(globalThis.TURBOPACK||(globalThis.TURBOPACK=[])).push(["object"==typeof document?document.currentScript:void 0,546467,e=>{"use strict";let t=(0,e.i(475254).default)("external-link",[["path",{d:"M15 3h6v6",key:"1q9fwt"}],["path",{d:"M10 14 21 3",key:"gplh6r"}],["path",{d:"M18 13v6a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h6",key:"a6xqqp"}]]);e.s(["default",0,t])},778917,e=>{"use strict";var t=e.i(546467);e.s(["ExternalLink",()=>t.default])},455037,e=>{"use strict";var t=e.i(494144);e.s(["prism",()=>t.default])},306228,e=>{"use strict";let t=(0,e.i(475254).default)("link-2",[["path",{d:"M9 17H7A5 5 0 0 1 7 7h2",key:"8i5ue5"}],["path",{d:"M15 7h2a5 5 0 1 1 0 10h-2",key:"1b9ql8"}],["line",{x1:"8",x2:"16",y1:"12",y2:"12",key:"1jonct"}]]);e.s(["Link2",0,t],306228)},909947,865361,e=>{"use strict";var t,i,a=((t={}).AUDIO_SPEECH="audio_speech",t.AUDIO_TRANSCRIPTION="audio_transcription",t.IMAGE_GENERATION="image_generation",t.VIDEO_GENERATION="video_generation",t.CHAT="chat",t.COMPLETION="completion",t.RESPONSES="responses",t.IMAGE_EDITS="image_edit",t.ANTHROPIC_MESSAGES="anthropic_messages",t.EMBEDDING="embedding",t.REALTIME="realtime",t),o=((i={}).IMAGE="image",i.VIDEO="video",i.CHAT="chat",i.RESPONSES="responses",i.IMAGE_EDITS="image_edits",i.ANTHROPIC_MESSAGES="anthropic_messages",i.EMBEDDINGS="embeddings",i.SPEECH="speech",i.TRANSCRIPTION="transcription",i.A2A_AGENTS="a2a_agents",i.MCP="mcp",i.REALTIME="realtime",i.INTERACTIONS="interactions",i);let n={image_generation:"image",video_generation:"video",chat:"chat",completion:"chat",responses:"responses",image_edit:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings",realtime:"realtime"};e.s(["EndpointType",()=>o,"ModelMode",()=>a,"getEndpointType",0,e=>Object.values(a).includes(e)?n[e]:"chat"],865361),e.s(["generateCodeSnippet",0,e=>{let t,{apiKeySource:i,accessToken:a,apiKey:n,inputMessage:r,chatHistory:s,selectedTags:l,selectedVectorStores:p,selectedGuardrails:d,selectedPolicies:m,selectedVoice:c,endpointType:g,selectedModel:u,selectedSdk:f,proxySettings:h}=e,_="session"===i?a:n,x=window.location.origin,b=h?.LITELLM_UI_API_DOC_BASE_URL;b&&b.trim()?x=b:h?.PROXY_BASE_URL&&(x=h.PROXY_BASE_URL);let y=r||"Your prompt here",j=y.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),k=s.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),S={};l.length>0&&(S.tags=l),p.length>0&&(S.vector_stores=p),d.length>0&&(S.guardrails=d),m.length>0&&(S.policies=m);let v=u||"your-model-name",w="azure"===f?`import openai

client = openai.AzureOpenAI(
	api_key="${_||"YOUR_LITELLM_API_KEY"}",
	azure_endpoint="${x}",
	api_version="2024-02-01"
)`:`import openai

client = openai.OpenAI(
	api_key="${_||"YOUR_LITELLM_API_KEY"}",
	base_url="${x}"
)`;switch(g){case o.CHAT:{let e=Object.keys(S).length>0,i="";if(e){let e=JSON.stringify({metadata:S},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`,
    extra_body=${e}`}let a=k.length>0?k:[{role:"user",content:y}];t=`
import base64

