gaudit 0.3.0

Library and CLI for GLIMPS Audit API

GLIMPS Audit Client Library and CLI

A comprehensive Python client library and command-line interface for interacting with the GLIMPS Audit API v2.0.4. This tool enables seamless integration with GLIMPS's binary analysis platform for software composition analysis and vulnerability detection.

Table of Contents

• Features
• Requirements
• Installation
• Quick Start
• Configuration
• Usage Guide
  • CLI Usage
  • Python Library Usage
• API Reference
• Examples
• Testing
• Development
• Troubleshooting
• Contributing
• License
• Support

Features

Core Capabilities

• Complete API Coverage: Full implementation of GLIMPS Audit API v2.0.4
• Dual Interface: Both programmatic (Python library) and command-line access
• Binary Analysis: Submit executables (PE/ELF) for comprehensive analysis
• Library Detection: Identify third-party libraries and their versions
• Dataset Management: Create, populate, and manage custom reference datasets with file upload support
• Vulnerability Correlation: Match binaries against known vulnerable components
• Flexible File Handling: Support for both pre-uploaded files and automatic upload workflows

Technical Features

• Cross-Platform Support: Windows, macOS, and Linux compatibility
• Secure Authentication: JWT-based authentication with automatic token refresh
• Comprehensive Testing: 80%+ code coverage with unit and integration tests
• Type Hints: Full type annotation support for better IDE integration
• Async Support: Efficient handling of long-running operations
• Configurable: Environment variables and configuration files support

Requirements

• Python 3.8 or higher
• pip package manager
• Active GLIMPS Audit account with API access

System Dependencies

• Operating System: Windows 10+, macOS 10.14+, or Linux (Ubuntu 18.04+)
• Network: Internet connection for API access
• Storage: ~50MB for installation

Installation

From PyPI (Recommended)

pip install gaudit

From Source (Latest Development)

# Clone the repository
git clone https://github.com/GLIMPS/gaudit.git
cd gaudit

# Install in production mode
pip install .

# Or install in development mode (editable)
pip install -e .

Development Installation

For contributors and developers:

# Clone and install with development dependencies
git clone https://github.com/GLIMPS/gaudit.git
cd gaudit
pip install -r requirements-dev.txt
pip install -e .

# Verify installation
gaudit --version

Docker Installation (Alternative)

FROM python:3.11-slim
RUN pip install gaudit

Quick Start

First-Time Setup

1. Obtain API Credentials

   • Sign up at https://gaudit.glimps.re
   • Navigate to your profile settings
   • Generate API credentials

2. Configure Authentication

   Using CLI (Interactive):

   gaudit login
   # Enter email and password when prompted
   

   Using Environment Variables:

   export GLIMPS_AUDIT_URL="https://gaudit.glimps.re"
   export GLIMPS_AUDIT_EMAIL="[e-mail]"
   export GLIMPS_AUDIT_PASSWORD="your-password"
   

3. Verify Setup

   gaudit whoami
   

Basic Workflow Example

# 1. Upload and analyze a binary
gaudit audit create --group security --file /path/to/application.exe --comment "Security scan"

# 2. Check analysis status
gaudit audit list --filter "application.exe"

# 3. Retrieve detailed results
gaudit audit get <audit-id>

# 4. Export results as JSON
gaudit audit get <audit-id> --json > results.json

# 5. Build a custom dataset for future comparisons
gaudit dataset create my_libs --comment "Our validated libraries"

# 6. Add files to your dataset (with auto-upload)
gaudit dataset add-files my_libs \
  --project "ValidatedLibs" \
  --file "/path/to/safe_lib_v1.dll@1.0.0" \
  --file "/path/to/safe_lib_v2.dll@2.0.0" \
  --auto-upload \
  --license "MIT"

Configuration

Configuration Hierarchy

The client uses the following priority order for configuration:

1. Command-line arguments (highest priority)
2. Environment variables
3. Configuration file
4. Default values (lowest priority)

Configuration File Location

Platform	Location
Linux	~/.config/gaudit/config.json
macOS	~/Library/Application Support/gaudit/config.json
Windows	%APPDATA%\gaudit\config.json

Configuration File Format

{
  "url": "https://gaudit.glimps.re",
  "email": "[e-mail]",
  "token": "eyJ0eXAiOiJKV1QiLCJhbGc...",
  "verify_ssl": true
}

