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 1 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Design a LangGraph workflow that orchestrates the financial analysis process. It should include nodes for: 1) Initial news ingestion, 2) Company data retrieval (financials, stock data), 3) Prediction market query, 4) Gemini 2.5 Pro Deep Think for synthesis and prediction, 5) Output generation. Emphasize how different data points will flow through the graph and how states will be managed.
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 is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
Agent for Autonomous Market Intelligence
This challenge tasks developers with building a sophisticated agentic system for real-time financial market analysis. The system will leverage the advanced reasoning capabilities of Gemini 2.5 Pro, particularly its 'Deep Think' mode, to process diverse financial data streams. Using LangGraph, you will orchestrate a dynamic, graph-based workflow where agents autonomously analyze news, company reports, and prediction market data (like from Kalshi or Polymarket, simulated) to generate actionable investment insights and risk assessments. The core of this challenge lies in implementing a hybrid reasoning system: instant processing for rapid market reactions and deep, extended thinking for complex economic forecasting. Agents will utilize the Model Context Protocol (MCP) for seamless tool integration, allowing them to fetch real-time stock prices, analyze financial statements via RAG, and query prediction markets. The goal is to create a robust, self-improving financial analyst capable of identifying opportunities and threats with high precision.
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.