AI Analysis
Final verdict: SUSPICIOUS
The package has minimal direct risks but raises concerns due to its newness and lack of metadata details, including no GitHub link.
- Brand new package
- Low metadata quality
- No GitHub link provided
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external communications.
- Shell: No shell executions detected, indicating no immediate risk from command execution vulnerabilities.
- 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 package shows some red flags such as being brand new, having no GitHub link, and low metadata quality, but there's no clear evidence of malicious intent.
Package Quality Overall: Low (1.2/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
○ Low
Documentation
1.0
No documentation detected
No documentation URL, doc files, or meaningful description found
○ 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
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
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 6.0
3 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor "Charles Harvey" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with archolith
Develop a Python-based mini-application named 'ContextCompressor' which leverages the 'archolith' package to manage and compress context for large language models (LLMs). This application should serve as a powerful tool for researchers and developers who work extensively with LLMs, aiming to optimize the performance of these models by efficiently managing their input context. Here’s a detailed outline of the application's functionality and features: 1. **User Input Interface**: Create a simple command-line interface (CLI) where users can input text or upload text files. This input will serve as the context that needs to be processed. 2. **Context Compression**: Utilize 'archolith' to analyze and compress the user-provided context. The goal is to reduce the size of the input while retaining its essential meaning and information relevant to the context. 3. **Knowledge Graph Generation**: After compression, generate a knowledge graph from the compressed context using 'archolith'. This graph should visually represent the key entities and relationships within the context, aiding in better understanding and management of the data. 4. **Output Presentation**: Provide the user with both the compressed text and the visual representation of the knowledge graph. Additionally, offer an option to save these outputs as files for further use. 5. **Performance Metrics**: Include basic performance metrics such as the reduction in text size and the time taken for processing, to help users understand the efficiency of the application. 6. **Customization Options**: Allow users to customize certain parameters related to the compression process and knowledge graph generation, such as the level of detail in the graph or the degree of compression desired. 7. **Integration with LLMs**: Demonstrate how the compressed context and generated knowledge graph can be directly integrated into an existing LLM setup, showcasing the improvement in model performance when using the optimized context. By completing this project, you will have developed a versatile tool that not only simplifies the handling of large textual inputs but also enhances the capabilities of LLMs through efficient context management.
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