#llmsmap.me

Independent technical audit

calk.me

calk.me

Overall AI readiness score

A combined result across all audit signals.

80of 100Excellent
AI readiness audit: 10/8/2026Public technical data

Overall AI readiness assessment

How ready calk.me is for AI systems

80/100

Calk: вес под контролем was independently audited by llmsmap. calk.me currently scores 80/100 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 2,242 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: 1. No Schema.org type was detected on the homepage. 11 OpenGraph tags were found and markup completeness is 60%, leaving more entity interpretation to crawlers.

The mobile Lighthouse profile adds Performance 99/100, 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
  • 1 sitemap declaration
  • Strong Google agentic signals

Priority improvements

  1. 1Add llms-full.txt for richer first-party context.
  2. 2Add JSON-LD for the organisation, website, and core entities.
  3. 3Complete OpenGraph and canonical homepage metadata.
llms.txt tokens2,242
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
99

Performance

100

Accessibility

100

Best Practices

100

Technical SEO

100

Agentic Browsing

What these results mean

The mobile page renders efficiently; its largest visible content block appeared in 1.8 s.

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

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.

FCP1.2 s

First content

LCP1.8 s

Main content

CLS0

Layout stability

TBT0 ms

Blocking time

SI2.5 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.
10/8/2026Lighthouse 13.5.0Mobile profile

AI readiness checks

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

llms.txt

File found and accessible

https://calk.me/llms.txt
llms-full.txt

Full version was not found

ai.txt

ai.txt file was not found

robots.txt

File found

https://calk.me/robots.txt
Sitemap in robots.txt1

1 sitemap found

Schema.org (JSON-LD)

Schema.org markup was not found on the homepage

OpenGraph60%

11 OG tags found

AI bot access

Based on robots.txt analysis

GPTBotNot mentioned
OAI-SearchBotAllowed
ChatGPT-UserNot mentioned
Google-ExtendedNot mentioned
ClaudeBotNot mentioned
Claude-SearchBotNot mentioned
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: 60%
og:urlhttps://calk.me/
og:site_nameCalk
og:titleCalk — the calorie counter you don
og:descriptionLog familiar meals from editable templates. Get a Nutrition Report during your free month, then use your weight trend to decide when to check in again.
og:localeen
og:typewebsite
og:image:typeimage/png
og:image:width1200
og:image:height630
og:image:altCalk — the calorie counter you don
# Calk

> Calk is a calorie and nutrition app built around meal templates. A meal template is a ready-made meal whose protein, base, cooking method, sauce, additions and portion can be changed before it is logged. If the meal is unchanged, choose the template and tap Eat. If one part changed, change that choice and keep the rest. Calk then turns a focused period of normal food logging into a Nutrition Report and uses the weight trend as the lighter signal between food checks.

Calk is released on iOS and Android. It can be downloaded from the App Store at https://apps.apple.com/app/id6760506316 and from Google Play at https://play.google.com/store/apps/details?id=me.calk.app. Calk works locally on the user's device. Food and weight can be entered without an internet connection. There are no ads. The website is published in English, Russian, Latin American Spanish, Spanish for Spain, Hebrew, German and Brazilian Portuguese. Each canonical article below is available across all seven site locales. The visible author of Calk's editorial content is Eli Petukhov.

## Start here

- [Calk — home](https://calk.me/): the product job, the meal-template workflow, the focused food-check loop and the free tier.
- [How calorie trackers compare](https://calk.me/articles/how-calorie-trackers-compare/): a sourced comparison of database search, photo estimation, barcode lookup and recipe entry with Calk's meal-template architecture.
- [How to export your MyFitnessPal recipes](https://calk.me/articles/export-transfer-myfitnesspal-recipes/): why MyFitnessPal's official export does not include saved recipe definitions and how Calk avoids rebuilding the archive before a switch.
- [What is a meal template?](https://calk.me/articles/how-the-food-constructor-works/): a direct explanation of how one meal can be reused and changed without rebuilding it.
- [How accurate is Calk?](https://calk.me/articles/how-accurate-is-calk/): the published recipe and packaged-food tests, their scope and the role of portion estimation.

## Data and evidence

- [Where Calk's food numbers come from](https://calk.me/articles/how-calk-tests-its-food-data/): how official food-composition databases, specialist publications, product labels and recipe references are combined and checked.
- [Why calorie databases disagree](https://calk.me/articles/database-lottery/): why search results for the same food can represent different recipes, preparations and serving sizes.
- [Cooking method and calories](https://calk.me/articles/cooking-method-calories/): how fat uptake, water loss and coatings or sauces change a dish.
- [Hidden calories guide](https://calk.me/articles/hidden-calories-guide/): practical checks for meat fat, cooking oil, sauces and raw-versus-cooked weights.
- [Photo calorie counting accuracy](https://calk.me/articles/photo-calorie-counting-accuracy/): what current photo systems have measured and which meal details remain unavailable from an image.
- [References](https://calk.me/references/): the studies, official guidance and primary product sources cited by the site.

