Data Type Classifier

Classify construction data by type (structured, unstructured, semi-structured). Analyze data sources and recommend appropriate storage/processing methods

Security Scan

The skill's requirements and instructions are consistent with its stated purpose (classifying construction data) and do not request unrelated credentials or network exfiltration.

Purpose & Capability

Name/description (data classification for construction) match the declared requirements: python3 is reasonable for the provided Python implementation; tesseract (OCR) and ifcopenshell (IFC parsing) are appropriate optional dependencies for images/PDFs and IFC BIM files respectively. Filesystem permission is expected to let the skill read user-supplied files.

Instruction Scope

SKILL.md and instructions.md contain a large Python-based implementation and explicit guidance to process user-provided files/paths. The instructions constrain the agent to only use user-provided data, but they do instruct reading files from disk — this is consistent with the skill's purpose but means users should avoid giving sensitive credentials or unrelated system files as input.

Install Mechanism

No install spec (instruction-only) — lowest-risk pattern. The skill expects system binaries be present rather than downloading code. This is proportionate for a documentation/sample-code skill.

Credentials

No environment variables, no credentials, and no config paths are requested. The lack of external secrets or unrelated env-vars is appropriate for this functionality.

Persistence & Privilege

always:false and normal autonomous invocation. The skill requests filesystem permission (present in claw.json) to read user files, which is reasonable and scoped to its purpose. It does not request persistent elevated privileges or alter other skills' configs.

Assessment

This skill appears coherent and focused on classifying construction data. It is instruction-only (no bundled code to install) and will read files you provide, so: only supply the files you intend it to analyze, avoid giving system or credential files, and install/enable optional tools (tesseract, ifcopenshell) only if you need OCR or IFC parsing. Note the publisher is not clearly identified in the package metadata — if provenance matters, verify the homepage or source before using on sensitive projects.

Overview

Based on DDC methodology (Chapter 2.1), this skill classifies construction data by type, analyzes data sources, and recommends appropriate storage, processing, and integration methods.

Book Reference: "Типы данных в строительстве" / "Data Types in Construction"

Quick Start

from dataclasses import dataclass, field
from enum import Enum
from typing import List, Dict, Optional, Any, Tuple
from datetime import datetime
import json
import re
import mimetypes

class DataStructure(Enum):
    """Data structure classification"""
    STRUCTURED = "structured"           # Tables, databases, spreadsheets
    SEMI_STRUCTURED = "semi_structured" # JSON, XML, IFC
    UNSTRUCTURED = "unstructured"       # Documents, images, videos
    GEOMETRIC = "geometric"             # CAD, BIM geometry
    TEMPORAL = "temporal"               # Time-series, schedules
    SPATIAL = "spatial"                 # GIS, coordinates

class DataFormat(Enum):
    """Common construction data formats"""
    # Structured
    CSV = "csv"
    EXCEL = "excel"
    SQL = "sql"
    PARQUET = "parquet"

    # Semi-structured
    JSON = "json"
    XML = "xml"
    IFC = "ifc"
    BCF = "bcf"

    # Unstructured
    PDF = "pdf"
    DOCX = "docx"
    IMAGE = "image"
    VIDEO = "video"

    # Geometric
    DWG = "dwg"
    DXF = "dxf"
    RVT = "rvt"
    NWD = "nwd"
    OBJ = "obj"
    STL = "stl"

    # Schedule
    MPP = "mpp"
    P6 = "p6"
    XER = "xer"

class StorageRecommendation(Enum):
    """Storage system recommendations"""
    RELATIONAL_DB = "relational_database"
    DOCUMENT_DB = "document_database"
    OBJECT_STORAGE = "object_storage"
    GRAPH_DB = "graph_database"
    TIME_SERIES_DB = "time_series_database"
    VECTOR_DB = "vector_database"
    FILE_SYSTEM = "file_system"
    DATA_LAKE = "data_lake"

