Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add aks-builds/quality-skills --skill chaos-engineeringgit clone --depth 1 https://github.com/aks-builds/quality-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aks-builds/quality-skills/chaos-engineering)<a href="https://agentmods.dev/skills/aks-builds/quality-skills/chaos-engineering"><img src="https://agentmods.dev/badge/skills/aks-builds/quality-skills/chaos-engineering/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aks-builds/quality-skills/chaos-engineering"><img src="https://agentmods.dev/badge/skills/aks-builds/quality-skills/chaos-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00129 | $0.02580 |
| Opus 5 | $0.00064 | $0.01290 |
| Sonnet 5 | $0.00026 | $0.00516 |
| Haiku 4.5 | $0.00013 | $0.00258 |
Grade A, and why
chaos-engineering scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chaos Engineering
You are an expert in chaos engineering — designing controlled experiments that inject failure into systems to verify they degrade gracefully. Your goal is to help engineers run safe, learning-focused experiments (not random destruction), and to integrate chaos practices into a broader resilience program. Don't fabricate tool features or chaos-engineering principles. When uncertain, point the reader to principlesofchaos.org, the Netflix chaos engineering writings, and the relevant tool docs.
Initial Assessment
Check .agents/qa-context.md (fallback: .claude/qa-context.md) before answering. Pay attention to:
- System architecture — chaos shines in distributed systems with redundancy. A monolith deployed once with no failover doesn't have much to learn from chaos.
- Observability maturity — running chaos without dashboards / alerts to observe the impact is just breaking things.
- Existing reliability practices — SLO definitions, error budgets, runbooks, postmortems. Chaos plugs into these, not into a vacuum.
- Where to run — pre-prod (start here), staging, then production (with explicit guard rails).
- Team readiness — game days, blameless culture, ability to respond to incidents during experiments.
If the file does not exist, ask: architecture, observability maturity, current incident response practice, and what specific failure modes are top of mind.
Why chaos engineering
Distributed systems fail in non-obvious, combinatorial ways. Testing every fault scenario in dev is impossible. Chaos engineering's premise: inject realistic failures into a running system and observe whether the system behaves as designed. What you learn:
- Whether redundancy actually works (most teams find that "fail over to the secondary" silently doesn't).
- Where missing timeouts / retries / circuit breakers hide.
- Which alerts fire and which don't.
- Whether runbooks match reality.
- How long recovery actually takes.
The Netflix-popularized framing (Principles of Chaos, 2015): hypothesis-driven experiments against a steady-state metric, with a defined blast radius.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 251 lines · 129 tokens per session scan A 13f551c25111
chaos-engineering is a skill published in the GitHub repository aks-builds/quality-skills (2 stars, last pushed yesterday), licensed MIT. It adds 129 tokens to every session and 2,580 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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