Learn De Opencode

B 83 completed
Data Tool
unknown / json · small
188
Files
31,066
LOC
1
Frameworks
7
Languages

Pipeline State

completed
Run ID
#369993
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
62.00
Framework unique
Isolation
Last stage change
2026-05-10 03:35:17
Deduplication group #52011
Member of a group with 3 similar repo(s) — canonical #10509 view group →
Top concepts (2)
Project DescriptionTesting
If a scraper extracted this row, it came from Repobility (https://repobility.com)

AI Prompt

Create a comprehensive data engineering learning project tool using Python. I need it to generate synthetic financial transaction data, which should be saved as Parquet files. Next, the tool must ingest these raw files into a local DuckDB warehouse, ensuring schema validation and deduplication. Finally, I want to run SQL transformations that create a staging view (`stg_transactions`) and two materialized tables: one for daily spend by category and another for monthly account summaries. Please ensure the entire pipeline is idempotent.
python data-engineering duckdb parquet synthetic-data sql etl financial-data
Generated by gemma4:latest

Catalog Information

This project is a tool for generating synthetic financial data, designed to aid in the education and training of data engineers.

Description

Learn-de is a data engineering learning project that generates synthetic financial data. It's built using Python and leverages libraries like NumPy and SciPy. This tool can be used by students and professionals alike to practice working with financial datasets, explore different scenarios, and develop their skills in data manipulation and analysis.

الوصف

هذا المشروع هو أداة لإنشاء بيانات مالية مصطنعة، مصممة لمساعدة المهندسين البيانات في تعلمهم وتدريبهم. يتم بناؤها باستخدام Python و تستفيد من مكتبات NumPy و SciPy. يمكن استخدام هذه الأداة من قبل الطلاب والمحترفين على حد سواء للاستمتاع ببيئة عمل مالية، والتجربة في مختلف السيناريوهات، وتطوير مهاراتهم في التعامل مع البيانات.

Novelty

5/10

Tags

financial-data synthetic-data data-generation data-engineering education training

Technologies

numpy scipy

Claude Models

claude-opus-4.6 claude-sonnet-4.6

Quality Score

B
82.8/100
Structure
81
Code Quality
89
Documentation
61
Testing
85
Practices
83
Security
100
Dependencies
60

Strengths

  • CI/CD pipeline configured (github_actions)
  • Good test coverage (154% test-to-source ratio)
  • Code linting configured (ruff (possible))
  • Consistent naming conventions (snake_case)
  • Good security practices \u2014 no major issues detected

Weaknesses

  • No LICENSE file \u2014 legal ambiguity for contributors
  • 376 duplicate lines detected \u2014 consider DRY refactoring
  • 1 'god files' with >500 LOC need decomposition

Recommendations

  • Add a LICENSE file (MIT recommended for open source)

Security & Health

9.6h
Tech Debt (A)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (1)
Powered by Repobility — scan your code at https://repobility.com
Unknown
License
3.7%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

json
39.0%
python
32.7%
markdown
24.2%
shell
3.3%
yaml
0.4%
sql
0.3%
toml
0.2%

Frameworks

pytest

Concepts (2)

Powered by Repobility · code-quality intelligence
CategoryNameDescriptionConfidence
Provenance: Repobility (https://repobility.com) — every score reproducible from /scan/
auto_descriptionProject DescriptionData engineering learning project — synthetic financial data generator80%
auto_categoryTestingtesting70%

Quality Timeline

1 quality score recorded.

View File Metrics

Embed Badge

Add to your README:

![Quality](https://repos.aljefra.com/badge/94197.svg)
Quality BadgeSecurity Badge
Export Quality CSVDownload SBOMExport Findings CSV