analysis-lcm

v0.1.4 safe
3.0
Low Risk

Analysis tools for LCM logs.

🤖 AI Analysis

Final verdict: SAFE

The package shows very low risk indicators with no network calls, shell executions, obfuscations, or credential harvesting attempts. The metadata risk is slightly elevated due to the maintainer having only one package, but there are no other red flags.

  • No network calls detected.
  • No shell execution patterns detected.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communications.
  • Shell: No shell execution patterns detected, indicating no immediate signs of malicious activities.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other red flags are present.

📦 Package Quality Overall: Low (1.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 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: is4s.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "IS4S" 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 analysis-lcm 
Create a fully-functional mini-application called 'LCM Log Analyzer' that leverages the 'analysis-lcm' Python package to analyze logs from LCM systems. This application should provide users with an intuitive interface for uploading LCM log files and offer detailed insights into the performance metrics contained within these logs. The primary goal of this project is to streamline the process of understanding complex LCM data, making it accessible and actionable for both technical and non-technical users.

**Steps to Build the Application:**
1. **Setup Project Environment**: Initialize a new Python project and install the 'analysis-lcm' package along with other necessary libraries such as Pandas for data manipulation, Matplotlib for plotting graphs, and Flask for creating a web interface.
2. **Data Upload Functionality**: Implement a feature that allows users to upload LCM log files directly through a web interface. Ensure that the application supports common log file formats such as CSV and JSON.
3. **Log Parsing and Analysis**: Use the 'analysis-lcm' package to parse the uploaded logs and extract key performance indicators (KPIs). These KPIs could include but are not limited to system uptime, error rates, response times, and throughput.
4. **Visualization Tools**: Develop visualizations using Matplotlib or similar libraries to display the extracted KPIs in an easily understandable format. Consider implementing interactive elements like sliders or dropdown menus to allow users to filter and explore different aspects of the data.
5. **Reporting and Export**: Allow users to generate reports based on their analysis and export these reports in formats such as PDF or Excel.
6. **User Interface Design**: Focus on designing a user-friendly interface with clear navigation and informative tooltips to assist users in interpreting the data.
7. **Testing and Validation**: Conduct thorough testing of all features to ensure reliability and accuracy. Validate the application’s performance using sample LCM log files provided by the 'analysis-lcm' package documentation.
8. **Deployment**: Once developed and tested, deploy the application on a server so that it can be accessed via a web browser.

**Suggested Features**:
- Real-time data updates as logs are parsed.
- Customizable alerts based on specific KPI thresholds.
- Historical data comparison to track performance over time.
- Integration with external systems for automated log collection.
- Detailed documentation and user guides for easy onboarding.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!