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@luoling8192/ai-coding-principles
Skills#Claude Code#Coding#Testing#Databases

Claude Code skills for coding discipline and DDIA principles

This repo bundles two Claude Code skills. One adds guardrails for writing code and tests, and the other provides concise guidance from Designing Data-Intensive Applications for data and system design work. Together they help keep agent-written code more disciplined and data decisions more grounded.

171 stars•11 forks•Updated 5mo ago
Who it's for

Builders who use Claude Code and want reusable rules for writing code, tests, and data systems.

What it delivers

You can keep your agent from using brittle shortcuts and get a clearer path for database and distributed system choices.

What it does

Coding discipline rules

Loads rules that block silent fallbacks, catch-all error handling, hardcoded lookup tables, and other common AI coding anti-patterns.

Test-first workflow

Encourages red-green testing so code changes are checked against failing tests before the fix.

Debug log retention

Keeps debug logs in place during a fix until a human confirms the problem is solved.

DDIA reference skill

Provides a compact guide to data modeling, storage engines, replication, partitioning, transactions, and stream or batch processing.

How to get it

  1. 1Run
    npx skills add luoling8192/ai-coding-principles

README

AI Coding Principles

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

中文文档

Skills

ai-coding-discipline

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

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

ddia-principles

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.

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

Installation

npx skills add luoling8192/ai-coding-principles

License

MIT

Files in the repo

Repository payload•5 top-level entries
  • ai-coding-discipline
  • ddia-principles
  • LICENSE
  • README.md
  • README.zh-CN.md

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