🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant network and shell execution risks, raising concerns about its safety and potential for misuse.

High network risk due to suspicious network connections
High shell risk from use of subprocess.run for command execution

Per-check LLM notes

Network: The presence of network connection attempts is suspicious and may indicate unauthorized data transfer or command and control activity.
Shell: Executing commands via subprocess.run can be used to perform arbitrary actions on the system, which is highly risky if not properly controlled and documented.
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 repository's low engagement and lack of maintainer information suggest potential risks, but there are no clear signs of typosquatting or malicious intent.

📦 Package Quality Overall: Medium (5.6/10)

✦ High Test Suite 9.0

Test suite present — 10 test file(s) found

10 test file(s) detected (e.g. test_config.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (20386 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ Medium Type Annotations 5.0

Partial type annotation coverage

88 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 5.0

Limited contributor diversity

1 unique contributor(s) across 30 commits in SurajKGoyal/amnesic
Single author but highly active (30 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

achable try: with socket.create_connection((conn.server, conn.port), timeout=2): return #

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

ipt try: result = subprocess.run( cmd, capture_output=True,

✓ 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: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ 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 amnesic

Create a mini-application called 'MemoryMender' that leverages the 'amnesic' package to enhance the semantic memory of a SQL database. This application will serve as a tool for users to annotate and query their database records with natural language descriptions, making it easier to retrieve information based on context rather than just raw data.

Step 1: Set up the environment
- Install Python and necessary libraries including 'amnesic', 'sqlalchemy' for database connectivity, and 'flask' for the web interface.
- Configure a local SQLite database or connect to an existing SQL database.

Step 2: Design the Database Schema
- Define the schema for the main table(s) where data will be stored.
- Create additional tables if needed to store annotations and metadata.

Step 3: Implement Semantic Annotation
- Use 'amnesic' to enable users to add semantic tags or natural language descriptions to each record.
- Ensure that these annotations are searchable and can be linked back to specific database entries.

Step 4: Develop Query Capabilities
- Integrate search functionality that allows users to query the database using natural language.
- Utilize 'amnesic' to interpret user queries and return relevant results based on both data content and associated annotations.

Step 5: Build the Web Interface
- Use Flask to create a simple web application that allows users to view, edit, and annotate database records.
- Provide forms for adding new records, editing existing ones, and searching the database using natural language queries.

Suggested Features:
- User authentication to secure access to the database.
- Real-time updates when new annotations are added or existing ones modified.
- Visualization tools to help users understand the relationships between annotated data points.
- Export capabilities to download annotated datasets in various formats such as CSV or JSON.

How 'amnesic' is utilized:
- 'amnesic' will be used to manage and interpret the semantic annotations attached to each database record. It will handle the storage, retrieval, and querying of these annotations to provide a richer, more contextual search experience.