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.
Initialize an OpenAI Agent using the OpenAI Agents SDK. Define a 'MetaboliteScientist' agent that has access to two tools: 'calculate_concentration(sequence_data)' and 'generate_metabolite_report(data)'. Use the following import: 'from openai_agents import Agent, Runner'. Describe how the agent should handle ambiguous sequencing reads.
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 already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
Aptamer-Seq Autonomous Metabolite Quantifier with OpenAI Agents & QA Wolf
Quantifying metabolites using structure-switching aptamers coupled to DNA sequencing is a breakthrough in molecular sensing. This challenge tasks you with building an autonomous agentic system using the OpenAI Agents SDK to process raw sequencing data and convert it into metabolite concentration profiles. Your agent will serve as an 'AI Scientist,' capable of selecting appropriate normalization methods, executing bioinformatics functions, and validating the resulting data visualizations. To ensure the reliability of the system's user interface, you will integrate QA Wolf for automated end-to-end testing of the generated metabolite dashboards. Participants must design a multi-turn conversation flow where the agent interacts with a data environment. The agent will use function calling to invoke DNA-sequencing analysis tools, perform structure-switching analysis, and output JSON-formatted metabolic profiles. This project mirrors the complexity of modern biotech labs where AI is used to bridge the gap between high-throughput sequencing and clinical metabolomics.
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.