Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 15 active lines to adapt.
Already linked to a challenge workflow.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Initialize your Google ADK environment and define a primary agent that leverages Gemini 2.5 Pro for its core generative capabilities. Structure the agent to handle multimodal inputs and produce structured multimodal outputs. Ensure the agent is configured to use Vertex AI for model access.
```python
import google.generativeai as genai
import vertexai
from vertexai.preview.generative_models import GenerationConfig, GenerativeModel, Part, Tool
# Initialize Vertex AI
vertexai.init(project="YOUR_GCP_PROJECT_ID", location="YOUR_GCP_REGION")
# Configure Gemini 2.5 Pro
model = GenerativeModel("gemini-1.5-pro-preview-0514") # Use 1.5 Pro as a stand-in if 2.5 Pro not public yet
def generate_multimodal_concept(topic: str, audience: str) -> dict:
prompt = f"Generate a short video concept for '{topic}' targeting '{audience}'. Provide a script, visual description, and audio cues in JSON format.\nScript:\nVisual Description:\nAudio Cues:"
response = model.generate_content(prompt, generation_config=GenerationConfig(response_mime_type="application/json"))
return response.candidates[0].content.parts[0].text
# In a full ADK agent, this would be wrapped as a tool or agent capability.
```Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
Multimodal Content Generator for Brand Safety
Create a Google ADK agent that generates innovative multimodal content concepts (e.g., short video scripts, visual descriptions, audio cues) tailored for specific platforms like YouTube or social media. The agent must meticulously adhere to brand safety guidelines and platform content policies. Leveraging Gemini's multimodal capabilities, it will perform self-correction, using external tools like Skyvern to scrape real-time policy updates and Voiceflow for a natural, conversational user interface. This challenge focuses on delivering creative content while ensuring strict compliance.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.