Borrowing it
Nothing to install: this file belongs to irahardianto/awesome-agv. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/irahardianto/awesome-agv/main/.agents/skills/chaos-testing/SKILL.mdgit clone --depth 1 https://github.com/irahardianto/awesome-agvWrote 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/irahardianto/awesome-agv/chaos-testing)<a href="https://agentmods.dev/skills/irahardianto/awesome-agv/chaos-testing"><img src="https://agentmods.dev/badge/skills/irahardianto/awesome-agv/chaos-testing/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/irahardianto/awesome-agv/chaos-testing"><img src="https://agentmods.dev/badge/skills/irahardianto/awesome-agv/chaos-testing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Agent Snooping · line 87 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00026 | $0.00669 |
| Opus 5 | $0.00013 | $0.00334 |
| Sonnet 5 | $0.00005 | $0.00134 |
| Haiku 4.5 | $0.00003 | $0.00067 |
Grade A, and why
chaos-testing 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 11d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- chaos-testing — 95% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chaos Testing Principles
Controlled failure injection to build confidence in system resilience.
When to Invoke
- Verifying system resilience before production deployment
- Designing game day exercises
- Testing circuit breakers, retries, and failover
- Validating disaster recovery plans
Methodology
1. Define Steady State
Identify measurable indicators of normal system behavior:
- Request success rate ≥ 99.9%
- P99 latency < 500ms
- Error rate < 0.1%
2. Form Hypothesis
"When [failure condition], the system will [expected behavior] because [mechanism]."
Example: "When database primary fails, the system will failover to replica within 30s because of automatic failover configuration."
3. Design Experiment
| Element | Description |
|---|---|
| Target | Which component to perturb |
| Failure mode | What kind of failure (latency, crash, partition) |
| Blast radius | Scope of impact (single instance, AZ, region) |
| Duration | How long the failure persists |
| Abort criteria | When to immediately stop the experiment |
| Rollback plan | How to restore normal operation |
4. Execute
- Start with smallest blast radius
- Monitor continuously during experiment
- Have rollback ready at all times
- Stop immediately if abort criteria met
5. Analyze & Learn
- Did system behave as hypothesized?
- What broke unexpectedly?
- What recovery mechanisms worked/failed?
- Document findings and action items
Failure Injection Types
| Type | Examples |
|---|---|
| Process | Kill process, OOM, CPU spike |
| Network | Latency injection, packet loss, partition |
| Infrastructure | Instance termination, AZ failure, disk full |
| Application | Exception injection, slow dependency, config error |
| Data | Corrupt cache, stale data, schema mismatch |
Safety Mechanisms (Non-Negotiable)
- Abort button — immediate experiment termination capability
- Blast radius limits — never affect >5% of production traffic initially
- Time-boxed — experiments have maximum duration
- Monitoring — real-time dashboards during experiments
- Business hours only — no chaos experiments during peak or off-hours
- Stakeholder communication — relevant teams informed before experiments
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.
- 11d ago First seen · 89 lines · 26 tokens per session scan A c0e7992f520d
chaos-testing is a skill published in the GitHub repository irahardianto/awesome-agv (156 stars, last pushed 20d ago), licensed MIT. It adds 26 tokens to every session and 669 once invoked, about $0.0001 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 2026-08-30.
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