Use when you have an approved research question and need a concrete analysis plan, before touching outcome data or fitting any model
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Use after framing a question and before designing an analysis, or when choosing a method, judging whether a result is novel, or needing a prior effect size for a power calculation
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Before designing an analysis, ground the question and your chosen methods in what is already known. Most questions have established methods, known confounds, and prior effect sizes. Reinventing a method badly — or rediscovering a known artifact and reporting it as a finding — wastes effort and erodes credibility.
Core principle: Find out what is already known before you generate new claims.
This is the science analog of reading the existing codebase before writing new code. It is a flexible skill — adapt depth to the stakes of the investigation.
framing-research-questions, before designing-the-analysisSurvey four things:
science-superpowers:dispatching-parallel-investigations for the workflow. This keeps your own context clean.After grounding, invoke science-superpowers:designing-the-analysis. Bring forward the adopted methods, the confound list, and the prior effect size for powering the design.
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로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Use when you have an approved research question and need a concrete analysis plan, before touching outcome data or fitting any model
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when facing 2+ independent investigations that can proceed without shared state - parallel literature survey, multi-dataset replication, or pre-specified robustness checks
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Use when your human partner has explicitly opted into exploratory or feasibility mode - a compute-heavy simulation, an unproven pipeline, an unbenchmarked solver, an untested cluster job - or when whether the work can run at all is still unknown, when a plan's largest configuration has never been executed, when a memory or wall-clock ceiling is estimated rather than measured, or when someone proposes pre-registering over runs that have never happened
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Use when you have a pre-registered analysis plan to execute inline in this session with review checkpoints, on a platform without subagents
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You MUST use this before any data analysis or investigation - before exploring a dataset, loading or profiling data, running a model, computing a statistic, or testing an idea, and before any outcome data is touched
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Use when a result is surprising, impossible, contradicts a sanity check, a pipeline fails, a model won't converge, or a replication fails - before adjusting anything
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