db-diagram

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Database ER Diagram Generator

数据库ER图生成工具

I'll help you generate comprehensive Entity-Relationship diagrams from your database schema, supporting multiple ORMs and output formats.

Arguments:

$ARGUMENTS

- schema files, output format (mermaid/plantuml/dbml), or ORM type

我将帮助您从数据库Schema生成全面的实体关系图，支持多种ORM和输出格式。

参数：

$ARGUMENTS

- Schema文件、输出格式（mermaid/plantuml/dbml）或ORM类型

Token Optimization

Token优化

This skill uses diagram generation-specific patterns to minimize token usage:

本技能使用特定于图生成的模式来最小化Token使用：

1. Schema Snapshot Caching (900 token savings)

1. Schema快照缓存（节省900个Token）

Pattern: Cache parsed schema structure to avoid re-analysis

Store schema in
```
db-diagram/schema-snapshot.json
```
(24 hour TTL)
Cache: tables, columns, relationships, constraints
Compare checksum on subsequent runs (100 tokens vs 1,000 tokens fresh)
Regenerate only if schema changed
Savings: 90% on repeat diagram generations

模式： 缓存解析后的Schema结构以避免重复分析

将Schema存储在
```
db-diagram/schema-snapshot.json
```
中（24小时TTL）
缓存内容：表、列、关系、约束
后续运行时比较校验和（100个Token vs 重新分析的1000个Token）
仅当Schema变化时重新生成
节省： 重复生成图时减少90%的Token使用

2. Early Exit for Unchanged Schemas (95% savings)

2. 未变更Schema的提前退出（节省95%的Token）

Pattern: Detect schema changes and return existing diagram

Check schema file mtimes vs diagram mtime (50 tokens)
If schema unchanged: return existing diagram path (80 tokens)
Distribution: ~60% of runs are "view diagram" on unchanged schema
Savings: 80 vs 2,000 tokens for diagram regeneration checks

模式： 检测Schema变化并返回现有图

检查Schema文件修改时间与图的修改时间（50个Token）
如果Schema未变更：返回现有图的路径（80个Token）
场景占比： 约60%的运行是在未变更Schema上的“查看图”操作
节省： 图重新生成检查仅需80个Token，而非2000个Token

3. Template-Based Diagram Generation (1,500 token savings)

3. 基于模板的图生成（节省1500个Token）

Pattern: Use Mermaid/PlantUML templates instead of creative generation

Standard templates for entity syntax, relationship arrows
Predefined formats for common diagram types
No creative diagram design logic needed
Savings: 85% vs LLM-generated diagram syntax

模式： 使用Mermaid/PlantUML模板而非创造性生成

实体语法、关系箭头的标准模板
常见图类型的预定义格式
无需创造性的图设计逻辑
节省： 相比LLM生成的图语法，减少85%的Token使用

4. Bash-Based Diagram Rendering (800 token savings)

4. 基于Bash的图渲染（节省800个Token）

Pattern: Use mermaid-cli or plantuml.jar for rendering

Generate Mermaid:
```
mmdc -i diagram.mmd -o diagram.png
```
(200 tokens)
Generate PlantUML:
```
java -jar plantuml.jar diagram.puml
```
(200 tokens)
No Task agents for rendering
Savings: 80% vs Task-based diagram generation

模式： 使用mermaid-cli或plantuml.jar进行渲染

生成Mermaid：
```
mmdc -i diagram.mmd -o diagram.png
```
（200个Token）
生成PlantUML：
```
java -jar plantuml.jar diagram.puml
```
（200个Token）
无需任务代理进行渲染
节省： 相比基于任务的图生成，减少80%的Token使用

5. Sample-Based Relationship Extraction (700 token savings)

5. 基于样本的关系提取（节省700个Token）

Pattern: Analyze first 20 tables for relationship patterns

Extract FK relationships from analyzed tables (500 tokens)
Infer patterns and apply to remaining tables
Full extraction only for schemas < 30 tables
Savings: 60% vs exhaustive relationship extraction

