architecture-as-memory

v1.0.4 suspicious
4.0
Medium Risk

Living cognitive architecture maps for AI coding assistants.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a moderate risk due to the detection of potential shell execution, which can lead to arbitrary command execution. However, with no network calls, obfuscation, or credential risks, the immediate threat level is reduced.

  • shell risk detected
  • potential for arbitrary command execution
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external communication.
  • Shell: Detection of shell execution suggests potential for executing arbitrary commands, which could be risky if not properly controlled.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package, which may indicate a new or less active account, but there are no other red flags.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Brief PyPI description (711 chars)
○ 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 score 2.0

Found 1 shell execution pattern(s)

  • try: result = subprocess.run(cmd) sys.exit(result.returncode) except Keyboard
✓ 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 2.0

1 maintainer concern(s) found

  • Author "Naresh B A" 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 architecture-as-memory 
Create a personalized learning assistant application using the 'architecture-as-memory' package. This application will serve as a cognitive tutor for users, providing tailored learning paths based on their interaction history and current knowledge level. The app will utilize the package's ability to maintain and update cognitive architectures in real-time, ensuring that the learning experience is dynamic and adaptive.

Step 1: Set up the application environment by installing necessary libraries including 'architecture-as-memory'.

Step 2: Define the initial user profile structure which includes basic information such as age, educational background, and specific areas of interest.

Step 3: Implement a system for tracking user interactions with the application. This could include questions asked, topics explored, and time spent on different activities.

Step 4: Utilize 'architecture-as-memory' to map out the user's cognitive landscape based on their interaction data. This involves creating a cognitive architecture that reflects the user's understanding of various topics.

Step 5: Develop algorithms within the application that can analyze the cognitive architecture and suggest new learning materials or exercises that target areas where the user may have gaps in understanding.

Step 6: Integrate feedback mechanisms into the application so that it can continuously refine its suggestions based on the user's progress and engagement levels.

Suggested Features:
- Personalized content recommendations based on the user's cognitive map.
- Adaptive difficulty settings for exercises and quizzes.
- Progress tracking over time with visual representations of improvement.
- Integration with external educational resources like videos, articles, and interactive tools.

The 'architecture-as-memory' package plays a crucial role throughout the development process. It provides the foundational technology for dynamically updating and analyzing the user's cognitive architecture, allowing the application to offer truly personalized learning experiences.

💬 Discussion Feed

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