Analyzed URL
https://omniasec.ai/
Measured 3 weeks ago
AI-Ready Score
Fair
out of 100
Token Savings
Score Breakdown
Emerging protocols
0 of 6 detectedWell-known endpoints AI agents look for. Detected here means an agent can discover and connect to your service automatically.
-
OAuth Protected Resource RFC 9728
/.well-known/oauth-protected-resource -
OAuth Discovery RFC 8414
/.well-known/oauth-authorization-server -
MCP Server Card SEP-1649 draft
/.well-known/mcp/server-card.json -
A2A Agent Card A2A v1.0
/.well-known/agent-card.json -
API Catalog RFC 9727
/.well-known/api-catalog -
Agent Skills index Discovery RFC v0.2.0 draft
/.well-known/agent-skills/index.json
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What we measured No JSON-LD / Schema.org found
No Schema.org structured data found. JSON-LD helps AI agents extract factual, structured information from your pages.
How to implement
Add a <script type="application/ld+json"> block with Schema.org markup. Use appropriate types: Article for blog posts, Product for products, Organization for your company page.
What we measured No Markdown for Agents support detected
Your site doesn't support Markdown for Agents. This Cloudflare standard lets AI agents request content in markdown format, reducing token usage by ~80%.
How to implement
Implement one or more: (1) Respond to Accept: text/markdown with markdown content. (2) Serve .md URLs (e.g., /page.md). (3) Add <link rel="alternate" type="text/markdown"> tags. (4) Add Link HTTP headers for markdown discovery.
What we measured No sitemap found
No sitemap found. A sitemap helps AI agents discover all pages on your site.
How to implement
Create a /sitemap.xml listing all your public pages. Most CMS platforms can generate this automatically.
What we measured No canonical URL
No canonical URL found. This helps AI agents identify the preferred version of a page and avoid duplicate content.
How to implement
Add a <link rel="canonical" href="..."> tag pointing to the canonical URL of the page.
What we measured No llms.txt found
Your site doesn't have an llms.txt file. This is the emerging standard for helping AI agents understand your site structure.
How to implement
Create an /llms.txt file following the llmstxt.org specification. Include a site description and links to your key pages.
What we measured No Content-Signal found (robots.txt or HTTP headers)
No Content-Signal directives found. These tell AI agents how they may use your content (search indexing, AI input, training data). The recommended location is robots.txt.
How to implement
Add Content-Signal to your robots.txt: User-agent: *\nContent-Signal: search=yes, ai-input=yes, ai-train=no. You can also add it as an HTTP header on markdown responses.
What we measured Content ratio: 5.9% (2138 content chars / 35943 HTML bytes)
Your page has a low ratio of actual content to total HTML. Much of the page weight is markup, scripts, or styles rather than content.
How to implement
Move CSS to external stylesheets, remove inline styles, minimize JavaScript, and ensure the HTML focuses on content structure.
What we measured 12 semantic elements, 85 divs (ratio: 12%)
Your page relies heavily on <div> elements. Semantic elements like <section>, <nav>, <header>, <footer>, and <aside> provide meaningful structure for AI agents.
How to implement
Replace generic <div> containers with appropriate semantic elements. Use <section> for thematic groups, <nav> for navigation, <header>/<footer> for page/section headers and footers.
What we measured 2/3 OG tags present
Missing or incomplete Open Graph tags. OG tags help AI agents (and social platforms) understand your page title, description, and image.
How to implement
Add og:title, og:description, and og:image meta tags to your page's <head>.
What we measured 4/275 elements with inline styles (1.5%)
Many elements have inline style attributes. These add noise for AI agents extracting content.
How to implement
Move all inline styles to CSS classes in your stylesheet. Use utility CSS frameworks like Tailwind if you need many unique styles.
## The AI Security *Command Center.* Orchestrate your security stack, analyze threats with AI, and stop attacks from one interface. [Try Omnia →](https://app.omniasec.ai/login) or install the CLI `curl -fsSL https://omniasec.ai/cli-install | bash` Our network ## Trusted Partners Capabilities ## Built for modern security teams ### Chat Ask Omnia to deobfuscate code, generate cybersecurity reports, look up hashes, or investigate threats — all through a single conversational interface. ### Custom Agents Create personalized AI agents with custom instructions tailored to your security needs. Define how Omnia thinks, responds, and acts for your specific use cases. ### Templates & Prompts Build reusable prompt templates for recurring tasks like incident triage, threat analysis, or compliance checks. Work faster without starting from scratch. ### Knowledge Upload your documents, reports, and intel feeds to create a private knowledge base. Omnia retrieves relevant context automatically when you need it. ### MCPs Connect your own Model Context Protocol servers to extend Omnia with custom tools, data sources, and integrations — no redeployment needed. ### Community Share and discover agents, prompts, workflows, templates, and knowledge bases created by other security professionals. Scale your operations with collective intelligence. Chat ## Your AI security analyst, one message away Deobfuscate code, generate threat reports, investigate hashes, and run security tasks — all through a conversational interface powered by Omnia's AI agents.  File Analysis ## Upload any file, uncover hidden threats Submit files for deep inspection powered by AI engines. Omnia decompiles binaries, disassembles code, and detects obfuscated malware and zero-day patterns that traditional scanners miss.  Market Intelligence ## Monitor every marketplace, spot risks early Search and audit packages across VS Code, npm, PyPI, and more. Instantly surface malicious extensions, typosquatting attempts, and compromised dependencies before they reach your stack.  Workflows ## Automate your security playbooks Build automated workflows that chain Omnia's AI agents to handle your cybersecurity tasks — from triage to response — without writing a single line of code. 
