Infra

C 64 completed
Devops
unknown / yaml · small
162
Files
15,257
LOC
0
Frameworks
3
Languages

Pipeline State

completed
Run ID
#360375
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Skipped
Decision
skip_scaffold_dup
Novelty
49.67
Framework unique
Isolation
Last stage change
2026-04-16 18:15:42
Deduplication group #48509
Member of a group with 1 similar repo(s) — canonical #80943 view group →
Top concepts (2)
Project DescriptionDocumentation
Source: Repobility analyzer · https://repobility.com

AI Prompt

Create a detailed documentation structure for a personal Kubernetes lab setup. I need to document the infrastructure, which includes features like GPU workload experimentation, custom operators, and workflow orchestration. The documentation should cover the entire stack, hardware details, repository layout, and the GitOps workflow using FluxCD. Please organize the content to include sections for service dashboards, useful commands, and general documentation guides, referencing components like ArgoCD, Grafana, and Jaeger.
kubernetes k8s yaml documentation devops gitops gpu operators airflow
Generated by gemma4:latest

Catalog Information

A personal Kubernetes lab for experimenting with GPU workloads, custom operators, and workflow orchestration.

Description

This project provides a fully configured home Kubernetes cluster designed for learning and experimentation. It supports GPU‑accelerated workloads, enabling users to test NVIDIA GPU deployments and performance tuning. The cluster includes a suite of observability tools such as Grafana, Prometheus, and Jaeger, as well as a GitOps pipeline powered by FluxCD for automated application delivery. Custom operators and Temporal workflows can be developed and deployed directly within the environment, offering hands‑on experience with advanced Kubernetes extensibility. The setup is ideal for developers, researchers, and students who want a sandbox to prototype, debug, and validate cloud‑native concepts.

الوصف

يُقدِّم هذا المشروع بيئة Kubernetes مُكوَّنة بالكامل في المنزل، مخصصة للتعلم والتجربة. يدعم تشغيل أعباء العمل التي تستفيد من وحدات معالجة الرسوميات NVIDIA، ما يتيح اختبار نشرات GPU وتحسين أدائها. يتضمن الكتلة مجموعة من أدوات المراقبة والمرئية مثل Grafana، Prometheus، وJaeger، بالإضافة إلى خط سير GitOps مدعوم بـ FluxCD لتسليم التطبيقات تلقائياً. يمكن تطوير ونشر مشغلات مخصصة وسير العمل باستخدام Temporal مباشرة داخل البيئة، ما يوفر تجربة عملية مع قابلية توسيع Kubernetes المتقدمة. تُعد هذه الإعدادات مثالية للمطورين والباحثين والطلاب الذين يرغبون في إنشاء بيئة تجريبية لاختبار وتصحيح وتحقق مفاهيم الحوسبة السحابية الأصلية.

Novelty

6/10

Tags

container-orchestration gpu-compute gitops-workflow workflow-automation monitoring-and-observability distributed-storage network-observability

Claude Models

claude-opus-4.6

Quality Score

C
64.3/100
Structure
43
Code Quality
100
Documentation
45
Testing
15
Practices
78
Security
100
Dependencies
50

Strengths

  • CI/CD pipeline configured (github_actions)
  • Low average code complexity \u2014 well-structured code
  • Good security practices \u2014 no major issues detected

Weaknesses

  • No LICENSE file \u2014 legal ambiguity for contributors
  • No tests found \u2014 high risk of regressions

Recommendations

  • Add a test suite \u2014 start with critical path integration tests
  • Add a linter configuration to enforce code style consistency
  • Add a LICENSE file (MIT recommended for open source)

Security & Health

4.1h
Tech Debt (A)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (1)
Want fix-PRs on findings? Install Repobility's GitHub App · github.com/apps/repobility-bot
Unknown
License
0.0%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

yaml
89.8%
markdown
10.0%
json
0.1%

Frameworks

None detected

Concepts (2)

Generated by the Repobility scanner · https://repobility.com
CategoryNameDescriptionConfidence
Repobility · code-quality intelligence platform · https://repobility.com
auto_descriptionProject DescriptionHome Kubernetes cluster — my learning lab for NVIDIA GPU workloads on k8s, building custom operators, Temporal workflows, and whatever else catches my interest.80%
auto_categoryDocumentationdocs70%

Quality Timeline

1 quality score recorded.

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