AI Analysis
Final verdict: SUSPICIOUS
The package has minimal risk factors identified in direct code analysis but raises concerns due to its novelty and limited maintainer history.
- Metadata risk score of 6 out of 10
- Lack of maintainer history and repository engagement
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external communication.
- Shell: No shell execution detected, indicating no immediate risk of unauthorized command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
- Metadata: The package is suspicious due to its newness, lack of maintainer history, and minimal repository engagement.
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: georgetown.edu>
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 2.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
Maintainer History
score 6.0
3 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor name is missing or very shortAuthor "" 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 agenticmeter
Create a financial management tool named 'AgentFin' using the Python package 'agenticmeter'. This tool will help developers and businesses track, budget, and optimize their spending on AI agent services from providers like OpenAI and Anthropic. Here’s a detailed plan for building this application: 1. **Setup and Installation**: Start by installing the 'agenticmeter' package along with any necessary dependencies. Ensure that you have access to APIs from at least one of the supported AI service providers. 2. **User Interface**: Develop a simple yet intuitive UI where users can input their API keys securely and manage their agent services. The UI should allow users to add, remove, or update API keys. 3. **Cost Tracking**: Implement real-time cost tracking for each AI agent request made through your application. Utilize 'agenticmeter' to monitor costs based on the type of service used (e.g., token usage). 4. **Budget Alerts**: Set up a feature that allows users to define monthly budgets for their AI agent services. Use 'agenticmeter' to calculate cumulative costs and send alerts when approaching or exceeding the defined budget. 5. **Counterfactual Cost Analysis**: Offer insights into potential cost savings by analyzing different scenarios. For example, if a user switches from one service provider to another, how much could they save? Use 'agenticmeter' to provide these counterfactual cost analyses. 6. **Waste Reduction Suggestions**: Analyze past requests and suggest ways to reduce unnecessary costs. This could include optimizing queries to use fewer tokens or suggesting more efficient models for certain tasks. 7. **Reporting**: Provide comprehensive reports detailing cost breakdowns, budget adherence, and cost-saving opportunities. These reports should be exportable as PDFs or CSV files. 8. **Security Measures**: Ensure all sensitive information, such as API keys, is stored securely. Use best practices for data encryption and secure authentication methods. By following these steps and utilizing the functionalities provided by 'agenticmeter', you'll create a powerful tool that helps users make informed decisions about their AI agent service expenses.