Home | Omnia [](https://omniasec.ai/) [Try Omnia](https://app.omniasec.ai/login) # The AI Security *Command Center.* Orchestrate your security stack, analyze threats with AI, and stop attacks from one interface. [Try Omnia →](https://app.omniasec.ai/login) or install the CLI `curl -fsSL https://omniasec.ai/cli-install | bash` Our network ## Trusted Partners                                 Capabilities ## Built for modern security teams ### Chat Ask Omnia to deobfuscate code, generate cybersecurity reports, look up hashes, or investigate threats — all through a single conversational interface. ### Custom Agents Create personalized AI agents with custom instructions tailored to your security needs. Define how Omnia thinks, responds, and acts for your specific use cases. ### Templates & Prompts Build reusable prompt templates for recurring tasks like incident triage, threat analysis, or compliance checks. Work faster without starting from scratch. ### Knowledge Upload your documents, reports, and intel feeds to create a private knowledge base. Omnia retrieves relevant context automatically when you need it. ### MCPs Connect your own Model Context Protocol servers to extend Omnia with custom tools, data sources, and integrations — no redeployment needed. ### Community Share and discover agents, prompts, workflows, templates, and knowledge bases created by other security professionals. Scale your operations with collective intelligence. Chat ## Your AI security analyst, one message away Deobfuscate code, generate threat reports, investigate hashes, and run security tasks — all through a conversational interface powered by Omnia's AI agents.  File Analysis ## Upload any file, uncover hidden threats Submit files for deep inspection powered by AI engines. Omnia decompiles binaries, disassembles code, and detects obfuscated malware and zero-day patterns that traditional scanners miss.  Market Intelligence ## Monitor every marketplace, spot risks early Search and audit packages across VS Code, npm, PyPI, and more. Instantly surface malicious extensions, typosquatting attempts, and compromised dependencies before they reach your stack.  Workflows ## Automate your security playbooks Build automated workflows that chain Omnia's AI agents to handle your cybersecurity tasks — from triage to response — without writing a single line of code.  Zero-day reads [View all →](https://omniasec.ai/blog/) [### Welcome to Omnia: Redefining Malware Analysis in the Age of AIMar 16, 2026](https://omniasec.ai/blog/welcome-to-omnia/) \> | ## Ready to see what Omnia can do? Join the community redefining how we fight cyber threats. [Try Omnia →](https://app.omniasec.ai/login)
Upload this file as /index.md on your server so AI agents can access a clean version of your page. You can also configure Accept: text/markdown content negotiation to serve it automatically.
Generated llms.txt for this single page
# omniasec.ai > Omnia ## Documentation - [Docs](https://omniasec.ai/docs/) ## Main - [Home | Omnia](https://omniasec.ai/): Omnia - [About](https://omniasec.ai/about/) - [Docs](https://omniasec.ai/docs/) - [Bug Bounty](https://omniasec.ai/bug-bounty/) ## Blog - [Blog](https://omniasec.ai/blog/) ## Legal - [Privacy Policy](https://omniasec.ai/privacy-policy/)
Full llms.txt requires domain-wide analysis (coming soon)
Upload this file to https://omniasec.ai/llms.txt at the root of your domain. AI agents like ChatGPT, Claude, and Perplexity check this file to understand your site structure.
