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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
2 KiB
2 KiB
| description |
|---|
| Interactive wizard to design and validate a chaos engineering experiment |
/chaos-experiment
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.
Usage
/chaos-experiment
/chaos-experiment --target checkout-svc --attack latency
Implementation
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-engineeringskill installed- Target identified
- Steady-state metric and dashboard available
- On-call team available
- Error budget known (or use defaults)
Post-conditions
.chaos-plan.jsonwritten for use withexperiment_postmortem.pylater- Markdown plan streamed for review
- Recommendation printed: PROCEED / REDUCE / ABORT