Chaos

Chaos Command

$ARGUMENTS

Triggers a controlled resilience experiment.

Usage

/chaos  [target]
# Example: /chaos latency backend-api
# Example: /chaos kill redis

Protocol

1. Safety Check: Verify env != PROD.
2. Baseline: Check system health is green.
3. Inject: Run the fault injection.
4. Observe: Monitor logs/metrics for 60s.
5. Recover: Restore system health.
6. Report: Did we survive?

Rules

• MUST verify target environment is non-production before injecting
• NEVER run against a system without a healthy baseline
• CRITICAL: abort immediately if recovery does not complete within the observation window
• MANDATORY: log every injected fault with timestamp and scope

Gotchas

• NODE_ENV=production on a developer's machine is common — checking that env var alone is not enough proof of non-prod. Combine with kubeconfig context, cloud account ID, or a project-specific env file check before injecting.
• docker stats reports cached values; the first sample immediately after injection is often pre-fault. Wait at least 5 seconds before reading metrics.
• Kubernetes liveness probes may self-heal the faulted pod inside the 60s observation window — the report shows green while the workload is still flapping. Check pod restart counters, not just health endpoints.
• Latency injected with tc (Linux traffic control) persists across container restarts on the host and across SIGTERM. Always pair the inject step with an explicit tc qdisc del dev root cleanup in the recover step — the fork context will not undo it for you.

When NOT to Use

• In production without an explicit, written runbook — use /workflow incident-response for real incidents
• When the system has no observability (no metrics, no logs) — fix observability first
• For load testing — use dedicated load-test tooling, not chaos injection
• During an active incident — stabilize first with /panic, then investigate

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: softspark
• Source: softspark/ai-toolkit
• License: MIT
• Homepage: https://softspark.eu

Install and usage instructions live in the source repository linked above.