stock-data-skill
OpenClaw Skill for stock data analysis
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.
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
SkillHub CLI
skillhub install datadrivenconstruction/open-construction-estimateInspect the source and compatibility requirements, then follow its installation steps.
Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.
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:
"Открытые базы данных расценок содержат более 55,000 позиций работ, что позволяет автоматизировать сметные расчеты для большинства проектов." — DDC LinkedIn
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)
# 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',...
These signals come from indexed source analysis and may lag behind changes. Check the current source files.
# 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',...OpenClaw Skill for stock data analysis
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