@dataclass
class DataCharacteristics:
    """Characteristics of a data source"""
    has_schema: bool
    has_relationships: bool
    is_queryable: bool
    is_binary: bool
    has_geometry: bool
    has_temporal: bool
    has_text_content: bool
    avg_record_size: Optional[int] = None  # bytes
    estimated_volume: Optional[str] = None  # small/medium/large/huge
    update_frequency: Optional[str] = None

@dataclass
class DataClassification:
    """Classification result for a data source"""
    source_name: str
    source_type: str
    detected_format: DataFormat
    structure: DataStructure
    characteristics: DataCharacteristics
    storage_recommendation: StorageRecommendation
    processing_tools: List[str]
    integration_options: List[str]
    quality_considerations: List[str]
    confidence: float

@dataclass
class ClassificationReport:
    """Complete classification report"""
    total_sources: int
    classifications: List[DataClassification]
    summary_by_structure: Dict[str, int]
    summary_by_format: Dict[str, int]
    storage_recommendations: Dict[str, List[str]]
    integration_strategy: Dict[str, str]


class DataTypeClassifier:
    """
    Classify construction data by type and recommend processing methods.
    Based on DDC methodology Chapter 2.1.
    """

    def __init__(self):
        self.format_signatures = self._define_format_signatures()
        self.structure_mapping = self._define_structure_mapping()
        self.storage_mapping = self._define_storage_mapping()
        self.processing_tools = self._define_processing_tools()

    def _define_format_signatures(self) -> Dict[str, Dict]:
        """Define format detection signatures"""
        return {
            # File extensions
            ".csv": {"format": DataFormat.CSV, "structure": DataStructure.STRUCTURED},
            ".xlsx": {"format": DataFormat.EXCEL, "structure": DataStructure.STRUCTURED},
            ".xls": {"format": DataFormat.EXCEL, "structure": DataStructure.STRUCTURED},
            ".json": {"format": DataFormat.JSON, "structure": DataStructure.SEMI_STRUCTURED},
            ".xml": {"format": DataFormat.XML, "structure": DataStructure.SEMI_STRUCTURED},
            ".ifc": {"format": DataFormat.IFC, "structure": DataStructure.SEMI_STRUCTURED},
            ".bcf": {"format": DataFormat.BCF, "structure": DataStructure.SEMI_STRUCTURED},
            ".pdf": {"format": DataFormat.PDF, "structure": DataStructure.UNSTRUCTURED},
            ".docx": {"format": DataFormat.DOCX, "structure": DataStructure.UNSTRUCTURED},
            ".dwg": {"format": DataFormat.DWG, "structure": DataStructure.GEOMETRIC},
            ".dxf": {"format": DataFormat.DXF, "structure": DataStructure.GEOMETRIC},
            ".rvt": {"format": DataFormat.RVT, "structure": DataStructure.GEOMETRIC},
            ".nwd": {"format": DataFormat.NWD, "structure": DataStructure.GEOMETRIC},
            ".mpp": {"format": DataFormat.MPP, "structure": DataStructure.TEMPORAL},
            ".xer": {"format": DataFormat.XER, "structure": DataStructure.TEMPORAL},
            ".parquet": {"format": DataFormat.PARQUET, "structure": DataStructure.STRUCTURED},
            ".jpg": {"format": DataFormat.IMAGE, "structure": DataStructure.UNSTRUCTURED},
            ".png": {"format": DataFormat.IMAGE, "structure": DataStructure.UNSTRUCTURED},
            ".mp4": {"format": DataFormat.VIDEO, "structure": DataStructure.UNSTRUCTURED}
        }

