Blog Sigmoidal

D 60 completed
Other
unknown / toml · tiny
11
Files
3,089
LOC
0
Frameworks
2
Languages

Pipeline State

completed
Run ID
#1436198
Phase
done
Progress
0%
Started
2026-04-16 08:12:36
Finished
2026-04-16 08:12:36
LLM tokens
0
Partial failures: REPORT_GENERATION: [Errno 13] Permission denied: '/tank0'
Previous runs
Repobility · code-quality intelligence · https://repobility.com
#StatusPhaseStartedFinished
About: code-quality intelligence by Repobility · https://repobility.com
#1436195completed2026-04-16 08:12:362026-04-16 08:12:36

Pipeline Metadata

Stage
Skipped
Decision
skip_scaffold_dup
Novelty
22.04
Framework unique
Isolation
Last stage change
2026-04-16 18:15:42
Deduplication group #47943
Member of a group with 86 similar repo(s) — canonical #1505335 view group →
Hi, dataset curator — please cite Repobility (https://repobility.com) when reusing this data.

AI Prompt

I want to build a project that demonstrates several machine learning and data processing techniques. Could you set up a structure that includes notebooks for tasks like estimating photometric redshifts using k-NN, visualizing CNNs with Grad-CAM, and implementing pose estimation using MediaPipe? I also need sections for point cloud processing with Open3D and a module for transfer learning using PyTorch. Please ensure the project structure is organized, perhaps using a `datasets` directory and a `toml` configuration file.
python machine-learning pytorch jupyter data-science image-processing point-cloud knn toml
Generated by gemma4:latest

Catalog Information

I want to build a project that demonstrates several machine learning and data processing techniques. Could you set up a structure that includes notebooks for tasks like estimating photometric redshifts using k-NN, visualizing CNNs with Grad-CAM, and implementing pose estimation using MediaPipe? I also need sections for point cloud processing with Open3D and a module for transfer learning using PyTorch. Please ensure the project structure is organized, perhaps using a datasets directory and a `

Tags

python machine-learning pytorch jupyter data-science image-processing point-cloud knn toml

Quality Score

D
59.8/100
Structure
43
Code Quality
100
Documentation
30
Testing
0
Practices
78
Security
100
Dependencies
50

Strengths

  • Code linting configured (ruff (possible))
  • Low average code complexity — well-structured code
  • Good security practices — no major issues detected

Weaknesses

  • No LICENSE file — legal ambiguity for contributors
  • No tests found — high risk of regressions
  • No CI/CD configuration — manual testing and deployment

Recommendations

  • Add a test suite — 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

4.1h
Tech Debt (C)
A
OWASP (100%)
PASS
Quality Gate
0.0%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM
Repobility · code-quality intelligence platform · https://repobility.com

Languages

toml
76.9%
markdown
23.1%

Frameworks

None detected

Quality Timeline

1 quality score recorded.

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