Data Model Designer
Design data models for construction projects. Create entity-relationship diagrams, define schemas, and generate database structures.
MIT-0 · Free to use, modify, and redistribute. No attribution required.
⭐ 0 · 3.2k · 24 current installs · 26 all-time installs
MIT-0
Security Scan
OpenClaw
Benign
medium confidencePurpose & Capability
Name/description align with the provided artifacts: the skill is instruction-only, includes Python-based model-generation code and guidance to accept project data and export schemas. Requiring python3 and filesystem access is consistent with reading input files (CSV/Excel/JSON) and exporting results.
Instruction Scope
SKILL.md and instructions.md focus on taking user-provided data, validating it, generating ERD/JSON/SQL outputs, and offering exports. They explicitly constrain processing to user-provided inputs. However the provided SKILL.md content in the package preview is truncated, so I cannot confirm there are no later instructions that perform unrelated actions (network calls, reading arbitrary system paths, or accessing environment variables).
Install Mechanism
Instruction-only skill with no install spec and no included executables — lowest-risk install model. It relies on an existing python3 binary which is reasonable for the code snippets included.
Credentials
The skill declares no required environment variables or credentials. claw.json requests filesystem permission which is proportionate for asking to read user-supplied files and write exports, but filesystem access is broad — it would allow reading any file the agent can access, so the user should ensure the agent is run with appropriate sandboxing and only provide files the skill should process.
Persistence & Privilege
The skill is not always-enabled and does not request persistence or modification of other skills. Autonomous invocation is allowed by platform default (disable-model-invocation:false) which is expected; there is no evidence the skill elevates privileges or modifies agent-wide configuration.
Assessment
This skill appears to do what it says: it uses Python code to generate ER diagrams, JSON schemas, and SQL from user-provided construction project data. Before installing, review the full SKILL.md (the preview here is truncated) to confirm there are no hidden network calls or instructions to read system files or environment variables. Be aware the package requests filesystem permission — only provide input files you intend the skill to access and run the agent in a sandboxed environment if you have sensitive files on the host. If you need higher assurance, request the complete SKILL.md and any example inputs/outputs, or run the skill in an isolated VM/container first.Like a lobster shell, security has layers — review code before you run it.
Current versionv2.1.0
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.
Runtime requirements
📐 Clawdis
OSmacOS · Linux · Windows
Binspython3
SKILL.md
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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