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- Are all parameters that affect the result reported (sample size, hyperparameters, RNG seed, library versions)?
- Does the design actually test the hypothesis, or some weaker proxy?
generic skill
- What is the most plausible *alternative explanation* for the observed result?
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> ${topic}
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Your job: produce a structural plan for the experiment script that the next stage will fill in. Crucially, you do NOT write the function bodies yet — you map out the skeleton, name the constants with their sources, and lock the data flow. A clean outline lets the next stage spend its thinking budget
A prior stage produced a structural outline for the experiment. Your job: fill in every function body in the scaffold. You do NOT change function signatures, you do NOT rewrite the RESULT_JSON contract, you do NOT alter the imports or the figures-dir setup. The outline locked those decisions; your o
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brainstormed for the same topic. Pick which is more promising as the
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This digest covers: **${window_from}** → **${window_to}**.
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You are NOT running a simulation. You are NOT inventing data. You are summarizing what the user actually dropped into the data dir.
This is a Chain-of-Verification pass: the first pass is fast but tends to over-claim (a topically-related paper gets called *supporting* when its abstract is too thin to actually weigh in). The verification pass catches that by forcing the model to articulate WHY a claim of support or conflict holds
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- Quest ID: `${quest_id}`
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