Overall AI readiness score
A combined result across all audit signals.
Average
AI readiness audit: 8/12/2026Public technical data
Overall AI readiness assessment
How ready deeppavlov.github.io is for AI systems
AutoIntent (deeppavlov.github.io) received an AI-readiness score of 61/100 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 0 declared sitemaps. 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 2026-08-12T00:16:57.078Z.
Audit context
llms.txt is accessible and contains 461 tokens. An expanded llms-full.txt is also available with 35,252 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 was not found, so explicit crawler policy could not be confirmed. 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 100/100, Accessibility 55/100, Best Practices 92/100, SEO 82/100, and experimental Agentic Browsing 50/100. 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
Priority improvements
- 1Publish robots.txt with explicit search and AI crawler rules.
- 2Declare the current sitemap.xml in robots.txt.
- 3Add JSON-LD for the organisation, website, and core entities.
- 4Complete OpenGraph and canonical homepage metadata.
llms.txt tokens461
llms-full.txt tokens35,252
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.
100
Performance
55
Accessibility
92
Best Practices
82
Technical SEO
50
Agentic Browsing
What these results mean
The mobile page renders efficiently; its largest visible content block appeared in 0.8 s.
Accessibility scored 55/100, Best Practices 92/100, and technical SEO 82/100. The experimental Agentic Browsing category scored 50/100. It measures signals Google currently tests for software agents and is shown separately from the llmsmap AI-readiness score.
1
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.
2
Improve interface contrast and readability
Increase contrast for text, states, and interactive controls so both people and visual agents can distinguish content from actions and supporting labels.
3
Clarify search and canonical signals
Verify unique titles and descriptions, crawlability, canonicals, descriptive link text, and language relationships. This reduces ambiguity for search engines and AI systems selecting a page.
FCP0.8 s
First content
LCP0.8 s
Main content
CLS0
Layout stability
TBT0 ms
Blocking time
SI0.8 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.
8/12/2026Lighthouse 13.4.1Mobile profile
AI readiness checks
Machine-readable files, crawler policy, discovery, and homepage markup.
llms-full.txt
Full version is available
https://deeppavlov.github.io/AutoIntent/versions/dev/llms-full.txtai.txt
ai.txt file was not found
robots.txt
robots.txt file was not found
Sitemap in robots.txt
Sitemap is not declared in robots.txt
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
# AutoIntent > AutoIntent is an open source tool for automatic configuration of text classification pipelines, with specialized support for intent prediction. ## Docs - [AutoIntent documentation](/index.html) - [Quickstart](/quickstart.html) - [Installation](/installation.html) - [Key Concepts](/concepts.html) - [User Guides](/user_guides.html) - [Basic Usage](/user_guides/index_basic_usage.html) - [Data](/user_guides/user_guides.basic_usage.01_data.html) - [Advanced Usage](/user_guides/index_advanced_usage.html) - [Data](/user_guides/user_guides.advanced.01_data.html) - [Embedder Configuration](/user_guides/user_guides.advanced.02_embedder_configuration.html) - [AutoML Customization](/user_guides/user_guides.advanced.03_automl.html) - [Run reporting](/user_guides/user_guides.advanced.04_reporting.html) - [Logging to stdout and file](/user_guides/user_guides.advanced.05_logging.html) - [Modules](/user_guides/user_guides.basic_usage.02_modules.html) - [AutoML Pipeline Configuration](/user_guides/user_guides.basic_usage.03_automl.html) - [Inference Pipeline](/user_guides/user_guides.basic_usage.04_inference.html) - [Data augmentation tutorials](/augmentation_tutorials/index.html) - [Balancing Datasets with DatasetBalancer](/augmentation_tutorials/balancer.html) - [Adversarial human-like augmentation](/augmentation_tutorials/adversarial.html) - [DSPY Augmentation](/augmentation_tutorials/dspy_augmentation.html) - [Intent Description Generation](/augmentation_tutorials/intent_description.html) - [Learn](/learn/index.html) - [AutoML and Hyperparameter Optimization](/learn/automl_theory.html) - [Dialogue Systems Theory and Practice](/learn/dialogue_systems.html) - [Optimization](/learn/optimization.html) - [Text Embeddings and Representation Learning](/learn/text_embeddings.html) - [Inference servers](/server.html)
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Added 8/12/2026