🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risk across multiple categories including network, shell, obfuscation, and credential risks. The primary concern lies with the metadata risk due to the maintainer's new or inactive account.

Low risk scores in network, shell, obfuscation, and credential checks.
Metadata risk due to the maintainer's account status.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package relies on external services.
Shell: The shell execution appears to be safe as the arguments are constants and not derived from user input.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has a new or inactive account and lacks author information, which raises some concern but does not strongly indicate malicious intent.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

] try: result = subprocess.run( # noqa: S603 — args are constants, not user input

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository frederick-douglas-pearce/agentfluent appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" 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 agentfluent

Create a Python-based personal productivity assistant named 'PromptPal' that leverages the 'agentfluent' library to track and analyze user interactions with its prompts. PromptPal should help users manage their daily tasks and goals by offering customized suggestions based on their interaction history. Here’s a step-by-step guide to building PromptPal:

1. **Setup**: Begin by installing the necessary packages including 'agentfluent'. Use virtual environments for better isolation.
2. **Core Functionality**: Develop the basic functionality where users can input their tasks and set reminders. Each task should have a unique ID for tracking purposes.
3. **Analytics Engine**: Implement a system using 'agentfluent' to record every interaction with these prompts. This includes when a user views a task, sets a reminder, or completes a task. 'agentfluent' will store this data locally, ensuring privacy.
4. **Diagnostics**: Utilize 'agentfluent's prompt diagnostics feature to identify which types of prompts are most effective. For example, determine if a morning reminder is more effective than an evening one.
5. **Customization**: Allow users to customize their experience by setting preferences such as preferred times for reminders or types of tasks they handle best at certain times of the day.
6. **Reporting**: Create a simple reporting mechanism that summarizes user interaction patterns over time, highlighting trends and areas for improvement in task management.
7. **Enhancements**: Consider adding features like integration with calendar apps, voice command support, or mood tracking to correlate task completion rates with user moods.

By following these steps, you'll create a fully-functional mini-app that not only helps users stay organized but also provides insights into their productivity habits through advanced analytics.