Accessibility
Content available without JavaScript
Main content starts at 22% of HTML
Page size: 35KB
AI Discoverability
All major AI search bots allowed
No sitemap found
robots.txt exists
No llms.txt found
Structured Data
No JSON-LD / Schema.org found
2/3 OG tags present
Meta description too short: 5 chars
No canonical URL
lang="en"
Semantic HTML
Clean heading hierarchy
Has <main>
12 semantic elements, 85 divs (ratio: 12%)
36/36 images with meaningful alt text
Avg div depth: 2.0, max: 3
Content Efficiency
96% token reduction (HTML→Markdown)
Content ratio: 5.9% (2138 content chars / 35943 HTML bytes)
HTML size: 35KB
4/275 elements with inline styles (1.5%)
{
"url": "https://omniasec.ai/",
"timestamp": 1788288938884,
"fetch": {
"mode": "simple",
"timeMs": 17,
"htmlSizeBytes": 35943,
"supportsMarkdown": false,
"markdownAgents": {
"contentNegotiation": false,
"mdUrl": {
"found": false,
"url": null
},
"linkTag": {
"found": false,
"url": null
},
"linkHeader": {
"found": false,
"url": null
},
"responseHeaders": {
"contentSignal": null,
"xMarkdownTokens": null,
"vary": null
},
"frontmatter": {
"present": false,
"fields": [],
"level": "none"
},
"level": "none"
},
"statusCode": 200
},
"extraction": {
"title": "Home | Omnia",
"excerpt": "Omnia",
"byline": null,
"siteName": null,
"lang": "en",
"contentLength": 2138,
"metadata": {
"description": "Omnia",
"ogTitle": "Home | Omnia",
"ogDescription": "Omnia",
"ogImage": null,
"ogType": "website",
"canonical": null,
"lang": "en",
"schemas": [],
"robotsMeta": null,
"author": null,
"generator": "Astro v5.17.3",
"markdownAlternateHref": null
}
},
"markdown": "## The AI Security *Command Center.*\n\nOrchestrate your security stack, analyze threats with AI, and stop attacks from one interface.\n\n[Try Omnia →](https://app.omniasec.ai/login)\n\nor install the CLI\n\n`curl -fsSL https://omniasec.ai/cli-install | bash`\n\nOur network\n\n## Trusted Partners\n\nCapabilities\n\n## Built for modern security teams\n\n### Chat\n\nAsk Omnia to deobfuscate code, generate cybersecurity reports, look up hashes, or investigate threats — all through a single conversational interface.\n\n### Custom Agents\n\nCreate personalized AI agents with custom instructions tailored to your security needs. Define how Omnia thinks, responds, and acts for your specific use cases.\n\n### Templates & Prompts\n\nBuild reusable prompt templates for recurring tasks like incident triage, threat analysis, or compliance checks. Work faster without starting from scratch.\n\n### Knowledge\n\nUpload your documents, reports, and intel feeds to create a private knowledge base. Omnia retrieves relevant context automatically when you need it.\n\n### MCPs\n\nConnect your own Model Context Protocol servers to extend Omnia with custom tools, data sources, and integrations — no redeployment needed.\n\n### Community\n\nShare and discover agents, prompts, workflows, templates, and knowledge bases created by other security professionals. Scale your operations with collective intelligence.\n\nChat\n\n## Your AI security analyst, one message away\n\nDeobfuscate code, generate threat reports, investigate hashes, and run security tasks — all through a conversational interface powered by Omnia's AI agents.\n\n\n\nFile Analysis\n\n## Upload any file, uncover hidden threats\n\nSubmit files for deep inspection powered by AI engines. Omnia decompiles binaries, disassembles code, and detects obfuscated malware and zero-day patterns that traditional scanners miss.\n\n\n\nMarket Intelligence\n\n## Monitor every marketplace, spot risks early\n\nSearch and audit packages across VS Code, npm, PyPI, and more. Instantly surface malicious extensions, typosquatting attempts, and compromised dependencies before they reach your stack.\n\n\n\nWorkflows\n\n## Automate your security playbooks\n\nBuild automated workflows that chain Omnia's AI agents to handle your cybersecurity tasks — from triage to response — without writing a single line of code.\n\n\n",
