#llmsmap.me

Independent technical audit

mlllm.io

mlllm.io

Overall AI readiness score

A combined result across all audit signals.

81of 100Excellent
AI readiness audit: 7/21/2026Public technical data

Overall AI readiness assessment

How ready mlllm.io is for AI systems

81/100

mlllm.io was independently audited by llmsmap. mlllm.io currently scores 81/100 for AI readiness. The result combines AI-specific files, crawler policy, sitemap discovery, structured homepage data, and—when available—the mobile Google Lighthouse technical profile.

Audit context

llms.txt is accessible and contains — tokens. No accessible llms-full.txt was detected, so deeper context still has to be assembled from regular pages. 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: 2. The homepage exposes Schema.org types WebSite, Organization, Person, CollectionPage; 6 OpenGraph tags were found and markup completeness is 80%.

The mobile Lighthouse profile adds Performance 100/100, Accessibility 96/100, Best Practices 100/100, SEO 92/100, and experimental Agentic Browsing 67/100. These signals have a limited weight: they complement rather than replace llms.txt, robots.txt, and structured-data checks.

Confirmed strengths

  • Accessible llms.txt
  • 2 sitemap declarations
  • Schema.org: WebSite, Organization
  • Complete social metadata

Priority improvements

  1. 1Add llms-full.txt for richer first-party context.
llms.txt tokens—
llms-full.txt tokens—
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.

Mobile · Lighthouse
100

Performance

96

Accessibility

100

Best Practices

92

Technical SEO

67

Agentic Browsing

What these results mean

The mobile page renders efficiently; its largest visible content block appeared in 1.1 s.

Accessibility scored 96/100, Best Practices 100/100, and technical SEO 92/100. The experimental Agentic Browsing category scored 67/100. 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

Optimise images and their loading order

Serve correctly sized modern formats, prioritise the primary visual, and lazy-load content below the first viewport.

3

Reduce network delay

Improve server response time, remove unnecessary redirects and repeat downloads, and use compression, caching, and selective preconnect hints.

4

Make controls unambiguous

Give buttons and links accessible names, associate labels with fields, and use ordered headings and semantic regions. The same structure helps screen readers and software agents understand actions.

FCP0.9 s

First content

LCP1.1 s

Main content

CLS0

Layout stability

TBT0 ms

Blocking time

SI2.2 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.
7/21/2026Lighthouse 13.4.0Mobile profile

AI readiness checks

Machine-readable files, crawler policy, discovery, and homepage markup.

llms.txt

File found and accessible

https://mlllm.io/llms.txt
llms-full.txt

Full version was not found

ai.txt

ai.txt file was not found

robots.txt

File found

https://mlllm.io/robots.txt
Sitemap in robots.txt2

2 sitemaps found

Schema.org (JSON-LD)

Types: WebSite, Organization, Person, CollectionPage

OpenGraph80%

6 OG tags found

AI bot access

Based on robots.txt analysis

GPTBotAllowed
OAI-SearchBotAllowed
ChatGPT-UserAllowed
Google-ExtendedAllowed
ClaudeBotAllowed
Claude-SearchBotAllowed
Claude-UserNot mentioned
BytespiderNot mentioned
CCBotNot mentioned
PerplexityBotAllowed
Perplexity-UserNot mentioned

Sitemaps

Declared discovery routes for crawlers and agents.

OpenGraph tags

Social preview metadata found on the homepage.

Markup completeness: 80%
og:typewebsite
og:titlemlllm.io - Live AI news and builder lab
og:descriptionLive AI news from TG-NEWS, English translations, Russian source editions, explainers, projects, and source-backed context.
og:urlhttps://mlllm.io/
og:image:altmlllm.io - Live AI news and builder lab

Schema.org markup

Structured entities and properties exposed on the homepage.

Types found: 4 · Properties: 8
WebSiteOrganizationPersonCollectionPage
namemlllm.io
urlhttps://mlllm.io/
publisher{"@id":"https://mlllm.io/#organization"}
logohttps://mlllm.io/assets/mlllm-og-card.svg
sameAshttps://github.com/sergekostenchuk, https://t.me/Serge_Kost, https://www.threads.com/@sergekost
descriptionLive AI news, explainers, projects, and source-backed editorial infrastructure.
inLanguageen
author{"@id":"https://mlllm.io/about/#person"}
# mlllm.io

mlllm.io is a public AI news and builder lab by Sergey Kostenchuk. It publishes short AI news briefs, expanded source-backed articles, public project pages, and practical notes about AI publishing pipelines, agent workflows, and LLM quality gates.

## Key Sections
- https://mlllm.io/start/ - First-reader guide to the site model, feeds, explainers, RSS, and llms.txt.
- https://mlllm.io/news/ - English short-news index for current AI signals.
- https://mlllm.io/ru/news/ - Russian short-news index.
- https://mlllm.io/articles/ - Expanded English articles linked from short news.
- https://mlllm.io/ru/articles/ - Expanded Russian articles linked from short news.
- https://mlllm.io/topics/ - Evergreen topic pages about the site architecture and publishing model.
- https://mlllm.io/projects/ - Public systems and open-source work behind the site.
- https://mlllm.io/blog/ - English builder notes and author essays.
- https://mlllm.io/ru/blog/ - Russian builder notes and author essays.
- https://mlllm.io/rss.xml - RSS 2.0 feed for current English AI news briefs.
- https://mlllm.io/about/ - Author profile and editorial scope.
- https://mlllm.io/community/ - Telegram, Threads, GitHub, and public collaboration links.

