AI Analysis
Final verdict: SUSPICIOUS
The package shows signs of potential command execution and has low activity indicators, raising concerns about its legitimacy and security posture.
- Detection of shell execution
- Low activity indicators
Per-check LLM notes
- Network: No network calls detected, which is neutral from a security perspective.
- Shell: Detection of shell execution suggests potential for executing arbitrary commands, which could be a security risk if not properly controlled.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package is new with low activity indicators, which raises some suspicion but does not conclusively indicate malice.
Package Quality Overall: Medium (5.8/10)
✦ High
Test Suite
9.0
Test suite present — 17 test file(s) found
Test runner config found: conftest.pyTest runner config found: conftest.pyTest runner config found: pyproject.toml17 test file(s) detected (e.g. conftest.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (9306 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 project97 type-annotated function signatures detected in source
◈ Medium
Multiple Contributors
6.0
Limited contributor diversity
2 unique contributor(s) across 100 commits in Sandjab/apicolTwo distinct contributors found
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)
l) try: result = subprocess.run(cmd, capture_output=True, text=True, timeout=timeout, check=
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 4.0
2 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor "JP" 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 apicol
Create a versatile chatbot application using the 'apicol' package, which abstracts away the complexities of interacting with various Large Language Models (LLMs). This application will allow users to chat with different LLMs seamlessly without needing to know the specifics of each model's API. Here’s a detailed guide on how to build it: 1. **Setup Project Environment**: Begin by setting up your Python environment. Ensure you have Python installed and create a virtual environment. Install the 'apicol' package and any other necessary dependencies. 2. **Define Core Functionality**: Use 'apicol' to define a function that can switch between different LLM backends (Anthropic, LiteLLM, etc.) based on user input or configuration settings. This function should handle authentication, API calls, and response parsing. 3. **User Interface**: Develop a simple command-line interface (CLI) where users can select an LLM backend and type messages to interact with the selected model. The CLI should display responses from the chosen LLM. 4. **Enhanced Features**: - Implement a feature that allows users to save their conversation history locally or remotely. - Add support for multiple concurrent conversations, each with its own LLM backend. - Introduce a feature to compare responses from different LLMs for the same query. 5. **Testing and Validation**: Write tests to ensure that the application works correctly with all supported LLMs. Test edge cases such as invalid inputs, API failures, and unexpected responses. 6. **Documentation**: Provide clear documentation on how to install the application, use the CLI, and configure it for different LLMs. 7. **Deployment**: Package the application as a standalone executable or Docker container for easy deployment. In this project, the 'apicol' package will be used extensively to abstract away the differences between various LLM APIs, allowing for a more unified and flexible development experience.
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