"fullPageMarkdown": "Home | Omnia\n\n[](https://omniasec.ai/)\n\n[Try Omnia](https://app.omniasec.ai/login)\n\n# The AI Security *Command Center.*\n\nOrchestrate your security stack, analyze threats with AI, and stop attacks from one interface.\n\n[Try Omnia →](https://app.omniasec.ai/login)\n\nor install the CLI\n\n`curl -fsSL https://omniasec.ai/cli-install | bash`\n\nOur network\n\n## Trusted Partners\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCapabilities\n\n## Built for modern security teams\n\n### Chat\n\nAsk Omnia to deobfuscate code, generate cybersecurity reports, look up hashes, or investigate threats — all through a single conversational interface.\n\n### Custom Agents\n\nCreate personalized AI agents with custom instructions tailored to your security needs. Define how Omnia thinks, responds, and acts for your specific use cases.\n\n### Templates & Prompts\n\nBuild reusable prompt templates for recurring tasks like incident triage, threat analysis, or compliance checks. Work faster without starting from scratch.\n\n### Knowledge\n\nUpload your documents, reports, and intel feeds to create a private knowledge base. Omnia retrieves relevant context automatically when you need it.\n\n### MCPs\n\nConnect your own Model Context Protocol servers to extend Omnia with custom tools, data sources, and integrations — no redeployment needed.\n\n### Community\n\nShare and discover agents, prompts, workflows, templates, and knowledge bases created by other security professionals. Scale your operations with collective intelligence.\n\nChat\n\n## Your AI security analyst, one message away\n\nDeobfuscate code, generate threat reports, investigate hashes, and run security tasks — all through a conversational interface powered by Omnia's AI agents.\n\n\n\nFile Analysis\n\n## Upload any file, uncover hidden threats\n\nSubmit files for deep inspection powered by AI engines. Omnia decompiles binaries, disassembles code, and detects obfuscated malware and zero-day patterns that traditional scanners miss.\n\n\n\nMarket Intelligence\n\n## Monitor every marketplace, spot risks early\n\nSearch and audit packages across VS Code, npm, PyPI, and more. Instantly surface malicious extensions, typosquatting attempts, and compromised dependencies before they reach your stack.\n\n\n\nWorkflows\n\n## Automate your security playbooks\n\nBuild automated workflows that chain Omnia's AI agents to handle your cybersecurity tasks — from triage to response — without writing a single line of code.\n\n\n\nZero-day reads\n\n[View all →](https://omniasec.ai/blog/)\n\n[### Welcome to Omnia: Redefining Malware Analysis in the Age of AIMar 16, 2026](https://omniasec.ai/blog/welcome-to-omnia/)\n\n\\> |\n\n## Ready to see what\nOmnia can do?\n\nJoin the community redefining how we fight cyber threats.\n\n[Try Omnia →](https://app.omniasec.ai/login)\n",
"markdownStats": {
"images": 4,
"links": 1,
"tables": 0,
"codeBlocks": 0,
"headings": 13
},
"tokens": {
"htmlTokens": 13402,
"markdownTokens": 582,
"reduction": 12820,
"reductionPercent": 96
},
"score": {
"score": 67,
"grade": "C",
"rubricVersion": 3,
"dimensions": {
"accessibility": {
"score": 94,
"weight": 30,
"grade": "A",
"checks": {
"content_without_js": {
"score": 100,
"weight": 55,
"evidence": "proven",
"details": "Content available without JavaScript"
},
"fast_content_position": {
"score": 75,
"weight": 25,
"evidence": "plausible",
"details": "Main content starts at 22% of HTML"
},
"reasonable_page_size": {
"score": 100,
"weight": 20,
"evidence": "plausible",
"details": "Page size: 35KB"
}
}
},
"aiDiscoverability": {
"score": 45,
"weight": 25,
"grade": "D",
"checks": {
"robots_allows_ai_bots": {
"score": 100,
"weight": 35,
"evidence": "proven",
"details": "All major AI search bots allowed"
},
"supports_markdown_negotiation": {
"score": 0,
"weight": 20,
"evidence": "plausible",
"details": "No Markdown for Agents support detected"
},
"has_sitemap": {
"score": 0,
"weight": 15,
"evidence": "plausible",
"details": "No sitemap found"
},
"has_robots_txt": {
"score": 100,
"weight": 10,
"evidence": "plausible",
"details": "robots.txt exists"
},
"has_llms_txt": {
"score": 0,
"weight": 10,
"evidence": "speculative",
"details": "No llms.txt found"
},
"has_content_signals": {
"score": 0,
"weight": 10,
"evidence": "speculative",
"details": "No Content-Signal found (robots.txt or HTTP headers)",
"mechanisms": {
"robotsTxt": false,
"httpHeader": false,
"policy": false
}
}
}
},
"structuredData": {
"score": 33,
"weight": 20,
"grade": "F",
"checks": {
"has_schema_org": {
"score": 0,
"weight": 35,
"evidence": "proven",
"details": "No JSON-LD / Schema.org found"
},
"has_open_graph": {
"score": 67,
"weight": 20,
"evidence": "plausible",
"details": "2/3 OG tags present"
},
"has_meta_description": {
"score": 50,
