Session Analysis

C 63 completed
Other
web_app / typescript · small
224
Files
42,033
LOC
3
Frameworks
7
Languages

Pipeline State

completed
Run ID
#394552
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
57.53
Framework unique
Isolation
Last stage change
2026-05-10 03:35:17
Deduplication group #49540
Member of a group with 14 similar repo(s) — canonical #116982 view group →
Top concepts (2)
Project DescriptionWeb Frontend
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AI Prompt

Create a real-time, privacy-focused web application for analyzing live tutoring sessions. The system needs to track metrics like eye contact, facial expressions, speaking patterns, and attention, all processed client-side. For tutors, it should provide a real-time engagement score with coaching nudges, and post-session analytics including an expression timeline and AI-generated feedback using Claude. For parents, it must generate shareable progress reports showing key learning moments. The application should use React and Next.js, and ideally include functionality for multi-participant mode.
typescript react next.js ai analytics web-app real-time education engagement typescript
Generated by gemma4:latest

Catalog Information

Real-time AI-powered engagement analytics for live tutoring sessions. Tracks eye contact, facial expressions, speaking patterns, and attention — then delivers coaching nudges to tutors and shareable progress reports to parents.

Description

Real-time AI-powered engagement analytics for live tutoring sessions. Tracks eye contact, facial expressions, speaking patterns, and attention — then delivers coaching nudges to tutors and shareable progress reports to parents.

Novelty

3/10

Tags

typescript react next.js ai analytics web-app real-time education engagement typescript

Technologies

anthropic nextjs react supabase tailwind

Claude Models

claude-opus-4-6

Quality Score

C
62.6/100
Structure
60
Code Quality
67
Documentation
57
Testing
50
Practices
63
Security
84
Dependencies
60

Strengths

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

Weaknesses

  • No LICENSE file \u2014 legal ambiguity for contributors
  • No CI/CD configuration \u2014 manual testing and deployment
  • 1 files with critical complexity need refactoring
  • 2390 duplicate lines detected \u2014 consider DRY refactoring
  • 6 'god files' with >500 LOC need decomposition

Recommendations

  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
  • Add a LICENSE file (MIT recommended for open source)

Security & Health

22.6h
Tech Debt (B)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (1)
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Unknown
License
2.5%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

typescript
61.3%
json
30.1%
markdown
5.3%
css
2.9%
sql
0.3%
yaml
0.1%
javascript
0.1%

Frameworks

React Next.js Jest

Concepts (2)

All metrics by Repobility · https://repobility.com
CategoryNameDescriptionConfidence
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auto_descriptionProject DescriptionReal-time AI-powered engagement analytics for live tutoring sessions. Tracks eye contact, facial expressions, speaking patterns, and attention — then delivers coaching nudges to tutors and shareable progress reports to parents.80%
auto_categoryWeb Frontendweb-frontend70%

Quality Timeline

1 quality score recorded.

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