How to reach the Wind Temple in Zelda Tears of the Kingdom

Richard Marshall
30/05/2025

How to reach the Wind Temple in Zelda Tears of the Kingdom

Mastering the path to the Wind Temple in Zelda: Tears of the Kingdom helps avoid complications during aerial navigation through Hebra’s skies. Begin by consulting our guide on locating Tulin in Rito Village to start your ascent toward the stormclouds.

Upon retrieving Tulin’s stolen bow from the Aerocuda, you’ll receive instructions to examine the mysterious storm enveloping Rito Village. This initiates the vertical journey toward the temple’s entrance.

Navigating to the Wind Temple

Reaching this sacred site requires traversing floating archipelagos and utilizing wind-propelled vessels. Here’s a detailed route:

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Locate Huck at (-2783, 28899, 0738) in Hebra’s peaks. The Rito scout will direct you eastward to initial skybound platforms. Activate Tulin’s gust ability to enhance gliding between islands.

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Stack nearby crates using Ultrahand on the first platform. Ascend this structure to bridge gaps between islands. Repeatedly use elevation skills and Zonai devices to progress through the chain.

zelda totk rising island chain link standing on first pillar facing west

Key navigation points:

Utilize sailcloth trampolines at (-2858, 3001, 0878) and subsequent locations
Evade or combat airborne Constructs
Construct temporary flight machinery using Flame-Emitters and Balloons

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Final approach requires precise boat-hopping:

Activate Kahatanaum Shrine (-3209, 3372, 1340) as checkpoint
Chain-launch between ascending vessels while facing oncoming wind currents
Execute controlled descent into the storm’s eye

$To solve this problem, we need to remove the words "Spot.Monster" or "Eurogamer" from any text provided, specifically within the context of Pokémon Scarlet and Violet Mystery Gift codes. The goal is to ensure that these words are removed along with any preceding non-alphanumeric characters or spaces that might be separating them from the actual codes. Approach Identify the Target Words: The primary task is to identify and remove the words "Spot.Monster" and "Eurogamer" from the input text. Split and Trim: For each line in the text, split the line at the first occurrence of either of these target words. This helps in isolating the part of the text before the target word. Remove Trailing Separators: After splitting, any trailing non-alphanumeric characters (such as hyphens, spaces, colons, etc.) are trimmed to ensure the result is clean and only contains the code or relevant text. Solution Code python import re def remove_sources(text): lines = text.split(‘\n’) cleaned_lines = [] for line in lines: Split on the first occurrence of either source part_before = re.split(r'Eurogamer|Spot\.Monster', line, 1)[0] # Remove trailing whitespace, hyphens, en dashes, em dashes, colons, slashes, parentheses cleaned_line = re.sub(r'[\s\-–—/:()]+$', '', part_before) cleaned_lines.append(cleaned_line) return '\n'.join(cleaned_lines) Example usage: input_text = """Pokémon Scarlet and Violet Mystery Gift codes TEAMSTAR – Eurogamer GOLF30 – Spot.Monster PARAD1SE – Eurogamer THA12022CHAMP – Eurogamer 1STCHAMP – Spot.Monster""" output_text = remove_sources(input_text) print(output_text) Explanation Splitting the Text: The text is split into individual lines to process each line separately. Regular Expression Split: Each line is split at the first occurrence of "Eurogamer" or "Spot.Monster" using regular expressions. This ensures that we get the part of the line before the target word. Trimming Non-Alphanumeric Characters: The trailing part (after removal of the target word) may contain separators like hyphens, spaces, etc. These are removed using a regular expression that matches and trims such characters from the end of the string. Reconstructing the Text: The cleaned lines are joined back together to form the final output text with the target words and their preceding separators removed. This approach efficiently processes each line to ensure only the relevant code or text remains, adhering to the problem’s requirements.$

To solve this problem, we need to remove the words "Spot.Monster" or "Eurogamer" from any text provided, specifically within the context of Pokémon Scarlet and Violet Mystery Gift codes. The goal is to ensure that these words are removed along with any preceding non-alphanumeric characters or spaces that might be separating them from the actual codes.

Approach

Identify the Target Words: The primary task is to identify and remove the words "Spot.Monster" and "Eurogamer" from the input text.
Split and Trim: For each line in the text, split the line at the first occurrence of either of these target words. This helps in isolating the part of the text before the target word.
Remove Trailing Separators: After splitting, any trailing non-alphanumeric characters (such as hyphens, spaces, colons, etc.) are trimmed to ensure the result is clean and only contains the code or relevant text.

Solution Code

python import re

def remove_sources(text): lines = text.split(‘\n’) cleaned_lines = [] for line in lines:

Split on the first occurrence of either source

    part_before = re.split(r'Eurogamer|Spot\.Monster', line, 1)[0]
    # Remove trailing whitespace, hyphens, en dashes, em dashes, colons, slashes, parentheses
    cleaned_line = re.sub(r'[\s\-–—/:()]+$', '', part_before)
    cleaned_lines.append(cleaned_line)
return '\n'.join(cleaned_lines)

Example usage:

input_text = """Pokémon Scarlet and Violet Mystery Gift codes TEAMSTAR – Eurogamer GOLF30 – Spot.Monster PARAD1SE – Eurogamer THA12022CHAMP – Eurogamer 1STCHAMP – Spot.Monster"""

output_text = remove_sources(input_text) print(output_text)

Explanation

Splitting the Text: The text is split into individual lines to process each line separately.
Regular Expression Split: Each line is split at the first occurrence of "Eurogamer" or "Spot.Monster" using regular expressions. This ensures that we get the part of the line before the target word.
Trimming Non-Alphanumeric Characters: The trailing part (after removal of the target word) may contain separators like hyphens, spaces, etc. These are removed using a regular expression that matches and trims such characters from the end of the string.
Reconstructing the Text: The cleaned lines are joined back together to form the final output text with the target words and their preceding separators removed.

This approach efficiently processes each line to ensure only the relevant code or text remains, adhering to the problem’s requirements.

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