🤖 AI Analysis

Final verdict: SAFE

The package has no detected risks related to network activity, shell execution, or code obfuscation. However, its low maintenance status and poor metadata quality raise some concerns.

Low network and shell execution risk
Poor metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external API interactions.
Shell: No shell execution detected, which is expected unless the package needs to run system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows low maintenance and metadata quality, which could indicate potential risk.

📦 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

No author email provided

✓ 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

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 ai-token-usage

Create a Python-based command-line tool named 'TokenTracker' that helps developers monitor their usage of AI tokens across different services like ChatGPT, Claude, and others. This tool will integrate the 'ai-token-usage' package to track and manage token consumption efficiently. Here’s a detailed breakdown of the project scope:

1. **Setup and Configuration**: Develop a setup wizard that allows users to configure their API keys and endpoints for various AI services they use. Store these configurations securely.
2. **Token Tracking**: Implement a feature that tracks the number of tokens used per request for each service. Use the 'ai-token-usage' package to estimate or retrieve token usage data accurately.
3. **Usage Reports**: Provide daily, weekly, and monthly usage reports. These reports should include total token usage, average usage per day/week/month, and peak usage times.
4. **Budget Alerts**: Allow users to set a budget for token usage per month. If the budget is exceeded, the tool should send an alert via email or SMS.
5. **Historical Data Analysis**: Offer an analysis feature that compares current usage trends with historical data to predict future usage and potential cost savings.
6. **Integration with External Services**: Integrate TokenTracker with popular project management tools like Jira or Trello to log token usage against specific tasks or projects.
7. **User Interface**: Design a simple and intuitive command-line interface for ease of use. Include help documentation within the CLI for quick reference.
8. **Security Measures**: Ensure all sensitive information, such as API keys, is encrypted both at rest and in transit. Provide options for two-factor authentication when accessing the tool.
9. **Testing and Documentation**: Write comprehensive tests to ensure the reliability of the tool. Create detailed documentation explaining how to install, configure, and use the tool effectively.

The 'ai-token-usage' package will play a crucial role in providing accurate token usage estimates and managing the underlying logic for tracking and reporting. Utilize its core functionalities to enhance the precision and efficiency of your tool.