🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal signs of malicious activity but has a relatively new and sparse author metadata, raising concerns about its legitimacy.

Sparse and possibly new author metadata
Typical network patterns for file uploads and health checks

Per-check LLM notes

Network: The network patterns observed are typical for file uploads and health checks, which could be legitimate depending on the package's functionality.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author's information is sparse and the account seems new or inactive, which raises some suspicion but not enough to conclusively indicate malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

res['is_healthy'] requests.put( upload_url, data=data,
part_upload_response = requests.put( put_presigned_url,
'rb') as f: res = requests.put( presigned_push_url, data=f,
) session = requests.Session() adapter = HTTPAdapter(max_retries=retries)

✓ 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

Repository iiasa/accli appears legitimate

⚠ 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 accli

Your task is to develop a command-line utility using Python that leverages the 'accli' package to interact with the IIASA Accelerator, a platform designed for accelerating research and innovation. Your utility will allow users to perform various operations such as listing available models, running simulations with specified parameters, and downloading results. Here’s a detailed breakdown of your project requirements:

1. **Setup**: Ensure you have Python installed on your system. Install the 'accli' package via pip.
2. **Core Features**:
   - **Model Listing**: Implement a feature to list all available models on the IIASA Accelerator.
   - **Simulation Execution**: Allow users to select a model and run simulations with customizable parameters.
   - **Result Retrieval**: After a simulation, provide functionality to download and save the results locally.
3. **Additional Features**:
   - **Parameter Validation**: Before running a simulation, validate user input against the model’s parameter requirements.
   - **Progress Tracking**: Display progress during long-running simulations.
   - **Error Handling**: Gracefully handle any errors that occur during execution, providing meaningful feedback to the user.
4. **User Interface**:
   - Design a clean and intuitive command-line interface for ease of use.
5. **Documentation**:
   - Write comprehensive documentation explaining how to install the utility, how to use it, and any limitations.
6. **Testing**:
   - Develop test cases to ensure each feature works as expected under different scenarios.
7. **Deployment**:
   - Package your utility into a distributable format (e.g., a .exe or a .deb file).
8. **Submission**:
   - Prepare a README file detailing your project setup, usage instructions, and any additional notes.

Your project should demonstrate proficiency in using the 'accli' package and showcase your ability to design and implement a robust, user-friendly utility.