Track data origin, transformations, and flow through construction systems. Essential for audit trails, compliance, and debugging data issues.
npx skills add https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill data-lineage-tracker
Track the origin, transformations, and flow of construction data through systems. Provides audit trails for compliance, helps debug data issues, and ensures data governance.
Construction projects require data accountability:
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional, Set
from datetime import datetime
from enum import Enum
import json
import hashlib
import uuid
class TransformationType(Enum):
EXTRACT = "extract"
TRANSFORM = "transform"
LOAD = "load"
AGGREGATE = "aggregate"
JOIN = "join"
FILTER = "filter"
CALCULATE = "calculate"
MANUAL_EDIT = "manual_edit"
IMPORT = "import"
EXPORT = "export"
@dataclass
class DataSource:
id: str
name: str
system: str
location: str
owner: str
created_at: datetime
@dataclass
class TransformationStep:
id: str
transformation_type: TransformationType
description: str
input_entities: List[str]
output_entities: List[str]
logic: str # SQL, Python, or description
performed_by: str # user or system
performed_at: datetime
parameters: Dict[str, Any] = field(default_factory=dict)
@dataclass
class DataEntity:
id: str
name: str
source_id: str
entity_type: str # table, file, field, record
created_at: datetime
version: int = 1
checksum: Optional[str] = None
parent_entities: List[str] = field(default_factory=list)
metadata: Dict[str, Any] = field(default_factory=dict)
@dataclass
class LineageRecord:
id: str
entity_id: str
transformation_id: str
upstream_entities: List[str]
downstream_entities: List[str]
recorded_at: datetime
class ConstructionDataLineageTracker:
"""Track data lineage for construction data flows."""
def __init__(self, project_id: str):
self.project_id = project_id
self.sources: Dict[str, DataSource] = {}
self.entities: Dict[str, DataEntity] = {}
self.transformations: Dict[str, TransformationStep] = {}
self.lineage_records: List[LineageRecord] = []
def register_source(self, name: str, system: str, location: str, owner: str) -> DataSource:
"""Register a new data source."""
source = DataSource(
id=f"SRC-{uuid.uuid4().hex[:8]}",
name=name,
system=system,
location=location,
owner=owner,
created_at=datetime.now()
)
self.sources[source.id] = source
return source
def register_entity(self, name: str, source_id: str, entity_type: str,
parent_entities: List[str] = None,
metadata: Dict = None) -> DataEntity:
"""Register a data entity (table, file, field)."""
entity = DataEntity(
id=f"ENT-{uuid.uuid4().hex[:8]}",
name=name,
source_id=source_id,
entity_type=entity_type,
created_at=datetime.now(),
parent_entities=parent_entities or [],
metadata=metadata or {}
)
self.entities[entity.id] = entity
return entity
def calculate_checksum(self, data: Any) -> str:
"""Calculate checksum for data verification."""
if isinstance(data, str):
content = data
else:
content = json.dumps(data, sort_keys=True, default=str)
return hashlib.sha256(content.encode()).hexdigest()[:16]
def record_transformation(self,
transformation_type: TransformationType,
description: str,
input_entities: List[str],
output_entities: List[str],
logic: str,
performed_by: str,
parameters: Dict = None) -> TransformationStep:
"""Record a data transformation."""
transformation = TransformationStep(
id=f"TRF-{uuid.uuid4().hex[:8]}",
transformation_type=transformation_type,
description=description,
input_entities=input_entities,
output_entities=output_entities,
logic=logic,
performed_by=performed_by,
performed_at=datetime.now(),
parameters=parameters or {}
)
self.transformations[transformation.id] = transformation
# Create lineage records
for output_id in output_entities:
record = LineageRecord(
id=f"LIN-{uuid.uuid4().hex[:8]}",
entity_id=output_id,
transformation_id=transformation.id,
upstream_entities=input_entities,
downstream_entities=[],
recorded_at=datetime.now()
)
self.lineage_records.append(record)
# Update downstream references for input entities
for input_id in input_entities:
for existing_record in self.lineage_records:
if existing_record.entity_id == input_id:
existing_record.downstream_entities.append(output_id)
return transformation
def trace_upstream(self, entity_id: str, depth: int = None) -> List[Dict]:
"""Trace all upstream sources of an entity."""
