メニュー
Plan CATE, causal forests, meta-learners, policy learning, conformal causal inference, fairness audits, subgroup stability, and heterogeneous-effect reporting. Use when causal effects vary across people, places, time, or policy rules.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
Plan and review computational fluid dynamics workflows with meshes, boundary conditions, turbulence models, OpenFOAM/FluidSim-style solvers, validation cases, convergence checks, and reproducible simulation artifacts. Use when users mention CFD, fluid dynamics, Navier-Stokes, meshing, turbulence, OpenFOAM, or flow simulation.
Run evaluator-driven hypothesis trees for improving research artifacts, prompts, analyses, and designs. Use when a task needs branch-and-prune exploration, rubric scoring, iterative refinement, or explicit comparison of competing scientific/workflow alternatives.
Detect repeated user workflows and draft native BioRouter skills, hooks, or checklist updates from observed work patterns. Use when users ask to turn repeated work, screen-history patterns, project routines, or recurring guardrails into reusable BioRouter skills.
Plan and audit BIDS neuroimaging datasets, derivatives, validation, metadata, and reproducible preprocessing. Use when users mention BIDS, fMRI, MRI, EEG/MEG/iEEG, DICOM conversion, dataset_description.json, participants.tsv, or neuroimaging derivatives.
Plan and review clinical AI modeling workflows with EHR cohorts, PyHealth-style tasks, validation, bias checks, calibration, safety boundaries, and no-PHI handling. Use when users ask about clinical prediction models, risk scores, treatment-response ML, medical reports, or clinical model evaluation.
Plan and review general time-series forecasting with classical models, ML, foundation models, backtesting, leakage checks, and uncertainty. Use for non-omics forecasting, sensor streams, operations, finance, clinical vitals, demand, or irregular temporal data.
SOC 職業分類に基づく
| name | causal-heterogeneity-policy-learning |
| description | Plan CATE, causal forests, meta-learners, policy learning, conformal causal inference, fairness audits, subgroup stability, and heterogeneous-effect reporting. Use when causal effects vary across people, places, time, or policy rules. |
| user-invocable | true |
| license | Apache-2.0 |
Source adaptation: BioRouter-original synthesis inspired by Auto-Empirical Research Skills capability clusters, rewritten under Apache-2.0. This is native BioRouter guidance and does not copy upstream text.
Use this skill when the user asks about CATE, causal forests, meta-learners, policy learning, heterogeneous treatment effects, fairness audits, or conformal causal inference. If the task is biomedical, clinical, or omics-specific, combine this with the appropriate BioSkills category and extension instead of treating the methods in isolation.
Collect:
Before writing conclusions:
empirical-research-router for full workflow staging.causal-identification-gates before causal language.econometrics-toolkit, statistical-modeling-bayes, or financial-accounting-econometrics for adjacent method detail.citation-temporal-integrity, claim-evidence-integrity, and replication-package-audit for publication-facing outputs.Return a compact plan with: