AI Analysis
Final verdict: SUSPICIOUS
The package shows minimal risk in terms of network and shell activities, and there is no evidence of obfuscation or credential harvesting. However, the maintainer's single package raises a slight concern about potential supply-chain risks.
- Metadata risk due to the maintainer having only one package
- Otherwise low-risk indicators across network, shell, obfuscation, and credential fronts
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access to function.
- Shell: No shell execution patterns detected, indicating no direct system command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, which could indicate a new or less active account, raising some suspicion but not enough to conclusively determine malintent.
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
Repository zydo/agent-readable appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "zydo and agent-readable contributors" appears to have only 1 package on PyPI (new or inactive account)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with agent-readable
Create a mini-application called 'DocGen' that leverages the 'agent-readable' Python package to generate and manage human-readable documentation for AI agents. The application should allow users to input metadata about their AI agents, such as capabilities, limitations, and communication protocols, and then automatically generate comprehensive documentation that is easy for both humans and other agents to understand. Key Features: 1. User Interface: Develop a simple, intuitive UI where users can input details about their AI agents. 2. Documentation Generation: Use 'agent-readable' to format and structure the provided information into well-organized, readable documents. 3. Customization Options: Allow users to choose different styles or formats for their documentation (e.g., Markdown, HTML). 4. Version Control: Implement basic version control so users can track changes and revert if necessary. 5. Export Functionality: Enable users to export their generated documentation in various formats (PDF, DOCX, etc.). 6. Integration Capabilities: Provide integration points for common CI/CD pipelines to automate documentation updates. How to Utilize 'agent-readable': - Import the 'agent-readable' package at the start of your Python script. - Define functions that utilize 'agent-readable' methods to parse and format the user-provided data into structured documentation. - Use 'agent-readable' to handle the conversion of raw data into different output formats based on user preferences. - Leverage 'agent-readable' features for maintaining consistency and clarity across all generated documents.