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

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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: claw.json, instructions.md, SKILL.md

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 2.1.0

Provenance

Publisher: datadrivenconstruction
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 7 sections Open source page

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, nrows=10, on_bad_lines='skip') has_header = not df.columns[0].replace('.', '').replace('-', '').isdigit() # Full row count with open(file_path, 'r', encoding=encoding, errors='replace') as f: row_count = sum(1 for _ in f) - (1 if has_header else 0) profile = CSVProfile( encoding=encoding, delimiter=delimiter, has_header=has_header, row_count=row_count, column_count=len(df.columns), columns=list(df.columns) ) self.last_profile = profile return profile def read_csv(self, file_path: str, encoding: Optional[str] = None, delimiter: Optional[str] = None, clean: bool = True) -> pd.DataFrame: """Read CSV with auto-detection.""" # Auto-detect if not provided if encoding is None: encoding = self.detect_encoding(file_path) if delimiter is None: delimiter = self.detect_delimiter(file_path, encoding) # Read with error handling df = pd.read_csv( file_path, encoding=encoding, delimiter=delimiter, on_bad_lines='skip', low_memory=False ) if clean: df = self.clean_dataframe(df) return df def clean_dataframe(self, df: pd.DataFrame) -> pd.DataFrame: """Clean construction CSV data.""" # Clean column names df.columns = [self._clean_column_name(c) for c in df.columns] # Remove empty rows and columns df = df.dropna(how='all') df = df.dropna(axis=1, how='all') # Strip whitespace from strings for col in df.select_dtypes(include=['object']): df[col] = df[col].str.strip() if df[col].dtype == 'object' else df[col] return df def _clean_column_name(self, name: str) -> str: """Clean column name.""" if not isinstance(name, str): return str(name) # Remove special characters, replace spaces clean = name.strip().lower() clean = clean.replace(' ', '_').replace('-', '_') clean = ''.join(c for c in clean if c.isalnum() or c == '_') return clean def merge_csvs(self, file_paths: List[str], on_column: Optional[str] = None) -> pd.DataFrame: """Merge multiple CSV files.""" dfs = [] for path in file_paths: df = self.read_csv(path) df['_source_file'] = Path(path).name dfs.append(df) if not dfs: return pd.DataFrame() if on_column and on_column in dfs[0].columns: result = dfs[0] for df in dfs[1:]: result = pd.merge(result, df, on=on_column, how='outer') return result return pd.concat(dfs, ignore_index=True) def split_csv(self, df: pd.DataFrame, group_column: str, output_dir: str) -> List[str]: """Split CSV by column values.""" output_path = Path(output_dir) output_path.mkdir(parents=True, exist_ok=True) files = [] for value in df[group_column].unique(): subset = df[df[group_column] == value] filename = f"{group_column}_{value}.csv" filepath = output_path / filename subset.to_csv(filepath, index=False) files.append(str(filepath)) return files def convert_types(self, df: pd.DataFrame, type_map: Dict[str, str] = None) -> pd.DataFrame: """Convert column types intelligently.""" df = df.copy() if type_map: for col, dtype in type_map.items(): if col in df.columns: try: df[col] = df[col].astype(dtype) except: pass else: # Auto-convert for col in df.columns: # Try numeric try: df[col] = pd.to_numeric(df[col]) continue except: pass # Try datetime try: df[col] = pd.to_datetime(df[col]) except: pass return df def export_csv(self, df: pd.DataFrame, file_path: str, encoding: str = 'utf-8-sig', delimiter: str = ',') -> str: """Export DataFrame to CSV.""" df.to_csv(file_path, encoding=encoding, sep=delimiter, index=False) return file_path # Specialized handlers class ScheduleCSVHandler(ConstructionCSVHandler): """Handler for project schedule CSVs.""" SCHEDULE_COLUMNS = ['task_id', 'task_name', 'start_date', 'end_date', 'duration', 'predecessors', 'resources'] def parse_schedule(self, file_path: str) -> pd.DataFrame: """Parse schedule CSV.""" df = self.read_csv(file_path) # Convert date columns for col in df.columns: if 'date' in col.lower() or 'start' in col.lower() or 'end' in col.lower(): try: df[col] = pd.to_datetime(df[col]) except: pass return df class CostCSVHandler(ConstructionCSVHandler): """Handler for cost/estimate CSVs.""" def parse_costs(self, file_path: str) -> pd.DataFrame: """Parse cost CSV.""" df = self.read_csv(file_path) # Find and convert numeric columns for col in df.columns: if any(word in col.lower() for word in ['cost', 'price', 'amount', 'total', 'qty', 'quantity']): df[col] = pd.to_numeric(df[col].replace(r'[\$,]', '', regex=True), errors='coerce') return df

Quick Start

handler = ConstructionCSVHandler() # Profile CSV first profile = handler.profile_csv("export.csv") print(f"Encoding: {profile.encoding}, Delimiter: '{profile.delimiter}'") # Read with auto-detection df = handler.read_csv("export.csv") print(f"Loaded {len(df)} rows, {len(df.columns)} columns")

1. Merge Multiple Exports

files = ["jan_export.csv", "feb_export.csv", "mar_export.csv"] merged = handler.merge_csvs(files)

2. Split by Category

handler.split_csv(df, group_column='category', output_dir='./split_files')

3. Schedule Import

schedule_handler = ScheduleCSVHandler() schedule = schedule_handler.parse_schedule("p6_export.csv")

Resources

DDC Book: Chapter 2.1 - Structured Data

Category context

Data access, storage, extraction, analysis, reporting, and insight generation.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

2 Docs1 Config

SKILL.md Primary doc
instructions.md Docs
claw.json Config