Ai Coding Principles

AI Coding Principles

A collection of Claude Code skills for coding discipline and system design knowledge.

[中文文档](./README.zh-CN.md)

Skills

[ai-coding-discipline](./ai-coding-discipline/SKILL.md)

Mandatory rules loaded during all code writing tasks that prevent common AI coding anti-patterns.

#	Rule	Summary
1	No Silent Fallbacks	Don't use ?? / `
2	No Catch-All try/catch	Business logic lets errors propagate; catch only at API boundaries
3	Tests Must Fail When Code Breaks	Verify specific outcomes, not just existence
4	No Hardcoded Lookup Tables	Implement real logic, not test-case-fitting shims
5	Red-Green Testing (TDD)	Write failing test first, then fix
6	Don't Remove Debug Logs During Fix	Logs stay until human confirms the fix works

[ddia-principles](./ddia-principles/SKILL.md)

Distilled reference guide based on Martin Kleppmann's *Designing Data-Intensive Applications*. Loaded when designing database schemas, choosing storage engines, implementing replication/partitioning, handling distributed transactions, or building batch/stream processing pipelines.

Part	Topics
I: Foundations	Reliability, Scalability, Maintainability; Data Models & Query Languages; Storage & Retrieval (B-tree vs LSM-tree, OLTP vs OLAP); Encoding & Evolution
II: Distributed Data	Replication (single/multi-leader, leaderless); Partitioning (key-range, hash, compound); Transactions & Isolation Levels; Distributed System Challenges; Consistency & Consensus
III: Derived Data	Batch Processing (MapReduce, Spark, Flink); Stream Processing (Kafka, CDC, Event Sourcing); Data Integration Patterns

Installation

npx skills add luoling8192/ai-coding-principles

License

[MIT](./LICENSE)