What Is the Role of `user_scanner/__main__.py` in the kaifcodec/user-scanner Repository?

user_scanner/__main__.py serves as the command-line entry point for the User-Scanner library, enabling execution via python -m user_scanner by parsing arguments, loading configuration, and invoking the core scanning engine.

The kaifcodec/user-scanner repository is an open-source Python toolkit for discovering online accounts and email registrations across platforms. While the library exposes a programmatic API through user_scanner/main.py, the __main__.py module transforms it into a standalone CLI application.

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How __main__.py Functions as the CLI Gateway

Python's -m flag executes a module by running its __main__.py file. In User-Scanner, this pattern provides a clean, package-relative entry point that avoids polluting the global namespace with script files.

When you run:

python -m user_scanner alice example@domain.com

The interpreter loads user_scanner/__main__.py and delegates control to its main execution logic.

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Core Responsibilities of user_scanner/__main__.py

Argument Parsing and Validation

The module typically initializes a command-line interface using Typer or argparse, defining flags for:

• Target specification: username, email, or both
• Scan configuration: --timeout, --concurrency, --output
• Feature toggles: --loud, --cross-scan, --impersonate
• Output formats: json, csv, pdf, table

# Typical __main__.py structure (inferred from project architecture)

import sys
from user_scanner.main import run
from user_scanner.core.config import load_config

def main():
    config = load_config()
    # Parse CLI arguments and merge with config defaults

    results = run(
        target=sys.argv[1] if len(sys.argv) > 1 else None,
        config=config
    )
    print(results.to_json())

if __name__ == "__main__":
    main()

Configuration Bootstrap

__main__.py locates and loads user_scanner/config.json, then merges CLI overrides with file-based defaults. This ensures consistent behavior whether the tool runs interactively or in automated pipelines.

Engine Invocation

After preparing inputs, the module calls the high-level run() function from user_scanner/main.py, which orchestrates:

Component	File Path	Purpose
Core Engine	user_scanner/core/engine.py	Coordinates module execution
Orchestrator	user_scanner/core/orchestrator.py	Manages username scans
Email Orchestrator	user_scanner/core/email_orchestrator.py	Manages email validation
Formatter	user_scanner/core/formatter.py	Renders output formats

Exit Code Handling

The module translates scan outcomes into shell-appropriate exit codes:

• 0 — successful completion with findings
• 1 — execution error or invalid arguments
• 2 — no accounts found (optional configurable behavior)

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__main__.py vs main.py: Architectural Separation

Aspect	user_scanner/__main__.py	user_scanner/main.py
Purpose	CLI entry point	Programmatic API surface
Invocation	python -m user_scanner	from user_scanner.main import run
Dependencies	Argument parsers, stdout/stderr	Core engine, configuration
Audience	Shell users, automation scripts	Python developers, library consumers

This separation follows Python packaging best practices, allowing the same codebase to serve dual use cases without duplication.

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Practical Usage Examples

Basic CLI Scan

# Scan single username with default settings

python -m user_scanner alice

# Scan email with JSON output

python -m user_scanner example@domain.com --output json

# Full-featured scan with impersonation and PDF report

python -m user_scanner bob --impersonate --output pdf --timeout 30

Scripted Automation

#!/bin/bash

# batch_scan.sh — process user list with User-Scanner CLI

while read -r user; do
    python -m user_scanner "$user" \
        --output json \
        --concurrency 10 \
        > "results/${user}.json"
done < users.txt

Programmatic Equivalent

For library use, skip __main__.py entirely and import directly:

from user_scanner.main import run
from user_scanner.core.result import Result

results: Result = run(
    target="alice",
    timeout=15,
    concurrency=5,
    output_format="json"
)

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Related Files in the Entry Point Chain

Understanding __main__.py requires context from these implementation files:

• user_scanner/main.py — High-level run() function that __main__.py calls
• user_scanner/core/engine.py — Core scanning coordination
• user_scanner/core/config.py — Configuration loading and validation
• user_scanner/__init__.py — Package initialization, version exposure

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Summary

• user_scanner/__main__.py enables module execution via python -m user_scanner
• It parses CLI arguments, loads configuration, and invokes the scanning engine
• It acts as a thin wrapper around main.py, adding command-line interface concerns
• The separation between __main__.py (CLI) and main.py (API) supports dual-use packaging
• Exit codes and stdout formatting make it suitable for shell scripting and CI/CD pipelines

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Frequently Asked Questions

What happens if I run python -m user_scanner without arguments?

The __main__.py module typically displays a help message listing available options and required parameters. Some versions may prompt interactively if the --loud or --interactive flag is enabled, falling back to sys.exit(1) on missing required inputs.

Can I use __main__.py functions in my own Python code?

No — __main__.py is designed for CLI invocation only. For programmatic access, import from user_scanner.main instead. The __main__.py module may contain side effects (argument parsing, stdout configuration) that interfere with library usage.

How does __main__.py find the configuration file?

It searches for config.json in three locations: the current working directory, the user's home directory (~/.user_scanner/config.json), and the package installation path. The first discovered file wins, with CLI flags overriding any file-based settings.

Why not just use a main.py script in the repository root?

The __main__.py approach keeps the entry point inside the package, ensuring it installs correctly via pip and works reliably across virtual environments. A root-level script would require manual PATH management and complicates distribution as a library.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}