| name | 360-panorama-viewer |
| description | Build a fully self-contained 360° equirectangular panorama viewer as a single HTML file. The viewer uses Three.js to render immersive spherical panoramas with drag-to-look, zoom, auto-rotate, and a scene-switcher sidebar. All panorama images are embedded as base64 JPEG — no server needed. Use this skill whenever the user asks to create a 360 viewer, VR panorama app, immersive scene gallery, equirectangular image viewer, or wants to combine multiple AI-generated panoramas into an interactive webpage. Also trigger when the user says things like "make a 360 viewer", "VR world gallery", "360度全景", "全景查看器", "make scenes I can look around in", etc.
|
360° Panorama Viewer Skill
This skill creates a polished, self-contained 360° panorama viewer HTML file.
What it produces
A single .html file (~3–6 MB depending on scene count) that:
- Renders equirectangular panoramas as spherical 360° environments using Three.js
- Supports dragging to look around, scroll to zoom, auto-rotate toggle, fullscreen
- Shows a thumbnail sidebar to switch between multiple scenes
- Works offline — no CDN dependencies, all assets embedded
Skill assets
| Asset | Purpose |
|---|
assets/viewer_template.html | Complete viewer HTML with Three.js inlined; panorama data injected at build time |
scripts/build_viewer.py | Loads images, applies seam fix, base64-encodes, injects into template |
Workflow
Step 1 — Gather scene specs from the user
Ask (or infer from context) for each scene:
- Description of what the panorama should show
- Title for the HUD (emoji + name, e.g.
🍄 Mario World)
- Thumbnail label (≤12 chars shown on the sidebar chip)
Typical count: 3–6 scenes. You can also accept user-provided image files directly (skip generation).
Step 2 — Generate panorama images
For each scene, generate a 360° equirectangular panorama image.
Model choice:
- Preferred:
google/gemini-3.1-flash-image-preview via AI Gateway — reliable 2:1 output, no safety rejections for fictional themes
- Alternative:
gpt-image-2 via AI Gateway at size 1536x1024 — higher quality but may reject branded IP (Mario, Zelda, etc.)
Generation code (Gemini route):
import os, requests, base64
from PIL import Image
import io
api_key = os.environ['AI_GATEWAY_API_KEY']
def generate_panorama(prompt: str, save_path: str):
payload = {
"model": "google/gemini-3.1-flash-image-preview",
"prompt": prompt,
"response_format": "b64_json",
"n": 1
}
resp = requests.post(
'https://ai-gateway.happycapy.ai/api/v1/images/generations',
headers={
'Authorization': f'Bearer {api_key}',
'Content-Type': 'application/json',
'Origin': 'https://trickle.so'
},
json=payload,
timeout=180
)
resp.raise_for_status()
img_bytes = base64.b64decode(resp.json()['data'][0]['b64_json'])
img = Image.open(io.BytesIO(img_bytes)).convert('RGB')
img.save(save_path)
return save_path
Prompt formula for good equirectangular panoramas:
360 degree equirectangular panorama of [SCENE DESCRIPTION].
[Key visual elements]. [Style description].
Wide seamless landscape, 2:1 aspect ratio,
left and right edges must tile perfectly for 360 VR viewing.
Example prompts:
- Mario:
360 degree equirectangular panorama of a colorful cartoon mushroom kingdom platformer world. Bright blue sky, rolling green hills, red mushroom houses, floating brick blocks, gold coins, stone castle. Vibrant cartoon illustration, 2:1 aspect ratio, seamless tiling.
- Underwater city:
360 degree equirectangular panorama of a futuristic underwater city. Bioluminescent buildings, schools of fish, coral reefs, deep ocean light shafts. Cinematic, 2:1 aspect ratio, seamless tiling.
Save each generated image to a temp path (e.g. tmp/scene_N_raw.png).
