⚑️ Developer Documentation & API Reference

The Open-Source Lighthouse for the Agentic Web

Audit, score, and optimize websites and storefronts for autonomous AI agents, LLMs, WebMCP, OpenAPI discovery, and machine-readable commerce protocols.

$ npx @forkpoint/agent-lighthouse https://yourstore.com
207
Specialized Audits
10
Audit Categories
0 ms
Browser Overhead (Dual AST)
MCP
Native AI Assistant Protocol

πŸš€ Quickstart & Installation

Run instant agentic readiness audits with zero installation or embed the scanner into your CI pipeline.

1. Instant Execution (Zero Install)

Use npx to audit any live storefront or staging environment in seconds:

$ npx @forkpoint/agent-lighthouse https://yourstore.com

2. Global / Project Installation

Install the CLI globally or save as a project developer dependency:

npm i -g @forkpoint/agent-lighthouse
pnpm add -D @forkpoint/agent-lighthouse

πŸ›οΈ Core Architecture

High-performance Gatherer–Audit–Scorer execution pipeline.

1. Gatherers
Parallel I/O, robots, sitemaps, DOM & JSON-LD parsing
2. CheckContext
Unified AST, Microdata, RDFa, headers & root files
3. Pure Audits
207 pure deterministic rules (10 categories)
4. Scoring & Vitals
Weighted category rollups & ALHR reports

Key Principle: Network I/O and HTML parsing occur strictly in Gatherers. Audits receive an immutable CheckContext and execute as pure, fully offline-testable functions.

πŸ“Š Scoring & 4 Readiness Vitals

How Agent Lighthouse evaluates readiness across key commerce pillars.

πŸ›’ Commerce Readiness (40%)

Weight: 0.40

Evaluates Schema.org Product, Offer, Price, Availability, Brand, WebMCP commerce actions, and product field integrity.

πŸ“„ Content Discoverability (25%)

Weight: 0.25

Verifies /llms.txt, /llms-full.txt, sitemaps, Markdown alternates, and RSS feeds.

πŸ€– Bot Accessibility (20%)

Weight: 0.20

Checks AI crawler permissions (GPTBot, ClaudeBot, PerplexityBot), absence of aggressive bot walls, and non-blocking forms.

πŸ”’ Technical & Security (15%)

Weight: 0.15

Audits HTTPS, HSTS, CSP, X-Content-Type-Options, CORS on AI files, server-rendered HTML, and fast response times.

πŸ” Interactive Audit Directory

Explore all 207 audits with evaluation rules, guidance, and remediation code snippets.

207 audits available
Loading audit database...

πŸ’» CLI Flags & CI Assertions

Options, presets, and budget assertions for terminal and CI runs.

Flag Default Description
-p, --preset <name> full Audit preset (ecommerce, saas, quick, full).
-o, --output <formats> terminal,html,json Comma-separated output formats: terminal, html, json, md.
-d, --output-dir <path> ./reports Output directory for generated reports.
-v, --view false Automatically open the generated HTML report in the default browser.
--min-score <n> 0 Minimum score (0–100) required to pass CI assertions (exits code 1 if failed).
--assert-category <id:n> - Category-specific budget (e.g. --assert-category structured-data:90).
--debug-audit <id> - Print deep diagnostic breakdown for specific audit ID (e.g. 3.8) or fails.
-c, --config <path> auto Path to configuration file (agent-lighthouse.config.json).
--silent false Suppress terminal progress output.