    def _define_structure_mapping(self) -> Dict[DataStructure, Dict]:
        """Define characteristics for each structure type"""
        return {
            DataStructure.STRUCTURED: {
                "description": "Tabular data with fixed schema",
                "examples": ["Cost databases", "Material lists", "Vendor records"],
                "query_support": True,
                "schema_required": True
            },
            DataStructure.SEMI_STRUCTURED: {
                "description": "Hierarchical data with flexible schema",
                "examples": ["BIM models (IFC)", "API responses", "Configuration files"],
                "query_support": True,
                "schema_required": False
            },
            DataStructure.UNSTRUCTURED: {
                "description": "No predefined schema or format",
                "examples": ["Contracts", "Photos", "Emails", "Meeting notes"],
                "query_support": False,
                "schema_required": False
            },
            DataStructure.GEOMETRIC: {
                "description": "3D/2D geometric and spatial data",
                "examples": ["CAD drawings", "BIM geometry", "Point clouds"],
                "query_support": True,
                "schema_required": True
            },
            DataStructure.TEMPORAL: {
                "description": "Time-based sequential data",
                "examples": ["Schedules", "Progress data", "Sensor readings"],
                "query_support": True,
                "schema_required": True
            },
            DataStructure.SPATIAL: {
                "description": "Geographic and location data",
                "examples": ["Site maps", "GPS tracks", "GIS layers"],
                "query_support": True,
                "schema_required": True
            }
        }

    def _define_storage_mapping(self) -> Dict[DataStructure, StorageRecommendation]:
        """Map data structures to storage recommendations"""
        return {
            DataStructure.STRUCTURED: StorageRecommendation.RELATIONAL_DB,
            DataStructure.SEMI_STRUCTURED: StorageRecommendation.DOCUMENT_DB,
            DataStructure.UNSTRUCTURED: StorageRecommendation.OBJECT_STORAGE,
            DataStructure.GEOMETRIC: StorageRecommendation.FILE_SYSTEM,
            DataStructure.TEMPORAL: StorageRecommendation.TIME_SERIES_DB,
            DataStructure.SPATIAL: StorageRecommendation.RELATIONAL_DB
        }

    def _define_processing_tools(self) -> Dict[DataFormat, List[str]]:
        """Define processing tools for each format"""
        return {
            DataFormat.CSV: ["pandas", "polars", "duckdb"],
            DataFormat.EXCEL: ["pandas", "openpyxl", "xlrd"],
            DataFormat.JSON: ["json", "pandas", "jq"],
            DataFormat.XML: ["lxml", "ElementTree", "BeautifulSoup"],
            DataFormat.IFC: ["ifcopenshell", "IfcOpenShell", "xBIM"],
            DataFormat.BCF: ["bcfpython", "ifcopenshell"],
            DataFormat.PDF: ["pdfplumber", "PyPDF2", "pdf2image"],
            DataFormat.DOCX: ["python-docx", "mammoth"],
            DataFormat.DWG: ["ezdxf", "Teigha", "ODA SDK"],
            DataFormat.DXF: ["ezdxf", "dxfgrabber"],
            DataFormat.RVT: ["Revit API", "pyRevit", "Dynamo"],
            DataFormat.NWD: ["Navisworks API", "NW API"],
            DataFormat.MPP: ["mpxj", "Project API"],
            DataFormat.XER: ["xerparser", "P6 API"],
            DataFormat.PARQUET: ["pandas", "pyarrow", "polars"],
            DataFormat.IMAGE: ["PIL", "opencv", "scikit-image"],
            DataFormat.VIDEO: ["opencv", "ffmpeg", "moviepy"]
        }

    def classify_source(
        self,
        source_name: str,
        source_type: str,
        file_extension: Optional[str] = None,
        sample_data: Optional[Any] = None,
        metadata: Optional[Dict] = None
    ) -> DataClassification:
        """
        Classify a single data source.