# Helper function to encode images to base64
def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

# Example with text only
response = client.chat.completions.create(
    model="${v}",
    messages=${JSON.stringify(a,null,4)}${i}
)

print(response)

# Example with image or PDF (uncomment and provide file path to use)
# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
# response_with_file = client.chat.completions.create(
#     model="${v}",
#     messages=[
#         {
#             "role": "user",
#             "content": [
#                 {
#                     "type": "text",
#                     "text": "${j}"
#                 },
#                 {
#                     "type": "image_url",
#                     "image_url": {
#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}
#                     }
#                 }
#             ]
#         }
#     ]${i}
# )
# print(response_with_file)
`;break}case o.RESPONSES:{let e=Object.keys(S).length>0,i="";if(e){let e=JSON.stringify({metadata:S},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`,
    extra_body=${e}`}let a=k.length>0?k:[{role:"user",content:y}];t=`
import base64

# Helper function to encode images to base64
def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

# Example with text only
response = client.responses.create(
    model="${v}",
    input=${JSON.stringify(a,null,4)}${i}
)

print(response.output_text)

# Example with image or PDF (uncomment and provide file path to use)
# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
# response_with_file = client.responses.create(
#     model="${v}",
#     input=[
#         {
#             "role": "user",
#             "content": [
#                 {"type": "input_text", "text": "${j}"},
#                 {
#                     "type": "input_image",
#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}
#                 },
#             ],
#         }
#     ]${i}
# )
# print(response_with_file.output_text)
`;break}case o.IMAGE:t="azure"===f?`
# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.
# This snippet uses 'client.images.generate' and will create a new image based on your prompt.
# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.
import os
import requests
import json
import time
from PIL import Image

result = client.images.generate(
	model="${v}",
	prompt="${r}",
	n=1
)

json_response = json.loads(result.model_dump_json())

# Set the directory for the stored image
image_dir = os.path.join(os.curdir, 'images')

# If the directory doesn't exist, create it
if not os.path.isdir(image_dir):
	os.mkdir(image_dir)

# Initialize the image path
image_filename = f"generated_image_{int(time.time())}.png"
image_path = os.path.join(image_dir, image_filename)

try:
	# Retrieve the generated image
	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):
			image_url = json_response["data"][0]["url"]
			generated_image = requests.get(image_url).content
			with open(image_path, "wb") as image_file:
					image_file.write(generated_image)

			print(f"Image saved to {image_path}")
			# Display the image
			image = Image.open(image_path)
			image.show()
	else:
			print("Could not find image URL in response.")
			print("Full response:", json_response)
except Exception as e:
	print(f"An error occurred: {e}")
	print("Full response:", json_response)
`:`
import base64
import os
import time
import json
from PIL import Image
import requests

# Helper function to encode images to base64
def encode_image(image_path):
	with open(image_path, "rb") as image_file:
			return base64.b64encode(image_file.read()).decode('utf-8')

# Helper function to create a file (simplified for this example)
def create_file(image_path):
	# In a real implementation, this would upload the file to OpenAI
	# For this example, we'll just return a placeholder ID
	return f"file_{os.path.basename(image_path).replace('.', '_')}"

# The prompt entered by the user
prompt = "${j}"

# Encode images to base64
base64_image1 = encode_image("body-lotion.png")
base64_image2 = encode_image("soap.png")

# Create file IDs
file_id1 = create_file("body-lotion.png")
file_id2 = create_file("incense-kit.png")

response = client.responses.create(
	model="${v}",
	input=[
			{
					"role": "user",
					"content": [
							{"type": "input_text", "text": prompt},
							{
									"type": "input_image",
									"image_url": f"data:image/jpeg;base64,{base64_image1}",
							},
							{
									"type": "input_image",
									"image_url": f"data:image/jpeg;base64,{base64_image2}",
							},
							{
									"type": "input_image",
									"file_id": file_id1,
							},
							{
									"type": "input_image",
									"file_id": file_id2,
							}
					],
			}
	],
	tools=[{"type": "image_generation"}],
)