模式： 分析前20个表以提取关系模式

从分析的表中提取外键关系（500个Token）
推断模式并应用到剩余表
仅当Schema表数<30时进行全量提取
节省： 相比全量关系提取，减少60%的Token使用

6. Progressive Diagram Complexity (1,000 token savings)

6. 渐进式图复杂度（节省1000个Token）

Pattern: Three-tier diagram depth

Level 1: Core tables only (5-10 tables) - 800 tokens
Level 2: All tables, key relationships - 1,500 tokens
Level 3: Full detail with columns - 2,500 tokens
Default: Level 2
Savings: 60% on default level

模式： 三级图深度

级别1：仅核心表（5-10个表）- 800个Token
级别2：所有表及关键关系 - 1500个Token
级别3：包含列的完整细节 - 2500个Token
默认：级别2
节省： 默认级别下减少60%的Token使用

7. Grep-Based Table Discovery (500 token savings)

7. 基于Grep的表发现（节省500个Token）

Pattern: Find table definitions with Grep

Grep for table patterns:
```
^model
```
,
```
CREATE TABLE
```
,
```
@Entity
```
(200 tokens)
Count tables without full parsing
Read only for relationship analysis
Savings: 75% vs reading all schema files

模式： 使用Grep查找表定义

Grep匹配表模式：
```
^model
```
,
```
CREATE TABLE
```
,
```
@Entity
```
（200个Token）
无需全量解析即可统计表数
仅在关系分析时读取内容
节省： 相比读取所有Schema文件，减少75%的Token使用

8. Incremental Diagram Updates (800 token savings)

8. 增量图更新（节省800个Token）

Pattern: Update only changed portions of diagram

Compare new schema with cached snapshot
Regenerate only modified table definitions
Preserve unchanged diagram sections
Savings: 70% vs full diagram regeneration

模式： 仅更新图中变更的部分

比较新Schema与缓存快照
仅重新生成修改的表定义
保留图中未变更的部分
节省： 相比全量图重新生成，减少70%的Token使用

Real-World Token Usage Distribution

实际Token使用分布

Typical operation patterns:

View existing diagram (unchanged schema): 80 tokens
Generate diagram (first time): 2,000 tokens
Update diagram (schema changes): 1,200 tokens
Full detail diagram: 2,500 tokens
Compare schemas: 1,500 tokens
Most common: View existing diagram or incremental updates

Expected per-generation: 1,500-2,500 tokens (50% reduction from 3,000-5,000 baseline) Real-world average: 700 tokens (due to cached snapshots, early exit, template-based generation)

典型操作模式：

查看现有图（Schema未变更）：80个Token
生成图（首次）：2000个Token
更新图（Schema变更）：1200个Token
完整细节图：2500个Token
比较Schema：1500个Token
最常见操作： 查看现有图或增量更新

单次生成预期： 1500-2500个Token（相比基线3000-5000个Token减少50%） 实际平均： 700个Token（得益于缓存快照、提前退出、基于模板的生成）

Session Intelligence

会话智能

I'll maintain diagram generation sessions for tracking schema evolution:

Session Files (in current project directory):

```
db-diagram/diagrams/
```
- Generated diagram files
```
db-diagram/schema-snapshot.json
```
- Current schema structure
```
db-diagram/state.json
```
- Generation history and settings
```
db-diagram/relationships.md
```
- Documented relationships

IMPORTANT: Session files are stored in a

db-diagram

folder in your current project root

Auto-Detection:

If schema detected: Generate updated diagram
If no schema: Guide through schema file location
Commands:
```
generate
```
,
```
update
```
,
```
compare
```
,
```
export
```

我将维护图生成会话以跟踪Schema演进：

会话文件（位于当前项目目录）：

```
db-diagram/diagrams/
```
- 生成的图文件
```
db-diagram/schema-snapshot.json
```
- 当前Schema结构
```
db-diagram/state.json
```
- 生成历史与设置
```
db-diagram/relationships.md
```
- 已记录的关系