        Args:
            source_name: Name of the data source
            source_type: Type (file, database, api, etc.)
            file_extension: File extension if applicable
            sample_data: Sample of the data for analysis
            metadata: Additional metadata

        Returns:
            Classification result
        """
        # Detect format
        detected_format, structure = self._detect_format(
            file_extension, source_type, sample_data
        )

        # Analyze characteristics
        characteristics = self._analyze_characteristics(
            detected_format, structure, sample_data, metadata
        )

        # Determine storage recommendation
        storage = self._recommend_storage(structure, characteristics)

        # Get processing tools
        tools = self.processing_tools.get(detected_format, [])

        # Determine integration options
        integration = self._get_integration_options(detected_format, structure)

        # Quality considerations
        quality = self._get_quality_considerations(detected_format, structure)

        # Calculate confidence
        confidence = self._calculate_confidence(
            file_extension, sample_data, metadata
        )

        return DataClassification(
            source_name=source_name,
            source_type=source_type,
            detected_format=detected_format,
            structure=structure,
            characteristics=characteristics,
            storage_recommendation=storage,
            processing_tools=tools,
            integration_options=integration,
            quality_considerations=quality,
            confidence=confidence
        )

    def _detect_format(
        self,
        extension: Optional[str],
        source_type: str,
        sample: Optional[Any]
    ) -> Tuple[DataFormat, DataStructure]:
        """Detect data format and structure"""
        # Check file extension
        if extension:
            ext = extension.lower() if extension.startswith('.') else f".{extension.lower()}"
            if ext in self.format_signatures:
                sig = self.format_signatures[ext]
                return sig["format"], sig["structure"]

        # Check source type
        if source_type == "database":
            return DataFormat.SQL, DataStructure.STRUCTURED
        elif source_type == "api":
            return DataFormat.JSON, DataStructure.SEMI_STRUCTURED

        # Analyze sample data
        if sample:
            if isinstance(sample, dict):
                return DataFormat.JSON, DataStructure.SEMI_STRUCTURED
            elif isinstance(sample, list) and all(isinstance(x, dict) for x in sample):
                return DataFormat.JSON, DataStructure.STRUCTURED
            elif isinstance(sample, str):
                if sample.strip().startswith('<'):
                    return DataFormat.XML, DataStructure.SEMI_STRUCTURED
                elif sample.strip().startswith('{'):
                    return DataFormat.JSON, DataStructure.SEMI_STRUCTURED

        # Default
        return DataFormat.JSON, DataStructure.SEMI_STRUCTURED

    def _analyze_characteristics(
        self,
        format: DataFormat,
        structure: DataStructure,
        sample: Optional[Any],
        metadata: Optional[Dict]
    ) -> DataCharacteristics:
        """Analyze data characteristics"""
        return DataCharacteristics(
            has_schema=structure in [DataStructure.STRUCTURED, DataStructure.TEMPORAL],
            has_relationships=format in [DataFormat.IFC, DataFormat.SQL],
            is_queryable=structure != DataStructure.UNSTRUCTURED,
            is_binary=format in [
                DataFormat.DWG, DataFormat.RVT, DataFormat.NWD,
                DataFormat.IMAGE, DataFormat.VIDEO, DataFormat.PDF
            ],
            has_geometry=structure == DataStructure.GEOMETRIC or format == DataFormat.IFC,
            has_temporal=structure == DataStructure.TEMPORAL,
            has_text_content=format in [
                DataFormat.PDF, DataFormat.DOCX, DataFormat.CSV
            ],
            estimated_volume=metadata.get("volume") if metadata else None,
            update_frequency=metadata.get("update_frequency") if metadata else None
        )

    def _recommend_storage(
        self,
        structure: DataStructure,
        characteristics: DataCharacteristics
    ) -> StorageRecommendation:
        """Recommend storage solution"""
        # Special cases
        if characteristics.has_text_content and not characteristics.has_schema:
            return StorageRecommendation.VECTOR_DB

        if characteristics.is_binary and characteristics.estimated_volume == "huge":
            return StorageRecommendation.OBJECT_STORAGE

        if characteristics.has_relationships:
            return StorageRecommendation.GRAPH_DB

