aiva-agent

v0.3.14 suspicious
4.0
Medium Risk

Clinical-genomics agent: ask natural-language questions over a local VCF and get literature-grounded answers.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has some legitimate functionality but raises concerns due to missing repository information and a short author name, which may indicate low credibility. Further investigation is recommended.

  • missing repository and short author name
  • legitimate network activity
Per-check LLM notes
  • Network: The use of HTTPX and JSON operations suggests the package is performing network requests to external services, which could be legitimate for data retrieval or API interactions.
  • Shell: No shell execution patterns were detected, indicating no immediate risk from command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The missing repository and short author name raise concerns, indicating potential low credibility.

📦 Package Quality Overall: Low (4.8/10)

✦ High Test Suite 9.0

Test suite present — 17 test file(s) found

  • Test runner config found: pyproject.toml
  • 17 test file(s) detected (e.g. test_agent_compose.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (20951 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 219 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • None: self._session = requests.Session() self._session.headers.update({"User-Agent": USER_A
  • httpx,json; print(json.dumps(httpx.get('<url>').json(), indent=2))"` if `jq` isn't installed. Honor
  • try: async with httpx.AsyncClient(timeout=_GNOMAD_HTTP_TIMEOUT) as client: resp =
  • veDB"] = 1 async with httpx.AsyncClient(timeout=HTTP_TIMEOUT) as client: if parsed["form
  • Any]] = [] async with httpx.AsyncClient(timeout=HTTP_TIMEOUT) as client: if ids:
  • variables async with httpx.AsyncClient(timeout=HTTP_TIMEOUT) as client: response = awai
✓ 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: mamidi.ai>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ 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 aiva-agent 
Develop a mini-application named 'GenoQuery' that allows researchers and clinicians to query genomic data stored in a local VCF file using natural language. This application will utilize the 'aiva-agent' Python package to provide scientifically grounded answers based on the queried genomic information. Here’s a detailed plan on how to build this application:

1. **Setup Environment**: Install Python and necessary libraries including 'aiva-agent'. Ensure you have access to a local VCF file containing genomic data.

2. **Application Structure**: Design a simple command-line interface (CLI) or a basic web interface using Flask. The CLI version should accept user input via command line arguments, while the web version should have an HTML form for input.

3. **Integration with AIVA-Agent**: Use 'aiva-agent' to parse the VCF file and answer natural language queries about the genomic data. Implement functions to process user inputs, pass them through the 'aiva-agent', and retrieve answers.

4. **Features**:
   - **Query Handling**: Allow users to ask questions like 'What are the known diseases associated with SNP rs1234567?' or 'Is there any evidence of gene-gene interactions involving BRCA1?'
   - **Response Formatting**: Display responses in a readable format, possibly including relevant citations from scientific literature.
   - **Error Handling**: Provide meaningful error messages if the query is not properly formatted or if there's no matching data in the VCF file.

5. **Testing**: Test your application with various types of queries to ensure it accurately retrieves and presents information from the genomic dataset.

6. **Documentation**: Write comprehensive documentation explaining how to use GenoQuery, including installation instructions, usage examples, and common troubleshooting tips.

7. **Deployment**: For the web version, deploy the application using a service like Heroku or AWS. Ensure that the deployed application can securely handle user queries and protect sensitive genomic data.

This project aims to demonstrate the power of natural language processing in genomics research and clinical practice, making complex genomic data more accessible to non-specialist users.

💬 Discussion Feed

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