Parkasaurus - Sea Monsters
Developer Washbear StudioPublisher Washbear Studio
Placeholder
Top Sellers#7,797New
Updated Oct 25, 2026.
Wishlist RankNot ranked
Updated Oct 25, 2026.
Metadata synced: Sep 22, 2026 · Not queued
Prices & bundle history
Steam ↗$6.99Checked · older than 24h
GG official storesNot recordedNo cached observation
GG keyshopsNot recordedNo cached observation
Steam reviews2867.85714285714286% positive
Recorded bundlesNot recordedNo cached observation
Cached US/USD prices; checkout prices may differ. Bundle history covers recorded observations and may be incomplete.
Bundle history & historical prices
Bundle history has not been recorded yet.
Rank History
Recent appearances for this app across complete public snapshots.
| Captured | Top Sellers | Wishlist Rank |
|---|---|---|
| Oct 25, 2026 | #7,797 | - |
| Sep 22, 2026 | #7,573 | - |
| Sep 21, 2026 | #5,602 | - |
| Sep 20, 2026 | #4,347 | - |
| Sep 19, 2026 | #5,727 | - |
| Sep 18, 2026 | #4,981 | - |
| Sep 17, 2026 | #3,799 | - |
| Sep 15, 2026 | #4,136 | - |
Data Coverage
What SteamFueled currently has for this app.
Store metadata
Synced Sep 22, 2026
Reviews
28 reviews
Tags
24 tags synced
Links
2 public links
Current players
Waiting for player count sync
Top Sellers
Rank #7,797
Wishlist Rank
Not in latest snapshot
Store Details
Public Steam metadata captured from appdetails. Contact-specific fields stay backend-only.
Platforms
Windows
Steam recommendations
Not synced
Review rating
Mixed
Weighted score
73.7%
Current players
Not synced
Achievements
Not synced
Metacritic
Not synced
Required age
Not listed
Snapshot Details
Source context for the latest complete public rank snapshots.
Top Sellers
#7,797
New
Previous - - Updated Oct 25, 2026
Snapshot size: 8,938 rows.Steam public Top Sellers rank order from the latest complete SteamFueled snapshot.
Wishlist Rank
-
-
Previous - - Updated Oct 25, 2026
Snapshot size: 5,401 rows.Steam public wishlist-rank order, not an exact private wishlist count.
Tags
Public Steam tags split by source. Store tags and genres are used more heavily for benchmark similarity than feature categories.