AI Analysis
The package exhibits minimal risks across all categories except for metadata, where the maintainer's profile is incomplete and potentially inactive, raising minor concerns.
- Low network and shell execution risk
- No obfuscation or credential harvesting detected
- Incomplete maintainer profile
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external communications.
- Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has an incomplete profile and appears to be new or inactive, which raises some concerns but not enough to conclusively indicate malicious intent.
Package Quality Overall: Medium (5.0/10)
Test suite present — 12 test file(s) found
Test runner config found: pyproject.toml12 test file(s) detected (e.g. test_audit.py)
No documentation detected
No documentation URL, doc files, or meaningful description found
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
170 type-annotated function signatures detected in source
Active multi-contributor project
3 unique contributor(s) across 100 commits in anip-protocol/anipSmall but multi-author team (3–4 contributors)
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: anip.dev>
All external links appear legitimate
Repository anip-protocol/anip appears legitimate
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a decentralized document management system using the 'anip-server' Python package. This system should allow users to upload documents securely and track changes over time while maintaining transparency through an audit trail. The application will leverage the core functionalities of 'anip-server', including delegation, audit, checkpoints, and Merkle trees. ### Features: 1. **User Authentication**: Implement a simple user authentication mechanism to ensure only authorized users can upload and modify documents. 2. **Document Upload**: Users should be able to upload various types of documents (e.g., PDFs, Word documents). 3. **Version Control**: Every change made to a document should create a new version, allowing users to revert to previous versions if necessary. 4. **Audit Trail**: Maintain a detailed log of all changes made to each document, including who made the change and when. 5. **Checkpoints**: Automatically save checkpoints at regular intervals to prevent data loss. 6. **Merkle Trees**: Utilize Merkle trees to verify the integrity of uploaded documents and ensure they haven't been tampered with. 7. **Delegation**: Allow users to delegate certain permissions to other users, such as read-only access or full editing rights. ### How 'anip-server' is Utilized: - **Delegation**: Use 'anip-server' to manage user permissions and delegate roles within the document management system. - **Audit**: Leverage the audit capabilities of 'anip-server' to maintain logs of all actions taken on documents. - **Checkpoints**: Implement checkpoints using 'anip-server' to automatically save states of documents at specified intervals. - **Merkle Trees**: Employ Merkle trees provided by 'anip-server' to ensure the integrity and authenticity of documents stored in the system.