# Process the response
image_generation_calls = [
	output
	for output in response.output
	if output.type == "image_generation_call"
]

image_data = [output.result for output in image_generation_calls]

if image_data:
	image_base64 = image_data[0]
	image_filename = f"edited_image_{int(time.time())}.png"
	with open(image_filename, "wb") as f:
			f.write(base64.b64decode(image_base64))
	print(f"Image saved to {image_filename}")
else:
	# If no image is generated, there might be a text response with an explanation
	text_response = [output.text for output in response.output if hasattr(output, 'text')]
	if text_response:
			print("No image generated. Model response:")
			print("\\n".join(text_response))
	else:
			print("No image data found in response.")
	print("Full response for debugging:")
	print(response)
`;break;case o.IMAGE_EDITS:t="azure"===f?`
import base64
import os
import time
import json
from PIL import Image
import requests

# Helper function to encode images to base64
def encode_image(image_path):
	with open(image_path, "rb") as image_file:
			return base64.b64encode(image_file.read()).decode('utf-8')

# The prompt entered by the user
prompt = "${j}"

# Encode images to base64
base64_image1 = encode_image("body-lotion.png")
base64_image2 = encode_image("soap.png")

# Create file IDs
file_id1 = create_file("body-lotion.png")
file_id2 = create_file("incense-kit.png")

response = client.responses.create(
	model="${v}",
	input=[
			{
					"role": "user",
					"content": [
							{"type": "input_text", "text": prompt},
							{
									"type": "input_image",
									"image_url": f"data:image/jpeg;base64,{base64_image1}",
							},
							{
									"type": "input_image",
									"image_url": f"data:image/jpeg;base64,{base64_image2}",
							},
							{
									"type": "input_image",
									"file_id": file_id1,
							},
							{
									"type": "input_image",
									"file_id": file_id2,
							}
					],
			}
	],
	tools=[{"type": "image_generation"}],
)

# Process the response
image_generation_calls = [
	output
	for output in response.output
	if output.type == "image_generation_call"
]

image_data = [output.result for output in image_generation_calls]

if image_data:
	image_base64 = image_data[0]
	image_filename = f"edited_image_{int(time.time())}.png"
	with open(image_filename, "wb") as f:
			f.write(base64.b64decode(image_base64))
	print(f"Image saved to {image_filename}")
else:
	# If no image is generated, there might be a text response with an explanation
	text_response = [output.text for output in response.output if hasattr(output, 'text')]
	if text_response:
			print("No image generated. Model response:")
			print("\\n".join(text_response))
	else:
			print("No image data found in response.")
	print("Full response for debugging:")
	print(response)
`:`
import base64
import os
import time

# Helper function to encode images to base64
def encode_image(image_path):
	with open(image_path, "rb") as image_file:
			return base64.b64encode(image_file.read()).decode('utf-8')

# Helper function to create a file (simplified for this example)
def create_file(image_path):
	# In a real implementation, this would upload the file to OpenAI
	# For this example, we'll just return a placeholder ID
	return f"file_{os.path.basename(image_path).replace('.', '_')}"

# The prompt entered by the user
prompt = "${j}"

# Encode images to base64
base64_image1 = encode_image("body-lotion.png")
base64_image2 = encode_image("soap.png")

# Create file IDs
file_id1 = create_file("body-lotion.png")
file_id2 = create_file("incense-kit.png")

response = client.responses.create(
	model="${v}",
	input=[
			{
					"role": "user",
					"content": [
							{"type": "input_text", "text": prompt},
							{
									"type": "input_image",
									"image_url": f"data:image/jpeg;base64,{base64_image1}",
							},
							{
									"type": "input_image",
									"image_url": f"data:image/jpeg;base64,{base64_image2}",
							},
							{
									"type": "input_image",
									"file_id": file_id1,
							},
							{
									"type": "input_image",
									"file_id": file_id2,
							}
					],
			}
	],
	tools=[{"type": "image_generation"}],
)