Environment Variables

Variable	Description	Default
GLIMPS_AUDIT_URL	API server URL	https://gaudit.glimps.re
GLIMPS_AUDIT_EMAIL	User email for authentication	None
GLIMPS_AUDIT_PASSWORD	User password for authentication	None
GLIMPS_AUDIT_TOKEN	JWT authentication token	None
GLIMPS_AUDIT_VERIFY_SSL	SSL certificate verification	true

Usage Guide

CLI Usage

Authentication Commands

# Interactive login
gaudit login

# Login with credentials
gaudit login --email [e-mail] --password secret

# Check authentication status
gaudit whoami

# Change password
gaudit change-password

# Logout (clear stored credentials)
gaudit logout

Audit Management

# Upload a file for analysis
gaudit audit upload /path/to/binary.exe

# Create audit with uploaded file
gaudit audit create \
  --group "production" \
  --file "6cbce50e71d810cd:binary.exe" \
  --comment "Production release v1.2.3" \
  --dataset "default,custom"

# Create audit with direct file upload
gaudit audit create \
  --group "testing" \
  --file /path/to/binary.exe \
  --comment "Test build"

# List all audits
gaudit audit list

# Filter audits
gaudit audit list --filter "keyword" --size 50 --sort desc

# Get detailed audit results
gaudit audit get 51d6999a-d54d-4ac7-8af3-0425d24fa615

# Download analyzed binary
gaudit audit download <audit-id> <file-id> --output recovered.exe

# Delete an audit
gaudit audit delete <audit-id>

# List available audit groups
gaudit audit groups

Dataset Management

# List all datasets
gaudit dataset list

# Create a new dataset
gaudit dataset create my_reference --comment "Custom reference libraries"

# List dataset entries
gaudit dataset entries my_reference --filter "openssl"

# Upload a file for dataset use
gaudit dataset upload /path/to/library.dll
# Output: File ID: 6cbce50e71d810cdf1342379b8fdbf16411d0aa25ff53f9a9568bae8bbc24ee8

# Add files to dataset - Three format options:
#
# Format 1: file_id:filename:version (for already uploaded files)
# Format 2: /path/to/file:version (with --auto-upload)
# Format 3: /path/to/file (defaults to version 1.0.0, with --auto-upload)

# Method 1: Using pre-uploaded file IDs
gaudit dataset add-files my_reference \
  --project "MyProject" \                                    # REQUIRED
  --file "6cbce50e71d810cd:library.dll@1.0.0" \             # file_id:name:version
  --file "7dbde60f82e920de:helper.dll@1.0.1" \
  --license "MIT" \
  --source "Internal Build"

# Method 2: Auto-upload local files
gaudit dataset add-files my_reference \
  --project "MyProject" \                                    # REQUIRED
  --file "/path/to/library.dll@1.0.0" \                     # path:version
  --file "/path/to/helper.dll@1.0.1" \
  --auto-upload \                                            # Auto-upload local files
  --license "MIT" \
  --home-page "https://example.com" \
  --description "Internal libraries"

# Method 3: Mixed - some IDs, some paths (with auto-upload)
gaudit dataset add-files my_reference \
  --project "MyProject" \
  --file "[e-mail]@2.0" \               # Already uploaded
  --file "/local/path/lib2.dll@2.1" \                       # Will be uploaded
  --auto-upload

# View what was added
gaudit dataset entries my_reference

# Update dataset to latest version
gaudit dataset update my_reference

# Delete a dataset (requires confirmation)
gaudit dataset delete my_reference

Library Search

# List all libraries
gaudit library list

# Search for specific libraries
gaudit library list --filter "openssl" --size 100

# Filter by architecture
gaudit library list --filter "amd64" --sort desc

User Management

# View user statistics
gaudit user stats

# Delete all user analyses
gaudit user delete-analyses

Python Library Usage

Basic Example

from gaudit import GlimpsAuditClient

# Initialize client
client = GlimpsAuditClient(
    url="https://gaudit.glimps.re",
    verify_ssl=True
)

# Authenticate
result = client.login("[e-mail]", "password")
print(f"Logged in as: {result['name']}")
print(f"Available services: {', '.join(result['services'])}")

# Upload and analyze a file
upload = client.upload_file_for_audit("application.exe")
file_id = upload["id"]

# Create audit with analysis parameters
audit = client.create_audit(
    group="security-scans",
    files={file_id: "application.exe"},
    comment="Automated security scan",
    services={
        "GlimpsLibCorrelate": {
            "dataset": "default,custom",
            "confidence": "1",
            "valid": "true"
        }
    }
)

audit_id = audit["aids"][0]
print(f"Audit created: {audit_id}")

