f"- **Tooling:** {a['tooling']}")
|
||||
lines.append("")
|
||||
lines.append("## Blast radius")
|
||||
lines.append(f"- **Scope:** {plan['blast_radius']['scope']}")
|
||||
lines.append("")
|
||||
lines.append("## Abort criteria")
|
||||
if plan["abort_criteria"]:
|
||||
for c in plan["abort_criteria"]:
|
||||
lines.append(f"- {c['signal']}")
|
||||
else:
|
||||
lines.append("- **WARNING: no abort criteria defined — DO NOT RUN**")
|
||||
lines.append("")
|
||||
lines.append("## Rollback procedure")
|
||||
lines.append(plan["rollback_procedure"])
|
||||
lines.append("")
|
||||
lines.append("## Monitoring")
|
||||
lines.append(f"- Dashboard: {plan['monitoring_dashboard']}")
|
||||
lines.append("")
|
||||
lines.append("## Learning question")
|
||||
lines.append(f"> {plan['learning_question']}")
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
|
||||
ap.add_argument("--target", required=True, help="Target system or service")
|
||||
ap.add_argument("--hypothesis", required=True, help='Hypothesis: "When X, metric Y stays Z"')
|
||||
ap.add_argument("--attack", required=True, choices=list(ATTACK_DEFAULTS.keys()))
|
||||
ap.add_argument("--magnitude", help="Attack magnitude (default: per-attack hint)")
|
||||
ap.add_argument("--duration-min", type=int, default=15)
|
||||
ap.add_argument("--steady-metric", help="Steady-state metric name (e.g., 'p99 latency')")
|
||||
ap.add_argument("--tolerance", help="Tolerance vs baseline (e.g., 'within ±5%%')")
|
||||
ap.add_argument("--blast-radius", help="Blast radius (e.g., '5%% of US traffic')")
|
||||
ap.add_argument("--abort-if", dest="abort_if", help='Abort criteria, OR-separated (e.g., "p99 > 1000ms OR error_rate > +1pp")')
|
||||
ap.add_argument("--rollback", help="Rollback procedure")
|
||||
ap.add_argument("--tooling", help="Chaos tool to use (default: per-attack hint)")
|
||||
ap.add_argument("--dashboard", help="Monitoring dashboard URL")
|
||||
ap.add_argument("--owner", help="Experiment owner")
|
||||
ap.add_argument("--learning", help="Learning question")
|
||||
ap.add_argument("--format", choices=["markdown", "json"], default="markdown")
|
||||
args = ap.parse_args()
|
||||
|
||||
plan = build_plan(args)
|
||||
if args.format == "json":
|
||||
print(json.dumps(plan, indent=2))
|
||||
else:
|
||||
print(render_markdown(plan))
|
||||
return 0 if plan["abort_criteria"] else 1
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
144
engineering/skills/chaos-engineering/scripts/experiment_postmortem.py
Executable file
144
engineering/skills/chaos-engineering/scripts/experiment_postmortem.py
Executable file
|
|
@ -0,0 +1,144 @@
|
|||
#!/usr/bin/env python3
|
||||
"""Generate a structured chaos experiment postmortem.
|
||||
|
||||
Takes an experiment plan (JSON from experiment_designer.py) plus a results
|
||||
file (free-form text or structured key=value lines), and produces a markdown
|
||||
postmortem with hypothesis verdict, learning, surprises, and follow-up actions.
|
||||
Catches common postmortem failure modes: no learning, no follow-up, blame-laden
|
||||
language.
|
||||
"""
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import sys
|
||||
from datetime import datetime, timezone
|
||||
|
||||
BLAME_PHRASES = [
|
||||
"fault of",
|
||||
"should have known",
|
||||
"stupid",
|
||||
"incompetent",
|
||||
"obvious",
|
||||
"lazy",
|
||||
"didn't bother",
|
||||
]
|
||||
|
||||
REQUIRED_RESULT_FIELDS = {
|
||||
"outcome": "Did the hypothesis hold? (held|refuted|inconclusive)",
|
||||
"duration_actual_min": "Actual experiment duration in minutes",
|
||||
"aborted": "Was the experiment aborted? (true|false)",
|
||||
}
|
||||
|
||||
|
||||
def _parse_results(path):
|
||||
"""Parse a results file. Lines like 'key=value' OR free text. Returns dict."""
