Sportradar Demo

F 49 completed
Other
monorepo / json · small
116
Files
25,980
LOC
4
Frameworks
10
Languages

Pipeline State

completed
Run ID
#394615
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
67.60
Framework unique
Isolation
Last stage change
2026-05-10 03:35:34
Deduplication group #52780
Member of a group with 2 similar repo(s) — this repo is canonical view group →
Top concepts (2)
Project DescriptionWeb Frontend
Repobility — the code-quality scanner for AI-generated software · https://repobility.com

AI Prompt

Create an agentic web application for NBA analytics called SportScout AI. The app should accept natural-language goals about NBA basketball. It needs to show a visible execution plan before running any tools. The system must support five server-side tools: one for querying players using pandas, one to fetch data from the Sportradar NBA API, one for comparing multiple entities and generating charts, one for searching session history using SQLite FTS5, and one to generate an Excel export. Additionally, it must support client-side actions like rendering various types of charts, displaying data tables, triggering file downloads, and showing toast notifications. The frontend should use React and Tailwind CSS, and the backend should utilize FastAPI.
python react fastapi tailwindcss agentic nba analytics web-app json typescript
Generated by gemma4:latest

Catalog Information

An agentic web application that accepts natural-language goals about NBA basketball, produces a visible execution plan, and completes tasks using both server-side tools and client-side actions.

Description

An agentic web application that accepts natural-language goals about NBA basketball, produces a visible execution plan, and completes tasks using both server-side tools and client-side actions.

Novelty

3/10

Tags

python react fastapi tailwindcss agentic nba analytics web-app json typescript

Claude Models

claude-opus-4-6

Quality Score

F
49.4/100
Structure
41
Code Quality
53
Documentation
50
Testing
0
Practices
74
Security
92
Dependencies
60

Strengths

  • Code linting configured (eslint, ruff (possible))
  • Good security practices \u2014 no major issues detected
  • Containerized deployment (Docker)

Weaknesses

  • No LICENSE file \u2014 legal ambiguity for contributors
  • No tests found \u2014 high risk of regressions
  • No CI/CD configuration \u2014 manual testing and deployment
  • 4 files with critical complexity need refactoring
  • 1536 duplicate lines detected \u2014 consider DRY refactoring
  • 6 'god files' with >500 LOC need decomposition

Recommendations

  • Add a test suite \u2014 start with critical path integration tests
  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
  • Add a LICENSE file (MIT recommended for open source)

Security & Health

13.3h
Tech Debt (B)
A
OWASP (100%)
FAIL
Quality Gate
A
Risk (13)
Citation: Repobility (2026). State of AI-Generated Code. https://repobility.com/research/
Unknown
License
3.4%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

json
45.5%
typescript
16.2%
python
12.9%
markdown
12.3%
javascript
12.3%
css
0.3%
yaml
0.3%
toml
0.1%
html
0.1%
text
0.0%

Frameworks

FastAPI React Tailwind CSS Vite

Concepts (2)

Findings produced by Repobility · scan your repo at https://repobility.com/scan/
CategoryNameDescriptionConfidence
Repobility · code-quality intelligence · https://repobility.com
auto_descriptionProject DescriptionAn agentic web application that accepts natural-language goals about NBA basketball, produces a visible execution plan, and completes tasks using both server-side tools and client-side actions.80%
auto_categoryWeb Frontendweb-frontend70%

Quality Timeline

1 quality score recorded.

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