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.
Develop the core Semantic Kernel agent. Define a set of 'skills' (functions) for the agent, such as 'RetrieveRelevantNews', 'GetMarketData', 'AssessRisk', and 'ProposeMitigation'. Implement these skills, integrating Cohere Command R+ for reasoning and generation, and Milvus for retrieval. Demonstrate how the agent processes an input query (e.g., 'Assess the supply risk for copper given recent news') and orchestrates these skills to produce a comprehensive risk assessment, similar to the `AnalyzeMineralSupplyRisk` evaluation task output.
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 is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
AI-Powered Critical Mineral Supply Chain Resilience with RAG and Vector DB
The global economy is increasingly reliant on critical minerals, but structural shortages, geopolitical tensions, and supply chain vulnerabilities pose significant risks. This challenge requires you to design and implement an AI-driven system to proactively identify, analyze, and mitigate potential disruptions in critical mineral supply chains. The system will ingest real-time news, economic data, and geopolitical intelligence to provide actionable insights for stakeholders in the mining and manufacturing sectors. Participants will build an AI agent that leverages Retrieval Augmented Generation (RAG) with Cohere Command R+ for advanced reasoning and Milvus as a high-performance vector database for semantic search. The agent, orchestrated by Semantic Kernel, will dynamically process vast amounts of unstructured and structured data, identify patterns, and generate risk assessments and mitigation strategies for minerals like copper and tungsten.
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.