Examples for using wan-2.7-image-pro through RunAPI from agent tools or API calls. Copy a prompt, then use it in Claude Code, Codex, Cursor, Windsurf, or your backend.
1. claude mcp add runapi -s user -- npx -y @runapi.ai/mcp
2. Restart Claude Code
3. Paste this prompt: Generate an image: "Clean UI design mockup of a SaaS analytics dashboard displayed on a 27-inch monitor. The interface shows a dark theme with a left sidebar navigation, a top metrics bar with four KPI cards (revenue, users, conversion, churn), a large area chart in the center showing 30-day trends in gradient blue-to-purple, and a data table below. The design uses a card-based layout with subtle rounded corners and thin borders. Typography is Inter font. Accent color is electric blue (#3B82F6). The monitor sits on a minimal white desk with a wireless keyboard in front."
1. codex plugin install runapi-mcp@agents
2. Restart Codex
3. Paste this prompt: Generate an image: "Clean UI design mockup of a SaaS analytics dashboard displayed on a 27-inch monitor. The interface shows a dark theme with a left sidebar navigation, a top metrics bar with four KPI cards (revenue, users, conversion, churn), a large area chart in the center showing 30-day trends in gradient blue-to-purple, and a data table below. The design uses a card-based layout with subtle rounded corners and thin borders. Typography is Inter font. Accent color is electric blue (#3B82F6). The monitor sits on a minimal white desk with a wireless keyboard in front."
1. npx @runapi.ai/mcp init cursor
2. Restart Cursor
3. Paste this prompt: Generate an image: "Clean UI design mockup of a SaaS analytics dashboard displayed on a 27-inch monitor. The interface shows a dark theme with a left sidebar navigation, a top metrics bar with four KPI cards (revenue, users, conversion, churn), a large area chart in the center showing 30-day trends in gradient blue-to-purple, and a data table below. The design uses a card-based layout with subtle rounded corners and thin borders. Typography is Inter font. Accent color is electric blue (#3B82F6). The monitor sits on a minimal white desk with a wireless keyboard in front."
1. npx @runapi.ai/mcp init windsurf
2. Restart Windsurf
3. Paste this prompt: Generate an image: "Clean UI design mockup of a SaaS analytics dashboard displayed on a 27-inch monitor. The interface shows a dark theme with a left sidebar navigation, a top metrics bar with four KPI cards (revenue, users, conversion, churn), a large area chart in the center showing 30-day trends in gradient blue-to-purple, and a data table below. The design uses a card-based layout with subtle rounded corners and thin borders. Typography is Inter font. Accent color is electric blue (#3B82F6). The monitor sits on a minimal white desk with a wireless keyboard in front."
curl -X POST https://runapi.ai/api/v1/wan/text_to_image \
-H "Authorization: Bearer $RUNAPI_KEY" \
-H "Content-Type: application/json" \
--data-binary @- <<'JSON'
{
"model": "wan-2.7-image-pro",
"prompt": "Clean UI design mockup of a SaaS analytics dashboard displayed on a 27-inch monitor. The interface shows a dark theme with a left sidebar navigation, a top metrics bar with four KPI cards (revenue, users, conversion, churn), a large area chart in the center showing 30-day trends in gradient blue-to-purple, and a data table below. The design uses a card-based layout with subtle rounded corners and thin borders. Typography is Inter font. Accent color is electric blue (#3B82F6). The monitor sits on a minimal white desk with a wireless keyboard in front."
}
JSON
import { WanClient } from "@runapi.ai/wan";
const client = new WanClient({
apiKey: process.env.RUNAPI_API_KEY,
});
const result = await client.textToImage.run({
"model": "wan-2.7-image-pro",
"prompt": "Clean UI design mockup of a SaaS analytics dashboard displayed on a 27-inch monitor. The interface shows a dark theme with a left sidebar navigation, a top metrics bar with four KPI cards (revenue, users, conversion, churn), a large area chart in the center showing 30-day trends in gradient blue-to-purple, and a data table below. The design uses a card-based layout with subtle rounded corners and thin borders. Typography is Inter font. Accent color is electric blue (#3B82F6). The monitor sits on a minimal white desk with a wireless keyboard in front."
