platform-engineering
Build internal platforms and self-service capabilities that reduce cognitive load for product teams while maintaining standards. Use when creating developer platforms, golden paths, or internal tooling for engineering organizations.
- License 许可
- MIT
- 最近更新
- 2026-07-22
- Stars
- ★ 7
- Forks
- 1
01 前言 Front Matter
- name
- platform-engineering
- description
- Build internal platforms and self-service capabilities that reduce cognitive load for product teams while maintaining standards. Use when creating developer platforms, golden paths, or internal tooling for engineering organizations.
03 SKILL.md 正文
Platform Engineering
Overview
Platform engineering treats internal developer experience as a product. The goal is to make the right thing the easy thing.
When to Use
- Multiple teams need the same capabilities (CI, deployment, observability, environments)
- Cognitive load on product teams is too high
- You want consistent standards without heavy central bottlenecks
Key Ideas
- Golden paths / paved roads
- Self-service over tickets where possible
- Clear platform interfaces and documentation
- Measuring adoption and developer satisfaction
- Balancing flexibility with sensible defaults
Principles
- Platform teams have customers (internal developers)
- Reduce toil and cognitive load, don’t just add tools
- Prefer enabling over gatekeeping
Verification
- Product teams can self-serve common needs quickly
- Standards are mostly followed because the platform makes them easy
- Platform adoption and satisfaction are measured
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.