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.
Describe your methodology for validating the top 5-10 candidate molecules. This should include methods for assessing synthetic accessibility, detailed quantum chemistry calculations (simulated, e.g., via a library) for a selected candidate's electronic properties, and a comprehensive comparison against existing commercial sunscreens. Discuss potential challenges in translating these candidates to real-world applications.
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 rubric, target behavior, and pass-fail criteria as the baseline for evaluation.
Adjust fixtures, mocks, and thresholds to the system under test instead of weakening the assertions.
Make sure the prompt catches regressions instead of just mirroring the happy-path examples.
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-Driven Sustainable Sunscreen Molecular Design with Optuna & ChromaDB
Inspired by the protective, UV-absorbing pigment found in octopuses, this challenge focuses on leveraging advanced computational chemistry and artificial intelligence to design novel, sustainable sunscreen molecules. Participants will develop a pipeline to generate and optimize molecular structures that mimic the broad-spectrum UV absorption and photostability of natural pigments, while ensuring environmental benignity and synthetic feasibility. The goal is to move beyond conventional, often environmentally harmful, UV filters by discovering bio-inspired alternatives. This project will involve integrating cheminformatics libraries with machine learning models to predict molecular properties. Candidates will use generative AI approaches to propose new chemical entities and apply optimization algorithms to refine their structures based on target properties like high UV absorption across UVA/UVB, photostability, low toxicity, and biodegradability. The outcome will be a set of prioritized molecular candidates with predicted properties, paving the way for more sustainable and effective sunscreens.
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.