重要提示： 会话文件存储在当前项目根目录的

db-diagram

文件夹中

自动检测：

如果检测到Schema：生成更新后的图
如果未找到Schema：引导您指定Schema文件位置
命令：
```
generate
```
,
```
update
```
,
```
compare
```
,
```
export
```

Phase 1: Schema Detection & ORM Recognition

阶段1：Schema检测与ORM识别

Extended Thinking for Schema Analysis

Schema分析的扩展思考

For complex database schemas, I'll use extended thinking to understand relationships:

<think> When analyzing database schemas: - Implicit relationships not explicitly defined in ORM - Many-to-many relationships through junction tables - Polymorphic associations and their representations - Inheritance strategies (single table, joined table, table per class) - Soft deletes and audit columns - Database-level constraints vs application-level validations - Normalized vs denormalized design patterns </think>

Triggers for Extended Analysis:

Complex multi-tenant schemas
Legacy databases with implicit conventions
Microservices with shared database patterns
Large schemas with 50+ tables

I'll automatically detect your database setup:

bash

#!/bin/bash

对于复杂数据库Schema，我将使用扩展思考来理解关系：

<think> 分析数据库Schema时： - ORM中未明确定义的隐式关系 - 通过中间表实现的多对多关系 - 多态关联及其表示方式 - 继承策略（单表、连接表、每个类对应表） - 软删除与审计列 - 数据库级约束与应用级验证 - 规范化与反规范化设计模式 </think>

触发扩展分析的场景：

复杂多租户Schema
具有隐式约定的遗留数据库
共享数据库模式的微服务
包含50+表的大型Schema

我将自动检测您的数据库配置：

bash

#!/bin/bash

ORM and schema detection

detect_database_stack() { echo "=== Database Stack Detection ==="

# Prisma detection
if [ -f "prisma/schema.prisma" ]; then
    echo "✓ Prisma detected: prisma/schema.prisma"
    ORM="prisma"
    SCHEMA_FILE="prisma/schema.prisma"
fi

# TypeORM detection
if find . -name "*.entity.ts" | head -1; then
    echo "✓ TypeORM detected: *.entity.ts files"
    ORM="typeorm"
    SCHEMA_FILES=$(find . -name "*.entity.ts")
fi

# Sequelize detection
if [ -d "models" ] && grep -q "sequelize" package.json 2>/dev/null; then
    echo "✓ Sequelize detected: models/ directory"
    ORM="sequelize"
    SCHEMA_FILES=$(find models -name "*.js" -o -name "*.ts")
fi

# SQLAlchemy (Python) detection
if find . -name "models.py" | head -1; then
    echo "✓ SQLAlchemy detected: models.py"
    ORM="sqlalchemy"
    SCHEMA_FILES=$(find . -name "models.py")
fi

# Django detection
if find . -path "*/models/*.py" | head -1; then
    echo "✓ Django detected: */models/*.py"
    ORM="django"
    SCHEMA_FILES=$(find . -path "*/models/*.py")
fi

# Drizzle detection
if find . -name "schema.ts" | grep -q drizzle; then
    echo "✓ Drizzle detected"
    ORM="drizzle"
    SCHEMA_FILES=$(find . -name "schema.ts")
fi

# Raw SQL detection
if find . -name "*.sql" | grep -qE "(schema|create|ddl)"; then
    echo "✓ SQL files detected"
    ORM="raw-sql"
    SCHEMA_FILES=$(find . -name "*.sql" | grep -iE "(schema|create|ddl)")
fi

if [ -z "$ORM" ]; then
    echo "⚠️  No recognized ORM/schema files found"
    echo "Supported: Prisma, TypeORM, Sequelize, SQLAlchemy, Django, Drizzle"
    return 1
fi

echo
echo "ORM: $ORM"
echo "Schema files: $SCHEMA_FILE $SCHEMA_FILES"

}

undefined

detect_database_stack() { echo "=== Database Stack Detection ==="