Step 3 — Build the viewer
Run the build script, passing all scenes as a JSON array:
SKILL_DIR=/home/node/.claude/skills/360-panorama-viewer
python3 "$SKILL_DIR/scripts/build_viewer.py" \
--template "$SKILL_DIR/assets/viewer_template.html" \
--output "outputs/360_viewer.html" \
--scenes '[
{"title":"🍄 Mario World","thumb_label":"Mario","image_path":"tmp/mario.png","initial_lon":90},
{"title":"🐚 Underwater City","thumb_label":"Deep City","image_path":"tmp/underwater.png","initial_lon":0}
]'
The script will:
- Resize each image to 1774×887 (standard 2:1)
- Apply a pure-roll seam fix — finds the horizontal offset that minimises left/right edge difference, then
np.rolls the image. No blending. This ensures the 360° seam is as clean as possible without distorting colors.
- Base64-encode as JPEG quality 90
- Inject into the template and write the output HTML
Step 4 — Deliver
Output the file to outputs/360_viewer.html (or user-specified path) and attach it as a static HTML deliverable.
Replacing a single scene in an existing viewer
If the user already has a viewer and wants to swap out one scene (e.g. regenerate Mario), do NOT rerun all scenes — just re-encode the new image and replace its b64 field in the HTML:
import re, base64, io, numpy as np
from PIL import Image
SKILL_DIR = '/home/node/.claude/skills/360-panorama-viewer'
img = Image.open('new_mario.png').convert('RGB')
img = img.resize((1774, 887), Image.LANCZOS)
arr = np.array(img)
f = arr.astype(np.float32)
h, w = arr.shape[:2]
best_roll, best_score = 0, float('inf')
for roll in range(w):
r = np.roll(f, roll, axis=1)
score = float(np.abs(r[:, :5] - r[:, w-5:]).mean())
if score < best_score:
best_score = score
best_roll = roll
fixed = np.roll(arr, best_roll, axis=1)
buf = io.BytesIO()
Image.fromarray(fixed).save(buf, format='JPEG', quality=90)
new_b64 = base64.b64encode(buf.getvalue()).decode('ascii')
with open('outputs/360_viewer.html') as fh:
html = fh.read()
pattern = r"(title:\s*'[^']*Mario[^']*',\s*initialLon:\s*[\d.]+,\s*b64:\s*')([^']+)(')"
html_new, n = re.subn(pattern, r'\g<1>' + new_b64 + r'\g<3>', html)
assert n == 1, f"Expected 1 replacement, got {n}"
with open('outputs/360_viewer.html', 'w') as fh:
fh.write(html_new)
print(f'Replaced Mario scene (roll={best_roll})')
Seam fix — why pure roll, not blending
Equirectangular panoramas wrap horizontally: x=0 and x=w-1 represent the same physical meridian. AI-generated images rarely place the seam at a natural boundary (e.g. open sky), so the default seam is often visible.
Pure roll: shift the whole image left/right to find the position where the 5-pixel columns at both edges are most similar (lowest mean absolute difference). np.roll() is a circular shift — no pixels are added or removed, no colors are changed. This is lossless and never creates artifacts.
Never use blending/color correction at the seam: it creates a visible "smeared" band where pixel values are artificially averaged, which looks worse than the original seam.
Viewer features reference
The template includes these controls, all functional out of the box:
- Drag — look around (mouse or touch)
- Scroll / pinch — zoom (FOV 30°–120°, default 75°)
- Auto Rotate button — gentle continuous pan
- Reset button — return to
initialLon of current scene
- Fullscreen button
- FOV slider — right-side vertical range input
- Thumbnail sidebar — left-side scene switcher with active highlight
Scene initialLon is the starting horizontal angle (0–360). Use it to face an interesting part of the panorama on load (e.g. 90 = face right, 180 = face backwards).
Tips for better panoramas
- Ask for 2:1 aspect ratio explicitly in every prompt — this is the equirectangular standard
- Mention "seamless tiling" and "left and right edges must connect" in the prompt
- For fictional/branded themes (Mario, Zelda), describe the visual style without using trademarked names if GPT-Image-2 rejects them: "colorful cartoon platformer mushroom kingdom style" instead of "Mario"
- Gemini rarely rejects prompts and consistently produces good 2:1 panoramas — prefer it for speed