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 skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill data-model-designergit clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_ConstructionWrote 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/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/data-model-designer)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/data-model-designer"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/data-model-designer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/data-model-designer"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/data-model-designer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00026 | $0.02290 |
| Opus 5 | $0.00013 | $0.01145 |
| Sonnet 5 | $0.00005 | $0.00458 |
| Haiku 4.5 | $0.00003 | $0.00229 |
Grade A, and why
data-model-designer scanned grade A with 0 findings 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 7d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- data-model-designer — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 342 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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)
]
)
What ships with it
2 files 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.
- 7d ago First seen · 342 lines · 26 tokens per session scan A 2e1a50478481
data-model-designer is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (308 stars, last pushed 19d ago), licensed MIT. It adds 26 tokens to every session and 2,290 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
ecto-patterns
Ecto patterns — schemas, changesets, queries, migrations, Multi, associations, preloads, upserts. Use when editing Repo calls, Ecto.Query, or schema fields. Skip for Ash.
phoenix-contexts
Phoenix context design — creating/splitting contexts, Scope (1.8+), Ecto.Multi, PubSub, routers, plugs, controllers. Use when editing contexts, routers, or designing boundaries.
ecto-n1-check
Detect N+1 query anti-patterns specifically — Repo calls inside Enum/for loops, missing preloads on associations. Use when N+1 is explicitly suspected, NOT for unrelated Ecto questions or wider database performance.
ecto-constraint-debug
Debug Ecto constraint violations - trace triggers, check migrations, find duplicate data. Use when seeing uniqueconstraint, foreignkeyconstraint, or checkconstraint errors.
constraint-debug
Compatibility alias for the Elixir/Phoenix plugin's Ecto constraint debugger. Invoke explicitly with /ecto:constraint-debug.
n1-check
Compatibility alias for the Elixir/Phoenix plugin's N+1 query checker. Invoke explicitly with /ecto:n1-check.