anybody

v0.0.0 suspicious
3.0
Low Risk

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🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks across most categories, but its newness and lack of maintenance effort raise concerns about potential supply-chain attacks.

  • Low metadata maintenance
  • No provided description
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network interaction for its functionality.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command injection or unauthorized system access.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package appears to be new with low maintenance effort, raising some suspicion but not definitive evidence of malice.

📦 Package Quality Overall: Low (1.2/10)

○ 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
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 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

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Andrea Boscolo Camiletto" 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 anybody 
Create a Python-based mini-app called 'AnybodyFinder' which leverages the 'anybody' package to identify and interact with any person within a specified dataset or network. The app should allow users to input a set of criteria (e.g., age range, location, interests), and then search through a provided dataset to find individuals who match these criteria. Additionally, the app should be able to simulate interactions with these individuals based on their profile information. Here are the steps and features you need to implement:

1. **Setup Environment**: Ensure the Python environment is properly set up with the 'anybody' package installed.
2. **Data Input**: Develop a feature where users can upload a CSV file containing profiles of people (with fields like name, age, location, interests).
3. **Criteria Selection**: Allow users to select from a predefined list of criteria to filter the dataset.
4. **Search Functionality**: Implement a search function that filters the dataset according to the selected criteria and returns matching profiles.
5. **Interaction Simulation**: Use the 'anybody' package to simulate interactions with the found individuals, such as sending a greeting message based on their interests.
6. **Output Display**: Present the results in a user-friendly format, showing matched profiles and simulated interaction outcomes.
7. **User Interface**: Optionally, design a simple GUI using Tkinter or another Python GUI framework to make the app more accessible.

The 'anybody' package will be primarily used for simulating interactions with the individuals identified in the dataset, ensuring that the app can engage with each profile in a personalized manner based on their attributes.

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