"weight": 20,
"evidence": "plausible",
"details": "Meta description too short: 5 chars"
},
"has_canonical_url": {
"score": 0,
"weight": 15,
"evidence": "plausible",
"details": "No canonical URL"
},
"has_lang_attribute": {
"score": 100,
"weight": 10,
"evidence": "plausible",
"details": "lang=\"en\""
}
}
},
"semanticHtml": {
"score": 88,
"weight": 15,
"grade": "B",
"checks": {
"proper_heading_hierarchy": {
"score": 100,
"weight": 30,
"evidence": "plausible",
"details": "Clean heading hierarchy"
},
"uses_article_or_main": {
"score": 100,
"weight": 25,
"evidence": "plausible",
"details": "Has <main>"
},
"semantic_elements": {
"score": 41,
"weight": 20,
"evidence": "plausible",
"details": "12 semantic elements, 85 divs (ratio: 12%)"
},
"meaningful_alt_texts": {
"score": 100,
"weight": 15,
"evidence": "plausible",
"details": "36/36 images with meaningful alt text"
},
"low_div_nesting": {
"score": 100,
"weight": 10,
"evidence": "speculative",
"details": "Avg div depth: 2.0, max: 3"
}
}
},
"contentEfficiency": {
"score": 73,
"weight": 10,
"grade": "C",
"checks": {
"token_reduction_ratio": {
"score": 100,
"weight": 40,
"evidence": "speculative",
"details": "96% token reduction (HTML→Markdown)"
},
"content_to_noise_ratio": {
"score": 25,
"weight": 30,
"evidence": "speculative",
"details": "Content ratio: 5.9% (2138 content chars / 35943 HTML bytes)"
},
"reasonable_page_weight": {
"score": 100,
"weight": 20,
"evidence": "speculative",
"details": "HTML size: 35KB"
},
"minimal_inline_styles": {
"score": 50,
"weight": 10,
"evidence": "speculative",
"details": "4/275 elements with inline styles (1.5%)"
}
}
}
}
},
"recommendations": [
{
"id": "add_schema_org",
"priority": "critical",
"category": "structuredData",
"titleKey": "rec.add_schema_org.title",
"descriptionKey": "rec.add_schema_org.description",
"howToKey": "rec.add_schema_org.howto",
"howToStepKeys": null,
"effort": "moderate",
"estimatedImpact": 7,
"maxImpact": 7,
"evidence": "proven",
"checkScore": 0,
"checkDetails": "No JSON-LD / Schema.org found"
},
{
"id": "add_markdown_negotiation",
"priority": "critical",
"category": "aiDiscoverability",
"titleKey": "rec.add_markdown_negotiation.title",
"descriptionKey": "rec.add_markdown_negotiation.description",
"howToKey": "rec.add_markdown_negotiation.howto",
"howToStepKeys": null,
"effort": "significant",
"estimatedImpact": 5,
"maxImpact": 5,
"evidence": "plausible",
"checkScore": 0,
"checkDetails": "No Markdown for Agents support detected"
},
{
"id": "add_sitemap",
"priority": "high",
"category": "aiDiscoverability",
"titleKey": "rec.add_sitemap.title",
"descriptionKey": "rec.add_sitemap.description",
"howToKey": "rec.add_sitemap.howto",
"howToStepKeys": null,
"effort": "quick-win",
"estimatedImpact": 3.8,
"maxImpact": 3.8,
"evidence": "plausible",
"checkScore": 0,
"checkDetails": "No sitemap found"
},
{
"id": "add_canonical_url",
"priority": "high",
"category": "structuredData",
"titleKey": "rec.add_canonical_url.title",
"descriptionKey": "rec.add_canonical_url.description",
"howToKey": "rec.add_canonical_url.howto",
"howToStepKeys": null,
"effort": "quick-win",
"estimatedImpact": 3,
"maxImpact": 3,
"evidence": "plausible",
"checkScore": 0,
"checkDetails": "No canonical URL"
},
{
"id": "add_llms_txt",
"priority": "high",
"category": "aiDiscoverability",
"titleKey": "rec.add_llms_txt.title",
"descriptionKey": "rec.add_llms_txt.description",
"howToKey": "rec.add_llms_txt.howto",
"howToStepKeys": null,
"effort": "quick-win",
"estimatedImpact": 2.5,
"maxImpact": 2.5,
"evidence": "speculative",
"checkScore": 0,
"checkDetails": "No llms.txt found"
},
{
"id": "add_content_signals",
"priority": "high",
"category": "aiDiscoverability",
"titleKey": "rec.add_content_signals.title",
"descriptionKey": "rec.add_content_signals.description",
"howToKey": "rec.add_content_signals.howto",
"howToStepKeys": null,
"effort": "quick-win",
"estimatedImpact": 2.5,
"maxImpact": 2.5,
"evidence": "speculative",
"checkScore": 0,
"checkDetails": "No Content-Signal found (robots.txt or HTTP headers)"
},
{
"id": "improve_content_ratio",
"priority": "medium",
"category": "contentEfficiency",
"titleKey": "rec.improve_content_ratio.title",
"descriptionKey": "rec.improve_content_ratio.description",
"howToKey": "rec.improve_content_ratio.howto",
"howToStepKeys": null,
"effort": "moderate",
"estimatedImpact": 2.3,
"maxImpact": 3,
"evidence": "speculative",