# Wait for completion and get results
import time
while True:
    details = client.get_audit(audit_id)
    if details["audit"].get("done_at"):
        break
    time.sleep(5)

# Process results
audit_data = details["audit"]
print(f"Analysis completed: {audit_data['done_at']}")
print(f"Libraries found: {len(audit_data.get('libraries', []))}")

for library in audit_data.get("libraries", []):
    print(f"- {library['name']}")
    for file, versions in library.get("files", {}).items():
        for version in versions:
            print(f"  {file} v{version['version']} (score: {version['score']})")

Advanced Usage

from gaudit import GlimpsAuditClient
from pathlib import Path
import json

class AuditAnalyzer:
    def __init__(self, client: GlimpsAuditClient):
        self.client = client
    
    def batch_analyze(self, directory: Path, group: str):
        """Analyze all executables in a directory"""
        results = []
        
        for file_path in directory.glob("**/*.exe"):
            try:
                # Upload file
                upload = self.client.upload_file_for_audit(str(file_path))
                
                # Create audit
                audit = self.client.create_audit(
                    group=group,
                    files={upload["id"]: file_path.name},
                    comment=f"Batch analysis: {file_path.parent}"
                )
                
                results.append({
                    "file": str(file_path),
                    "audit_id": audit["aids"][0],
                    "status": "submitted"
                })
                
            except Exception as e:
                results.append({
                    "file": str(file_path),
                    "error": str(e)
                })
        
        return results
    
    def add_to_dataset(self, dataset_name: str, file_path: Path, project_name: str):
        """Add a file to a dataset with required fields"""
        # Upload file first
        upload = self.client.upload_file_for_dataset(str(file_path))
        
        # Add to dataset - project_name and files are REQUIRED
        result = self.client.add_dataset_entries(
            dataset_name=dataset_name,
            project_name=project_name,  # REQUIRED field
            files=[{                     # REQUIRED field
                "id": upload["id"],
                "binary_name": file_path.name,
                "version": "1.0.0"
            }],
            source_name="Internal Build",
            license="MIT",
            home_page="https://example.com",
            project_description="Custom library for analysis"
        )
        
        return result
    
    def export_results(self, audit_id: str, output_file: Path):
        """Export audit results to JSON file"""
        details = self.client.get_audit(audit_id)
        
        # Extract relevant information
        export_data = {
            "audit_id": audit_id,
            "filename": details["audit"]["filename"],
            "analysis_date": details["audit"]["done_at"],
            "libraries": []
        }
        
        for lib in details["audit"].get("libraries", []):
            lib_info = {
                "name": lib["name"],
                "description": lib.get("desc", ""),
                "versions": []
            }
            
            for file, versions in lib.get("files", {}).items():
                for ver in versions:
                    lib_info["versions"].append({
                        "file": file,
                        "version": ver["version"],
                        "score": ver["score"],
                        "license": ver.get("license", "Unknown")
                    })
            
            export_data["libraries"].append(lib_info)
        
        # Save to file
        with open(output_file, "w") as f:
            json.dump(export_data, f, indent=2)
        
        return export_data

# Usage
client = GlimpsAuditClient()
client.login("[e-mail]", "password")

analyzer = AuditAnalyzer(client)

# Batch analyze files
results = analyzer.batch_analyze(Path("/path/to/binaries"), "batch-scan")

# Add file to dataset with required project_name
dataset_result = analyzer.add_to_dataset(
    dataset_name="my_reference",
    file_path=Path("/path/to/library.dll"),
    project_name="MyProject"  # This is REQUIRED
)

API Reference

Client Initialization

GlimpsAuditClient(url: str = "https://gaudit.glimps.re", verify_ssl: bool = True)

Authentication Methods

Method	Description	Returns
login(email, password)	Authenticate with credentials	Auth response with token
refresh_token()	Refresh authentication token	New auth response
is_token_valid()	Check token validity	Boolean
ensure_authenticated()	Ensure valid authentication	None or raises exception

Audit Methods

Method	Description	Returns
create_audit(group, files, comment, services)	Create new audit	Audit creation response
list_audits(filter, sort_order, page_number, page_size)	List audits	Paginated audit list
get_audit(audit_id)	Get audit details	Full audit information
delete_audit(audit_id)	Delete an audit	Status response
upload_file_for_audit(file_path)	Upload file for analysis	File upload response
download_audit_binary(audit_id, file_id, save_path)	Download analyzed file	Binary content
generate_idc(audit_id, library_ids)	Generate IDA IDC script	IDC script content

