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.
Already linked to a challenge workflow.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Define three agents in OpenAI Swarm: 'Auditor', 'Analyst', and 'Strategist'. Assign the 'Auditor' a tool to fetch FDA documents and the 'Analyst' a tool to summarize clinical metrics using Gemma 2.
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.
BioPharma Market Intelligence Swarm with Gemma 2 and OpenAI Swarm
Cytokinetics and Bristol Myers Squibb (BMS) are currently competing in the heart drug market following the FDA approval of Cytokinetics' Myqorzo. This challenge involves building a multi-agent orchestration system to perform real-time competitive intelligence. You will use OpenAI Swarm to coordinate specialized agents—an 'FDA Auditor', a 'Clinical Trial Analyst', and a 'Market Strategist'—to analyze the competitive landscape of obstructive hypertrophic cardiomyopathy (oHCM) treatments. You will utilize Gemma 2 as the local LLM for the 'FDA Auditor' and 'Clinical Trial Analyst' roles to process long regulatory documents, while using Tavily's search tool to feed real-time market data into the Swarm. The goal is to generate a comprehensive risk-benefit report that compares Myqorzo against Camzyos, highlighting specific trial outcomes and market availability windows.
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.