Overall AI readiness score
A combined result across all audit signals.
Excellent
AI readiness audit: 7/21/2026Public technical data
Overall AI readiness assessment
How ready oxunrowing.app is for AI systems
Oxun Rowing was independently audited by llmsmap. oxunrowing.app currently scores 77/100 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 427 tokens. An expanded llms-full.txt is also available with — tokens, giving agents more direct context. A separate ai.txt policy is published.
robots.txt is available. 11 of 11 tracked AI bots are not blocked. Declared sitemaps: 0. No Schema.org type was detected on the homepage. 3 OpenGraph tags were found and markup completeness is 30%, leaving more entity interpretation to crawlers.
The mobile Lighthouse profile adds Performance 100/100, Accessibility 93/100, Best Practices 92/100, SEO 83/100, 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
- Published AI policy
- Strong Google agentic signals
Priority improvements
- 1Declare the current sitemap.xml in robots.txt.
- 2Add JSON-LD for the organisation, website, and core entities.
- 3Complete OpenGraph and canonical homepage metadata.
llms.txt tokens427
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.
100
Performance
93
Accessibility
92
Best Practices
83
Technical SEO
100
Agentic Browsing
What these results mean
The mobile page renders efficiently; its largest visible content block appeared in 0.8 s.
Accessibility scored 93/100, Best Practices 92/100, and technical SEO 83/100. 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
Optimise images and their loading order
Serve correctly sized modern formats, prioritise the primary visual, and lazy-load content below the first viewport.
4
Reduce network delay
Improve server response time, remove unnecessary redirects and repeat downloads, and use compression, caching, and selective preconnect hints.
FCP0.8 s
First content
LCP0.8 s
Main content
CLS0
Layout stability
TBT20 ms
Blocking time
SI1.4 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.
7/21/2026Lighthouse 13.4.0Mobile profile
AI readiness checks
Machine-readable files, crawler policy, discovery, and homepage markup.
Sitemap in robots.txt
Sitemap is not declared in robots.txt
Schema.org (JSON-LD)
Schema.org markup was not found on the homepage
OpenGraph30%
3 OG tags found
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
OpenGraph tags
Social preview metadata found on the homepage.
og:titleOxun Rowing Appog:urlhttps://oxunrowing.app# Oxun Rowing Also known as Oxun, Oxun Rowing App. > Oxun is a cross-platform tracker for indoor and outdoor rowing, supporting iOS, watchOS, iPadOS. It provides advanced metrics, HealthKit integration, and seamless device connectivity for rowers across multiple rowing machine manufacturers. Key points: - Built for both indoor and outdoor rowing tracking - For outdoor rowers, it's designed to be a replacement for traditional rowing race trackers. - Supports major rowing machine brands through Bluetooth FTMS and specific protocols - Integrates with Apple HealthKit for comprehensive fitness tracking ## Core Features - Indoor Rowing Features: Connect to Bluetooth-enabled rowing machines, track real-time metrics including split time, stroke rate, power output - Outdoor Rowing Features: GPS tracking, route mapping, motion data capture for stroke analysis - Logbook and Analytics Dashboard: Comprehensive workout history, performance metrics across indoor and outdoor sessions. - HealthKit Integration: Syncs workout data with Apple Health, allowing for a unified health and fitness tracking experience ## Help and Support - [Supported Rowing Machines](https://oxunrowing.app/indoor-rowing/supported-rowing-machines): Full list of compatible rowing machines and their specific features - [Connecting to Apple Health](https://oxunrowing.app/help/rowing-apple-health/): A detailed guide on how to connect Oxun with Apple Health for syncing workout data. > For feature suggestions or support, please contact us via the app or via email at support@oxunrowing.app. We are always looking to improve the app and value user feedback and aim to reply promptly to all inquiries within a couple of days.
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Added 7/19/2026