StatTrak™ Klasyczny nóż (★) | Tajga (po testach bojowych)

StatTrak™ Klasyczny nóż (★) | Tajga (po testach bojowych), sklasyfikowany pod Knives i głównie dostępny w Field-Tested, cechuje się zakresem wartości float od 0.06 do 0.8. Ten zakres ogranicza jego dostępność do warunków takich jak Field-Tested. StatTrak™ Klasyczny nóż (★) | Tajga (po testach bojowych) jest znany z wzoru Tajga i został opracowany przez Valve. Obecnie wyceniany jest na około 113.32 na rynkach takich jak , pozostaje unpopular wśród kolekcjonerów.

StatTrak™ Klasyczny nóż (★) | Tajga (po testach bojowych) wyróżnia się jako jeden z 6 dostępnych skinów . Ze swoim statusem rzadkości klasy „tajne”, zdobycie Tajga jest wyzwaniem, ponieważ ma tylko szans na upadek.

StatTrak™ Klasyczny nóż (★) | Tajga (po testach bojowych) zdobył znaczącą uwagę z ponad 920 głosami na TRUSTPILOT.COM, osiągając imponującą średnią ocenę 4.02.

The ★ StatTrak™ Classic Knife | Boreal Forest is a practical and versatile knife featuring various shades of green and brown, evocative of a dense forest. It is a part of the Shattered Web Case collection and was introduced to CS:GO in November 2019.

As a 'Covert' tier melee weapon, the Classic Knife | Boreal Forest adds immense value to your inventory. Its exceptional rarity and unique design have created high desirability amongst the CS:GO community.

The Modern Hunter finish, which includes the Boreal Forest pattern, was one of the earliest camo patterns featured in CS:GO. The debut of the Classic Knife | Boreal Forest in the Operation Shattered Web reinvented this beloved pattern with new prestige.

The Classic Knife | Boreal Forest is a sought-after item amongst streamers and professional players alike, due to its distinctive military look and the nostalgia it conveys.

The Classic Knife is a melee weapon and therefore compatible with all characters in CS:GO.

The knife's unique features lie in its camouflage design inspired by the heavy, overgrown leaves of a Boreal Forest, thus giving it its name.

This skin was released as part of the Shattered Web Case during Operation Shattered Web.

Boreal Forest is one of the first finishes that were introduced to the CS:GO arsenal, making the Classic Knife skin featuring this pattern a homage to the game's history.

Designed by Valve.

The Boreal Forest skin design makes use of varying green and brown color palettes to create an intricate camouflage pattern, blending in with various in-game environments.

Players greatly appreciate the Classic Knife | Boreal Forest for it’s a nostalgic value and the StatTrak™ technology that keeps count of the player's kills, adding a competitive edge to gameplay.

Bringing back one of the earliest weapon finishes on the trusty Classic Knife during Operation Shattered Web marked a significant move from Valve, intending to blend nostalgia and novelty.

Its popularity lies in the unique camo design, nostalgic value linked to early CS:GO days, StatTrak™ feature and its status as a valuable covert item.

The Operation Shattered Web was a fundamental event that significantly boosted the skin's popularity by introducing it to a broadened audience.

Valve brought forth the Classic Knife | Boreal Forest to generate a fusion of the old and new; this creation was full of historic significance and modern appeal.

The Boreal Forest design is symbolic of the wilderness and survival, embodying the player's tenacity and will to win.

The presence of a Classic Knife | Boreal Forest in a player's inventory often evokes a sense of nostalgia, bringing back fond memories of early versions of the game.

The release of the Classic Knife | Boreal Forest sparked excitement in the community, as it showcased a blend of history and innovation in CS:GO's evolving weapon skin arsenal.

Due to its high value and status symbol, the Classic Knife | Boreal Forest makes frequent appearances in professional esports tournaments.

Many players talk about the thrill and excitement of obtaining the Classic Knife | Boreal Forest due its rarity, the beloved Boreal Forest pattern, and the StatTrak feature.