We analyzed this app across 3 independent signals and corroborated nothing unfair. That is a signal, not an audit.
This analysis describes version 1.2.6 of the app, analyzed 2026-10-05 . The app may have changed since.
The store currently lists version 1.2.6, updated 2026-09-14.
What the analysis found
One source only — shown, not confirmed 2
A single kind of evidence. We publish these because withholding them would be the bigger distortion, and we do not treat any of them as a finding we stand behind.
What reviewers said
Verbatim excerpts from public store reviews, shown as corroborating user reports. They are not Appworthy audit evidence, and a quotation is not proof of the finding it sits under.
Aggressive ad load
Ads — Game was great until suddenly there are tons of ads. Deleted
False Advertisement — I saw an add that uses images to match in a certain way to find a code to which you input in a number pad with a tree symbol on the top. I love puzzle games like that so I instantly downloaded the app. Once I entered…
Sneaky, Dirty Ad Tactic — Started off well enough. The quotes were interesting and challenging, but I started to notice that it was designed to cause you to fail by not filling in the letters as discovered and instead making you seek…
Reports that the ads misrepresent the app
False advertising with a different game — Got shown footage from “someone” playing a game called “Her Trees: Puzzle House” in an ad for this game and no footage of the actual game. I promptly deleted it and got the ad again—that “someone”…
False Advertising — The ad for this game shows visual puzzles in which you manipulate objects on the screen to reveal hidden letters or clues. This is ABSOLUTELY not that game. Why is this deliberate deception allowed in app advertising?…
Nothing like the ads — Surprise surprise, another app misrepresenting itself in ads. It's all just quotes and movie lines. Pay for clues, 3 mistake tally, the usual PTW strategy. Pass this "game".
How the automated analysis works
This assessment is produced automatically. Three independent signals are read — user reviews, the store listing and the store privacy disclosure — and each candidate finding is filed under the sources that support it.
No charge stands on one kind of evidence — with one published exception. A finding is either a measured fact that anyone can reproduce (an ad SDK is in the package; every subscription bills weekly; the privacy label omits a tracker we can see), or it is an interpretation — and an interpretation needs independent sources to agree before it reaches the catch list. The exception is a harm only users can see: a charge may publish from reviews alone, as “Reported by N users”, once enough reviewers raise it to clear that charge’s own published bar. That agreement rule is what replaced the human reviewer, not a model’s confidence.
There is no overall score, on purpose. A 0–99 number was published here until 2026-09-15. It was withdrawn because it discriminated too weakly to be the instrument — 44.7% of every pair of apps tied — and because a single number is a verdict where a count is an observation. What publishes instead is a row of measurements, each of which says what was counted and, where we could not look, says that instead.
Findings under One source only are real reports we do not stand behind as findings. They are shown because withholding them entirely would hide a genuine complaint, and labelled because publishing them as charges would be an accusation off a single source.
Several measurements above say “Not assessed” because no artefact we hold can answer them. A human review is what resolves those — and it can remove a favourable band as easily as add one.
Hands-on inspections are not yet open.