Fetch construction material prices from open APIs. Track price trends, regional variations, and update cost databases.

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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

Material prices fluctuate constantly. This skill fetches prices from open sources, tracks trends, and updates cost databases with current market data.

Python Implementation

import requests import pandas as pd from typing import Dict, Any, List, Optional from dataclasses import dataclass, field from datetime import datetime, timedelta from enum import Enum import json class MaterialCategory(Enum): """Construction material categories.""" CONCRETE = "concrete" STEEL = "steel" LUMBER = "lumber" COPPER = "copper" ALUMINUM = "aluminum" CEMENT = "cement" AGGREGATES = "aggregates" ASPHALT = "asphalt" @dataclass class MaterialPrice: """Material price point.""" material: str price: float unit: str currency: str source: str date: datetime region: str = "" @dataclass class PriceTrend: """Price trend analysis.""" material: str current_price: float week_change: float month_change: float year_change: float trend_direction: str # 'up', 'down', 'stable' class OpenPriceAPI: """Client for open material price APIs.""" # Commodity price sources FRED_BASE = "https://api.stlouisfed.org/fred/series/observations" # FRED Series IDs for construction commodities FRED_SERIES = { 'steel': 'WPU101', 'lumber': 'WPS0811', 'concrete': 'WPU133', 'copper': 'PCOPPUSDM', 'aluminum': 'PALUMUSDM' } def __init__(self, fred_api_key: Optional[str] = None): self.fred_api_key = fred_api_key def get_fred_prices(self, material: str, start_date: str = None, end_date: str = None) -> List[MaterialPrice]: """Get prices from FRED API.""" if material.lower() not in self.FRED_SERIES: return [] series_id = self.FRED_SERIES[material.lower()] if start_date is None: start_date = (datetime.now() - timedelta(days=365)).strftime('%Y-%m-%d') if end_date is None: end_date = datetime.now().strftime('%Y-%m-%d') params = { 'series_id': series_id, 'observation_start': start_date, 'observation_end': end_date, 'file_type': 'json' } if self.fred_api_key: params['api_key'] = self.fred_api_key try: response = requests.get(self.FRED_BASE, params=params) if response.status_code != 200: return [] data = response.json() observations = data.get('observations', []) prices = [] for obs in observations: try: price = float(obs['value']) prices.append(MaterialPrice( material=material, price=price, unit='index', currency='USD', source='FRED', date=datetime.strptime(obs['date'], '%Y-%m-%d'), region='US' )) except (ValueError, KeyError): continue return prices except Exception as e: print(f"Error fetching FRED data: {e}") return [] def to_dataframe(self, prices: List[MaterialPrice]) -> pd.DataFrame: """Convert prices to DataFrame.""" data = [{ 'material': p.material, 'price': p.price, 'unit': p.unit, 'currency': p.currency, 'source': p.source, 'date': p.date, 'region': p.region } for p in prices] return pd.DataFrame(data) class ConstructionPriceTracker: """Track and analyze construction material prices.""" # Default regional factors REGIONAL_FACTORS = { 'US_National': 1.0, 'US_Northeast': 1.15, 'US_Southeast': 0.95, 'US_Midwest': 0.92, 'US_West': 1.10, 'Germany': 1.25, 'UK': 1.20, 'France': 1.18 } def __init__(self): self.price_cache: Dict[str, pd.DataFrame] = {} def calculate_trend(self, prices: pd.DataFrame) -> PriceTrend: """Calculate price trend from historical data.""" if prices.empty or 'price' not in prices.columns: return None prices = prices.sort_values('date') current = prices['price'].iloc[-1] # Calculate changes week_ago_idx = len(prices) - 7 if len(prices) >= 7 else 0 month_ago_idx = len(prices) - 30 if len(prices) >= 30 else 0 year_ago_idx = len(prices) - 365 if len(prices) >= 365 else 0 week_price = prices['price'].iloc[week_ago_idx] month_price = prices['price'].iloc[month_ago_idx] year_price = prices['price'].iloc[year_ago_idx] week_change = ((current - week_price) / week_price * 100) if week_price else 0 month_change = ((current - month_price) / month_price * 100) if month_price else 0 year_change = ((current - year_price) / year_price * 100) if year_price else 0 # Determine trend if month_change > 5: trend = 'up' elif month_change < -5: trend = 'down' else: trend = 'stable' return PriceTrend( material=prices['material'].iloc[0], current_price=current, week_change=round(week_change, 2), month_change=round(month_change, 2), year_change=round(year_change, 2), trend_direction=trend ) def apply_regional_factor(self, base_price: float, region: str) -> float: """Apply regional price factor.""" factor = self.REGIONAL_FACTORS.get(region, 1.0) return base_price * factor def update_cost_database(self, cost_df: pd.DataFrame, price_updates: Dict[str, float], date_column: str = 'last_updated') -> pd.DataFrame: """Update cost database with new prices.""" updated = cost_df.copy() for material, price in price_updates.items(): # Find rows with this material mask = updated['material'].str.lower() == material.lower() if mask.any(): # Calculate adjustment factor old_price = updated.loc[mask, 'unit_price'].mean() factor = price / old_price if old_price > 0 else 1 # Update prices updated.loc[mask, 'unit_price'] *= factor updated.loc[mask, date_column] = datetime.now() return updated class MaterialPriceEstimator: """Estimate material prices when API data unavailable.""" # Reference prices (USD per unit, as of 2024) REFERENCE_PRICES = { 'concrete_m3': 120, 'rebar_ton': 800, 'structural_steel_ton': 1200, 'lumber_mbf': 450, 'copper_wire_kg': 12, 'brick_1000': 550, 'cement_ton': 130, 'sand_m3': 35, 'gravel_m3': 40, 'drywall_m2': 8, 'insulation_m2': 25 } def estimate_price(self, material: str, region: str = 'US_National', inflation_adjustment: float = 0) -> float: """Estimate current price for material.""" base_price = self.REFERENCE_PRICES.get(material, 0) if base_price == 0: return 0 # Apply inflation adjusted = base_price * (1 + inflation_adjustment) # Apply regional factor tracker = ConstructionPriceTracker() return tracker.apply_regional_factor(adjusted, region) def bulk_estimate(self, materials: List[str], region: str = 'US_National') -> pd.DataFrame: """Estimate prices for multiple materials.""" estimates = [] for material in materials: price = self.estimate_price(material, region) estimates.append({ 'material': material, 'estimated_price': price, 'region': region, 'source': 'estimate', 'date': datetime.now() }) return pd.DataFrame(estimates)

Quick Start

# Initialize price API api = OpenPriceAPI(fred_api_key="your_key") # Get steel prices steel_prices = api.get_fred_prices('steel') df = api.to_dataframe(steel_prices) print(df.tail()) # Analyze trend tracker = ConstructionPriceTracker() trend = tracker.calculate_trend(df) print(f"Steel trend: {trend.trend_direction}, YoY: {trend.year_change}%")

1. Update Cost Database

tracker = ConstructionPriceTracker() # New prices from market updates = {'steel': 1250, 'concrete': 135, 'lumber': 480} # Update database updated_db = tracker.update_cost_database(cost_df, updates)

2. Regional Pricing

base_price = 120 # concrete USD/m3 berlin_price = tracker.apply_regional_factor(base_price, 'Germany') print(f"Berlin price: ${berlin_price}/m3")

3. Bulk Estimation

estimator = MaterialPriceEstimator() materials = ['concrete_m3', 'rebar_ton', 'lumber_mbf'] estimates = estimator.bulk_estimate(materials, region='US_West') print(estimates)

Resources

DDC Book: Chapter 2.2 - Open Data Sources FRED API: https://fred.stlouisfed.org/docs/api/

Category context

Code helpers, APIs, CLIs, browser automation, testing, and developer operations.

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