πŸ“¦ Node.js / TypeScript SDK

Integrate Agent Lighthouse scanning and HTML report generation programmatically.

scan-and-report.ts
import { runScan } from '@forkpoint/agent-lighthouse-core';
import { generateHtmlReport, generateMarkdownSummary } from '@forkpoint/agent-lighthouse-report';
import { writeFileSync } from 'node:fs';

// 1. Run live scan
const report = await runScan('https://yourstore.com', {
  onEvent: (event) => {
    if (event.type === 'phase:done') {
      console.log(`[${Math.round(event.fraction * 100)}%] ${event.phase} done`);
    }
  },
});

console.log(`Overall Readiness Score: ${report.overallScore}/100 (${report.scoreTier})`);

// 2. Generate standalone Lighthouse HTML report
const html = generateHtmlReport(report);
writeFileSync('./reports/agent-report.html', html);

// 3. Generate Markdown summary for PR comments
const markdown = generateMarkdownSummary(report);
console.log(markdown);

πŸ€– Model Context Protocol (MCP) Server

Enable AI coding assistants (Claude Desktop, Cursor, Antigravity) to audit and fix stores interactively.

Claude Desktop & IDE Configuration

claude_desktop_config.json / mcp_config.json
{
  "mcpServers": {
    "agent-lighthouse": {
      "command": "npx",
      "args": ["-y", "@forkpoint/agent-lighthouse-mcp"]
    }
  }
}
audit_website
Full 207-audit scan with scores, failed audits & remediation fixes.
inspect_product_schema
Deep inspection of Schema.org Product, Offer, Brand, and Price.
verify_agent_tools
Probes OpenAPI endpoints, agents.json, and WebMCP shopping actions.

πŸ›‘οΈ GitHub Actions CI Pipeline

Automate agentic readiness regression testing on every pull request.

.github/workflows/agent-lighthouse.yml
name: Agent Lighthouse Audit

on:
  pull_request:
    branches: [main]

jobs:
  audit:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout repository
        uses: actions/checkout@v4

      - name: Run Agent Lighthouse Audit
        run: |
          npx @forkpoint/agent-lighthouse https://staging.yourstore.com \
            --preset ecommerce \
            --min-score 80 \
            --output terminal,html,json,md \
            --output-dir ./reports

      - name: Upload HTML Report Artifact
        uses: actions/upload-artifact@v4
        with:
          name: agent-lighthouse-report
          path: ./reports/agent-lighthouse-report.html

πŸ“Š Interactive Report Inspector

Drag and drop any scan JSON report to view the interactive score gauges and category breakdown.

πŸ“₯
Drop your agent-lighthouse-report.json here
or click to browse from your computer

πŸ“ˆ 100-Store Benchmark Findings

Key takeaways from our empirical audit of 100 top e-commerce storefronts.

0%
OpenAPI, WebMCP, or MCP action surfaces
4%
Stores with llms.txt or full-text AI files
53.6
Average readiness score out of 100
85+
Common score after fast foundational fixes

Benchmark Story

  • The agentic gap: top storefronts have basic SEO, but rarely expose machine-readable action surfaces.
  • Discovery gap: only a small minority expose /llms.txt or /llms-full.txt.
  • Fastest boost: add /llms.txt, an AI catalog, and accessible icon button names.

Post-ready Angle

Most commerce sites are built for human browsers and classic crawlers. Agent Lighthouse measures the layer needed for autonomous shopping agents: discovery files, crawler permissions, structured facts, and safe action paths.

Read benchmark notes →

πŸ… Public Score Showcase

Shareable examples for launch posts, client before/after reports, and community submissions.

docs platform
90+
Strong discovery files

Good llms.txt, semantic structure, and crawler access make docs easier for AI coding agents to cite.

storefront
50-70
Classic SEO, weak agent actions

Product schema is common, but OpenAPI, WebMCP, and clean machine-readable checkout paths are usually missing.

before / after
+30
Fast lift from foundations

Adding AI discovery files, crawler policy clarity, and accessible controls creates a visible score jump.

Have a public result?

Submit a score and remediation notes so other teams can learn from real examples.

Share a Site Score

🏷️ Badge Generator

Create a README badge for public site scores and before/after launch posts.


            

πŸ“£ Launch Kit

Ready-to-use copy and assets for Show HN, Product Hunt, Reddit, Dev.to, and outreach.