visited = set()
lineage = []
def trace(eid: str, current_depth: int):
if eid in visited:
return
if depth is not None and current_depth > depth:
return
visited.add(eid)
entity = self.entities.get(eid)
if not entity:
return
# Find transformations that produced this entity
for record in self.lineage_records:
if record.entity_id == eid:
transformation = self.transformations.get(record.transformation_id)
if transformation:
lineage.append({
'entity': entity.name,
'entity_id': eid,
'depth': current_depth,
'transformation': transformation.description,
'transformation_type': transformation.transformation_type.value,
'performed_at': transformation.performed_at.isoformat(),
'performed_by': transformation.performed_by,
'upstream': record.upstream_entities
})
for upstream_id in record.upstream_entities:
trace(upstream_id, current_depth + 1)
trace(entity_id, 0)
return sorted(lineage, key=lambda x: x['depth'])
def trace_downstream(self, entity_id: str, depth: int = None) -> List[Dict]:
"""Trace all downstream dependencies of an entity."""
visited = set()
dependencies = []
def trace(eid: str, current_depth: int):
if eid in visited:
return
if depth is not None and current_depth > depth:
return
visited.add(eid)
entity = self.entities.get(eid)
if not entity:
return
# Find entities that use this entity
for record in self.lineage_records:
if eid in record.upstream_entities:
transformation = self.transformations.get(record.transformation_id)
if transformation:
dependencies.append({
'entity': self.entities[record.entity_id].name if record.entity_id in self.entities else record.entity_id,
'entity_id': record.entity_id,
'depth': current_depth,
'transformation': transformation.description,
'transformation_type': transformation.transformation_type.value
})
trace(record.entity_id, current_depth + 1)
trace(entity_id, 0)
return sorted(dependencies, key=lambda x: x['depth'])
def get_entity_history(self, entity_id: str) -> List[Dict]:
"""Get complete history of changes to an entity."""
history = []
for record in self.lineage_records:
if record.entity_id == entity_id:
transformation = self.transformations.get(record.transformation_id)
if transformation:
history.append({
'timestamp': transformation.performed_at.isoformat(),
'action': transformation.transformation_type.value,
'description': transformation.description,
'performed_by': transformation.performed_by,
'inputs': [
self.entities[eid].name if eid in self.entities else eid
for eid in record.upstream_entities
]
})
return sorted(history, key=lambda x: x['timestamp'])
def impact_analysis(self, entity_id: str) -> Dict:
"""Analyze impact of changes to an entity."""
downstream = self.trace_downstream(entity_id)
impact = {
'entity': self.entities[entity_id].name if entity_id in self.entities else entity_id,
'total_affected': len(downstream),
'affected_by_depth': {},
'affected_entities': downstream
}
for dep in downstream:
depth = dep['depth']
impact['affected_by_depth'][depth] = impact['affected_by_depth'].get(depth, 0) + 1
return impact
def validate_lineage(self) -> List[str]:
"""Validate lineage for completeness and consistency."""
issues = []
# Check for orphan entities (no source or transformation)
for eid, entity in self.entities.items():
has_lineage = any(r.entity_id == eid for r in self.lineage_records)
if not has_lineage and entity.entity_type != 'source':
issues.append(f"Entity '{entity.name}' has no lineage record")
# Check for broken references
all_entity_ids = set(self.entities.keys())
for record in self.lineage_records:
for upstream_id in record.upstream_entities:
if upstream_id not in all_entity_ids:
issues.append(f"Lineage references unknown entity: {upstream_id}")
# Check for circular dependencies
for eid in self.entities:
upstream = set()
to_check = [eid]
while to_check:
current = to_check.pop()
if current in upstream:
issues.append(f"Circular dependency detected involving entity: {self.entities[eid].name}")
break
upstream.add(current)
for record in self.lineage_records:
if record.entity_id == current:
to_check.extend(record.upstream_entities)
return issues
def generate_lineage_graph(self, entity_id: str) -> str:
"""Generate Mermaid diagram of lineage."""