"checkScore": 25,
"checkDetails": "Content ratio: 5.9% (2138 content chars / 35943 HTML bytes)"
},
{
"id": "add_semantic_elements",
"priority": "medium",
"category": "semanticHtml",
"titleKey": "rec.add_semantic_elements.title",
"descriptionKey": "rec.add_semantic_elements.description",
"howToKey": "rec.add_semantic_elements.howto",
"howToStepKeys": null,
"effort": "moderate",
"estimatedImpact": 1.8,
"maxImpact": 3,
"evidence": "plausible",
"checkScore": 41,
"checkDetails": "12 semantic elements, 85 divs (ratio: 12%)"
},
{
"id": "add_open_graph",
"priority": "medium",
"category": "structuredData",
"titleKey": "rec.add_open_graph.title",
"descriptionKey": "rec.add_open_graph.description",
"howToKey": "rec.add_open_graph.howto",
"howToStepKeys": null,
"effort": "quick-win",
"estimatedImpact": 1.3,
"maxImpact": 4,
"evidence": "plausible",
"checkScore": 67,
"checkDetails": "2/3 OG tags present"
},
{
"id": "remove_inline_styles",
"priority": "low",
"category": "contentEfficiency",
"titleKey": "rec.remove_inline_styles.title",
"descriptionKey": "rec.remove_inline_styles.description",
"howToKey": "rec.remove_inline_styles.howto",
"howToStepKeys": null,
"effort": "moderate",
"estimatedImpact": 0.5,
"maxImpact": 1,
"evidence": "speculative",
"checkScore": 50,
"checkDetails": "4/275 elements with inline styles (1.5%)"
}
],
"llmsTxtPreview": "# omniasec.ai\n\n> Omnia\n\n## Documentation\n- [Docs](https://omniasec.ai/docs/)\n\n## Main\n- [Home | Omnia](https://omniasec.ai/): Omnia\n- [About](https://omniasec.ai/about/)\n- [Docs](https://omniasec.ai/docs/)\n- [Bug Bounty](https://omniasec.ai/bug-bounty/)\n\n## Blog\n- [Blog](https://omniasec.ai/blog/)\n\n## Legal\n- [Privacy Policy](https://omniasec.ai/privacy-policy/)\n\n",
"llmsTxtExisting": null,
"emergingProtocols": {
"oauthProtectedResource": {
"exists": false,
"url": "https://omniasec.ai/.well-known/oauth-protected-resource"
},
"oauthDiscovery": {
"exists": false,
"url": "https://omniasec.ai/.well-known/oauth-authorization-server"
},
"mcpServerCard": {
"exists": false,
"url": "https://omniasec.ai/.well-known/mcp/server-card.json",
"draft": true
},
"a2aAgentCard": {
"exists": false,
"url": "https://omniasec.ai/.well-known/agent-card.json"
},
"apiCatalog": {
"exists": false,
"url": "https://omniasec.ai/.well-known/api-catalog"
},
"agentSkills": {
"exists": false,
"url": "https://omniasec.ai/.well-known/agent-skills/index.json",
"draft": true
},
"count": 0,
"total": 6
},
"botAccess": {
"probed": true,
"bot": "OAI-SearchBot",
"controlStatus": 200,
"botStatus": 200,
"discriminates": false,
"refusedAsBot": false,
"edge": null,
"verifiable": false,
"detail": "This origin answers OAI-SearchBot exactly as it answers any other client (200). No edge-level filtering of AI crawlers observed."
},
"snippets": [
{
"id": "add_llms_txt",
"title": "Create /llms.txt",
"description": "Upload this file to your web root. It tells AI agents what your site is about and which pages matter.",
"language": "markdown",
"code": "# omniasec.ai\n\n> Omnia\n\n## Documentation\n- [Docs](https://omniasec.ai/docs/)\n\n## Main\n- [Home | Omnia](https://omniasec.ai/): Omnia\n- [About](https://omniasec.ai/about/)\n- [Docs](https://omniasec.ai/docs/)\n- [Bug Bounty](https://omniasec.ai/bug-bounty/)\n\n## Blog\n- [Blog](https://omniasec.ai/blog/)\n\n## Legal\n- [Privacy Policy](https://omniasec.ai/privacy-policy/)\n\n",
"filename": "/llms.txt"
},
{
"id": "add_open_graph",
"title": "Add missing Open Graph tags",
"description": "Open Graph tags control how your page looks when shared on social media and how AI platforms preview your URL in answers.",
"language": "html",
"code": "<meta property=\"og:image\" content=\"https://yoursite.com/og-image.jpg\">\n<meta property=\"og:url\" content=\"https://omniasec.ai/\">\n<meta property=\"og:type\" content=\"website\">",
"filename": "<head>",
"stacks": [
{
"id": "html",
"label": "HTML <head>",
"language": "html",
"filename": "<head>",
"code": "<meta property=\"og:image\" content=\"https://yoursite.com/og-image.jpg\">\n<meta property=\"og:url\" content=\"https://omniasec.ai/\">\n<meta property=\"og:type\" content=\"website\">"
},
{
"id": "wordpress",
"label": "WordPress",
"language": "php",
"filename": "functions.php",
