Track data origin, transformations, and flow through construction systems.

MITAuto-check passedData & Analytics

Install Data Lineage Tracker

skills CLI
$ npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill data-lineage-tracker -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction data-lineage-tracker --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction.git skills-src && mkdir -p .claude/skills && cp -r skills-src/2_DDC_Book/2.6-Data-Quality-Validation/data-lineage-tracker .claude/skills/data-lineage-tracker && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
data-lineage-tracker
GitHub stars
344
Used in
1 other repo
Token cost
~4.4k tokens
SKILL.md length
84 words
Files
3
Skills in repo
43
Repo updated
First seen
Licence
MIT

At a glance

Track data origin, transformations, and flow through construction systems.

  • Tasks that involve Data governance
  • SKILL.md covers Overview, Business Case, Technical Implementation and Quick Start, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Tasks that involve Data cleaning

What it does

Data Lineage Tracker is an agent skill from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. Track data origin, transformations, and flow through construction systems. Essential for audit trails, compliance, and debugging data issues.

Its SKILL.md is about 4.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `claw.json` and `instructions.md`).

It sits in Data & Analytics, covering Data governance and Data cleaning. The repository describes itself as: 221 AI skills for construction: BIM analysis, cost estimation, scheduling, document control, and automation with Claude Code. The licence is MIT.

When your agent uses it

  • Tasks that involve Data governance
  • Tasks that involve Data cleaning

Example prompts

  • “/data-lineage-tracker”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit ce45bbf. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Data Lineage Tracker loads about 4.4k tokens when it runs. Until then it costs about 41 tokens; SKILL.md has 84 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~41
When it runs · the whole SKILL.md, loaded when a task matches
~4.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction at commit ce45bbf, republished under its MIT licence (© datadrivenconstruction). 84 words, ~4,433 tokens.

Download SKILL.mdSave it as .claude/skills/data-lineage-tracker/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
data-lineage-tracker
description
Track data origin, transformations, and flow through construction systems. Essential for audit trails, compliance, and debugging data issues.
homepage
https://datadrivenconstruction.io

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

python
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)

Quick Start

python
# 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()

Resources

  • Data Governance: DAMA DMBOK lineage guidelines
  • Audit Requirements: SOX, ISO compliance

© datadrivenconstruction, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in 2_DDC_Book/2.6-Data-Quality-Validation/data-lineage-tracker of datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction.

  • SKILL.md
  • claw.json
  • instructions.md

Open the folder on GitHubat commit ce45bbf

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Data Lineage Tracker next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Data Lineage Tracker compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Data Lineage Tracker this skilldatadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction3441 repos~4.4kAutomated safety check: PassMIT
Data Quality Frameworkswshobson/agents40k11 repos~1.1kAutomated safety check: PassMIT
Research Data Feasibility and Leakage ChecksLight0305/Light-skills641—~4.9kAutomated safety check: PassMIT
Datalineage Summarygoogle/skills21k—~1.7kAutomated safety check: PassApache-2.0
Monte Carlo Context Detectionsickn33/agentic-awesome-skills47k1 repos~2.6kAutomated safety check: WarnMIT
Querying AWS Sagemaker Catalogaws/agent-toolkit-for-aws2.8k—~2.6kAutomated safety check: PassApache-2.0

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Questions about Data Lineage Tracker

What does Data Lineage Tracker do?

Track data origin, transformations, and flow through construction systems. Data Lineage Tracker is an agent skill from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. Track data origin, transformations, and flow through construction systems.

When should I use Data Lineage Tracker?

Data Lineage Tracker fits situations like: tasks that involve Data governance; tasks that involve Data cleaning.

How do I install Data Lineage Tracker in Claude Code?

Run `npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill data-lineage-tracker -a claude-code`. Or copy the skill folder (2_DDC_Book/2.6-Data-Quality-Validation/data-lineage-tracker in datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction) into .claude/skills/data-lineage-tracker in your project. Claude Code loads it when a task matches its description.

How do I install Data Lineage Tracker in Codex?

Run `npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill data-lineage-tracker -a codex`. Or copy the skill folder (2_DDC_Book/2.6-Data-Quality-Validation/data-lineage-tracker in datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction) into .agents/skills/data-lineage-tracker in your project. Codex loads it when a task matches its description.

Can I use Data Lineage Tracker in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill data-lineage-tracker -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/data-lineage-tracker, .gemini/skills/data-lineage-tracker, .github/skills/data-lineage-tracker and .opencode/skills/data-lineage-tracker in your project.

What does Data Lineage Tracker need to run?

SKILL.md names no scripts, command-line tools or credentials: Data Lineage Tracker is instructions for the agent only. Our summary lists: Python 3.

Does Data Lineage Tracker access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Data Lineage Tracker safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Data Lineage Tracker use?

Data Lineage Tracker is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Data Lineage Tracker use?

About 4.4k tokens (SKILL.md is roughly 18k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Data Lineage Tracker?

Skills that share tags, products or a category with Data Lineage Tracker: Data Quality Frameworks (wshobson/agents, 40k stars), Research Data Feasibility and Leakage Checks (Light0305/Light-skills, 641 stars), Datalineage Summary (google/skills, 21k stars) and Monte Carlo Context Detection (sickn33/agentic-awesome-skills, 47k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Data Lineage Tracker?

datadrivenconstruction (a GitHub user) maintains it in datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction, which has 344 GitHub stars. The repository holds 43 skills in this directory. The repository was last updated on August 22, 2026.

Source: datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.