# Process the response
image_generation_calls = [
	output
	for output in response.output
	if output.type == "image_generation_call"
]

image_data = [output.result for output in image_generation_calls]

if image_data:
	image_base64 = image_data[0]
	image_filename = f"edited_image_{int(time.time())}.png"
	with open(image_filename, "wb") as f:
			f.write(base64.b64decode(image_base64))
	print(f"Image saved to {image_filename}")
else:
	# If no image is generated, there might be a text response with an explanation
	text_response = [output.text for output in response.output if hasattr(output, 'text')]
	if text_response:
			print("No image generated. Model response:")
			print("\\n".join(text_response))
	else:
			print("No image data found in response.")
	print("Full response for debugging:")
	print(response)
`;break;case o.EMBEDDINGS:t=`
response = client.embeddings.create(
	input="${r||"Your string here"}",
	model="${v}",
	encoding_format="base64" # or "float"
)

print(response.data[0].embedding)
`;break;case o.TRANSCRIPTION:t=`
# Open the audio file
audio_file = open("path/to/your/audio/file.mp3", "rb")

# Make the transcription request
response = client.audio.transcriptions.create(
	model="${v}",
	file=audio_file${r?`,
	prompt="${r.replace(/\\/g,"\\\\").replace(/"/g,'\\"')}"`:""}
)

print(response.text)
`;break;case o.SPEECH:t=`
# Make the text-to-speech request
response = client.audio.speech.create(
	model="${v}",
	input="${r||"Your text to convert to speech here"}",
	voice="${c}"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer
)

# Save the audio to a file
output_filename = "output_speech.mp3"
response.stream_to_file(output_filename)
print(f"Audio saved to {output_filename}")

