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csv-handler

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

datadrivenconstruction0 installations selon la source☆ 0 Étoiles du dépôt

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install datadrivenconstruction/csv-handler

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

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,...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/datadrivenconstruction/csv-handler
Version
—
Commit
439dc78df6c6699a8cae98f5809ed072bf23307b
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

Contenu extrait

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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