「sci」の検索結果

Provides edge case, corner case, boundary condition, and limit testing patterns for Java unit tests. Validates minimum/maximum values, null cases, empty collections, numeric overflow/underflow, floating-point precision, and off-by-one scenarios using JUnit 5 and AssertJ. Use when writing .java test files to ensure code handles limits, corner cases, and special inputs correctly.

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

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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

日本語の概要は準備中です。原文の説明を表示しています。

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

日本語の概要は準備中です。原文の説明を表示しています。

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

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

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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 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

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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

日本語の概要は準備中です。原文の説明を表示しています。

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

日本語の概要は準備中です。原文の説明を表示しています。

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

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

日本語の概要は準備中です。原文の説明を表示しています。

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

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Train ML models on Databricks. Use for: classification/regression/deep-learning (XGBoost, scikit-learn, LightGBM, PyTorch) with Optuna, @prod/@challenger aliases, batch scoring (spark_udf for plain models, fe.score_batch for feature-store-backed), custom PyFunc, custom ResponsesAgent (LangGraph + UC Function/Vector Search); UC feature tables + FeatureLookup + point-in-time joins + Lakebase online store; declarative Feature Views (create_feature, DeltaTableSource, RollingWindow/SlidingWindow/TumblingWindow, materialize_features, streaming Kafka features). NOT for: endpoint ops (databricks-model-serving), MLflow evaluation (databricks-mlflow-evaluation).

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Take any claim and design + execute a complete research protocol to test it. Apply scientific method automatically: design study, gather data, run analysis, provide verdict with confidence level.

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論文投稿用の出版品質グラフ・図表を作成するメタスキル。 「論文用のグラフを作って」「Nature用の図を作成」「出版品質の可視化」等のリクエストで発動。

Generates Mermaid and ASCII diagrams of palace structure, knowledge topology, and synapse connectivity. Use when inspecting or presenting a palace visually.

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Pre-implementation gate covering think-first, simplicity, surgical edits, and verifiable goals. Use when starting implementation to verify the approach.

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Guide minimal code via a decision ladder with full safety, edge, and negative-case coverage. Use when adding code, choosing a dependency, or auditing a diff.

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Use when a manuscript comes back with reviewer or editor comments. Numbers every comment, classifies it MAJOR/MINOR/REBUTTAL, drafts a point-by-point response with tracked manuscript changes, routes new analyses to /analyze-stats and writes the cover letter.

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Renamed to /model-assessment in v6 (removed in v7).

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Use when finding papers or building a reference list. Searches PubMed, Semantic Scholar and bioRxiv/medRxiv, includes only references verified through an API, and generates BibTeX. Auditing an existing reference list is /verify-refs.

日本語の概要は準備中です。原文の説明を表示しています。