# Prisma detection
if [ -f "prisma/schema.prisma" ]; then
    echo "✓ Prisma detected: prisma/schema.prisma"
    ORM="prisma"
    SCHEMA_FILE="prisma/schema.prisma"
fi

# TypeORM detection
if find . -name "*.entity.ts" | head -1; then
    echo "✓ TypeORM detected: *.entity.ts files"
    ORM="typeorm"
    SCHEMA_FILES=$(find . -name "*.entity.ts")
fi

# Sequelize detection
if [ -d "models" ] && grep -q "sequelize" package.json 2>/dev/null; then
    echo "✓ Sequelize detected: models/ directory"
    ORM="sequelize"
    SCHEMA_FILES=$(find models -name "*.js" -o -name "*.ts")
fi

# SQLAlchemy (Python) detection
if find . -name "models.py" | head -1; then
    echo "✓ SQLAlchemy detected: models.py"
    ORM="sqlalchemy"
    SCHEMA_FILES=$(find . -name "models.py")
fi

# Django detection
if find . -path "*/models/*.py" | head -1; then
    echo "✓ Django detected: */models/*.py"
    ORM="django"
    SCHEMA_FILES=$(find . -path "*/models/*.py")
fi

# Drizzle detection
if find . -name "schema.ts" | grep -q drizzle; then
    echo "✓ Drizzle detected"
    ORM="drizzle"
    SCHEMA_FILES=$(find . -name "schema.ts")
fi

# Raw SQL detection
if find . -name "*.sql" | grep -qE "(schema|create|ddl)"; then
    echo "✓ SQL files detected"
    ORM="raw-sql"
    SCHEMA_FILES=$(find . -name "*.sql" | grep -iE "(schema|create|ddl)")
fi

if [ -z "$ORM" ]; then
    echo "⚠️  No recognized ORM/schema files found"
    echo "Supported: Prisma, TypeORM, Sequelize, SQLAlchemy, Django, Drizzle"
    return 1
fi

echo
echo "ORM: $ORM"
echo "Schema files: $SCHEMA_FILE $SCHEMA_FILES"

}

undefined

Phase 2: Schema Parsing

阶段2：Schema解析

I'll parse schema definitions into a structured format:

我将把Schema定义解析为结构化格式：

Prisma Schema Parser

Prisma Schema解析器

bash

undefined

bash

undefined

Parse Prisma schema

parse_prisma_schema() { local schema_file=$1

echo "Parsing Prisma schema..."

# Extract models
awk '/^model / {
    model=$2;
    print "MODEL:" model;
    in_model=1;
    next;
}
in_model && /^}/ {
    in_model=0;
    print "END_MODEL";
    next;
}
in_model && /^[[:space:]]+[a-zA-Z]/ {
    print "FIELD:" $0;
}
/^enum / {
    print "ENUM:" $2;
}' "$schema_file" > db-diagram/parsed-schema.txt

