Lots of room to improve. Start with a README and CI.
Offline phishing detection model for websites using a hybrid CNN–LSTM architecture. Operates without internet access, classifying URLs as legitimate or potentially malicious based on learned patterns.
Documentation
82
No CONTRIBUTING.md found (−47 pts base + up to −53 pts more for content).
→ Add a CONTRIBUTING.md telling newcomers how to get involved. Include setup, code style, test, and PR instructions.
README documents how to install the project.
README is present.
Licensed under MIT.
Engineering
9
No tests detected anywhere in the repository.
→ Add automated tests. They prove the code works and give contributors confidence to make changes.
No CI configuration detected in this repository.
→ If your CI lives elsewhere (a private repo that builds this one) or this project is itself a CI/CD tool, mark this check Not Applicable. Otherwise add a GitHub Actions workflow that runs tests on each push. It takes 15 minutes and reassures contributors their changes won't break things.
No linter or formatter config found.
→ Add a linter config such as .eslintrc.json, .prettierrc, ruff.toml, or .golangci.yml to enforce consistent code style.
No issue or PR templates found (−100 pts).
→ Add .github/ISSUE_TEMPLATE/ with bug_report.md and feature_request.md to guide contributors. It dramatically improves issue quality.
Lockfile present (requirements.txt). Installs are reproducible.
Project health
100
Dependency manifest found (requirements.txt).
Repository has a description.
Actively maintained (pushed within the last month).
.gitignore present.
Repository health signals
Activity, community, and responsiveness at scan time
Activity
- —Commits (30d / 90d)
- 0Forks
- 0Releases
Community
- —Community health
- —authors own >50% of commits
- 8Watchers
Responsiveness
- <1hMedian issue response
- —Median PR merge time
- 0Open issues
Repository files13 root entries
- docs
- .gitignoreGood: .gitignore present.
- dataset_phishing.csv
- history.npy
- LICENSEGood: Licensed under MIT.
- my_model.keras
- README.mdGood: README is present.Good: README is well structured with multiple sections.Good: README includes screenshots or visuals. Great for first impressions.Good: README has code examples.Good: README links to a live demo or deployed app.Good: README includes status badges.Good: README documents how to install the project.Good: README documents how to run the project.
- requirements.txtGood: Lockfile present (requirements.txt). Installs are reproducible.Good: Dependency manifest found (requirements.txt).
- URL_Phishing_Detection.ipynb
- X_test.npy
- X_train.npy
- Y_test.npy
- Y_train.npy