🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risk in most categories but requires further scrutiny due to potential shell execution and poor metadata quality.

Potential shell execution needs investigation
Incomplete author information and low metadata quality

Per-check LLM notes

Network: No network calls detected, indicating low risk of data exfiltration or command and control communication.
Shell: Detected shell execution may be for legitimate purposes like handling user input or configuration but requires further investigation to confirm benign intent.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: Low risk but requires attention due to incomplete author information and low metadata quality.

📦 Package Quality Overall: Low (3.8/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

○ Low Documentation 1.0

No documentation detected

No documentation URL, doc files, or meaningful description found

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

403 type-annotated function signatures detected in source

✦ High Multiple Contributors 10.0

Active multi-contributor project

20 unique contributor(s) across 100 commits in annetutil/annet
Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

_, term_columns_str = os.popen("stty size", "r").read().split() term_co
t_path(touch=True) proc = subprocess.Popen([editor, path]) proc.wait() @subcommand(parent=context

✓ 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

Repository annetutil/annet appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" 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 annet

Create a social networking analysis tool using the Python package 'annet'. This tool will allow users to input a dataset representing connections between individuals (e.g., friendships, professional relationships) and analyze these networks to discover key influencers, community structures, and potential areas of improvement within the network. Here are the steps and features your project should include:

1. **Data Input**: Design a user-friendly interface where users can upload their data in CSV format. The data should contain at least two columns: 'from_node' and 'to_node', representing connections between nodes.
2. **Network Visualization**: Use 'annet' to visualize the network graphically. Highlight clusters and key nodes visually to make the network structure more understandable.
3. **Key Influencers Identification**: Implement functionality to identify key influencers based on centrality measures (e.g., degree centrality, betweenness centrality). These influencers are crucial for spreading information within the network.
4. **Community Detection**: Utilize 'annet' to detect communities within the network. Explain to the user how different communities are identified and visualized.
5. **Analysis Reports**: Generate detailed reports summarizing the findings from the network analysis, including visualizations and statistical metrics. Allow users to download these reports as PDF files.
6. **Interactive Exploration**: Enable users to interactively explore the network by hovering over nodes to see additional details about them, such as their degree and centrality scores.
7. **Customization Options**: Provide options for users to customize their analysis, such as selecting which centrality measures to use and choosing the layout of the network visualization.
8. **User Documentation**: Include comprehensive documentation explaining how to use the tool effectively and how 'annet' functions contribute to the analysis process.

Ensure your application is intuitive and accessible, making complex network analysis approachable for non-experts. Emphasize the use of 'annet' for its capabilities in handling and analyzing network data efficiently.