|
||||
if not os.path.isfile(path):
|
||||
return {"_raw_text": ""}
|
||||
with open(path, "r", encoding="utf-8", errors="replace") as f:
|
||||
text = f.read()
|
||||
parsed = {}
|
||||
for line in text.splitlines():
|
||||
m = re.match(r"^\s*([\w_.\-]+)\s*=\s*(.+?)\s*$", line)
|
||||
if m:
|
||||
parsed[m.group(1)] = m.group(2)
|
||||
parsed["_raw_text"] = text
|
||||
return parsed
|
||||
|
||||
|
||||
def _check_blame(text):
|
||||
found = []
|
||||
low = text.lower()
|
||||
for phrase in BLAME_PHRASES:
|
||||
if phrase in low:
|
||||
found.append(phrase)
|
||||
return found
|
||||
|
||||
|
||||
def build_postmortem(plan, results, follow_ups):
|
||||
raw_text = results.get("_raw_text", "")
|
||||
blame = _check_blame(raw_text)
|
||||
pm = {
|
||||
"experiment_id": plan.get("experiment_id", "?"),
|
||||
"target": plan.get("target", "?"),
|
||||
"created": datetime.now(timezone.utc).isoformat(),
|
||||
"hypothesis": plan.get("hypothesis", "?"),
|
||||
"outcome": results.get("outcome", "<UNRECORDED — must record>"),
|
||||
"aborted": results.get("aborted", "<unrecorded>"),
|
||||
"duration_actual_min": results.get("duration_actual_min", "<unrecorded>"),
|
||||
"duration_planned_min": plan.get("attack", {}).get("duration_min", "?"),
|
||||
"what_we_learned": results.get("learned", "<UNRECORDED — must record at least one learning>"),
|
||||
"what_surprised_us": results.get("surprised", "<unrecorded>"),
|
||||
"what_failed": results.get("failed", "<none recorded>"),
|
||||
"what_held": results.get("held", "<none recorded>"),
|
||||
"follow_ups": follow_ups,
|
||||
"blame_warnings": blame,
|
||||
"raw_results_excerpt": raw_text[:500],
|
||||
}
|
||||
return pm
|
||||
|
||||
|
||||
def render_markdown(pm):
|
||||
lines = []
|
||||
lines.append(f"# Postmortem: {pm['experiment_id']}")
|
||||
lines.append("")
|
||||
lines.append(f"- **Target:** `{pm['target']}`")
|
||||
lines.append(f"- **Postmortem date:** {pm['created']}")
|
||||
lines.append(f"- **Outcome:** {pm['outcome']}")
|
||||
lines.append(f"- **Aborted:** {pm['aborted']}")
|
||||
lines.append(f"- **Duration:** planned={pm['duration_planned_min']}min, actual={pm['duration_actual_min']}min")
|
||||
lines.append("")
|
||||
lines.append("## Hypothesis")
|
||||
lines.append(f"> {pm['hypothesis']}")
|
||||
lines.append("")
|
||||
lines.append("## What we learned")
|
||||
lines.append(pm["what_we_learned"])
|
||||
lines.append("")
|
||||
lines.append("## What surprised us")
|
||||
lines.append(pm["what_surprised_us"])
|
||||
lines.append("")
|
||||
lines.append("## What failed")
|
||||
lines.append(pm["what_failed"])
|
||||
lines.append("")
|
||||
lines.append("## What held")
|
||||
lines.append(pm["what_held"])
|
||||
lines.append("")
|
||||
lines.append("## Follow-up actions")
|
||||
if pm["follow_ups"]:
|
||||
for f in pm["follow_ups"]:
|
||||
lines.append(f"- [ ] {f}")
|
||||
else:
|
||||
lines.append("- _none recorded — every experiment should produce ≥1 follow-up_")
|
||||
if pm["blame_warnings"]:
|
||||
lines.append("")
|
||||
lines.append("## ⚠️ Blame warning")
|
||||
lines.append("Blame-laden language detected — postmortems should be blameless.")
|
||||
for b in pm["blame_warnings"]:
|
||||
lines.append(f"- '{b}'")
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
|
||||
ap.add_argument("--plan", required=True, help="Path to experiment plan JSON (from experiment_designer.py --format json)")
|
||||
ap.add_argument("--result-log", required=True, help="Path to result log (free-form text OR key=value lines)")
|
||||
ap.add_argument("--follow-up", action="append", default=[], help="A follow-up action; repeat for multiple")
|
||||
ap.add_argument("--format", choices=["markdown", "json"], default="markdown")
|
||||
args = ap.parse_args()
|
||||
|
||||
if not os.path.isfile(args.plan):
|
||||
print(f"ERROR: plan not found: {args.plan}", file=sys.stderr)
|
||||
return 2
|
||||
with open(args.plan, "r", encoding="utf-8") as f:
|
||||
plan = json.load(f)
|
||||
results = _parse_results(args.result_log)
|
||||
pm = build_postmortem(plan, results, args.follow_up)
|
||||
if args.format == "json":
|
||||
print(json.dumps(pm, indent=2))
|
||||
else:
|
||||
print(render_markdown(pm))
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
|
|
@ -226,6 +226,7 @@ nav:
|
|||
- "Karpathy Coder": skills/engineering/karpathy-coder.md
|
||||
- "Feature Flags Architect": skills/engineering/feature-flags-architect.md
|
||||
- "Kubernetes Operator": skills/engineering/kubernetes-operator.md
|
||||
- "Chaos Engineering": skills/engineering/chaos-engineering.md
|
||||
- AgentHub:
|
||||
- "AgentHub": skills/engineering/agenthub.md
|
||||
- "/hub:init": skills/engineering/agenthub-init.md
|
||||
|
|
@ -433,3 +434,4 @@ nav:
|
|||
- "/karpathy-check": commands/karpathy-check.md
|
||||
- "/flag-cleanup": commands/flag-cleanup.md
|
||||
- "/operator-audit": commands/operator-audit.md
|
||||
- "/chaos-experiment": commands/chaos-experiment.md
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue