data-model-designer
Design data models for construction projects. Create entity-relationship diagrams, define schemas, and generate database structures.
Install / Use
npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill data-model-designerInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Data & AnalyticsSupported Platforms
Our assessment of data-model-designer
data-model-designer scores 89/100 on our quality scale, 342nd of 603 Data & Analytics skills we index.
Its SKILL.md is 11 KB long, well organised into 15 sections with 5 code examples: a thorough specification that gives an agent plenty to work with.
It has 333 GitHub stars, a meaningful sign that others use it.
Maintenance, license and trust
- The repository was last updated about 2 months ago, so data-model-designer is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
data-model-designer compared with similar skills
All 4 of these similar skills score higher than data-model-designer; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| data-model-designer (this skill)by datadrivenconstruction | 89 | 333 | 2mo ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 106.4k | 5d ago | CLAUDE.md |
| taste-skillby Leonxlnx | 100 | 105.7k | today | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 78.3k | today | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 90.4k | today | MCP Server |
Frequently asked questions
- How do I install data-model-designer?
- Run
npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill data-model-designer. The install tabs above show the steps for each supported agent. - Which AI agents does data-model-designer work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is data-model-designer safe to use?
- It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is data-model-designer still maintained?
- The repository was last updated about 2 months ago, so data-model-designer is actively maintained.
Skill content
View source on GitHubname: "data-model-designer" description: "Design data models for construction projects. Create entity-relationship diagrams, define schemas, and generate database structures." homepage: "https://datadrivenconstruction.io" metadata: {"openclaw": {"emoji": "📐", "os": ["darwin", "linux", "win32"], "homepage": "https://datadrivenconstruction.io", "requires": {"bins": ["python3"]}}}
Data Model Designer
Business Case
Problem Statement
Construction data management challenges:
- Fragmented data across systems
- Inconsistent data structures
- Missing relationships between entities
- Difficult data integration
Solution
Systematic data model design for construction projects, defining entities, relationships, and schemas for effective data management.
Technical Implementation
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from enum import Enum
import json
class DataType(Enum):
STRING = "string"
INTEGER = "integer"
FLOAT = "float"
BOOLEAN = "boolean"
DATE = "date"
DATETIME = "datetime"
TEXT = "text"
JSON = "json"
class RelationType(Enum):
ONE_TO_ONE = "1:1"
ONE_TO_MANY = "1:N"
MANY_TO_MANY = "N:M"
class ConstraintType(Enum):
PRIMARY_KEY = "primary_key"
FOREIGN_KEY = "foreign_key"
UNIQUE = "unique"
NOT_NULL = "not_null"
@dataclass
class Field:
name: str
data_type: DataType
nullable: bool = True
default: Any = None
description: str = ""
constraints: List[ConstraintType] = field(default_factory=list)
@dataclass
class Entity:
name: str
description: str
fields: List[Field] = field(default_factory=list)
primary_key: str = "id"
@dataclass
class Relationship:
name: str
from_entity: str
to_entity: str
relation_type: RelationType
from_field: str
to_field: str
class ConstructionDataModel:
"""Design data models for construction projects."""
def __init__(self, project_name: str):
self.project_name = project_name
self.entities: Dict[str, Entity] = {}
self.relationships: List[Relationship] = []
def add_entity(self, entity: Entity):
"""Add entity to model."""
self.entities[entity.name] = entity
def add_relationship(self, relationship: Relationship):
"""Add relationship between entities."""
self.relationships.append(relationship)
def create_entity(self, name: str, description: str,
fields: List[Dict[str, Any]]) -> Entity:
"""Create entity from field definitions."""
entity_fields = [
Field(
name=f['name'],
data_type=DataType(f.get('type', 'string')),
nullable=f.get('nullable', True),
default=f.get('default'),
description=f.get('description', ''),
constraints=[ConstraintType(c) for c in f.get('constraints', [])]
)
for f in fields
]
entity = Entity(name=name, description=description, fields=entity_fields)
self.add_entity(entity)
return entity
def create_relationship(self, from_entity: str, to_entity: str,
relation_type: str = "1:N",
from_field: str = None) -> Relationship:
"""Create relationship between entities."""
rel = Relationship(
name=f"{from_entity}_{to_entity}",
from_entity=from_entity,
to_entity=to_entity,
relation_type=RelationType(relation_type),
from_field=from_field or f"{to_entity.lower()}_id",
to_field="id"
)
self.add_relationship(rel)
return rel
def generate_sql_schema(self, dialect: str = "postgresql") -> str:
"""Generate SQL DDL statements."""
sql = []
type_map = {
DataType.STRING: "VARCHAR(255)",
DataType.INTEGER: "INTEGER",
DataType.FLOAT: "DECIMAL(15,2)",
DataType.BOOLEAN: "BOOLEAN",
DataType.DATE: "DATE",
DataType.DATETIME: "TIMESTAMP",
DataType.TEXT: "TEXT",
DataType.JSON: "JSONB" if dialect == "postgresql" else "JSON"
}
for name, entity in self.entities.items():
columns = []
for fld in entity.fields:
col = f" {fld.name} {type_map.get(fld.data_type, 'VARCHAR(255)')}"
if not fld.nullable:
col += " NOT NULL"
if ConstraintType.PRIMARY_KEY in fld.constraints:
col += " PRIMARY KEY"
columns.append(col)
sql.append(f"CREATE TABLE {name} (\n" + ",\n".join(columns) + "\n);")
for rel in self.relationships:
sql.append(f"""ALTER TABLE {rel.from_entity}
ADD CONSTRAINT fk_{rel.name}
FOREIGN KEY ({rel.from_field}) REFERENCES {rel.to_entity}({rel.to_field});""")
return "\n\n".join(sql)
def generate_json_schema(self) -> Dict[str, Any]:
"""Generate JSON Schema representation."""
schemas = {}
for name, entity in self.entities.items():
properties = {}
required = []
for fld in entity.fields:
prop = {"description": fld.description}
if fld.data_type == DataType.STRING:
prop["type"] = "string"
elif fld.data_type == DataType.INTEGER:
prop["type"] = "integer"
elif fld.data_type == DataType.FLOAT:
prop["type"] = "number"
elif fld.data_type == DataType.BOOLEAN:
prop["type"] = "boolean"
else:
prop["type"] = "string"
properties[fld.name] = prop
if not fld.nullable:
required.append(fld.name)
schemas[name] = {
"type": "object",
"title": entity.description,
"properties": properties,
"required": required
}
return schemas
def generate_er_diagram(self) -> str:
"""Generate Mermaid ER diagram."""
lines = ["erDiagram"]
for name, entity in self.entities.items():
for fld in entity.fields[:5]:
lines.append(f" {name} {{")
lines.append(f" {fld.data_type.value} {fld.name}")
lines.append(" }")
for rel in self.relationships:
rel_symbol = {
RelationType.ONE_TO_ONE: "||--||",
RelationType.ONE_TO_MANY: "||--o{",
RelationType.MANY_TO_MANY: "}o--o{"
}.get(rel.relation_type, "||--o{")
lines.append(f" {rel.from_entity} {rel_symbol} {rel.to_entity} : \"{rel.name}\"")
return "\n".join(lines)
def validate_model(self) -> List[str]:
"""Validate data model for issues."""
issues = []
for rel in self.relationships:
if rel.from_entity not in self.entities:
issues.append(f"Missing entity: {rel.from_entity}")
if rel.to_entity not in self.entities:
issues.append(f"Missing entity: {rel.to_entity}")
for name, entity in self.entities.items():
has_pk = any(ConstraintType.PRIMARY_KEY in f.constraints for f in entity.fields)
if not has_pk:
issues.append(f"Entity '{name}' has no primary key")
return issues
class ConstructionEntities:
"""Standard construction data entities."""
@staticmethod
def project_entity() -> Entity:
return Entity(
name="projects",
description="Construction projects",
fields=[
Field("id", DataType.INTEGER, False, constraints=[ConstraintType.PRIMARY_KEY]),
Field("code", DataType.STRING, False, constraints=[ConstraintType.UNIQUE]),
Field("name", DataType.STRING, False),
Field("status", DataType.STRING),
Field("start_date", DataType.DATE),
Field("end_date", DataType.DATE),
Field("budget", DataType.FLOAT)
]
)
@staticmethod
def activity_entity() -> Entity:
return Entity(
name="activities",
description="Schedule activities",
fields=[
Field("id", DataType.INTEGER, False, constraints=[ConstraintType.PRIMARY_KEY]),
Field("project_id", DataType.INTEGER, False),
Field("wbs_code", DataType.STRING),
Field("name", DataType.STRING, False),
Field("start_date", DataType.DATE),
Field("end_date", DataType.DATE),
Field("percent_complete", DataType.FLOAT)
]
)
@staticmethod
def cost_item_entity() -> Entity:
return Entity(
name="cost_items",
description="Project cost items",
fields=[
Field("id", DataType.INTEGER, False, constraints=[ConstraintType.PRIMARY_KEY]),
Field("project_id", DataType.INTEGER, False),
Field("wbs_code", DataType.STRING),
Field("description", DataType.STRING),
Field("budgeted_cost", DataType.FLOAT),
Field("actual_cost", DataType.FLOAT)
]
)
Quick Start
# Create model
model = ConstructionDataModel("Office Building A")
# Add standard entities
model.add_entity(ConstructionEntities.project_entity())
model.add_entity(ConstructionEntities.activity_entity())
model.add_entity(ConstructionEntities.cost_item_entity())
# Add relationships
model.create_relationship("activities", "projects")
model.create_relationship("cost_items", "projects")
# Generate SQL
sql = model.generate_sql_schema("postgresql")
print(sql)
Common Use Cases
1. Custom Entity
model.create_entity(
name="change_orders",
description="Project change orders",
fields=[
{"name": "id", "type": "integer", "nullable": False, "constraints": ["primary_key"]},
{"name": "project_id", "type": "integer", "nullable": False},
{"name": "amount", "type": "float"},
{"name": "status", "type": "string"}
]
)
2. Generate ER Diagram
er_diagram = model.generate_er_diagram()
print(er_diagram)
3. Validate Model
issues = model.validate_model()
for issue in issues:
print(f"Issue: {issue}")
Resources
- DDC Book: Chapter 2.5 - Data Models and Standards
- Website: https://datadrivenconstruction.io
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