"code": "<?php\n// Quick Open Graph tags without a plugin (skip if Yoast / Rank Math is active)\nadd_action('wp_head', function () {\n if (!is_singular()) return;\n $post = get_queried_object();\n $title = get_the_title($post);\n $desc = get_the_excerpt($post) ?: wp_trim_words(strip_tags($post->post_content), 30);\n $image = get_the_post_thumbnail_url($post, 'large') ?: 'https://yoursite.com/og-image.jpg';\n $url = get_permalink($post);\n printf('<meta property=\"og:title\" content=\"%s\">' . \"\\n\", esc_attr($title));\n printf('<meta property=\"og:description\" content=\"%s\">' . \"\\n\", esc_attr($desc));\n printf('<meta property=\"og:image\" content=\"%s\">' . \"\\n\", esc_url($image));\n printf('<meta property=\"og:url\" content=\"%s\">' . \"\\n\", esc_url($url));\n echo '<meta property=\"og:type\" content=\"article\">' . \"\\n\";\n}, 5);"
},
{
"id": "nextjs",
"label": "Next.js",
"language": "typescript",
"filename": "app/page.tsx",
"code": "// Next.js App Router — Metadata API\nimport type { Metadata } from 'next';\n\nexport const metadata: Metadata = {\n title: \"Home | Omnia\",\n description: \"Omnia\",\n openGraph: {\n title: \"Home | Omnia\",\n description: \"Omnia\",\n url: \"https://omniasec.ai/\",\n images: [\"https://yoursite.com/og-image.jpg\"],\n type: 'website',\n },\n};"
}
]
},
{
"id": "add_canonical_url",
"title": "Add canonical URL",
"description": "The canonical URL tells AI agents which version of the page is the \"official\" one, avoiding duplicate content issues.",
"language": "html",
"code": "<link rel=\"canonical\" href=\"https://omniasec.ai/\">",
"filename": "<head>"
},
{
"id": "add_schema_org",
"title": "Add Schema.org JSON-LD",
"description": "Structured data helps AI agents understand the type, author, and purpose of your content.",
"language": "html",
"code": "<script type=\"application/ld+json\">\n{\n \"@context\": \"https://schema.org\",\n \"@type\": \"WebPage\",\n \"name\": \"Home | Omnia\",\n \"description\": \"Omnia\",\n \"url\": \"https://omniasec.ai/\",\n \"inLanguage\": \"en\"\n}\n</script>",
"filename": "<head>"
},
{
"id": "add_sitemap",
"title": "Create /sitemap.xml",
"description": "A sitemap helps AI agents discover all your pages. Most CMS platforms generate one automatically.",
"language": "xml",
"code": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<urlset xmlns=\"http://www.sitemaps.org/schemas/sitemap/0.9\">\n <url>\n <loc>https://omniasec.ai/</loc>\n <lastmod>2026-09-01</lastmod>\n </url>\n</urlset>",
"filename": "/sitemap.xml"
},
{
"id": "add_content_signals",
"title": "Add Content-Signal directives",
"description": "Content-Signal tells AI agents how they may use your content. The canonical location is robots.txt, but you can also expose it as an HTTP header from any stack.",
"language": "txt",
"code": "User-agent: *\nContent-Signal: search=yes, ai-input=yes, ai-train=no",
"filename": "/robots.txt",
"stacks": [
{
"id": "robots",
"label": "robots.txt",
"language": "txt",
"filename": "/robots.txt",
"code": "User-agent: *\nContent-Signal: search=yes, ai-input=yes, ai-train=no"
},
{
"id": "nginx",
"label": "Nginx",
"language": "nginx",
"filename": "server block",
"code": "# Inside your server { } block:\nadd_header Content-Signal \"search=yes, ai-input=yes, ai-train=no\" always;"
},
{
"id": "apache",
"label": "Apache",
"language": "apache",
"filename": ".htaccess",
"code": "# In .htaccess (or VirtualHost):\nHeader set Content-Signal \"search=yes, ai-input=yes, ai-train=no\""
},
{
"id": "wordpress",
"label": "WordPress",
"language": "php",
"filename": "functions.php",
"code": "<?php\n// In your theme's functions.php or a small mu-plugin\nadd_action('send_headers', function () {\n header('Content-Signal: search=yes, ai-input=yes, ai-train=no');\n});\n\n// Optional: also append the directive to the dynamic robots.txt\nadd_filter('robots_txt', function ($output) {\n return $output . \"\\nContent-Signal: search=yes, ai-input=yes, ai-train=no\\n\";\n}, 10, 1);"
},
{
"id": "nextjs",
"label": "Next.js",
"language": "typescript",
"filename": "middleware.ts",
"code": "// middleware.ts (Next.js 13+ App Router or Pages Router)\nimport { NextResponse } from 'next/server';\nexport function middleware() {\n const res = NextResponse.next();\n res.headers.set(\n 'Content-Signal',\n 'search=yes, ai-input=yes, ai-train=no'\n );\n return res;\n}\nexport const config = { matcher: '/:path*' };"
},
{
"id": "cloudflare",
"label": "Cloudflare Workers",
"language": "javascript",
"filename": "worker.js",
