implementation

Deep Think Mode for Anomaly Detection

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: Build a Secure Stablecoin Payment System with LangGraph, Gemini 2.5 Pro, and MCP

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Linked challenge

Build a Secure Stablecoin Payment System with LangGraph, Gemini 2.5 Pro, and MCP

Prompt source

Original prompt text with formatting preserved for inspection.

1 lines

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No variables

0 checklist items

Develop the 'RiskAssessment' node in your LangGraph workflow. This node should leverage Gemini 2.5 Pro's Deep Think mode to analyze transaction patterns, sender history, and recipient reputation. Provide a prompt template for Gemini 2.5 Pro that guides it to identify potentially fraudulent or high-risk transactions and recommend appropriate actions (e.g., flag for manual review, block transaction).

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.

Prompt diagnostics

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Purpose

implementation

This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked challenge

Build a Secure Stablecoin Payment System with LangGraph, Gemini 2.5 Pro, and MCP

Leveraging the latest advancements in agentic AI, this challenge focuses on creating a robust and secure multi-agent system for managing and executing stablecoin payments. Inspired by Stripe and Paradigm's Tempo project, you will design agents capable of complex financial reasoning, transaction validation, and secure execution. The system will utilize graph-based workflows to orchestrate approval processes, integrate with external financial APIs via the MCP, and employ advanced hybrid reasoning to handle both routine and exceptional payment scenarios. This challenge emphasizes the development of agents that can operate reliably in a high-stakes financial environment, ensuring compliance and security.

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Initial Assessment Prompt

planning

Content Classification Prompt

implementation

Problem Definition and Requirements Gathering

To establish a clear understanding of the problem space and define comprehensive requirements for the AI solution.