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 bestagentkits/agency-skills --skill chaos-engineeringgit clone --depth 1 https://github.com/bestagentkits/agency-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/bestagentkits/agency-skills/chaos-engineering)<a href="https://agentmods.dev/skills/bestagentkits/agency-skills/chaos-engineering"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-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/bestagentkits/agency-skills/chaos-engineering"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-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 | $0.00152 | $0.02504 |
| Opus 5 | $0.00076 | $0.01252 |
| Sonnet 5 | $0.00030 | $0.00501 |
| Haiku 4.5 | $0.00015 | $0.00250 |
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 8d 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 — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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):
- Build a hypothesis around steady-state behavior. Not "what breaks?" but "X holds; will it still hold under fault Y?"
- Vary real-world events. Inject realistic failures: kill nodes, slow networks, lose cache, throttle dependencies.
- Run experiments in production. Staging never has the same failure modes. Start small.
- 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
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
What ships with it
10 files 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.
- agents/openai.yaml 204 B
- assets/experiment_template.md 2.4 KB
- assets/postmortem_template.md 1.5 KB
- references/attack_taxonomy.md 5.7 KB
- references/chaos_principles.md 5.3 KB
- references/experiment_design.md 5.6 KB
- references/tooling_landscape.md 6.1 KB
- scripts/blast_radius_calculator.py 4.9 KB runs code
- scripts/experiment_designer.py 6.3 KB runs code
- scripts/experiment_postmortem.py 5.1 KB runs code
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.
- 8d ago First seen · 232 lines · 152 tokens per session scan A fde41aeb8b99
chaos-engineering is a skill published in the GitHub repository bestagentkits/agency-skills (11 stars, last pushed 2mo ago), licensed MIT. It adds 152 tokens to every session and 2,504 once invoked, about $0.0008 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 2026-09-03.
Other skills, from other repositories
chaos-engineering
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.…
chaos-engineering
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.…
chaos-engineering-expert
Expert in chaos engineering principles, failure injection, resilience testing, Chaos Monkey, Gremlin, and building fault-tolerant systems. Use when the user mentions reliability, testing, SRE, resilience, failure injection, or resilience testing, or when the task involves Chaos Engineering Principles, Failure Types…
Advanced Chaos Engineering
Advanced chaos engineering patterns using Chaos Monkey, Litmus, and Gremlin for testing distributed system resilience under failure conditions.
Kubernetes Chaos Testing
Chaos testing for Kubernetes workloads using Chaos Mesh, Litmus, and custom fault injection for pod, network, and disk failures.
chaos-fault-injection
Deliberately inject faults — dropped connections, corrupted writes, latency, malformed responses, resource exhaustion — and assert the system's expected recovery (escalation, hardstop, rollback).