naturomics/CapsNet-Tensorflow
A Tensorflow implementation of CapsNet(Capsules Net) in paper Dynamic Routing Between Capsules
- Repository: https://github.com/naturomics/CapsNet-Tensorflow
- Commit:
5b464caab361ec402c1b17acb9bc2680e5fbb7de - Generated: 2026-11-13T23:22:24+00:00
- Scorer 0.1.0, scoring prov-4+qual-2+sec-2, report schema v2
Scores
| Pillar | Score | Signal | Confidence | Coverage |
|---|---|---|---|---|
| AI provenance (estimated AI-generated share) | 0 | none-detected | 65% | 100% |
| Code quality | 45 | poor | 90% | 100% |
| Security | 79 | adequate | 85% | 100% |
AI provenance
No AI attribution or AI-consistent velocity signals detected.
- Explicitly attributed share (lower bound): 0.0%
- Unattributed share in AI-consistent velocity bursts: 0.0%
- Estimated AI-generated share: 0.0%
| Month | Lines added | Attributed to AI |
|---|---|---|
| 2017-10 | 638 | 0% |
| 2017-11 | 1,572 | 0% |
| 2017-12 | 269 | 0% |
| 2018-01 | 1 | 0% |
| 2018-02 | 50 | 0% |
| 2018-03 | 1 | 0% |
| 2018-09 | 62 | 0% |
AI provenance describes how code was produced; it is not a quality judgment. Unless attribution is explicit, these are signals consistent with AI generation, not proof. Squash/rebase workflows, imported code, and rewritten history reduce accuracy.
Code quality
Code quality is poor: test-to-source ratio 0.00, no CI test run detected; 0% of function code in complex or long functions; 6% duplicated lines. Weakest area: lint.
| Area | Score |
|---|---|
| Complexity (lizard) | 100 |
| Duplication | 68 |
| Code smells (Opengrep) | 0 |
| Tests and CI | 0 |
| Documentation | 80 |
| Type safety | 40 |
| Process (Scorecard review/maintenance) | n/a |
- Functions: 30; mean complexity 2.9, 90th percentile 6; 0 complex and 0 long functions
- Duplicated lines: 6.5% of 712 compared
- Tests: 0 test lines for 983 source lines (ratio 0.00); CI test commands: none detected; 0% of recent source commits also change tests
- Docs missing or thin: changelog
Security
Security posture is adequate: no high-impact findings. Weakest area: posture.
| Area | Score |
|---|---|
| Secrets (gitleaks, tree + history) | 100 |
| Static analysis (Opengrep) | 92 |
| Vulnerable dependencies (OSV) | n/a |
| CI workflows (zizmor) | n/a |
| Posture (policy, updates, signing, lockfiles) | 0 |
| OpenSSF Scorecard | n/a |
- Posture checks passed: lockfiles; missing: security policy, dependency updates, signed commits
Top findings
| Severity | Finding | Location |
|---|---|---|
| medium | opengrep/python.lang.security.audit.dynamic-urllib-use-detected: Detected a dynamic value being used with urllib. urllib supports 'file://' schemes, so a dynamic value controlled by a malicious actor may allow them to read arbitrary files. Audit uses of urllib calls to ensure user data cannot control the URLs, or consider using the 'requests' library instead. [confidence low] |
download_data.py:42 |
Static signals only: the scorer never runs the project's code. Secret findings are pattern matches that were not verified, and secret values are never stored.
Distribution signals
- readme-download-lure: gives an archive password
Signals consistent with a repository that exists to distribute a download. They are heuristics, not a verdict; release files were not downloaded or inspected.
Repository profile
- Source lines: 983; test lines: 0
| Language | Lines |
|---|---|
| Python | 983 |