Dataset Methods

Method	Description	Parameters	Returns
list_datasets()	List all datasets	None	Dataset list
create_dataset(name, comment)	Create new dataset	name (required), comment (optional)	Dataset creation response
get_dataset_entries(dataset_name, size, from_index, filter)	Get dataset entries	dataset_name (required), others optional	Paginated entry list
add_dataset_entries(dataset_name, project_name, files, ...)	Add entries to dataset	dataset_name, project_name, files (all required)	Addition status
delete_dataset(dataset_name)	Delete a dataset	dataset_name (required)	Status response
update_dataset(dataset_name)	Update dataset version	dataset_name (required)	None (202 Accepted)

Note: For add_dataset_entries, both project_name and files are mandatory parameters according to the API specification.

Examples

Example: Vulnerability Scanner

#!/usr/bin/env python3
"""
Scan binaries for known vulnerable libraries
"""

from gaudit import GlimpsAuditClient
from pathlib import Path
import sys

def scan_for_vulnerabilities(client: GlimpsAuditClient, file_path: Path):
    """Scan a binary for vulnerable libraries"""
    
    print(f"Scanning {file_path.name}...")
    
    # Upload file
    upload = client.upload_file_for_audit(str(file_path))
    
    # Create audit with vulnerability detection
    audit = client.create_audit(
        group="vulnerability-scan",
        files={upload["id"]: file_path.name},
        comment="Vulnerability scan",
        services={
            "GlimpsLibCorrelate": {
                "dataset": "vulnerable_libs",
                "confidence": "1",
                "valid": "true"
            }
        }
    )
    
    audit_id = audit["aids"][0]
    
    # Wait for completion
    import time
    max_wait = 300  # 5 minutes
    start_time = time.time()
    
    while time.time() - start_time < max_wait:
        details = client.get_audit(audit_id)
        if details["audit"].get("done_at"):
            break
        time.sleep(5)
    
    # Check for vulnerable libraries
    vulnerabilities = []
    for lib in details["audit"].get("libraries", []):
        # Check against vulnerability database
        # (This is a simplified example)
        if lib["name"] in ["log4j", "openssl-1.0.1"]:
            vulnerabilities.append({
                "library": lib["name"],
                "severity": "HIGH",
                "description": f"Known vulnerable library: {lib['name']}"
            })
    
    return vulnerabilities

# Usage
if __name__ == "__main__":
    client = GlimpsAuditClient()
    client.login("[e-mail]", "password")
    
    vulnerabilities = scan_for_vulnerabilities(
        client, 
        Path(sys.argv[1])
    )
    
    if vulnerabilities:
        print("VULNERABILITIES FOUND:")
        for vuln in vulnerabilities:
            print(f"  - {vuln['library']}: {vuln['description']}")
        sys.exit(1)
    else:
        print("No known vulnerabilities detected")
        sys.exit(0)

Example: Dataset Management

#!/usr/bin/env python3
"""
Complete example of dataset creation and management
"""

from gaudit import GlimpsAuditClient
from pathlib import Path

def create_and_populate_dataset(client: GlimpsAuditClient):
    """Example showing complete dataset workflow with required fields"""
    
    # Step 1: Create a new dataset
    dataset_name = "custom_libs_v1"
    dataset = client.create_dataset(
        name=dataset_name,
        comment="Custom library dataset for internal projects"
    )
    print(f"Created dataset: {dataset['kind']}")
    
    # Step 2: Upload files to be added to the dataset
    library_files = [
        "/path/to/mylib_v1.0.dll",
        "/path/to/mylib_v1.1.dll",
        "/path/to/helper.so"
    ]
    
    uploaded_files = []
    for file_path in library_files:
        upload = client.upload_file_for_dataset(file_path)
        uploaded_files.append({
            "id": upload["id"],
            "binary_name": Path(file_path).name,
            "version": "1.0.0"  # Extract version from filename or metadata
        })
        print(f"Uploaded: {Path(file_path).name} -> {upload['id']}")
    
    # Step 3: Add files to dataset with REQUIRED fields
    result = client.add_dataset_entries(
        dataset_name=dataset_name,
        project_name="InternalProject",  # REQUIRED: Must specify project
        files=uploaded_files,             # REQUIRED: Must provide files list
        source_name="Internal Build System",
        license="Proprietary",
        home_page="https://internal.example.com/project",
        project_description="Internal libraries for company projects"
    )
    
    if result["status"]:
        print(f"Successfully added {len(uploaded_files)} files to dataset")
        for file_result in result.get("files", []):
            status = "✓" if file_result["status"] else "✗"
            print(f"  {status} {file_result['id']}")
    
    # Step 4: Verify dataset entries
    entries = client.get_dataset_entries(dataset_name)
    print(f"\nDataset now contains {entries['count']} entries:")
    for entry in entries.get("entries", []):
        print(f"  - {entry['binary_name']} ({entry['architecture']})")
        print(f"    Project: {entry.get('project_name', 'N/A')}")
        print(f"    SHA256: {entry['sha256']}")
    
    return dataset_name

# Usage
if __name__ == "__main__":
    client = GlimpsAuditClient()
    client.login("[e-mail]", "password")
    
    try:
        dataset_name = create_and_populate_dataset(client)
        print(f"\nDataset '{dataset_name}' created and populated successfully!")
    except Exception as e:
        print(f"Error: {e}")
        # Common errors:
        # - Missing project_name: "Bad Request: project_name is a required field"
        # - Missing files: "Bad Request: files is a required field"
        # - Invalid file ID: "File not found"

Example: Compliance Reporter

#!/usr/bin/env python3
"""
Generate compliance reports for analyzed binaries
"""

from gaudit import GlimpsAuditClient
from datetime import datetime
import json

def generate_compliance_report(client: GlimpsAuditClient, audit_id: str):
    """Generate a compliance report from audit results"""
    
    details = client.get_audit(audit_id)
    audit = details["audit"]
    
    report = {
        "report_date": datetime.now().isoformat(),
        "audit_id": audit_id,
        "file": {
            "name": audit["filename"],
            "type": audit["filetype"],
            "architecture": audit["arch"],
            "size": audit["size"],
            "hashes": {h["Name"]: h["Value"] for h in audit["hashes"]}
        },
        "compliance": {
            "total_libraries": len(audit.get("libraries", [])),
            "licensed_libraries": [],
            "unlicensed_libraries": [],
            "copyleft_licenses": [],
            "permissive_licenses": []
        }
    }
    
    # Analyze licenses
    for lib in audit.get("libraries", []):
        for file, versions in lib.get("files", {}).items():
            for version in versions:
                license_type = version.get("license", "Unknown")
                lib_entry = {
                    "name": lib["name"],
                    "file": file,
                    "version": version["version"],
                    "license": license_type
                }
                
                if license_type == "Unknown":
                    report["compliance"]["unlicensed_libraries"].append(lib_entry)
                else:
                    report["compliance"]["licensed_libraries"].append(lib_entry)
                    
                    # Categorize by license type
                    if license_type in ["GPL", "LGPL", "AGPL"]:
                        report["compliance"]["copyleft_licenses"].append(lib_entry)
                    elif license_type in ["MIT", "BSD", "Apache"]:
                        report["compliance"]["permissive_licenses"].append(lib_entry)
    
    # Add compliance summary
    report["summary"] = {
        "compliant": len(report["compliance"]["unlicensed_libraries"]) == 0,
        "copyleft_risk": len(report["compliance"]["copyleft_licenses"]) > 0,
        "license_coverage": (
            len(report["compliance"]["licensed_libraries"]) / 
            report["compliance"]["total_libraries"] * 100
            if report["compliance"]["total_libraries"] > 0 else 0
        )
    }
    
    return report

# Usage
client = GlimpsAuditClient()
client.login("[e-mail]", "password")

report = generate_compliance_report(client, "audit-id-123")
print(json.dumps(report, indent=2))

Testing

Running Tests

# Run all unit tests
pytest

# Run with coverage report
pytest --cov=gaudit --cov-report=html

# Run specific test module
pytest tests/test_client.py

# Run tests matching pattern
pytest -k "test_login"

# Run with verbose output
pytest -v

# Using the test runner script
python run_tests.py --coverage

Integration Testing

Integration tests require a live API server:

# Set environment variables
export GLIMPS_TEST_API_URL="https://gaudit.glimps.re"
export GLIMPS_TEST_EMAIL="[e-mail]"
export GLIMPS_TEST_PASSWORD="test-password"

# Run integration tests
pytest -m integration

# Or using the test runner
python run_tests.py --integration

Test Coverage

Current test coverage targets:

• Minimum: 80%
• Target: 90%+
• Current: Check with pytest --cov=gaudit

Development

Project Structure

gaudit/
├── src/gaudit/         # Source code
│   ├── __init__.py    # Package initialization
│   ├── client.py      # API client implementation
│   ├── cli.py         # CLI implementation
│   └── config.py      # Configuration management
├── tests/             # Test suite
│   ├── test_client.py # Client tests
│   ├── test_cli.py    # CLI tests
│   └── utils.py       # Test utilities
├── docs/              # Documentation
│   └── openapi.yml    # API specification
├── examples/          # Example scripts
├── requirements.txt   # Production dependencies
└── pyproject.toml     # Project configuration

Development Setup

# Clone repository
git clone https://github.com/GLIMPS/gaudit.git
cd gaudit

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
pip install -r requirements-dev.txt
pip install -e .

