Overall AI readiness score
A combined result across all audit signals.
Excellent
AI readiness audit: 9/3/2026Public technical data
Overall AI readiness assessment
How ready docs.alphaos.net is for AI systems
Alpha Network (docs.alphaos.net) received an AI-readiness score of 76/100 in an automated technical audit. llms.txt was accessible, llms-full.txt was accessible, and ai.txt was not found. The robots.txt analysis found 0 explicitly allowed and 0 blocked AI crawlers, with 1 declared sitemap. Homepage markup completeness was 0%; no Schema.org types were detected and 0 OpenGraph tags were detected. Results reflect the public site response observed on 2026-09-03T01:41:06.571Z.
Audit context
llms.txt is accessible and contains 872 tokens. An expanded llms-full.txt is also available with 18,426 tokens, giving agents more direct context. No separate ai.txt policy was detected; it is optional, but can clarify training, retrieval, and attribution preferences.
robots.txt is available. 11 of 11 tracked AI bots are not blocked. Declared sitemaps: 1. No Schema.org type was detected on the homepage. 0 OpenGraph tags were found and markup completeness is 0%, leaving more entity interpretation to crawlers.
The mobile Lighthouse profile adds Performance 88/100, Accessibility 100/100, Best Practices 100/100, SEO 100/100, and experimental Agentic Browsing 100/100. These signals have a limited weight: they complement rather than replace llms.txt, robots.txt, and structured-data checks.
Confirmed strengths
- Accessible llms.txt
- Expanded llms-full.txt
- 1 sitemap declaration
- Strong Google agentic signals
Priority improvements
- 1Add JSON-LD for the organisation, website, and core entities.
- 2Complete OpenGraph and canonical homepage metadata.
- 3Reduce mobile rendering delay and main-thread work.
llms.txt tokens872
llms-full.txt tokens18,426
ai.txt
sitemap.xml
Google Lighthouse technical profile
A mobile Lighthouse measurement. Google’s experimental Agentic Browsing category is explained separately and does not replace the broader llmsmap AI-readiness score.
88
Performance
100
Accessibility
100
Best Practices
100
Technical SEO
100
Agentic Browsing
What these results mean
Mobile performance is 88/100, with the largest visible content block appearing in 3.5 s and the browser main thread blocked for 40 ms. Layout shift was 0.012. The main thread is where the browser runs JavaScript, calculates layout, and paints the page; long work there delays both user input and browser-agent actions.
Accessibility scored 100/100, Best Practices 100/100, and technical SEO 100/100. The experimental Agentic Browsing category scored 100/100. It measures signals Google currently tests for software agents and is shown separately from the llmsmap AI-readiness score.
1
Free the main thread and accelerate the first view
Split long JavaScript tasks, defer non-critical scripts and styles, and shorten blocking request chains. This helps the primary content appear sooner and makes controls usable earlier.
2
Reduce code that loads without helping the page
Remove unused CSS and JavaScript, load heavy widgets on demand, and limit third-party scripts. Less code means less parsing and background work on the device.
3
Reduce network delay
Improve server response time, remove unnecessary redirects and repeat downloads, and use compression, caching, and selective preconnect hints.
FCP2.4 s
First content
LCP3.5 s
Main content
CLS0.012
Layout stability
TBT40 ms
Blocking time
SI2.4 s
Visual speed
Metric glossary
- FCP · First content
- When the first text or image appeared on screen.
- LCP · Main content
- When the largest visible element in the first viewport rendered.
- CLS · Layout stability
- How much content shifted unexpectedly while loading; lower is better.
- TBT · Blocking time
- How long the browser main thread could not respond quickly to input.
- SI · Visual speed
- How quickly the visible viewport filled with content.
9/3/2026Lighthouse 13.4.1Mobile profile
AI readiness checks
Machine-readable files, crawler policy, discovery, and homepage markup.
ai.txt
ai.txt file was not found
Sitemap in robots.txt1
1 sitemap found
Schema.org (JSON-LD)
Schema.org markup was not found on the homepage
OpenGraph
OpenGraph tags were not found on the homepage
AI bot access
Based on robots.txt analysis
GPTBotNot mentioned
OAI-SearchBotNot mentioned
ChatGPT-UserNot mentioned
Google-ExtendedNot mentioned
ClaudeBotNot mentioned
Claude-SearchBotNot mentioned
Claude-UserNot mentioned
BytespiderNot mentioned
CCBotNot mentioned
PerplexityBotNot mentioned
Perplexity-UserNot mentioned
Sitemaps
Declared discovery routes for crawlers and agents.
