K-Dense-AI/science-superpowers

preregistering-analysis

Use before running any confirmatory analysis or looking at outcome data, when testing a hypothesis, computing a p-value, or about to claim an effect - locks predictions and decision rules before results are seen

원문 언어: 영어

establishing-feasibility-first

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…

원문 언어: 영어

framing-research-questions

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

원문 언어: 영어

using-science-superpowers

Use when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions

원문 언어: 영어

reporting-and-archiving-findings

Use when an analysis is complete and verified, and you need to decide how to report it and archive the work for reproducibility

원문 언어: 영어

verifying-results-before-claiming

Use when about to claim a result, effect, significance, or that an analysis reproduces, before reporting or writing it up - requires running the analysis fresh and reading the actual output first; evidence before claims always

원문 언어: 영어

designing-the-analysis

Use when you have an approved research question and need a concrete analysis plan, before touching outcome data or fitting any model

원문 언어: 영어

dispatching-parallel-investigations

Use when facing 2+ independent investigations that can proceed without shared state - parallel literature survey, multi-dataset replication, or pre-specified robustness checks

원문 언어: 영어

executing-analysis

Use when you have a pre-registered analysis plan to execute inline in this session with review checkpoints, on a platform without subagents

원문 언어: 영어

investigating-anomalous-results

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

원문 언어: 영어

receiving-critical-review

Use when receiving critical feedback on an analysis or manuscript, before implementing suggestions, especially if feedback seems unclear or methodologically questionable - requires verification, not performative agreement or blind changes

원문 언어: 영어

requesting-red-team-review

Use after completing an analysis or before reporting a finding, to have a skeptical reviewer attack the conclusion before you believe it

원문 언어: 영어

setting-up-reproducible-analysis

Use when starting analysis work that needs isolation, or before executing a pre-registered plan - ensures an isolated, reproducible workspace with pinned environment, fixed seeds, and immutable raw data

원문 언어: 영어

subagent-driven-analysis

Use when executing a pre-registered analysis plan with mostly independent steps in the current session

원문 언어: 영어

surveying-prior-work

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

원문 언어: 영어

writing-science-skills

Use when creating new skills, editing existing skills, or verifying skills work before deployment

원문 언어: 영어