🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network, shell, obfuscation, and credential harvesting, but the metadata risk is high due to recent repository creation, low activity, and poor metadata quality. This combination raises suspicion but does not definitively indicate malicious intent.

High metadata risk
Low activity and poor metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar attacks.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: High risk due to recent repository creation, low activity, and poor metadata quality.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 10.0

Git history flags: Repository created very recently: 7 day(s) ago (2026-05-29T12:16:12Z)

Repository created very recently: 7 day(s) ago (2026-05-29T12:16:12Z)
Repository has zero stars and zero forks
Single contributor with only 3 commit(s) — possibly throwaway account
All 3 commits happened within 24 hours

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "Tian Li" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with CosmoDJ

Create a Python-based mini-application called 'CosmoLensForecast' which leverages the CosmoDJ package to facilitate cosmological distance calculations specifically tailored for lensing forecast analysis. This application should allow users to input various parameters related to cosmology such as redshift values, angular diameter distances, and comoving distances. Users should be able to perform the following actions:

1. Calculate angular diameter distances between two points in space given their redshifts.
2. Compute comoving distances from the observer to different objects based on their redshift.
3. Estimate the lensing efficiency of a given cosmic structure.
4. Visualize the calculated distances and efficiencies using matplotlib or any other suitable plotting library.
5. Save the results of the calculations and visualizations into a CSV file or a PDF report.

The application should have a user-friendly command-line interface where users can input their data and select which operations they wish to perform. Additionally, include a brief explanation of the physical significance of each calculation performed and how it relates to our understanding of the universe. Use CosmoDJ's functions to handle all cosmological distance computations efficiently and accurately.