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.
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Structured source with 19 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Initialize a Google ADK agent in Vertex AI. Define its capabilities to ingest multimodal input: text from news articles, sentiment scores, and numerical market data from a `Prediction Market API`. The agent should be able to receive a prompt like 'Analyze the market for the upcoming election' and respond with a basic summary of current data.
```python
from vertexai.preview.generative_models import GenerativeModel, Part
from google.cloud import aiplatform
aiplatform.init(project="your-gcp-project-id", location="us-central1")
model = GenerativeModel("gemini-1.5-pro-preview-0514") # Use an appropriate Gemini model for ADK
def create_prediction_market_agent():
# This is a conceptual example, actual ADK setup involves defining specific flows and tools
# You would define your agent's schema and actions through Vertex AI Console or SDK configuration.
pass # Implement ADK agent setup here
# Example of multimodal input for a Gemini model call within ADK context
# You'll adapt this for specific ADK tools/actions
# response = model.generate_content([
# Part.from_text("Recent news about candidate A:"),
# Part.from_uri(uri="gs://your-bucket/news_image.jpg", mime_type="image/jpeg"),
# Part.from_text("Market sentiment: bullish for A.")
# ])
```
Begin by structuring your ADK agent to define a tool for `fetch_market_data` from your simulated API and another for `analyze_news_sentiment` using Claude Opus 4.1.Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
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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
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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 Prediction Market Agent
This challenge focuses on building a highly sophisticated agent for analyzing real-time prediction markets. Leveraging Google's Agent Development Kit (ADK) with its strong multimodal capabilities, developers will create an agent that aggregates diverse data sources—market data, news feeds, social media sentiment, and expert opinions—to forecast event outcomes and identify potential arbitrage opportunities. This goes beyond simple data analysis to synthesize complex information for strategic decision-making. The system will use Claude Opus 4.5 for nuanced textual analysis and advanced strategy generation, interpreting qualitative market signals. Grok 4 Heavy will be integrated for high-performance, real-time complex pattern recognition and scenario simulation, enabling rapid assessment of market dynamics. Firebender AI will serve as a specialized service, offering advanced market data aggregation and pattern recognition as a tool that the Google ADK agent orchestrates. The agent will interact with a simulated Prediction Market API to fetch real-time contract data and execute simulated trades. This challenge emphasizes multimodal data fusion, multi-LLM orchestration, and the integration of specialized agentic services to create a powerful, real-time market intelligence system capable of providing actionable insights to 'pro gamblers' and financial strategists.
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.