🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risk in terms of network, shell, and obfuscation activities but has a moderate metadata risk due to the maintainer's inactivity and lack of community engagement.

Low risk in network, shell, and obfuscation activities.
Moderate risk due to maintainer inactivity and low community engagement.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
Shell: No shell execution patterns detected, indicating no immediate risk of command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
Metadata: The maintainer seems new or inactive, and the repository lacks community engagement.

📦 Package Quality Overall: Low (4.6/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

3 test file(s) detected (e.g. test_notebook.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (7411 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 5.0

Partial type annotation coverage

31 type-annotated function signatures detected in source

○ Low Multiple Contributors 2.0

Single-author or unverifiable project

1 unique contributor(s) across 17 commits in freesemt/ai-context-tools
Single author with few commits — possibly a personal or throwaway project

🔬 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

No author email provided

✓ 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 2.0

1 maintainer concern(s) found

Author "freesemt" 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 ai-context-tools

Create a mini-application called 'ContextualQueryTool' that leverages the 'ai-context-tools' Python package to enhance the user experience when querying large datasets with complex context requirements. This tool will enable users to input specific queries related to their dataset and receive contextual responses based on predefined rules and conditions set within the application.

Step 1: Set up the project environment by installing necessary packages including 'ai-context-tools'. Ensure that your setup supports Python 3.8 or higher.

Step 2: Design a simple yet intuitive user interface where users can enter their query and select from a list of pre-defined contexts (e.g., 'Financial Analysis', 'Medical Research', 'Environmental Studies'). Use the 'ai-context-tools' package to manage these contexts efficiently.

Step 3: Implement a backend functionality using 'ai-context-tools' to process each query according to the selected context. For example, if the context is 'Financial Analysis', the tool should apply financial-specific rules and filters to the data before presenting results.

Step 4: Integrate an example dataset into your application for demonstration purposes. This dataset should contain diverse types of data relevant to multiple contexts to showcase the flexibility of 'ai-context-tools'.

Step 5: Enhance the application by adding features such as saving previous queries with their respective contexts for future reference, allowing users to export results in various formats (CSV, PDF), and providing real-time feedback on query execution status.

How 'ai-context-tools' is utilized:
- To define and manage different contexts effectively, utilizing its core features for setting up context-aware workflows.
- To process incoming queries through a context-aware pipeline, ensuring that the response is tailored precisely to the user's needs based on the selected context.
- To maintain efficiency and scalability in handling complex queries and large datasets by leveraging the optimized functionalities provided by 'ai-context-tools'.