4% MethodArchitectureRetainersBlogFounders
Client NodeLet's Chat
Documentation

The Protocol // Open Source.

Declassified developer utilities, core search algorithms, and automation scripts engineered by the deployment team.

Overview

The Programmatic SEO & GEO Audit CLI engine is a proprietary, asynchronous python script built to scan enterprise domains and evaluate their readiness for Generative Engine Optimization (GEO).

By bypassing standard subjective metrics, this engine strictly evaluates a domain's structural ability to be ingested, crawled, and cited by large language models (LLMs) like ChatGPT, Claude, and Gemini.

Prerequisites

Ensure your local environment meets the minimum deployment standards before running the auditor.

  • Python 3.10+ installed
  • Network environment unblocked by major corporate WAFs
  • Virtual environment (venv or pipenv recommended)

Installation

Install the required asynchronous network and scraping libraries via pip.

pip install aiohttp beautifulsoup4

Core Audit Logic

The PipelineAuditor class is the heart of the engine. It initiates an asynchronous HTTP request to bypass standard thread blocking, scraping the DOM specifically for JSON-LD schemas and telemetry nodes.

pipeline_auditor.py
400">"text-blue-400">import asyncio
400">"text-blue-400">import aiohttp
400">"text-blue-400">from bs4 400">"text-blue-400">import BeautifulSoup

400">"text-blue-400">class PipelineAuditor:
    400">"text-blue-400">def __init__(self, target_url: str):
        self.url = target_url
        self.metrics = {400">"programmatic_density": 0.0, 400">"llm_hooks": 0, 400">"latency_ms": 0.0}

    400">"text-blue-400">async 400">"text-blue-400">def evaluate_architecture(self):
        400">"text-blue-400">async 400">"text-blue-400">with aiohttp.ClientSession() 400">"text-blue-400">as session:
            start_time = asyncio.get_event_loop().time()
            400">"text-blue-400">async 400">"text-blue-400">with session.get(self.url) 400">"text-blue-400">as response:
                self.metrics[400">"latency_ms"] = (asyncio.get_event_loop().time() - start_time) * 1000
                html = 400">"text-blue-400">await response.text()
                
                # Scan for programmatic structure and generative intent
                soup = BeautifulSoup(html, 'html.parser')
                data_nodes = soup.find_all(attrs={400">"data-telemetry": True})
                self.metrics[400">"programmatic_density"] = len(data_nodes) / max(len(soup.find_all()), 1)
                
                # Check for Answer Engine (AEO) structural schem400">"text-blue-400">as
                schem400">"text-blue-400">as = soup.find_all('script', type='application/ld+json')
                self.metrics[400">"llm_hooks"] = len(schem400">"text-blue-400">as)
                
                400">"text-blue-400">return self.compile_telemetry_report()

    400">"text-blue-400">def compile_telemetry_report(self):
        print(f400">"// INITIATING PIPELINE AUDIT FOR: {self.url}")
        print(f400">"[STATUS] Network Latency: {self.metrics['latency_ms']:.2f}ms")
        print(f400">"[STATUS] Programmatic Node Density: {self.metrics['programmatic_density']:.2%}")
        print(f400">"[STATUS] Generative Engine Hook Count: {self.metrics['llm_hooks']}")
        400">"text-blue-400">if self.metrics[400">"llm_hooks"] == 0:
            print(400">"[CRITICAL] Threat detected: Domain is invisible to modern AI search architectures.")

Execution CLI

Once initialized, you can execute the script directly from your terminal by importing the class and running the async loop.

import asyncio

auditor = PipelineAuditor("https://example.com")
report = asyncio.run(auditor.evaluate_architecture())