chaos-engineering
Deliberately inject failures to discover weaknesses and improve system resilience in a controlled way. Use when validating resilience mechanisms or building confidence in production reliability.
- License 许可
- MIT
- 最近更新
- 2026-07-22
- Stars
- ★ 7
- Forks
- 1
01 前言 Front Matter
- name
- chaos-engineering
- description
- Deliberately inject failures to discover weaknesses and improve system resilience in a controlled way. Use when validating resilience mechanisms or building confidence in production reliability.
03 SKILL.md 正文
Chaos Engineering
Overview
Chaos engineering is the disciplined practice of experimenting on a system to build confidence in its ability to withstand turbulent conditions.
When to Use
- After basic monitoring and resilience patterns are in place
- Before or after major architectural changes
- To validate timeouts, retries, fallbacks, and redundancy
Process
- Define steady-state behavior (what “healthy” looks like)
- Hypothesize how the system will respond to a failure
- Inject a realistic failure in a controlled way
- Observe and compare to the hypothesis
- Improve the system and the controls based on findings
Principles
- Start small and in non-production or limited blast-radius environments
- Automate experiments where possible
- Always have abort conditions and clear ownership
- Focus on learning, not on causing outages
Verification
- Resilience mechanisms behave as expected under failure
- Gaps discovered are turned into concrete improvements
- Experiments are repeatable and safe
04 同仓库更多 Skills
compliance-monitoring
Design compliance monitoring and testing programs that verify controls work in practice and surface issues before regulators do. Use when building monitoring plans, compliance testing, or continuous control monitoring.
complaints-handling
Operate complaints-handling processes that meet regulatory standards — logging, timely resolution, root-cause analysis, and fair outcomes. Use when designing or improving regulated complaints frameworks or analyzing complaint themes for conduct risk.
aml-kyc-basics
Apply AML/KYC fundamentals — customer due diligence, monitoring, suspicious activity escalation, and recordkeeping — for financial crime compliance. Use when designing onboarding controls, transaction monitoring concepts, or AML program components in financial services.
ai-model-compliance
Apply compliance controls to AI and automated decision systems — transparency, fairness, human oversight, documentation, and regulatory alignment. Use when deploying AI in regulated processes (credit, fraud, customer decisions) or responding to AI governance expectations.
data-quality-management
Manage data quality end-to-end — dimensions, rules, remediation, and prevention — beyond one-off cleaning scripts. Use when building sustained data quality programs or fixing systemic accuracy, completeness, or consistency problems.
data-lineage
Capture and use data lineage — where data comes from, how it transforms, and what depends on it — for impact analysis, debugging, and trust. Use when tracing metric origins, assessing change impact, or diagnosing broken pipelines.