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open-construction-estimate

Access and utilize open construction pricing databases. Match BIM elements to standardized work items, calculate costs using public unit price databases with 55,000+ work items.

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/open-construction-estimate

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

Open Construction Estimate

Overview

This skill leverages open construction pricing databases for automated cost estimation. Match project elements to standardized work items and calculate costs using publicly available unit prices.

Data Sources:

  • OpenConstructionEstimate (55,000+ work items)
  • RSMeans Online (subscription)
  • Government pricing databases
  • Regional cost indexes

"Открытые базы данных расценок содержат более 55,000 позиций работ, что позволяет автоматизировать сметные расчеты для большинства проектов." — DDC LinkedIn

Quick Start

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# Load work items database
work_items = pd.read_csv("open_construction_estimate.csv")
print(f"Loaded {len(work_items)} work items")

# Simple matching function
vectorizer = TfidfVectorizer(ngram_range=(1, 2))
item_vectors = vectorizer.fit_transform(work_items['description'])

def find_matching_items(query, top_n=5):
    query_vec = vectorizer.transform([query])
    similarities = cosine_similarity(query_vec, item_vectors)[0]
    top_indices = similarities.argsort()[-top_n:][::-1]

    return work_items.iloc[top_indices][['code', 'description', 'unit', 'unit_price']]

# Find matches
matches = find_matching_items("reinforced concrete wall 300mm")
print(matches)

Open Database Structure

Database Schema
# Standard work items database structure
WORK_ITEMS_SCHEMA = {
    'code': 'Work item code (e.g., 03.31.13.13)',
    'description': 'Full description of work',
    'short_description': 'Abbreviated description',
    'unit': 'Unit of measure (m³, m², ton, pcs)',
    'unit_price': 'Base unit price',
    'labor_cost': 'Labor component per unit',
    'material_cost': 'Material component per unit',
    'equipment_cost': 'Equipment component per unit',
    'labor_hours': 'Labor hours per unit',...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/datadrivenconstruction/open-construction-estimate
Version
—
Commit
e4e062379b49ba8958ba3fd6e487abd809e1f79e
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
# Open Construction Estimate

## Overview

This skill leverages open construction pricing databases for automated cost estimation. Match project elements to standardized work items and calculate costs using publicly available unit prices.

**Data Sources:**
- OpenConstructionEstimate (55,000+ work items)
- RSMeans Online (subscription)
- Government pricing databases
- Regional cost indexes

> "Открытые базы данных расценок содержат более 55,000 позиций работ, что позволяет автоматизировать сметные расчеты для большинства проектов."
> — DDC LinkedIn

## Quick Start

```python
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# Load work items database
work_items = pd.read_csv("open_construction_estimate.csv")
print(f"Loaded {len(work_items)} work items")

# Simple matching function
vectorizer = TfidfVectorizer(ngram_range=(1, 2))
item_vectors = vectorizer.fit_transform(work_items['description'])

def find_matching_items(query, top_n=5):
    query_vec = vectorizer.transform([query])
    similarities = cosine_similarity(query_vec, item_vectors)[0]
    top_indices = similarities.argsort()[-top_n:][::-1]

    return work_items.iloc[top_indices][['code', 'description', 'unit', 'unit_price']]

# Find matches
matches = find_matching_items("reinforced concrete wall 300mm")
print(matches)
```

## Open Database Structure

### Database Schema

```python
# Standard work items database structure
WORK_ITEMS_SCHEMA = {
    'code': 'Work item code (e.g., 03.31.13.13)',
    'description': 'Full description of work',
    'short_description': 'Abbreviated description',
    'unit': 'Unit of measure (m³, m², ton, pcs)',
    'unit_price': 'Base unit price',
    'labor_cost': 'Labor component per unit',
    'material_cost': 'Material component per unit',
    'equipment_cost': 'Equipment component per unit',
    'labor_hours': 'Labor hours per unit',...

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