🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some concerning aspects such as missing metadata and potentially unnecessary network calls, raising suspicion but lacking definitive evidence of malicious intent.

missing repository link
low-effort metadata

Per-check LLM notes

Network: The network call patterns indicate the package is making HTTP requests, which could be legitimate if it's designed to fetch data from external sources.
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 missing repository and low-effort metadata suggest potential risk, but without clear malicious indicators, the risk is moderate.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

_id_cache"): with httpx.Client(timeout=10) as client: resp = client.get(f"{
"sdk", } with httpx.Client(timeout=self.timeout) as client: with client.str
ts/{result_key}" with httpx.Client(timeout=60) as client: resp = client.get(url, he
t"] = "parquet" with httpx.Client(timeout=60) as client: resp = client.get(url, he
result_key}" with httpx.Client(timeout=60) as client: resp = client.get(url

✓ 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 score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" 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 agenticearth

Create a mini-application named 'GeoQueryBot' that allows users to interactively query and visualize geospatial data using natural language inputs. GeoQueryBot should leverage the 'agenticearth' package to fetch over 150 live geospatial datasets. Here’s a step-by-step guide on how to develop the application:

1. **Setup**: Install the necessary Python packages including 'agenticearth', 'matplotlib' for plotting, and 'flask' for web serving.
2. **User Interface**: Develop a simple web interface using Flask where users can input their queries in natural language. The UI should include fields for entering the query and options to select the type of visualization (e.g., map, chart).
3. **Backend Logic**: Implement the backend logic using the 'agenticearth' package to interpret the user's query and retrieve the relevant geospatial dataset(s). Ensure the backend supports error handling and retries for failed requests.
4. **Data Processing**: Once the data is fetched, process it to ensure it is suitable for visualization. This may involve cleaning, filtering, or transforming the data based on the user's query.
5. **Visualization**: Use 'matplotlib' to generate visualizations of the geospatial data according to the user's preferences. For example, if the user asks about population density, the app should display a heat map or choropleth map.
6. **Interactive Features**: Enhance the application by adding interactive features such as zooming into specific regions on the map, hovering over points to get more information, and exporting visualizations as images.
7. **Testing & Validation**: Test the application thoroughly to ensure it handles various types of queries correctly and efficiently. Validate the results against known datasets or manually verified data.
8. **Documentation**: Provide clear documentation on how to install and use the application, including examples of supported queries and expected outputs.

Suggested Features:
- Support for multiple languages for natural language processing.
- Integration with other geospatial tools like OpenStreetMap for enhanced mapping capabilities.
- Option to save and share visualizations directly from the application.
- Detailed logging and analytics to track usage patterns and improve the system.

By following these steps and incorporating the suggested features, you will create a powerful and user-friendly tool for exploring and understanding geospatial data through natural language queries.