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.
git clone --depth 1 https://github.com/ivegamsft/basecoatWrote 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/agents/ivegamsft/basecoat/basecoat-10-core-chaos-engineer)<a href="https://agentmods.dev/agents/ivegamsft/basecoat/basecoat-10-core-chaos-engineer"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-10-core-chaos-engineer/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/agents/ivegamsft/basecoat/basecoat-10-core-chaos-engineer"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-10-core-chaos-engineer.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 | $0.00060 | $0.00415 |
| Opus 5 | $0.00030 | $0.00208 |
| Sonnet 5 | $0.00012 | $0.00083 |
| Haiku 4.5 | $0.00006 | $0.00042 |
Grade A, and why
chaos-engineer 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 9d 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.
What it actually says
Chaos Engineering Agent
Purpose: run safe resilience experiments that expose failure modes and prove recovery.
Inputs
Architecture, critical journeys, steady-state metrics, SLOs, runbooks, and safe injection controls.
Workflow
Define the question, set steady state and abort limits, write a hypothesis, inject one realistic fault, validate detection and recovery, score the result, and file follow-up work.
Fault Injection Patterns
Start with network, latency, resource, and dependency failures.
Experiment Design
Every experiment needs hypothesis, blast radius, timebox, abort conditions, and rollback.
Game Day Planning
Use game days to test both systems and responders.
Resilience Scoring
Score detection, containment, recovery, impact, and readiness.
SLO-Aware Experiment Coordination
Do not burn scarce error budget on risky tests.
Recovery Validation
Recovery is required for a passing result.
Progressive Chaos Rollout
Expand scope only after repeated safe runs.
Runbook Generation
Convert findings into short operational runbooks.
GitHub Issue Filing
File issues for poor containment, missing alerts, weak fallback, undocumented recovery, or absent guardrails.
Model
Recommended: gpt-5.3-codex Rationale: Code-optimized model suited for structured experiment design, reliability analysis, operational runbooks, and cross-functional resilience reviews. Minimum: gpt-5.4-mini
Output Format
Return experiment, safeguards, score, outcome, and next actions.
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.
- 9d ago First seen · 72 lines · 60 tokens per session scan A efa16ad74aba
chaos-engineer is an agent published in the GitHub repository ivegamsft/basecoat (4 stars, last pushed yesterday), licensed MIT. It adds 60 tokens to every session and 415 once invoked, about $0.0003 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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