AI Analysis
Final verdict: SAFE
The package has minimal risk indicators with no network calls, shell executions, or obfuscation techniques detected. The metadata risk is slightly elevated due to sparse author details, but there are no clear signs of malicious intent.
- No network calls
- No shell executions
- Sparse author details
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require internet access.
- Shell: No shell executions detected, indicating the package likely does not execute external commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The author's details are sparse, indicating potential lack of transparency, but no clear signs of malicious intent.
Package Quality Overall: Medium (5.8/10)
✦ High
Test Suite
9.0
Test suite present — 26 test file(s) found
Test runner config found: pyproject.toml26 test file(s) detected (e.g. test_batch.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (5164 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
65 type-annotated function signatures detected in source
✦ High
Multiple Contributors
8.0
Active multi-contributor project
4 unique contributor(s) across 24 commits in Arcanada-one/output-guardSmall but multi-author team (3–4 contributors)
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
Email domain looks legitimate: arcanada.one>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository Arcanada-one/output-guard appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 arcanada-output-guard
Create a command-line utility called 'OutputGuardian' that leverages the 'arcananda-output-guard' package to ensure the integrity of structured outputs from various Large Language Models (LLMs). This utility should be capable of validating, repairing, and retrying the retrieval of structured data formats such as JSON, YAML, TOML, and Python literals. Here’s a detailed breakdown of the project requirements: 1. **Setup**: Begin by installing the 'arcananda-output-guard' package along with any necessary dependencies for handling different data formats. 2. **Input Handling**: Design a user-friendly interface where users can input their structured data queries or directly feed in unstructured text responses from LLMs. 3. **Validation**: Implement a validation mechanism using 'arcananda-output-guard' to check if the retrieved output matches the expected structure (e.g., JSON format). 4. **Repair Mechanism**: If the output fails validation, use the repair functionality provided by 'arcananda-output-guard' to attempt fixing common issues such as syntax errors or missing fields. 5. **Retry Logic**: Integrate a retry logic that allows the utility to automatically retry fetching the correct structured output from the LLM if the initial response is invalid. 6. **Output Display**: Once the output is successfully validated or repaired, display it in its intended format (e.g., pretty-printed JSON). 7. **Logging**: Include logging capabilities to keep track of validation attempts, repairs made, and successful outputs. 8. **Customization Options**: Allow users to customize the retry count, time intervals between retries, and specific error handling strategies. 9. **Documentation**: Provide comprehensive documentation detailing how to install 'OutputGuardian', how to use it with different LLMs, and examples of typical use cases. This project aims to streamline the process of working with structured data outputs from LLMs, making it easier for developers and researchers to focus on leveraging these models without worrying about the intricacies of data format handling.
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