algovoi-composite-trust-query

v0.1.1 safe
4.0
Medium Risk

AlgoVoi composite trust query response format reference implementation -- verifier-side categorical conclusion over audit chains of compliance, settlement, cancellation, and refund receipts under urn:x402:canonicalisation:jcs-rfc8785-v1

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across various checks, with no detected network calls, shell executions, or obfuscation. However, the metadata risk score is elevated due to sparse maintainer information and lack of community engagement.

  • No network calls or shell executions detected.
  • Sparse maintainer information 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 executions detected, reducing the likelihood of potential malicious activities.
  • 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 maintainer's author information is sparse, and the repository lacks community engagement.

📦 Package Quality Overall: Medium (5.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_composite_trust_query.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.algovoi.co.uk/composite-trust-query
  • Detailed PyPI description (9541 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 4 type-annotated function signatures (partial)
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 8 commits in chopmob-cloud/algovoi-composite-trust-query
  • 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

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 algovoi-composite-trust-query 
Create a mini-application called 'TrustAuditTool' using Python that leverages the 'algovoi-composite-trust-query' package to provide a user-friendly interface for verifying compliance, settlement, cancellation, and refund transactions. The tool should enable users to input specific transaction IDs and receive a comprehensive report on the trustworthiness of each transaction based on audit chain analysis. Here’s how you can structure the application:

1. **Setup**: Begin by setting up your Python environment and installing the 'algovoi-composite-trust-query' package. Ensure you have all necessary dependencies installed.
2. **User Interface**: Develop a simple command-line interface (CLI) or a basic web interface where users can input transaction IDs.
3. **Transaction Verification**: Utilize the 'algovoi-composite-trust-query' package to process these transaction IDs and generate a report that includes compliance status, settlement status, cancellation status, and refund status.
4. **Report Generation**: Display the results in an easily understandable format, highlighting any issues or flags that might indicate non-compliance or discrepancies.
5. **Enhancements**: Consider adding features such as saving reports to a local file, exporting them in PDF or CSV formats, and allowing users to compare multiple transactions at once.
6. **Security Measures**: Implement basic security measures to protect user inputs and ensure data privacy.

The goal is to create a tool that not only simplifies the complex task of auditing transactions but also provides actionable insights for improving transactional integrity.