everything-claude-code-zh is a Chinese translation of a collection of configurations for Claude Code and other AI coding agents. It provides agents, skills, hooks, commands, rules, and MCP configurations intended to support development workflows such as memory persistence, security scanning, evaluation, and research-first work. The catalogue includes commands, skills, agents, instructions, and a plugin from this configuration set.
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.
npx agentmods add skills/xu-xiang/everything-claude-code-zh/backend-patternsnpx skills add xu-xiang/everything-claude-code-zh --skill backend-patternsgit clone --depth 1 https://github.com/xu-xiang/everything-claude-code-zhWrote 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/skills/xu-xiang/everything-claude-code-zh/backend-patterns)<a href="https://agentmods.dev/skills/xu-xiang/everything-claude-code-zh/backend-patterns"><img src="https://agentmods.dev/badge/skills/xu-xiang/everything-claude-code-zh/backend-patterns.svg" alt="Measured on agentmods" 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 | $0.00036 | $0.03601 |
| Opus 5 | $0.00018 | $0.01801 |
| Sonnet 5 | $0.00007 | $0.00720 |
| Haiku 4.5 | $0.00004 | $0.00360 |
Grade A, and why
backend-patterns scanned grade A with 1 finding 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 6d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
const requests = this.requests.get(identifier) || [] How it starts
The opening of the file, as written. The whole thing — 599 lines — stays where its author put it; the contents beside it link to each section on GitHub.
后端开发模式 (Backend Development Patterns)
后端架构模式以及构建可扩展服务端应用的最佳实践。
启用时机 (When to Activate)
- 设计 REST 或 GraphQL API 端点时
- 实现仓库(Repository)、服务(Service)或控制器(Controller)层时
- 优化数据库查询(N+1、索引、连接池)时
- 添加缓存(Redis、内存缓存、HTTP 缓存头)时
- 设置后台作业或异步处理时
- 为 API 构建错误处理和验证结构时
- 编写中间件(鉴权、日志、速率限制)时
API 设计模式 (API Design Patterns)
RESTful API 结构
// ✅ 基于资源的 URL
GET /api/markets # 列出资源
GET /api/markets/:id # 获取单个资源
POST /api/markets # 创建资源
PUT /api/markets/:id # 替换资源
PATCH /api/markets/:id # 更新资源
DELETE /api/markets/:id # 删除资源
// ✅ 用于过滤、排序、分页的查询参数
GET /api/markets?status=active&sort=volume&limit=20&offset=0
仓库模式 (Repository Pattern)
// 抽象数据访问逻辑
interface MarketRepository {
findAll(filters?: MarketFilters): Promise<Market[]>
findById(id: string): Promise<Market | null>
create(data: CreateMarketDto): Promise<Market>
update(id: string, data: UpdateMarketDto): Promise<Market>
delete(id: string): Promise<void>
}
class SupabaseMarketRepository implements MarketRepository {
async findAll(filters?: MarketFilters): Promise<Market[]> {
let query = supabase.from('markets').select('*')
if (filters?.status) {
query = query.eq('status', filters.status)
}
if (filters?.limit) {
query = query.limit(filters.limit)
}
const { data, error } = await query
if (error) throw new Error(error.message)
return data
}
// 其他方法...
}
服务层模式 (Service Layer Pattern)
// 业务逻辑与数据访问分离
class MarketService {
constructor(private marketRepo: MarketRepository) {}
async searchMarkets(query: string, limit: number = 10): Promise<Market[]> {
// 业务逻辑
const embedding = await generateEmbedding(query)
const results = await this.vectorSearch(embedding, limit)
// 获取完整数据
const markets = await this.marketRepo.findByIds(results.map(r => r.id))
// 按相似度排序
return markets.sort((a, b) => {
const scoreA = results.find(r => r.id === a.id)?.score || 0
const scoreB = results.find(r => r.id === b.id)?.score || 0
return scoreA - scoreB
})
}
private async vectorSearch(embedding: number[], limit: number) {
// 向量搜索实现
}
}
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago First seen · 599 lines · 36 tokens per session scan A 325904ac26ee
backend-patterns is a skill published in the GitHub repository xu-xiang/everything-claude-code-zh (1,929 stars, last pushed 6mo ago), licensed MIT. It adds 36 tokens to every session and 3,601 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
qdrant
Vector search engine for production RAG systems.
backend-patterns
Backend architecture patterns, API design, database optimization, and server-side best practices for Node.js, Express, and Next.js API routes. Use when building or reviewing Node.js, Express, or Next.js API routes and their data access.
airtable
Airtable REST API via curl. Records CRUD, filters, upserts.
chroma
Embedding database for RAG and semantic search.
pinecone
Managed vector DB for production RAG and search.
database-migrations
Database migration best practices for schema changes, data migrations, rollbacks, and zero-downtime deployments across PostgreSQL, MySQL, and common ORMs (Prisma, Drizzle, Django, TypeORM, golang-migrate). Use when planning or implementing database schema changes.