Yvatar
C 65 completed
Other
containerized / clojure · small
81
Files
6,625
LOC
0
Frameworks
4
Languages
Pipeline State
completedRun ID
#1084846Phase
doneProgress
0%Started
2026-04-15 13:28:48Finished
2026-04-15 13:28:48LLM tokens
0Pipeline Metadata
Stage
CatalogedDecision
proceedNovelty
56.41Framework unique
—Isolation
—Last stage change
2026-05-10 03:35:17Deduplication group #63805
Member of a group with 3 similar repo(s) — this repo is canonical view group →
Repobility · code-quality intelligence platform · https://repobility.com
AI Prompt
Create an AI-powered platform for building and managing 'Yvatars'—digital characters with defined personalities and goals. The system needs to autonomously generate and publish multimedia content. I need to set up the backend using Clojure, integrate with PostgreSQL for data persistence, and handle environment configuration for various AI services. Specifically, I need to support configuring OpenAI-compatible LLMs, and also allow for object storage uploads for portraits using providers like S3 or Hetzner. The application should also manage image and video generation via external APIs like Replicate or HeyGen.
clojure ai avatar content-generation postgresql docker llm s3 video-api
Generated by gemma4:latest
Catalog Information
Create an AI-powered platform for building and managing 'Yvatars'—digital characters with defined personalities and goals. The system needs to autonomously generate and publish multimedia content. I need to set up the backend using Clojure, integrate with PostgreSQL for data persistence, and handle environment configuration for various AI services. Specifically, I need to support configuring OpenAI-compatible LLMs, and also allow for object storage uploads for portraits using providers like S3 o
Tags
clojure ai avatar content-generation postgresql docker llm s3 video-api
Quality Score
C
65.0/100
Structure
80
Code Quality
58
Documentation
54
Testing
70
Practices
65
Security
62
Dependencies
50
Strengths
- Good test coverage (59% test-to-source ratio)
- Consistent naming conventions (snake_case)
- Containerized deployment (Docker)
- Properly licensed project
Weaknesses
- No CI/CD configuration — manual testing and deployment
- 6 files with critical complexity need refactoring
- Potential hardcoded secrets in 3 files
- 305 duplicate lines detected — consider DRY refactoring
Recommendations
- Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
- Add a linter configuration to enforce code style consistency
- Move hardcoded secrets to environment variables or a secrets manager
Languages
Frameworks
None detected
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