Aumai Modeloci
B 85 completed
Library
cli / python · tiny
22
Files
1,400
LOC
1
Frameworks
4
Languages
Pipeline State
completedRun ID
#304020Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
SkippedDecision
skip_scaffold_dupNovelty
31.36Framework unique
—Isolation
—Last stage change
2026-04-16 18:15:42Deduplication group #47941
Member of a group with 1 similar repo(s) — canonical #9446 view group →
Top concepts (6)
Project DescriptiontestingTestingFactoryDatabaseTesting
Repobility · MCP-ready · https://repobility.com
🧪 Code Distillation
Browse all specs →Sample distilled functions (click for full spec)
ModelUnpackager.verify_layersVerifies the integrity of every layer blob contained within a specified archive file. It takes the path to the archive as input and returns a list of tuples, where each tuple contains the expected digest string and a boolean indicating if the layer's actual content matches that digest. The function
ModelUnpackager.unpackExtracts the contents of a specified archive file into a designated directory, ensuring the output directory exists first. It reads the resulting configuration file, named config.json, from the extracted contents and returns a validated OCIConfig object. The function handles potential path traversal
ModelPackager._create_layer_blobCreates a compressed tar archive containing the specified file, saving this resulting binary data to a file within the designated blobs directory using the content's SHA-256 hash as the filename. It accepts the source file path, the directory for storing blobs, and a base directory for path relativi
AI Prompt
Build me a command-line tool in Python that handles OCI-compliant packaging for machine learning models. The tool should have core functionality, and I need to ensure it's easy to contribute to, following best practices. Please structure it so I can easily find documentation for getting started and reference the API. I also need to make sure the project structure supports testing using pytest.
python cli mlops packaging pytest command-line oci machine-learning
Generated by gemma4:latest
Catalog Information
This project provides OCI-compliant packaging for machine learning models.
Description
Aumai-modeloci is a tool that enables the creation of OCI-compliant packages for machine learning models. This allows developers to easily deploy and manage their ML models in a standardized way, following the Open Container Initiative (OCI) specifications. The project uses Python as its primary language and Click for command-line interface management.
الوصف
هذا المشروع يقدم حزمة OCI المعمقة لطرق التعلم الآلي. يتيح هذا الأداة للمطورين إنشاء حزم متوافقة مع OCI للطرق التعلم الآلي بسهولة، مما يسمح لهم بتركيب وتحديث طرق التعلم الآلي في شكل موحد، وفقًا لمعيار OCI المفتوح. يستخدم المشروع لغة البرمجة Python كاللغة الرئيسية و Click لإدارة الواجهة السطرية.
Novelty
5/10Tags
machine-learning model-packaging oci-compliance containerization
Technologies
click pydantic
Claude Models
claude-opus-4.6
Quality Score
B
84.7/100
Structure
93
Code Quality
81
Documentation
85
Testing
85
Practices
69
Security
100
Dependencies
90
Strengths
- CI/CD pipeline configured (github_actions)
- Good test coverage (60% test-to-source ratio)
- Code linting configured (ruff (possible))
- Consistent naming conventions (snake_case)
- Good security practices \u2014 no major issues detected
- Properly licensed project
Security & Health
4.6h
Tech Debt (D)
Medium
DORA Rating
A
OWASP (100%)
Open data scored by Repobility · https://repobility.com
PASS
Quality Gate
A
Risk (8)
Apache-2.0
License
2.3%
Duplication
Languages
Frameworks
pytest
Symbols
variable16
function7
method6
class5
constant3
Concepts (6)
| Category | Name | Description | Confidence | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Want fix-PRs on findings? Install Repobility's GitHub App · github.com/apps/repobility-bot | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_description | Project Description | > OCI-compliant packaging for ML models | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| arch_layer | testing | Detected testing layer | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_category | Testing | testing | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| design_pattern | Factory | Found factory/create_ naming patterns | 60% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Database | Detected from 5 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Testing | Detected from 3 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Repobility — same analyzer, your code, free for public repos · /scan/
Embed Badge
Add to your README:
BinComp Dependency Hardening
All packages →2 of this repo's dependencies have been scanned for binary hardening. Grade reflects RELRO / stack canary / FORTIFY / PIE coverage.