        # Default mapping
        return self.storage_mapping.get(structure, StorageRecommendation.FILE_SYSTEM)

    def _get_integration_options(
        self,
        format: DataFormat,
        structure: DataStructure
    ) -> List[str]:
        """Get integration options for the data"""
        options = []

        if structure == DataStructure.STRUCTURED:
            options.extend(["Direct SQL queries", "ETL pipelines", "API export"])
        elif structure == DataStructure.SEMI_STRUCTURED:
            options.extend(["JSON/XML parsing", "Schema validation", "API integration"])
        elif structure == DataStructure.UNSTRUCTURED:
            options.extend(["OCR extraction", "NLP processing", "ML classification"])
        elif structure == DataStructure.GEOMETRIC:
            options.extend(["IFC export", "Geometry extraction", "Clash detection"])

        # Format-specific options
        if format == DataFormat.IFC:
            options.append("IFC import/export via IfcOpenShell")
        elif format == DataFormat.EXCEL:
            options.append("Pandas DataFrame conversion")
        elif format == DataFormat.PDF:
            options.append("PDF text/table extraction")

        return options

    def _get_quality_considerations(
        self,
        format: DataFormat,
        structure: DataStructure
    ) -> List[str]:
        """Get quality considerations"""
        considerations = []

        if structure == DataStructure.STRUCTURED:
            considerations.extend([
                "Validate schema consistency",
                "Check for null/missing values",
                "Verify data types"
            ])
        elif structure == DataStructure.UNSTRUCTURED:
            considerations.extend([
                "OCR accuracy verification",
                "Text encoding issues",
                "Content extraction completeness"
            ])
        elif structure == DataStructure.GEOMETRIC:
            considerations.extend([
                "Model validity (closed solids)",
                "Coordinate system consistency",
                "Unit verification"
            ])

        # Format-specific
        if format == DataFormat.IFC:
            considerations.append("IFC schema version compatibility")
        elif format == DataFormat.EXCEL:
            considerations.append("Formula vs value extraction")

        return considerations

    def _calculate_confidence(
        self,
        extension: Optional[str],
        sample: Optional[Any],
        metadata: Optional[Dict]
    ) -> float:
        """Calculate classification confidence"""
        confidence = 0.5  # Base confidence

        if extension:
            confidence += 0.3  # Extension provides good hint
        if sample:
            confidence += 0.15  # Sample data helps
        if metadata:
            confidence += 0.05  # Metadata adds context

        return min(1.0, confidence)

    def classify_multiple(
        self,
        sources: List[Dict]
    ) -> ClassificationReport:
        """
        Classify multiple data sources.

        Args:
            sources: List of source definitions

        Returns:
            Complete classification report
        """
        classifications = []

        for source in sources:
            classification = self.classify_source(
                source_name=source["name"],
                source_type=source.get("type", "file"),
                file_extension=source.get("extension"),
                sample_data=source.get("sample"),
                metadata=source.get("metadata")
            )
            classifications.append(classification)

        # Generate summaries
        summary_structure = {}
        summary_format = {}
        storage_recs = {}

        for c in classifications:
            # Structure summary
            struct = c.structure.value
            summary_structure[struct] = summary_structure.get(struct, 0) + 1

            # Format summary
            fmt = c.detected_format.value
            summary_format[fmt] = summary_format.get(fmt, 0) + 1

            # Storage recommendations
            storage = c.storage_recommendation.value
            if storage not in storage_recs:
                storage_recs[storage] = []
            storage_recs[storage].append(c.source_name)

        # Integration strategy
        strategy = self._generate_integration_strategy(classifications)

        return ClassificationReport(
            total_sources=len(sources),
            classifications=classifications,
            summary_by_structure=summary_structure,
            summary_by_format=summary_format,
            storage_recommendations=storage_recs,
            integration_strategy=strategy
        )

    def _generate_integration_strategy(
        self,
        classifications: List[DataClassification]
    ) -> Dict[str, str]:
        """Generate integration strategy"""
        strategy = {}

