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 jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction --skill data-lineage-trackergit clone --depth 1 https://github.com/jdmorag97-rgb/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/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/data-lineage-tracker)<a href="https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/data-lineage-tracker"><img src="https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/data-lineage-tracker/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/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/data-lineage-tracker"><img src="https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/data-lineage-tracker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00030 | $0.03540 |
| Opus 5 | $0.00015 | $0.01770 |
| Sonnet 5 | $0.00006 | $0.00708 |
| Haiku 4.5 | $0.00003 | $0.00354 |
Grade A, and why
data-lineage-tracker 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 9d 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.
This is a copy
100% identical to data-lineage-tracker — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 474 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Lineage Tracker for Construction
Overview
Track the origin, transformations, and flow of construction data through systems. Provides audit trails for compliance, helps debug data issues, and ensures data governance.
Business Case
Construction projects require data accountability:
- Audit Compliance: Know where every number came from
- Issue Resolution: Trace data problems to their source
- Change Impact: Understand what downstream systems are affected
- Regulatory Requirements: Maintain data provenance for legal/insurance
Technical Implementation
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)
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.
- 9d ago First seen · 474 lines · 30 tokens per session scan A 3a6941682e27
data-lineage-tracker is a skill published in the GitHub repository jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction (2 stars, last pushed 6mo ago), licensed MIT. It adds 30 tokens to every session and 3,540 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to data-lineage-tracker, differing in 0 lines, and is treated as a copy.
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