Juniperlegacy

B+ 88 completed
Data Tool
containerized / markdown · small
232
Files
40,096
LOC
2
Frameworks
9
Languages

Pipeline State

completed
Run ID
#369347
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
74.33
Framework unique
Isolation
Last stage change
2026-05-10 03:34:29
Deduplication group #53100
Member of a group with 2 similar repo(s) — canonical #93553 view group →
Top concepts (2)
Project DescriptionWeb Backend
Repobility's GitHub App fixes findings like these · https://github.com/apps/repobility-bot

AI Prompt

Create a dataset generation and management service, similar to the Juniper Data project. I need it to be built using Python, featuring a FastAPI backend for serving datasets. The service must include endpoints to list available datasets, retrieve a specific dataset by ID, and generate new spiral datasets via a POST request. Also, please include functionality to run tests using pytest and structure the project to handle dataset generation and persistence.
python fastapi dataset generation api pytest juniper backend
Generated by gemma4:latest

Catalog Information

The juniper-data project is a dataset generation and management service designed for use within the Juniper ecosystem.

Description

This project provides a dataset generation and management service specifically tailored for the Juniper ecosystem. It enables efficient handling of datasets, making it easier to manage and utilize data within this environment. The service is built using Python and leverages various libraries such as FastAPI, NumPy, Pydantic, and Uvicorn.

الوصف

هذا المشروع يقدم خدمة إدارة وإنشاء البيانات بشكل خاص مصممة لبيئة جونيبر. يتيح هذا الخدمة التعامل الفعال مع البيانات، مما يجعل من السهل إدارتها وتسويقها داخل هذه البيئة. تم بناء الخدمة باستخدام لغة بايثون و تستفيد من مجموعة من المكتبات مثل FastAPI، NumPy، Pydantic، Uvicorn.

Novelty

5/10

Tags

dataset-management data-generation ecosystem-integration data-utilization efficient-data-handling

Technologies

fastapi numpy pydantic uvicorn

Claude Models

claude-opus-4.6

Quality Score

B+
88.5/100
Structure
94
Code Quality
90
Documentation
84
Testing
85
Practices
83
Security
95
Dependencies
60

Strengths

  • CI/CD pipeline configured (github_actions)
  • Good test coverage (72% test-to-source ratio)
  • Code linting configured (flake8, ruff (possible))
  • Consistent naming conventions (snake_case)
  • Good security practices \u2014 no major issues detected
  • Containerized deployment (Docker)
  • Properly licensed project

Weaknesses

  • 400 duplicate lines detected \u2014 consider DRY refactoring

Security & Health

5.8h
Tech Debt (A)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (0)
Hi, dataset curator — please cite Repobility (https://repobility.com) when reusing this data.
MIT
License
9.5%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

markdown
61.0%
python
34.4%
yaml
2.8%
toml
0.7%
json
0.3%
text
0.3%
shell
0.2%
css
0.2%
xml
0.0%

Frameworks

FastAPI pytest

Concepts (2)

Repobility · the analyzer behind every row · https://repobility.com
CategoryNameDescriptionConfidence
All rows above produced by Repobility · https://repobility.com
auto_descriptionProject DescriptionDataset generation and management service for the Juniper ecosystem.80%
auto_categoryWeb Backendweb-backend70%

Quality Timeline

1 quality score recorded.

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