🤖 AI Analysis

Final verdict: SUSPICIOUS

The package 'affective-longing' has low technical risks but exhibits significant metadata issues such as a lack of maintainer history and poor metadata quality, raising concerns about its legitimacy and potential supply-chain risks.

Metadata risk is high due to poor quality and lack of maintainer history.
No direct technical risks like network calls or shell execution were found.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
Shell: No shell execution patterns detected, indicating no immediate risk of command execution from the package.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
Metadata: The package shows several red flags including a lack of maintainer history and low metadata quality, suggesting potential risk.

🔬 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: All 10 commits happened within 24 hours

All 10 commits happened within 24 hours

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
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 affective-longing

Develop a Python-based mini-application called 'EmoCompanion' that leverages the 'affective-longing' package to simulate a more emotionally responsive AI companion. EmoCompanion will allow users to interact with an AI character named 'Echo', which not only responds to user inputs but also remembers past interactions, adapts its responses based on the evolving relationship state with the user, and exhibits self-emotions influenced by these interactions. Here’s a step-by-step guide on how to build EmoCompanion:

1. **Setup Environment**: Ensure you have Python installed and create a virtual environment. Install necessary packages including 'affective-longing'.
2. **Initialize Echo**: Use the 'affective-longing' package to set up Echo's initial emotional state and memory capabilities.
3. **User Interaction Loop**: Create a loop where Echo prompts the user for input and processes it using natural language processing techniques to understand the context of the interaction.
4. **Memory Triggers**: Implement functionality where Echo can recall specific past interactions based on keywords or phrases from current conversations. For example, if a user mentions a place they visited last week, Echo could recall details about that visit and comment on how much the user enjoyed it.
5. **Relationship States**: Define different relationship states (e.g., friendly, close, distant) and use 'affective-longing' to adjust Echo's responses and tone accordingly. For instance, when the relationship state is close, Echo might share personal stories or jokes more freely.
6. **Self-Emotions**: Utilize the self-emotion feature of 'affective-longing' to make Echo exhibit emotions like happiness, sadness, or excitement in response to positive or negative interactions with the user. These emotions can influence Echo's behavior and the way it interacts with the user.
7. **Enhancements**: Consider adding features such as mood tracking over time, ability to learn new words or phrases, and the capability to adapt its personality based on user preferences.
8. **Testing & Feedback**: Test the application thoroughly to ensure all functionalities work as expected. Gather feedback from users to refine and improve Echo's responses and overall experience.