lines = ["```mermaid", "graph LR"]
upstream = self.trace_upstream(entity_id, depth=5)
downstream = self.trace_downstream(entity_id, depth=5)
# Add nodes
added_nodes = set()
for item in upstream + downstream:
node_id = item['entity_id'].replace('-', '_')
if node_id not in added_nodes:
entity = self.entities.get(item['entity_id'])
name = entity.name if entity else item['entity_id']
lines.append(f" {node_id}[{name}]")
added_nodes.add(node_id)
# Add target node
target_node = entity_id.replace('-', '_')
if target_node not in added_nodes:
entity = self.entities.get(entity_id)
name = entity.name if entity else entity_id
lines.append(f" {target_node}[{name}]:::target")
# Add edges
for item in upstream:
for upstream_id in item.get('upstream', []):
from_node = upstream_id.replace('-', '_')
to_node = item['entity_id'].replace('-', '_')
lines.append(f" {from_node} --> {to_node}")
for item in downstream:
from_node = entity_id.replace('-', '_')
to_node = item['entity_id'].replace('-', '_')
if to_node != from_node:
lines.append(f" {from_node} --> {to_node}")
lines.append(" classDef target fill:#f96")
lines.append("```")
return "\n".join(lines)
def export_lineage(self) -> Dict:
"""Export complete lineage data."""
return {
'project_id': self.project_id,
'exported_at': datetime.now().isoformat(),
'sources': {k: {
'id': v.id,
'name': v.name,
'system': v.system,
'location': v.location,
'owner': v.owner
} for k, v in self.sources.items()},
'entities': {k: {
'id': v.id,
'name': v.name,
'source_id': v.source_id,
'entity_type': v.entity_type,
'parent_entities': v.parent_entities
} for k, v in self.entities.items()},
'transformations': {k: {
'id': v.id,
'type': v.transformation_type.value,
'description': v.description,
'input_entities': v.input_entities,
'output_entities': v.output_entities,
'performed_by': v.performed_by,
'performed_at': v.performed_at.isoformat()
} for k, v in self.transformations.items()},
'lineage_records': [{
'id': r.id,
'entity_id': r.entity_id,
'transformation_id': r.transformation_id,
'upstream_entities': r.upstream_entities
} for r in self.lineage_records]
}
def generate_report(self) -> str:
"""Generate lineage report."""
lines = [f"# Data Lineage Report: {self.project_id}", ""]
lines.append(f"**Generated:** {datetime.now().strftime('%Y-%m-%d %H:%M')}")
lines.append(f"**Sources:** {len(self.sources)}")
lines.append(f"**Entities:** {len(self.entities)}")
lines.append(f"**Transformations:** {len(self.transformations)}")
lines.append("")
# Sources
lines.append("## Data Sources")
for source in self.sources.values():
lines.append(f"- **{source.name}** ({source.system})")
lines.append(f" - Location: {source.location}")
lines.append(f" - Owner: {source.owner}")
lines.append("")
# Validation
issues = self.validate_lineage()
if issues:
lines.append("## Lineage Issues")
for issue in issues:
lines.append(f"- ⚠️ {issue}")
lines.append("")
# Transformation summary
lines.append("## Transformation Summary")
type_counts = {}
for t in self.transformations.values():
type_counts[t.transformation_type.value] = type_counts.get(t.transformation_type.value, 0) + 1
for t_type, count in sorted(type_counts.items()):
lines.append(f"- {t_type}: {count}")
return "\n".join(lines)
# Initialize tracker
tracker = ConstructionDataLineageTracker("PROJECT-001")
# Register sources
procore = tracker.register_source("Procore", "SaaS", "cloud", "PM Team")
sage = tracker.register_source("Sage 300", "Database", "on-prem", "Finance")
# Register entities
budget = tracker.register_entity("Project Budget", procore.id, "table")
costs = tracker.register_entity("Job Costs", sage.id, "table")
report = tracker.register_entity("Cost Variance Report", procore.id, "file")
# Record transformation
tracker.record_transformation(
transformation_type=TransformationType.JOIN,
description="Join budget and actual costs for variance calculation",
input_entities=[budget.id, costs.id],
output_entities=[report.id],
logic="SELECT b.*, c.actual, (b.budget - c.actual) as variance FROM budget b JOIN costs c ON b.cost_code = c.cost_code",
performed_by="ETL Pipeline"
)
# Trace lineage
upstream = tracker.trace_upstream(report.id)
print("Upstream lineage:", upstream)
# Generate graph
print(tracker.generate_lineage_graph(report.id))
# Export for audit
lineage_data = tracker.export_lineage()
Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.
Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.
Build and distribute Expo development clients locally or via TestFlight
Use when you have a written implementation plan to execute in a separate session with review checkpoints
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
Take datadrivenconstruction/data-lineage-tracker from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.