## Best Used For
- Building an AI news pipeline -> https://mlllm.io/topics/ai-news-pipeline/
- Understanding TG-NEWS -> https://mlllm.io/projects/tg-news/
- Inspecting a live cursor-trail reveal Agent Skills demo -> https://mlllm.io/projects/mouse-trail-masking-reveal/
- Designing LLM publication QA gates -> https://mlllm.io/topics/llm-publication-qa/
- Structuring one brief and one longform article per story -> https://mlllm.io/topics/story-surface-contract/

## Author Essays
- https://mlllm.io/ru/blog/skill-kotoryy-sozdaet-skilly/ - Скилл, который создаёт скиллы. Авторская статья о Skill Creator V2 как мета-скилле для создания других agent skills: сначала классифицировать работу, определить риск и evidence, сгенерировать skill, проверить, упаковать и оставить будущие skills тестируемыми.
- /ru/blog/glubokaya-taksonomiya-klassov-skillov-dlya-skill-creator-v2/ - Глубокая таксономия классов скиллов для Skill Creator V2. Очищенная публичная companion note к Skill Creator V2. Объясняет, почему плоская таксономия скиллов ломается, какие шесть осей используются, как отличать class, subclass, tag и tool surface, и как таксономия влияет на evidence gates и skill groups.
- https://mlllm.io/blog/the-skill-that-builds-skills/ - The Skill That Builds Skills. An author essay about Skill Creator V2 as a meta-skill for creating other agent skills: classify the work first, define risk and evidence, generate the skill, review it, package it, and keep future skills testable and auditable.
- /blog/deep-skill-class-taxonomy-for-skill-creator-v2/ - Deep Skill Class Taxonomy for Skill Creator V2. A cleaned public companion research note for Skill Creator V2. It explains why flat skill taxonomies break, what six axes are used, when something is a class, subclass, tag, or tool surface, and how the taxonomy affects evidence gates and skill groups.
- https://mlllm.io/ru/blog/kak-mouse-trail-masking-reveal-stal-agent-skill/ - Как Mouse Trail Masking Reveal стал Agent Skill. Авторская статья о Mouse Trail Masking Reveal: связке из двух Agent Skills, которая проверяет совпадение base/reveal изображений и строит canvas-based soft cursor-trail reveal hero с maskCanvas, revealCanvas, настройками эффекта и browser validation.
- https://mlllm.io/blog/how-mouse-trail-masking-reveal-became-an-agent-skill/ - How Mouse Trail Masking Reveal Became an Agent Skill. An author essay about Mouse Trail Masking Reveal: a two-skill Agent Skills workflow that validates base/reveal image alignment and builds a canvas-based soft cursor-trail reveal hero with maskCanvas, revealCanvas, tuning controls, and browser validation.
- https://mlllm.io/ru/blog/kak-rabota-nad-seo-llm-mlllm-io-prevratilas-v-sistemu-skills/ - Как работа над SEO/LLM mlllm.io превратилась в систему skills. Авторская статья о том, как SEO и LLM-friendly оптимизация mlllm.io превратилась в переиспользуемую группировку Codex skills с оркестрацией, валидацией, schema, crawler access, перелинковкой, UX и task-plan процессом.
- https://mlllm.io/blog/how-mlllm-io-seo-llm-work-became-a-skill-cluster/ - How mlllm.io SEO/LLM Work Became a Skill Cluster. An author essay about turning the SEO and LLM-friendly optimization work behind mlllm.io into a reusable Codex skill cluster with orchestration, validation, schema, crawler access, link architecture, UX, and task-plan based execution.
- https://mlllm.io/ru/blog/kak-ya-sozdal-ai-skill-senior-xray-vpn-architect/ - Как я создал AI-скилл Senior Xray VPN Architect. Авторская статья о проектировании AI-скилла Senior Xray VPN Architect для легитимной приватной VPN/proxy-инфраструктуры: роль, safety-границы, режимы работы, reference-файлы, split tunneling, client compatibility, DNS/routing, актуализация источников, линтер и monitoring.
- https://mlllm.io/blog/how-i-built-the-senior-xray-vpn-architect-ai-skill/ - How I Built the Senior Xray VPN Architect AI Skill. An author essay about designing the Senior Xray VPN Architect AI skill for legitimate private VPN/proxy infrastructure: role, safety boundaries, work modes, reference files, split tunneling, client compatibility, DNS/routing, source freshness, linting, and monitoring.

## Do Not Use
- https://mlllm.io/admin/ - private admin area.
- https://mlllm.io/api/ - private/admin and ingestion APIs.
- https://mlllm.io/plans/ - internal planning artifacts.
- https://mlllm.io/server/ - internal server code paths.
- Draft, unpublished, or unreviewed locale URLs.
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Added 7/4/2026

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mlllm.io - AI readiness audit | llmsmap.me