# Alpha Network ## Alpha Network Whitepaper - [World's first decentralized data execution layer of AI](https://docs.alphaos.net/whitepaper/worlds-first-decentralized-data-execution-layer-of-ai.md): Crypto Industry for Advancing AI Development. - [Market Opportunity](https://docs.alphaos.net/whitepaper/market-opportunity.md): For AI Training Data Scarcity - [AlphaOS](https://docs.alphaos.net/whitepaper/alphaos.md): A One-Stop AI-Driven Solution for the Web3 Ecosystem - [How does it Work?](https://docs.alphaos.net/whitepaper/alphaos/how-does-it-work.md) - [Use Cases](https://docs.alphaos.net/whitepaper/alphaos/use-cases.md): How users can use AlphaOS in Web3 to experience the efficiency and security improvements brought by AI? - [Update History](https://docs.alphaos.net/whitepaper/alphaos/update-history.md): Key feature update history of AlphaOS. - [Terms of Service](https://docs.alphaos.net/whitepaper/alphaos/terms-of-service.md) - [Privacy Policy](https://docs.alphaos.net/whitepaper/alphaos/privacy-policy.md) - [Alpha Chain](https://docs.alphaos.net/whitepaper/alpha-chain.md): A Decentralized Blockchain Solution for Private Data Storage and Trading of AI Training Data - [Blockchain Architecture](https://docs.alphaos.net/whitepaper/alpha-chain/blockchain-architecture.md): Robust Data Dynamics in the Alpha Chain Utilizing RPC Node Fluidity and Network Topology Optimization - [Roles](https://docs.alphaos.net/whitepaper/alpha-chain/roles.md) - [Provider](https://docs.alphaos.net/whitepaper/alpha-chain/roles/provider.md) - [Labelers](https://docs.alphaos.net/whitepaper/alpha-chain/roles/labelers.md) - [Preprocessors](https://docs.alphaos.net/whitepaper/alpha-chain/roles/preprocessors.md) - [Data Privacy and Security](https://docs.alphaos.net/whitepaper/alpha-chain/data-privacy-and-security.md) - [Decentralized Task Allocation Virtual Machine](https://docs.alphaos.net/whitepaper/alpha-chain/decentralized-task-allocation-virtual-machine.md) - [Data Utilization and AI Training](https://docs.alphaos.net/whitepaper/alpha-chain/data-utilization-and-ai-training.md) - [Blockchain Consensus](https://docs.alphaos.net/whitepaper/alpha-chain/blockchain-consensus.md) - [Distributed Crawler Protocol (DCP)](https://docs.alphaos.net/whitepaper/distributed-crawler-protocol-dcp.md): A Decentralized and Privacy-First Solution for AI Data Collection - [Distributed VPN Protocol (DVP)](https://docs.alphaos.net/whitepaper/distributed-vpn-protocol-dvp.md): A decentralized VPN protocol enabling DePin devices to share bandwidth, ensuring Web3 users access resources securely and anonymously while protecting their location privacy. - [Architecture](https://docs.alphaos.net/whitepaper/distributed-vpn-protocol-dvp/architecture.md): The DVP protocol is built on the following core components: - [Benefits](https://docs.alphaos.net/whitepaper/distributed-vpn-protocol-dvp/benefits.md): The Distributed VPN Protocol (DVP) offers several significant benefits: - [Tokenomics](https://docs.alphaos.net/whitepaper/tokenomics.md) - [DePin's Sustainable Revenue](https://docs.alphaos.net/whitepaper/depins-sustainable-revenue.md): Integrating Distributed Crawler Protocol (DCP) with Alpha Network for Sustainable DePin Ecosystems - [Committed to Global Poverty Alleviation](https://docs.alphaos.net/whitepaper/committed-to-global-poverty-alleviation.md) - [@alpha-network/keccak256-zk](https://docs.alphaos.net/whitepaper/open-source-contributions/alpha-network-keccak256-zk.md)
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Added 9/3/2026