});
console.log(result.id);
require "runapi/wan"
client = RunApi::Wan::Client.new
result = client.text_to_image.run(
model: "wan-2.7-image-pro",
prompt: "Clean UI design mockup of a SaaS analytics dashboard displayed on a 27-inch monitor. The interface shows a dark theme with a left sidebar navigation, a top metrics bar with four KPI cards (revenue, users, conversion, churn), a large area chart in the center showing 30-day trends in gradient blue-to-purple, and a data table below. The design uses a card-based layout with subtle rounded corners and thin borders. Typography is Inter font. Accent color is electric blue (#3B82F6). The monitor sits on a minimal white desk with a wireless keyboard in front."
)
puts result.id
package main
import (
"context"
"fmt"
"log"
"net/http"
"os"
"strings"
)
func main() {
body := strings.NewReader("{\"model\":\"wan-2.7-image-pro\",\"prompt\":\"Clean UI design mockup of a SaaS analytics dashboard displayed on a 27-inch monitor. The interface shows a dark theme with a left sidebar navigation, a top metrics bar with four KPI cards (revenue, users, conversion, churn), a large area chart in the center showing 30-day trends in gradient blue-to-purple, and a data table below. The design uses a card-based layout with subtle rounded corners and thin borders. Typography is Inter font. Accent color is electric blue (#3B82F6). The monitor sits on a minimal white desk with a wireless keyboard in front.\"}")
req, err := http.NewRequestWithContext(context.Background(), http.MethodPost, "https://runapi.ai/api/v1/wan/text_to_image", body)
if err != nil {
log.Fatal(err)
}
req.Header.Set("Authorization", "Bearer "+os.Getenv("RUNAPI_API_KEY"))
req.Header.Set("Content-Type", "application/json")
resp, err := http.DefaultClient.Do(req)
if err != nil {
log.Fatal(err)
}
defer resp.Body.Close()
fmt.Println(resp.Status)
}
wan-2.7-image-pro/api/v1/wan/text_to_imageGet API Key
Clean UI design mockup of a SaaS analytics dashboard displayed on a 27-inch monitor. The interface shows a dark theme with a left sidebar navigation, a top metrics bar with four KPI cards (revenue, users, conversion, churn), a large area chart in the center showing 30-day trends in gradient blue-to-purple, and a data table below. The design uses a card-based layout with subtle rounded corners and thin borders. Typography is Inter font. Accent color is electric blue (#3B82F6). The monitor sits on a minimal white desk with a wireless keyboard in front.
curl -X POST https://runapi.ai/api/v1/wan/text_to_image \
-H "Authorization: Bearer $RUNAPI_KEY" \
-H "Content-Type: application/json" \
--data-binary @- <<'JSON'
{
"model": "wan-2.7-image-pro",
"prompt": "Clean UI design mockup of a SaaS analytics dashboard displayed on a 27-inch monitor. The interface shows a dark theme with a left sidebar navigation, a top metrics bar with four KPI cards (revenue, users, conversion, churn), a large area chart in the center showing 30-day trends in gradient blue-to-purple, and a data table below. The design uses a card-based layout with subtle rounded corners and thin borders. Typography is Inter font. Accent color is electric blue (#3B82F6). The monitor sits on a minimal white desk with a wireless keyboard in front."
}
JSON
FAQ
Using wan-2.7-image-pro prompts
What is %{model}?
%{model} is available through RunAPI as part of the unified model catalog. These prompts show practical input patterns that agents and backend services can reuse.
How do I use these prompts?
Copy any prompt and paste it into Claude Code, Codex, Cursor, or Windsurf after installing the RunAPI MCP Server. Developers can also copy the API example and send the prompt directly.
Do these prompts cost money to browse?
Browsing and copying prompt examples is free. Generation requests only cost money when you call a RunAPI model with your API key.
Can I adapt the prompts for production?
Yes. Treat each prompt as a starting point, then add your brand rules, output dimensions, safety constraints, and application-specific context before using it in production.