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csv-handler

Handle CSV files from construction software exports. Auto-detect delimiters, encodings, and clean messy data.

datadrivenconstruction0 source installs☆ 0 Repository stars

Installation

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Skill instructions

CSV Handler for Construction Data

Overview

CSV is the universal exchange format in construction - from scheduling exports to cost databases. This skill handles encoding issues, delimiter detection, and data cleaning.

Python Implementation

import pandas as pd
import csv
from typing import Dict, Any, List, Optional, Tuple
from pathlib import Path
from dataclasses import dataclass
import chardet


@dataclass
class CSVProfile:
    """Profile of CSV file."""
    encoding: str
    delimiter: str
    has_header: bool
    row_count: int
    column_count: int
    columns: List[str]


class ConstructionCSVHandler:
    """Handle CSV files from construction software."""

    COMMON_DELIMITERS = [',', ';', '\t', '|']
    COMMON_ENCODINGS = ['utf-8', 'utf-8-sig', 'latin-1', 'cp1252', 'iso-8859-1']

    def __init__(self):
        self.last_profile: Optional[CSVProfile] = None

    def detect_encoding(self, file_path: str) -> str:
        """Detect file encoding."""
        with open(file_path, 'rb') as f:
            raw = f.read(10000)
        result = chardet.detect(raw)
        return result.get('encoding', 'utf-8') or 'utf-8'

    def detect_delimiter(self, file_path: str, encoding: str) -> str:
        """Detect CSV delimiter."""
        with open(file_path, 'r', encoding=encoding, errors='replace') as f:
            sample = f.read(5000)

        # Count occurrences
        counts = {d: sample.count(d) for d in self.COMMON_DELIMITERS}

        # Return most common that appears consistently
        if counts:
            return max(counts, key=counts.get)
        return ','

    def profile_csv(self, file_path: str) -> CSVProfile:
        """Profile CSV file."""
        encoding = self.detect_encoding(file_path)
        delimiter = self.detect_delimiter(file_path, encoding)

        # Read sample
        df = pd.read_csv(file_path, encoding=encoding, delimiter=delimiter,...

Source information

Source
github
Repository
openclaw/skills
Path
skills/datadrivenconstruction/csv-handler
Version
—
Commit
439dc78df6c6699a8cae98f5809ed072bf23307b
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

Extracted content

SKILL.md
# CSV Handler for Construction Data

## Overview
CSV is the universal exchange format in construction - from scheduling exports to cost databases. This skill handles encoding issues, delimiter detection, and data cleaning.

## Python Implementation

```python
import pandas as pd
import csv
from typing import Dict, Any, List, Optional, Tuple
from pathlib import Path
from dataclasses import dataclass
import chardet


@dataclass
class CSVProfile:
    """Profile of CSV file."""
    encoding: str
    delimiter: str
    has_header: bool
    row_count: int
    column_count: int
    columns: List[str]


class ConstructionCSVHandler:
    """Handle CSV files from construction software."""

    COMMON_DELIMITERS = [',', ';', '\t', '|']
    COMMON_ENCODINGS = ['utf-8', 'utf-8-sig', 'latin-1', 'cp1252', 'iso-8859-1']

    def __init__(self):
        self.last_profile: Optional[CSVProfile] = None

    def detect_encoding(self, file_path: str) -> str:
        """Detect file encoding."""
        with open(file_path, 'rb') as f:
            raw = f.read(10000)
        result = chardet.detect(raw)
        return result.get('encoding', 'utf-8') or 'utf-8'

    def detect_delimiter(self, file_path: str, encoding: str) -> str:
        """Detect CSV delimiter."""
        with open(file_path, 'r', encoding=encoding, errors='replace') as f:
            sample = f.read(5000)

        # Count occurrences
        counts = {d: sample.count(d) for d in self.COMMON_DELIMITERS}

        # Return most common that appears consistently
        if counts:
            return max(counts, key=counts.get)
        return ','

    def profile_csv(self, file_path: str) -> CSVProfile:
        """Profile CSV file."""
        encoding = self.detect_encoding(file_path)
        delimiter = self.detect_delimiter(file_path, encoding)

        # Read sample
        df = pd.read_csv(file_path, encoding=encoding, delimiter=delimiter,...

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