# Run linting
ruff check src tests

# Format code
ruff format src tests

# Run tests
pytest

Code Style

This project follows:

• PEP 8 style guide
• Type hints for all public methods
• Docstrings for all modules, classes, and functions
• Maximum line length: 119 characters

Pre-commit Hooks

# Install pre-commit
pip install pre-commit

# Install hooks
pre-commit install

# Run manually
pre-commit run --all-files

Troubleshooting

Common Issues

Authentication Failures

Problem: "API Error 401: Unauthorized"

Solutions:

1. Verify credentials are correct
2. Check if token has expired: gaudit whoami
3. Re-authenticate: gaudit login
4. Verify API URL is correct

SSL Certificate Errors

Problem: SSL certificate verification failed

Solutions:

1. Update certificates: pip install --upgrade certifi
2. For testing only: gaudit --insecure login
3. Set custom CA bundle: export REQUESTS_CA_BUNDLE=/path/to/ca-bundle.crt

File Upload Errors

Problem: "invalid file type" or "invalid file size"

Solutions:

1. Verify file is PE (Windows) or ELF (Linux) executable
2. Check file size is under 20MB limit
3. Ensure file is not corrupted: file /path/to/binary

Dataset Entry Errors

Problem: "Bad Request" when adding dataset entries

Solutions:

1. Ensure project_name is provided (REQUIRED field)
2. Ensure files array is provided (REQUIRED field)
3. Verify file IDs exist from prior upload
4. Check dataset name exists and you have permissions

Example of correct usage:

client.add_dataset_entries(
    dataset_name="my_dataset",
    project_name="MyProject",  # REQUIRED
    files=[{                    # REQUIRED
        "id": "file_id_from_upload",
        "binary_name": "library.dll"
    }]
)

Connection Timeouts

Problem: Requests timing out

Solutions:

1. Check network connectivity
2. Verify firewall settings
3. Try using a different DNS server
4. Contact your network administrator

Debug Mode

Enable detailed logging for troubleshooting:

import logging

# Enable debug logging
logging.basicConfig(level=logging.DEBUG)

# Now client will show detailed requests/responses
client = GlimpsAuditClient()

Getting Help

1. Check the FAQ
2. Search existing issues
3. Contact support: [e-mail]
4. Join our community forum

Contributing

We welcome contributions! Please see our Contributing Guide for details.

Quick Contribution Guide

1. Fork the repository
2. Create a feature branch:

   git checkout -b feature/amazing-feature
   

3. Make your changes and add tests
4. Ensure tests pass:

   pytest
   ruff check .
   

5. Commit with descriptive message:

   git commit -m "Add amazing feature: description of changes"
   

6. Push to your fork:

   git push origin feature/amazing-feature
   

7. Open a Pull Request

Development Guidelines

• Write tests for new features
• Update documentation for API changes
• Follow existing code style
• Add type hints for new functions
• Keep commits atomic and descriptive

License

This project is licensed under the MIT License. See the LICENSE file for details.

Support

Resources

• Documentation: https://docs.glimps.re
• API Reference: https://api.glimps.re/docs
• Support Portal: https://support.glimps.re
• Email: [e-mail]
• Company Website: https://www.glimps.re

Professional Support

For enterprise support, custom integrations, or training, contact [e-mail]

Changelog

Latest Updates

• Enhanced Dataset Management: Added dataset upload and dataset add-files commands for complete dataset workflow
• Auto-Upload Feature: New --auto-upload flag for automatic file upload when adding to datasets
• Improved Error Messages: Better handling of required fields with helpful error messages

See CHANGELOG.md for complete version history and release notes.

Acknowledgments

• GLIMPS development team for the API platform
• Open source community for invaluable tools and libraries
• All contributors and users providing feedback and improvements

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Copyright (c) 2025 GLIMPS Prevent tomorrow’s threats today.