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 rjmurillo/ai-agents --skill chaos-experimentgit clone --depth 1 https://github.com/rjmurillo/ai-agentsWrote 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/rjmurillo/ai-agents/chaos-experiment)<a href="https://agentmods.dev/skills/rjmurillo/ai-agents/chaos-experiment"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/chaos-experiment/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/rjmurillo/ai-agents/chaos-experiment"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/chaos-experiment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 361 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- low Excessive Agency · line 240 Skill's behavior or capabilities extend beyond its stated purpose. Scope creep allows an agent to perform actions unrelated to its documented functionality, increasing the attack surface.Fix: Limit the skill's scope to its documented purpose. Remove instructions that enable the agent to perform actions outside its stated functionality.
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 | $0.00084 | $0.03097 |
| Opus 5.5 | $0.00034 | $0.01239 |
| Sonnet 5.5 | $0.00017 | $0.00619 |
| Haiku 4.5 | $0.00008 | $0.00310 |
Grade A, and why
chaos-experiment 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 24d 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 — 370 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chaos Experiment Designer
Design rigorous chaos engineering experiments that build confidence in system resilience.
Quick Start
# Describe what you want to test:
"Design a chaos experiment for our API gateway failover"
"Plan a game day for database resilience"
"Test whether our circuit breakers work under load"
The skill guides you through 6 phases: Scope, Baseline, Hypothesis, Injection, Execute, Analyze.
Triggers
chaos experimentfailure injectiongame daytest resiliencechaos engineering
Quick Reference
| Phase | Purpose | Output |
|---|---|---|
| 1. Scope | Define system boundaries and objectives | System under test, success criteria |
| 2. Baseline | Establish steady state metrics | Quantified normal behavior |
| 3. Hypothesis | Form falsifiable hypothesis | Clear prediction statement |
| 4. Injection | Design failure scenarios | Injection plan with blast radius |
| 5. Execute | Run controlled experiment | Observation log |
| 6. Analyze | Compare actual vs expected | Findings and action items |
When to Use
Use this skill when:
- Planning a game day or failure injection exercise
- Building confidence in system resilience before production launch
- Investigating whether auto-scaling, circuit breakers, or failover mechanisms work as designed
- After a real incident, to validate that fixes prevent recurrence
Use threat-modeling instead when:
- Identifying security threats (not resilience)
- Evaluating attack surfaces rather than failure modes
Use pre-mortem instead when:
- Identifying project risks (not infrastructure failures)
- Working at planning stage before any system exists
Process Overview
Scope → Baseline → Hypothesis → Injection Plan → Execute → Analyze
│ │ │ │ │ │
└─ Stakeholder sign-off
└─ 7-30 day metric collection
└─ Falsifiable prediction
└─ Rollback-ready plan
└─ Observation log
└─ Verdict + action items
What ships with it
6 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.
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.
- 24d ago Changed a5ae89853600
- 29d ago Changed · +5 lines 2280038d6ef1
- 1mo ago Changed 05833fa1984b
- 1mo ago Changed · +3 lines d41fd33609ce
- 1mo ago First seen · 362 lines · 84 tokens per session scan A 3bebfe39adb5
chaos-experiment is a skill published in the GitHub repository rjmurillo/ai-agents (49 stars, last pushed yesterday), licensed MIT. It adds 84 tokens to every session and 3,097 once invoked, about $0.0003 per session on Opus 5.5. 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.
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