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.
请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。
SkillHub CLI
skillhub install datadrivenconstruction/open-construction-estimate打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。
将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。
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',...
这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。
# 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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