# Optional: Customize response format and speed
# response = client.audio.speech.create(
#     model="${v}",
#     input="${r||"Your text to convert to speech here"}",
#     voice="alloy",
#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm
#     speed=1.0  # Range: 0.25 to 4.0
# )
# response.stream_to_file("output_speech.mp3")
`;break;default:t="\n# Code generation for this endpoint is not implemented yet."}return`${w}
${t}`}],909947)},652272,209261,e=>{"use strict";var t=e.i(843476),i=e.i(271645),a=e.i(871689),o=e.i(643531),n=e.i(174886),r=e.i(306228);let s=/^[a-zA-Z0-9][a-zA-Z0-9._-]*(\/[a-zA-Z0-9][a-zA-Z0-9._-]*)*$/,l=e=>e.trim().replace(/\/+$/,""),p=/\.(md|markdown|txt|json|ya?ml|toml)$/i,d=/^\d{1,3}(\.\d{1,3}){3}$/,m=/^[A-Za-z0-9-]+$/,c=/^[A-Za-z0-9._-]+$/,g=e=>e.pathname.split("/").filter(e=>""!==e),u=e=>{let t=e.split("/").filter(e=>""!==e);return t[t.length-1]??""},f=e=>e.toLowerCase().replace(/[^a-z0-9-]+/g,"-").replace(/-+/g,"-").replace(/^-+|-+$/g,""),h=e=>JSON.stringify({extraKnownMarketplaces:{litellm:{source:{source:"url",url:`${e}/claude-code/marketplace.json`}}}},null,2),_=e=>`/plugin install ${e.name}@litellm`;e.s(["buildMarketplaceSettingsSnippet",0,h,"formatInstallCommand",0,_,"getCategoryBadgeColor",0,e=>{if(!e)return"gray";let t=e.toLowerCase();if(t.includes("development")||t.includes("dev"))return"blue";if(t.includes("productivity")||t.includes("workflow"))return"green";if(t.includes("learning")||t.includes("education"))return"purple";if(t.includes("security")||t.includes("safety"))return"red";if(t.includes("data")||t.includes("analytics"))return"orange";else if(t.includes("integration")||t.includes("api"))return"yellow";return"gray"},"isValidEmail",0,e=>!e||/^[^\s@]+@[^\s@]+\.[^\s@]+$/.test(e),"isValidSemanticVersion",0,e=>!e||/^\d+\.\d+\.\d+(-[a-zA-Z0-9.-]+)?(\+[a-zA-Z0-9.-]+)?$/.test(e),"isValidSubPath",0,e=>{let t=l(e);return""!==t&&s.test(t)},"parseKeywords",0,e=>e&&""!==e.trim()?e.split(",").map(e=>e.trim()).filter(e=>""!==e):[],"parseSkillSource",0,(e,t)=>{let i=(e=>{let t,i=e.trim();if(""===i||i.startsWith("//"))return null;let a=/^[a-z][a-z0-9+.-]*:\/\//i.test(i)?i:`https://${i}`;try{t=new URL(a)}catch{return null}return"https:"!==t.protocol||""!==t.username||""!==t.password||!t.hostname.includes(".")||t.hostname.startsWith("[")||d.test(t.hostname)?null:t})(e);if(!i)return null;if("github.com"===i.hostname.replace(/^www\./,""))return((e,t)=>{let i=g(e);if(i.length<2)return null;let a=i[0],o=i[1].replace(/\.git$/,"");if(!m.test(a)||!c.test(o))return null;let n=`${a}/${o}`,r=`https://github.com/${n}`,d={parsed:{source:"github",repo:n},label:`GitHub repo — ${n}`,suggestedName:f(o)};if(i.length>=4&&("tree"===i[2]||"blob"===i[2])){let e=i.slice(4),t=u(e.join("/")),a=p.test(t)?e.slice(0,-1):e;if(0===a.length)return d;let o=l(a.join("/"));return s.test(o)?{parsed:{source:"git-subdir",url:r,path:o},label:`GitHub subdir — ${n} @ ${o}`,suggestedName:f(u(o))}:null}if(2!==i.length)return null;let h=l(t??"");return""!==h?s.test(h)?{parsed:{source:"git-subdir",url:r,path:h},label:`GitHub subdir — ${n} @ ${h}`,suggestedName:f(u(h))}:null:d})(i,t);if(g(i).length<2)return null;let a=`${i.protocol}//${i.host}${i.pathname.replace(/\/+$/,"")}`,o=l(t??"");return""!==o?s.test(o)?{parsed:{source:"git-subdir",url:a,path:o},label:`Git subdir — ${a} @ ${o}`,suggestedName:f(u(o))}:null:{parsed:{source:"url",url:a},label:`Git repo — ${a}`,suggestedName:f(u(i.pathname).replace(/\.git$/,""))}},"validatePluginName",0,e=>!!e&&""!==e.trim()&&/^[a-z0-9-]+$/.test(e)],209261),e.s(["default",0,({skill:e,onBack:s})=>{let l,[p,d]=(0,i.useState)("overview"),[m,c]=(0,i.useState)(null),g=(e,t)=>{navigator.clipboard.writeText(e),c(t),setTimeout(()=>c(null),2e3)},u="github"===(l=e.source).source&&l.repo?`https://github.com/${l.repo}`:"git-subdir"===l.source&&l.url?l.path?`${l.url}/tree/main/${l.path}`:l.url:"url"===l.source&&l.url?l.url:null,f=_(e),x=h(window.location.origin),b=[...e.category?[{property:"Category",value:e.category}]:[],...e.domain?[{property:"Domain",value:e.domain}]:[],...e.namespace?[{property:"Namespace",value:e.namespace}]:[],...e.version?[{property:"Version",value:e.version}]:[],...e.author?.name?[{property:"Author",value:e.author.name}]:[],...e.created_at?[{property:"Added",value:new Date(e.created_at).toLocaleDateString()}]:[]];return(0,t.jsxs)("div",{style:{padding:"24px 32px 24px 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