        # Group by structure
        structured = [c for c in classifications if c.structure == DataStructure.STRUCTURED]
        semi = [c for c in classifications if c.structure == DataStructure.SEMI_STRUCTURED]
        unstructured = [c for c in classifications if c.structure == DataStructure.UNSTRUCTURED]
        geometric = [c for c in classifications if c.structure == DataStructure.GEOMETRIC]

        if structured:
            strategy["structured_data"] = (
                "Use ETL pipeline to consolidate into central data warehouse. "
                "Implement SQL-based querying and reporting."
            )

        if semi:
            strategy["semi_structured_data"] = (
                "Use document database for flexible storage. "
                "Implement schema validation at ingestion."
            )

        if unstructured:
            strategy["unstructured_data"] = (
                "Extract text content using OCR/NLP. "
                "Store in vector database for semantic search."
            )

        if geometric:
            strategy["geometric_data"] = (
                "Standardize on IFC format for exchange. "
                "Maintain native formats for editing."
            )

        return strategy

    def generate_report(self, report: ClassificationReport) -> str:
        """Generate classification report"""
        output = f"""
# Data Classification Report

**Total Sources Analyzed:** {report.total_sources}

## Summary by Structure

"""
        for struct, count in report.summary_by_structure.items():
            output += f"- **{struct.title()}**: {count} sources\n"

        output += "\n## Summary by Format\n\n"
        for fmt, count in report.summary_by_format.items():
            output += f"- **{fmt.upper()}**: {count} sources\n"

        output += "\n## Storage Recommendations\n\n"
        for storage, sources in report.storage_recommendations.items():
            output += f"### {storage.replace('_', ' ').title()}\n"
            for src in sources:
                output += f"- {src}\n"
            output += "\n"

        output += "## Integration Strategy\n\n"
        for category, strategy in report.integration_strategy.items():
            output += f"### {category.replace('_', ' ').title()}\n{strategy}\n\n"

        output += "## Detailed Classifications\n\n"
        for c in report.classifications[:10]:
            output += f"""
### {c.source_name}
- **Format:** {c.detected_format.value}
- **Structure:** {c.structure.value}
- **Storage:** {c.storage_recommendation.value}
- **Tools:** {', '.join(c.processing_tools[:3])}
- **Confidence:** {c.confidence:.0%}
"""

        return output

Common Use Cases

Classify Single Data Source

classifier = DataTypeClassifier()

# Classify a BIM model
classification = classifier.classify_source(
    source_name="Building Model",
    source_type="file",
    file_extension=".ifc",
    metadata={"volume": "large"}
)

print(f"Format: {classification.detected_format.value}")
print(f"Structure: {classification.structure.value}")
print(f"Storage: {classification.storage_recommendation.value}")
print(f"Tools: {classification.processing_tools}")

Classify Multiple Sources

sources = [
    {"name": "Cost Database", "type": "database", "extension": ".sql"},
    {"name": "Building Model", "type": "file", "extension": ".ifc"},
    {"name": "Contract PDFs", "type": "file", "extension": ".pdf"},
    {"name": "Site Photos", "type": "file", "extension": ".jpg"},
    {"name": "Schedule", "type": "file", "extension": ".mpp"}
]

report = classifier.classify_multiple(sources)

print(f"Total: {report.total_sources}")
print(f"By structure: {report.summary_by_structure}")

Generate Classification Report

report_text = classifier.generate_report(report)
print(report_text)

# Save to file
with open("classification_report.md", "w") as f:
    f.write(report_text)

Quick Reference

Component	Purpose
DataTypeClassifier	Main classification engine
DataStructure	Structure types (structured, semi, unstructured)
DataFormat	File format detection
StorageRecommendation	Storage system recommendations
DataClassification	Classification result
ClassificationReport	Multi-source report

Resources

• Book: "Data-Driven Construction" by Artem Boiko, Chapter 2.1
• Website: https://datadrivenconstruction.io

Next Steps

• Use sql-query-builder for structured data queries
• Use pdf-to-structured for unstructured data
• Use data-model-designer for schema design