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 16 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Design a basic conversational flow in Voiceflow that allows a user to specify a content topic and target audience. The Voiceflow agent should then call your Google ADK agent (via a webhook or API endpoint) to generate the multimodal concept. Implement logic within your ADK agent for self-correction: after generating a concept, it should cross-reference with policies fetched by Skyvern and refine the concept if violations are found.
```python
# Conceptual ADK self-correction loop
def generate_and_check_content(topic, audience, policies):
initial_concept = generate_multimodal_concept(topic, audience)
violations = check_concept_against_policies(initial_concept, policies)
if violations:
# Use Gemini again to refine the concept based on violations
corrected_concept = model.generate_content(
f"Refine this content concept to remove violations: {initial_concept}. Violations: {violations}",
generation_config=GenerationConfig(response_mime_type="application/json")
).candidates[0].content.parts[0].text
return corrected_concept, violations
return initial_concept, []
# Voiceflow integration would involve setting up an API endpoint that triggers this ADK logic.
```Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
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.