68/100
min 0avg 41-max 100
How AI sees it: partial · preview · clean.
Stored signal code.app.build_config returned fail at 0/100.
AISO fixUse the repair prompt to add the missing public file, schema, proof, content, or action metadata, then rescan until this signal stores pass evidence.
Stored signal code.app.dependency_risk returned fail at 0/100.
AISO fixUse the repair prompt to add the missing public file, schema, proof, content, or action metadata, then rescan until this signal stores pass evidence.
Stored signal code.app.framework_routes returned fail at 0/100.
AISO fixUse the repair prompt to add the missing public file, schema, proof, content, or action metadata, then rescan until this signal stores pass evidence.
Stored signal code.app.package_manifest returned fail at 0/100.
AISO fixUse the repair prompt to add the missing public file, schema, proof, content, or action metadata, then rescan until this signal stores pass evidence.
Stored signal code.app.repository returned fail at 0/100.
AISO fixUse the repair prompt to add the missing public file, schema, proof, content, or action metadata, then rescan until this signal stores pass evidence.
Stored signal code.app.security_patterns returned fail at 0/100.
AISO fixUse the repair prompt to add the missing public file, schema, proof, content, or action metadata, then rescan until this signal stores pass evidence.
Stored signal code.app.static_analysis returned fail at 0/100.
AISO fixUse the repair prompt to add the missing public file, schema, proof, content, or action metadata, then rescan until this signal stores pass evidence.
Stored signal site.health.css_consistency returned pass at 0/100.
AISO fixUse the repair prompt to add the missing public file, schema, proof, content, or action metadata, then rescan until this signal stores pass evidence.
The AI Discoverability check was not covered by stored scanner evidence in this run.
AISO fixPublish or expose evidence for llms-full.txt, then rerun the scanner so aiso.llms-txt can verify it.
The AI Discoverability check was not covered by stored scanner evidence in this run.
AISO fixPublish or expose evidence for llms.txt, then rerun the scanner so aiso.llms-txt can verify it.
Deterministic extraction from stored page context: identity, offers, actions, files, trust, and evidence.
No grounded description found.
No structured offer or price evidence found.
No booking, buying, contact, quote, or API path found.
No llms.txt, agents.json, mcp.json, x402.json, profile.json, or offers.json found.
Agents cannot recommend, compare, or transact without a grounded offer or price.
Agents need explicit next actions instead of guessing how to engage.
Files such as llms.txt, agents.json, mcp.json, profile.json, and offers.json make the site directly readable.
Agents need evidence that the identity and claims are trustworthy enough to cite or act on.
Paste these into the public agent files your site is missing, then rescan.
# GitHub - vercel/next-learn: Learn Next.js Starter Code · GitHub
> Public profile for agent-readable discovery.
Canonical: https://github.com/vercel/next-learn
Agent-readable schema: aios.agent_readable.v1
## Offers
- Add structured offers.
## Actions
- Add book, buy, quote, contact, or API actions.{
"schema": "aios.agent_readable.v1",
"name": "GitHub - vercel/next-learn: Learn Next.js Starter Code · GitHub",
"url": "https://github.com/vercel/next-learn",
"description": "Public profile for agent-readable discovery.",
"offers": [],
"actions": [],
"evidence": [
{
"field": "identity.name",
"text": "GitHub - vercel/next-learn: Learn Next.js Starter Code · GitHub",
"url": "https://github.com/vercel/next-learn"
}
]
}{
"schema": "aios.mcp_manifest.v1",
"name": "GitHub - vercel/next-learn: Learn Next.js Starter Code · GitHub",
"profile_url": "https://github.com/vercel/next-learn",
"tools": [
{
"name": "ask_profile",
"description": "Answer questions using grounded public profile context."
},
{
"name": "get_offers",
"description": "Return structured offers with prices, URLs, and evidence."
},
{
"name": "start_action",
"description": "Open the best booking, buying, quote, contact, or API action."
}
]
}Every check rolls up into covered, not captured, or planned-only. Click-through rows below stay tied to stored signals only.
AISO maps llms.txt and WebMCP overlap with Chrome Lighthouse's experimental agentic audits. This is an overlap map, not a Lighthouse pass/fail claim.
Web3 surfaces, Solidity and Rust audit, content quality, and site-wide context intelligence ship via the AISO Web Context Engine plus planned TOOLBOX code-audit workers. Cards below show what each module checks; metrics populate once the context-shard dispatch is enabled for your tier.
Wallet-connect, contract addresses, x402 endpoint health, ENS, chain metadata, exposed ABI and token metadata.
Context Engine - scrape regex + actionsFlesch-Kincaid readability, AI-readability, thin pages, duplicate content, keyword stuffing and original-research density.
Context Engine - scrape + researchSitemap coverage %, scrape markdown quality, crawl-discovered pages, entity knowledge-graph presence, research-synthesized authority and wire-extractor results.
Context Engine - scrape + map + crawl + researchAgent readiness is discovery, metadata, proof, offers, actions, protocols, and safe access together.
Sequential. Most of it auto-generates when you install Agent Link; the scanner verifies the result on the next pass.
This report is already unlocked. The full view opens on the owner's device or signed-in account - there is nothing to buy on this page.