"code": "// Cloudflare Worker that proxies your origin and adds the header\nexport default {\n async fetch(request, env, ctx) {\n const res = await fetch(request);\n const newRes = new Response(res.body, res);\n newRes.headers.set(\n 'Content-Signal',\n 'search=yes, ai-input=yes, ai-train=no'\n );\n return newRes;\n },\n};"
},
{
"id": "express",
"label": "Express / Fastify",
"language": "javascript",
"filename": "server.js",
"code": "// Express\napp.use((req, res, next) => {\n res.setHeader('Content-Signal', 'search=yes, ai-input=yes, ai-train=no');\n next();\n});\n\n// Fastify\nfastify.addHook('onSend', (request, reply, payload, done) => {\n reply.header('Content-Signal', 'search=yes, ai-input=yes, ai-train=no');\n done();\n});"
}
]
},
{
"id": "add_markdown_negotiation",
"title": "Support Markdown for Agents",
"description": "Let AI agents request a clean Markdown version of any page via content negotiation, .md alternate URLs, link tags or Link headers.",
"language": "html",
"code": "<!-- Mechanism 3: link tag advertising the .md alternate -->\n<link rel=\"alternate\" type=\"text/markdown\" href=\"/page.md\">",
"filename": "<head>",
"stacks": [
{
"id": "html",
"label": "HTML <head>",
"language": "html",
"filename": "<head>",
"code": "<!-- Mechanism 3: link tag advertising the .md alternate -->\n<link rel=\"alternate\" type=\"text/markdown\" href=\"/page.md\">"
},
{
"id": "express",
"label": "Express",
"language": "javascript",
"filename": "server.js",
"code": "// Mechanisms 1 + 4: content negotiation + Link header\napp.get('/page', (req, res) => {\n res.setHeader('Vary', 'Accept');\n res.setHeader('Link', '</page.md>; rel=\"alternate\"; type=\"text/markdown\"');\n if ((req.headers.accept || '').includes('text/markdown')) {\n res.type('text/markdown; charset=utf-8');\n return res.send(renderMarkdown('page'));\n }\n res.render('page');\n});"
},
{
"id": "fastify",
"label": "Fastify",
"language": "javascript",
"filename": "server.js",
"code": "// Mechanisms 1 + 4: content negotiation + Link header\nfastify.get('/page', async (req, reply) => {\n reply.header('Vary', 'Accept');\n reply.header('Link', '</page.md>; rel=\"alternate\"; type=\"text/markdown\"');\n if ((req.headers.accept || '').includes('text/markdown')) {\n return reply.type('text/markdown; charset=utf-8').send(renderMarkdown('page'));\n }\n return reply.view('/page.ejs');\n});"
},
{
"id": "nextjs",
"label": "Next.js",
"language": "typescript",
"filename": "app/page/route.ts",
"code": "// Next.js App Router — Route Handler returning Markdown\nimport { NextRequest } from 'next/server';\nimport { renderMarkdown } from '@/lib/md';\nexport async function GET(req: NextRequest) {\n const accept = req.headers.get('accept') || '';\n if (accept.includes('text/markdown')) {\n return new Response(await renderMarkdown('page'), {\n headers: {\n 'Content-Type': 'text/markdown; charset=utf-8',\n 'Vary': 'Accept',\n },\n });\n }\n // Fall through to the page component\n return new Response(null, { status: 404 });\n}"
},
{
"id": "wordpress",
"label": "WordPress",
"language": "php",
"filename": "functions.php",
"code": "<?php\n// Mechanism 1: respond to Accept: text/markdown on the same URL\nadd_action('template_redirect', function () {\n if (!is_singular()) return;\n $accept = $_SERVER['HTTP_ACCEPT'] ?? '';\n if (strpos($accept, 'text/markdown') === false) return;\n header('Content-Type: text/markdown; charset=utf-8');\n header('Vary: Accept');\n $post = get_queried_object();\n echo \"# \" . get_the_title($post) . \"\\n\\n\";\n echo wp_strip_all_tags(apply_filters('the_content', $post->post_content));\n exit;\n});"
},
{
"id": "static",
"label": "Hugo / Jekyll / Astro",
"language": "txt",
"filename": "static/page.md",
"code": "# Mechanism 2: serve .md alongside .html\n# Hugo: place page.md in /static/ — built unchanged\n# Jekyll: drop page.md in /assets/ — copied as-is\n# Astro: src/pages/page.md.ts that exports a GET returning markdown\n\n# Then advertise with mechanism 3 in <head>:\n# <link rel=\"alternate\" type=\"text/markdown\" href=\"/page.md\">"
}
]
}
]
}
Use our API to get this programmatically (coming soon)
This JSON is for internal use — unlike the Markdown and llms.txt files, it's not meant to be uploaded to your site. Save it as a baseline to track your score over time, share it with your dev team, or integrate it into your CI/CD pipeline.
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