🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to low maintainer activity and poor metadata quality. While there are no immediate signs of malicious intent, these factors raise concerns about its reliability and long-term maintenance.

Low maintainer activity
Poor metadata quality

Per-check LLM notes

Network: The network call pattern suggests the package fetches files from a remote repository, which could be legitimate for version control integration or similar purposes.
Shell: No shell execution patterns were detected, indicating minimal risk related to direct command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity.
Metadata: The package shows low maintainer activity and poor metadata quality, raising some suspicion but not definitive signs of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

raw file fetcher _session = requests.Session() def fetch_raw(repo: str, commit: str, filepath: str) -> O

✓ 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 agentbase-cli

Create a Python-based utility called 'TokenTamer' which leverages the 'agentbase-cli' package to optimize the efficiency of code agents in terms of token usage. This utility should be designed to help developers analyze and reduce the token consumption of their code agents, ensuring they remain within budget constraints while maintaining functionality. The project should include the following features:

1. **Code Agent Analysis**: Implement a feature that takes in a Python script as input and analyzes it using the 'agentbase-cli' package to determine the number of tokens used by the code agent.
2. **Optimization Suggestions**: Based on the analysis, provide suggestions for reducing token usage without compromising the code's functionality. This could include refactoring loops, simplifying complex expressions, or suggesting alternative algorithms.
3. **Interactive Mode**: Develop an interactive mode where users can input snippets of code and receive real-time feedback on token usage and optimization tips.
4. **Report Generation**: Allow users to generate detailed reports summarizing the analysis and optimization process, including before-and-after comparisons of token usage.
5. **Integration with Version Control Systems**: Enable TokenTamer to integrate with popular version control systems like Git, allowing developers to track changes in token usage over time.
6. **CLI Interface**: Ensure that TokenTamer provides a user-friendly command-line interface (CLI) for easy interaction.

The 'agentbase-cli' package should be utilized throughout the development process to handle the core functionalities related to analyzing and optimizing code agents' token usage. Your goal is to create a tool that not only helps developers understand the impact of their code on token consumption but also empowers them to write more efficient and cost-effective code.