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Get Started Free →datadrivenconstruction/etl-pipeline
Build automated ETL (Extract-Transform-Load) pipelines for construction data. Process PDFs, Excel, BIM exports. Generate reports, dashboards, and integrate with other systems. Orchestrate with Airflow or n8n.
Passed 9 of 22 test cases with the skill, and 7 of 22 without it.
2 fixed7 passed either way13 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
What keys are expected in the configuration dictionary passed to ConstructionETLPipeline to define inputs, price lookup, validation, and outputs?
| Checked | Without skill | With skill |
|---|---|---|
| Uses configuration keys excel_folder, pdf_folder, prices_file, validation_rules, excel_output, and database | Missed | Passed |
The instructions your model reads when the skill loads.
Based on DDC methodology (Chapter 4.2), this skill enables building automated data pipelines that extract information from various sources, transform it into useful formats, and load it into target systems or generate reports.
Book Reference: "ETL и автоматизация процессов" / "ETL and Process Automation"
"ETL: переход от ручного управления к автоматизации позволяет компаниям обрабатывать данные без постоянного человеческого вмешательства." — DDC Book, Chapter 4.2
┌─────────┐ ┌───────────┐ ┌────────┐
│ EXTRACT │ -> │ TRANSFORM │ -> │ LOAD │
└─────────┘ └───────────┘ └────────┘
│ │ │
▼ ▼ ▼
Sources Process Outputs
- PDF - Clean - Excel
- Excel - Validate - PDF
- CSV - Calculate - Database
- BIM - Merge - API
- API - Aggregate - Dashboard
import pandas as pd
# Simple ETL Pipeline
def simple_etl_pipeline(input_file, output_file):
# EXTRACT
df = pd.read_excel(input_file)
# TRANSFORM
df = df.dropna() # Clean
df['Total'] = df['Quantity'] * df['Unit_Price'] # Calculate
test cases passed, vs 7 / 22 without
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