# Extract relationships
grep -E "@relation|@@" "$schema_file" > db-diagram/relationships.txt

}


**Parsed Schema Structure:**
```json
{
  "models": [
    {
      "name": "User",
      "fields": [
        {"name": "id", "type": "Int", "primaryKey": true, "autoIncrement": true},
        {"name": "email", "type": "String", "unique": true},
        {"name": "name", "type": "String", "nullable": true},
        {"name": "posts", "type": "Post[]", "relation": true}
      ]
    },
    {
      "name": "Post",
      "fields": [
        {"name": "id", "type": "Int", "primaryKey": true},
        {"name": "title", "type": "String"},
        {"name": "authorId", "type": "Int"},
        {"name": "author", "type": "User", "relation": {"from": "authorId", "to": "id"}}
      ]
    }
  ],
  "relationships": [
    {
      "from": "Post",
      "to": "User",
      "type": "many-to-one",
      "fromField": "author",
      "toField": "posts"
    }
  ]
}

parse_prisma_schema() { local schema_file=$1

echo "Parsing Prisma schema..."

# Extract models
awk '/^model / {
    model=$2;
    print "MODEL:" model;
    in_model=1;
    next;
}
in_model && /^}/ {
    in_model=0;
    print "END_MODEL";
    next;
}
in_model && /^[[:space:]]+[a-zA-Z]/ {
    print "FIELD:" $0;
}
/^enum / {
    print "ENUM:" $2;
}' "$schema_file" > db-diagram/parsed-schema.txt

# Extract relationships
grep -E "@relation|@@" "$schema_file" > db-diagram/relationships.txt

}


**解析后的Schema结构：**
```json
{
  "models": [
    {
      "name": "User",
      "fields": [
        {"name": "id", "type": "Int", "primaryKey": true, "autoIncrement": true},
        {"name": "email", "type": "String", "unique": true},
        {"name": "name", "type": "String", "nullable": true},
        {"name": "posts", "type": "Post[]", "relation": true}
      ]
    },
    {
      "name": "Post",
      "fields": [
        {"name": "id", "type": "Int", "primaryKey": true},
        {"name": "title", "type": "String"},
        {"name": "authorId", "type": "Int"},
        {"name": "author", "type": "User", "relation": {"from": "authorId", "to": "id"}}
      ]
    }
  ],
  "relationships": [
    {
      "from": "Post",
      "to": "User",
      "type": "many-to-one",
      "fromField": "author",
      "toField": "posts"
    }
  ]
}

TypeORM Entity Parser

TypeORM实体解析器

typescript

// Parse TypeORM entities (conceptual - would use AST parsing)
/*
@Entity()
export class User {
  @PrimaryGeneratedColumn()
  id: number;

  @Column({ unique: true })
  email: string;

  @OneToMany(() => Post, post => post.author)
  posts: Post[];
}

Extracts to:
- Entity: User
- Primary Key: id (auto-generated)
- Unique: email
- Relationship: OneToMany to Post
*/

typescript

// Parse TypeORM entities (conceptual - would use AST parsing)
/*
@Entity()
export class User {
  @PrimaryGeneratedColumn()
  id: number;

  @Column({ unique: true })
  email: string;

  @OneToMany(() => Post, post => post.author)
  posts: Post[];
}

Extracts to:
- Entity: User
- Primary Key: id (auto-generated)
- Unique: email
- Relationship: OneToMany to Post
*/

SQLAlchemy Parser

SQLAlchemy解析器

python

undefined

python

undefined

Parse SQLAlchemy models (conceptual)

""" class User(Base): tablename = 'users'

id = Column(Integer, primary_key=True)
email = Column(String, unique=True)
posts = relationship('Post', back_populates='author')

Extracts to:

Table: users
Model: User
Primary Key: id
Relationship: one-to-many to Post """

undefined

""" class User(Base): tablename = 'users'

id = Column(Integer, primary_key=True)
email = Column(String, unique=True)
posts = relationship('Post', back_populates='author')

Extracts to:

Table: users
Model: User
Primary Key: id
Relationship: one-to-many to Post """

undefined

Phase 3: Diagram Generation

阶段3：图生成

I'll generate diagrams in multiple formats:

我将生成多种格式的图：

Format 1: Mermaid (Default)

格式1：Mermaid（默认）

Advantages:

GitHub/GitLab native rendering
Interactive in many markdown viewers
Easy to version control
Simple syntax

bash

undefined

优势：

GitHub/GitLab原生渲染
在许多Markdown查看器中支持交互
易于版本控制
语法简单

bash

undefined

Generate Mermaid ER diagram

generate_mermaid() { local output_file="db-diagram/diagrams/schema.mmd"

cat > "$output_file" <<'EOF'

erDiagram USER ||--o{ POST : "writes" USER { int id PK string email UK string name datetime createdAt }

POST ||--o{ COMMENT : "has"
POST {
    int id PK
    string title
    text content
    int authorId FK
    datetime publishedAt
}

COMMENT {
    int id PK
    text content
    int postId FK
    int userId FK
    datetime createdAt
}

USER ||--o{ COMMENT : "writes"

POST }o--|| CATEGORY : "belongs to"
CATEGORY {
    int id PK
    string name UK
    string slug
}

POST }o--o{ TAG : "tagged with"
TAG {
    int id PK
    string name UK
}

POST_TAG {
    int postId FK
    int tagId FK
}
POST ||--o{ POST_TAG : ""
TAG ||--o{ POST_TAG : ""

EOF

echo "Mermaid diagram generated: $output_file"
echo
echo "View in GitHub/GitLab, or use:"
echo "  - https://mermaid.live"
echo "  - VSCode Mermaid Preview extension"

}


**Relationship Notation:**

||--o{ : one to many }o--|| : many to one ||--|| : one to one }o--o{ : many to many

undefined

generate_mermaid() { local output_file="db-diagram/diagrams/schema.mmd"

cat > "$output_file" <<'EOF'

erDiagram USER ||--o{ POST : "writes" USER { int id PK string email UK string name datetime createdAt }

POST ||--o{ COMMENT : "has"
POST {
    int id PK
    string title
    text content
    int authorId FK
    datetime publishedAt
}

COMMENT {
    int id PK
    text content
    int postId FK
    int userId FK
    datetime createdAt
}

USER ||--o{ COMMENT : "writes"

POST }o--|| CATEGORY : "belongs to"
CATEGORY {
    int id PK
    string name UK
    string slug
}

POST }o--o{ TAG : "tagged with"
TAG {
    int id PK
    string name UK
}

POST_TAG {
    int postId FK
    int tagId FK
}
POST ||--o{ POST_TAG : ""
TAG ||--o{ POST_TAG : ""

EOF

echo "Mermaid diagram generated: $output_file"
echo
echo "View in GitHub/GitLab, or use:"
echo "  - https://mermaid.live"
echo "  - VSCode Mermaid Preview extension"

}


**关系表示法：**

||--o{ : 一对多 }o--|| : 多对一 ||--|| : 一对一 }o--o{ : 多对多

undefined

Format 2: PlantUML

格式2：PlantUML

Advantages:

Highly customizable
Professional appearance
Extensive styling options
Good for documentation

bash

undefined

优势：

高度可定制
外观专业
丰富的样式选项
适合文档使用

bash

undefined

Generate PlantUML diagram

generate_plantuml() { local output_file="db-diagram/diagrams/schema.puml"

cat > "$output_file" <<'EOF'

@startuml Database Schema

!define Table(name,desc) class name as "desc" << (T,#FFAAAA) >> !define primary_key(x) <b>PK: x</b> !define foreign_key(x) color:redFK: x</color> !define unique(x) color:greenUK: x</color>

Table(User, "users") { primary_key(id): INT unique(email): VARCHAR name: VARCHAR createdAt: TIMESTAMP }

Table(Post, "posts") { primary_key(id): INT title: VARCHAR content: TEXT foreign_key(authorId): INT publishedAt: TIMESTAMP }

Table(Comment, "comments") { primary_key(id): INT content: TEXT foreign_key(postId): INT foreign_key(userId): INT createdAt: TIMESTAMP }

Table(Category, "categories") { primary_key(id): INT unique(name): VARCHAR slug: VARCHAR }

Table(Tag, "tags") { primary_key(id): INT unique(name): VARCHAR }

Table(PostTag, "post_tags") { foreign_key(postId): INT foreign_key(tagId): INT }

User "1" -- "0.." Post : writes User "1" -- "0.." Comment : writes Post "1" -- "0.." Comment : has Post "0.." -- "1" Category : belongs to Post "0.." -- "0.." Tag : tagged with (Post, Tag) .. PostTag

@enduml EOF

echo "PlantUML diagram generated: $output_file"
echo
echo "Generate image:"
echo "  plantuml $output_file"
echo "  # or use: https://www.plantuml.com/plantuml/"

}

undefined

generate_plantuml() { local output_file="db-diagram/diagrams/schema.puml"

cat > "$output_file" <<'EOF'