## Tracking, reports and maintenance

- [Calorie tracking without the daily grind](https://calk.me/articles/calorie-tracking-without-the-grind/): the complete focused food-check protocol.
- [Why calorie counters fail after the first month](https://calk.me/articles/why-calorie-counters-fail/): why the work can remain after the useful discoveries slow down.
- [What a 30-day food audit reveals](https://calk.me/articles/what-a-30-day-food-audit-reveals/): the questions a month of ordinary logging can answer.
- [How Calk reads a month of eating](https://calk.me/articles/how-calk-reads-your-month-like-a-dietitian/): how the Nutrition Report connects repeated meals to calories, satiety, nutrient coverage and variety.
- [Maintain weight without daily tracking](https://calk.me/articles/maintain-weight-without-daily-tracking/): how to separate a focused food check from background weight-trend monitoring.
- [Understanding your weight trend](https://calk.me/articles/understanding-your-weight-trend/): why decisions use a trend rather than one weigh-in.
- [Maintainers' corner](https://calk.me/corners/maintainers/): the maintenance reading path.

## Nutrition guides

- [Insights](https://calk.me/insights/): Calk's plain-language reference for protein, fiber, fats, salt, nutrient density, variety and other report signals.
- [Eating habits for weight loss](https://calk.me/articles/eating-habits-for-weight-loss/): how repeatable meals, satiety and calorie density fit together.
- [Slow weight loss](https://calk.me/articles/slow-weight-loss/): the difference between rapid scale movement and sustained fat loss.
- [Intuitive eating and data](https://calk.me/articles/intuitive-eating-and-data/): when internal cues are enough and when a short measurement period can answer a specific question.

## Comparisons with other trackers

- [Calk vs MyFitnessPal](https://calk.me/articles/calk-vs-myfitnesspal/): how a saved set of diary rows differs from an editable meal template once the dish changes.
- [Calk vs YAZIO, Cal AI and FatSecret](https://calk.me/articles/calk-vs-yazio-cal-ai-fatsecret/): where each app starts logging, and what each asks of the reader in the second month.
- [Calk vs Cronometer](https://calk.me/articles/calk-vs-cronometer/): micronutrient depth against meal structure, and which question each answers.
- [Calk vs MacroFactor](https://calk.me/articles/calk-vs-macrofactor/): adaptive targets against a food check with an end.
- [The calorie-tracker market map](https://calk.me/articles/calorie-tracker-market-map/): the categories trackers fall into and what distinguishes them.

## Logging, accuracy and effort

- [Counting calories without weighing](https://calk.me/articles/calorie-counting-without-weighing/): what an estimate can and cannot answer, and where the error actually lands.
- [How long calorie logging takes](https://calk.me/articles/how-long-calorie-logging-takes/): the measured daily cost of a food diary and what drives it.
- [Tracking fatigue](https://calk.me/articles/tracking-fatigue-calorie-counting/): why logging stops, and which parts of it are worth keeping.
- [Why a calorie tracker does not count fiber](https://calk.me/articles/calorie-tracker-not-counting-fiber/): why an empty database field arrives in a diary as a zero.
- [Eating out and travel](https://calk.me/articles/eating-out-and-travel/): logging a meal whose recipe belongs to someone else's kitchen.
- [Calories in everyday dishes](https://calk.me/calories-in/): per-dish calculators built from the same meal templates the app uses, with the assumptions shown.

## Understanding the numbers

- [Calkpedia](https://calk.me/calkpedia/): short, plain-language answers to the questions the app raises while it is being used.
- [Methodology](https://calk.me/methodology/): how Calk's food data is assembled, tested and published.
- [Nutrition Report](https://calk.me/nutrition-report/): what a month of ordinary logging produces, page by page.
- [Variety Map](https://calk.me/variety-map/): which foods a month of logging actually covered, and which groups stayed faded.
- [Why diets do not work](https://calk.me/articles/why-diets-dont-work/): what changes when the goal moves from a diet to the meals already being eaten.
- [The maintenance problem](https://calk.me/articles/the-maintenance-problem/): why keeping weight is a different job from losing it.
- [Losing weight without counting calories](https://calk.me/articles/weight-loss-without-calorie-tracking/): what the promise leaves out.
- [Weight fluctuations and the water cycle](https://calk.me/articles/weight-fluctuations-water-cycle-trend/): why the scale moves for reasons that are not fat.
- [Tracking for weight gain](https://calk.me/articles/tracking-for-weight-gain/): the same tools pointed the other way.
- [Food variety and your gut](https://calk.me/articles/food-variety-and-your-gut/): what the evidence supports about range of foods, and what it does not.
- [Food order and steady energy](https://calk.me/articles/food-order-steady-energy/): what changing the order of a meal does and does not do.
- [Eating when appetite returns](https://calk.me/articles/eating-when-appetite-returns/): what to do when hunger comes back after a restricted period.

## Privacy and legal

- [Privacy and your data](https://calk.me/articles/privacy-and-your-data/): local processing, offline use, analytics choices and data handling.
- [Privacy policy](https://calk.me/legal/privacy-policy/)
- [Terms of service](https://calk.me/legal/tos/)

## Scope notes

- Calk does not diagnose, treat or promise health outcomes. It is not a medical device.
- The published `99.7% within 20%` accuracy claim refers to 1,803 tested recipe variants and their independent references. It is not a guarantee for every logged plate or portion.
- Calk's packaged-food results are reported separately from its recipe results.
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Added 10/8/2026

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calk.me - AI readiness audit | llmsmap.me