🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to its network and shell execution capabilities, raising concerns about potential unauthorized data transmission and code execution.

network risk 7/10
shell risk 8/10

Per-check LLM notes

Network: The network calls may indicate the package is designed to communicate with external servers, which could be for legitimate purposes but also raises concerns about potential unauthorized data transmission.
Shell: The use of subprocess.run suggests the package executes shell commands, which can be risky if not properly sanitized or intended for malicious actions like executing arbitrary code on the user's system.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author's information is sparse, indicating potential lack of transparency or newness to the platform.

📦 Package Quality Overall: Low (4.6/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://github.com/Sapientropic/AIppocampus/tree/main/docs
Detailed PyPI description (18958 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 7.0

Partial type annotation coverage

Type checker (mypy / pyright / pytype) referenced in project
734 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 6.0

Limited contributor diversity

2 unique contributor(s) across 100 commits in Sapientropic/AIppocampus
Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

"] = config.user_id req = urllib.request.Request( url, data=json.dumps(body, ensure_a
, ) try: with urllib.request.urlopen(req, timeout=config.timeout) as resp: re

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

tr(anchors), ] proc = subprocess.run( cmd, text=True, encoding="utf-8", errors="replace",
e == "nt": proc = subprocess.run( ["powershell", "-NoProfile", "-ExecutionPol
else: proc = subprocess.run( command, input=stdin_text,
-> dict[str, Any]: proc = subprocess.run( cmd, text=True, encoding="utf-8", errors="replace",
Any]: try: proc = subprocess.run( cmd, text=True, encodin
list[str]) -> str: proc = subprocess.run( cmd, text=True, encoding="utf-8", errors="replace",

✓ 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

Repository Sapientropic/AIppocampus 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 aippocampus

Create a Python-based mini-application named 'AI Tutor Companion' which integrates the 'aippocampus' package to enhance the learning experience of students through personalized AI tutoring sessions. This application will serve as a continuity layer for long-running AI agent relationships, ensuring that each session is seamlessly connected, allowing the AI tutor to remember previous interactions and provide more tailored advice and feedback. The app should include the following features:

1. User Registration and Login: Allow users to register and log in using their email or social media accounts.
2. Personalized Tutoring Sessions: Users can start a new tutoring session where they can ask questions on various topics such as mathematics, science, or literature. The AI tutor should be able to recall previous sessions and provide contextually relevant responses.
3. Progress Tracking: Track the progress of each user over time, including topics covered, areas of improvement, and strengths. This information should be stored and accessible within the 'aippocampus' continuity layer.
4. Customizable Learning Paths: Based on user performance and preferences, the AI tutor should suggest customized learning paths that focus on specific areas of improvement.
5. Interactive Feedback System: Implement an interactive feedback system where users can rate the quality of the tutoring session and provide suggestions for improvement. This feedback should help refine future interactions.
6. Integration with External Resources: Allow the AI tutor to recommend external resources such as videos, articles, and practice problems based on the current topic of discussion.

To utilize the 'aippocampus' package, you will need to implement a source-backed continuity layer that allows the AI tutor to maintain context across multiple sessions. This involves storing session data in a structured format and retrieving it when necessary. Additionally, the package should support features like versioning, so that older sessions can be revisited if needed. Ensure that the application is designed with scalability in mind, allowing for the addition of new topics and features without disrupting existing functionality.