菜单
Plan scientific ML workflows with scikit-learn, PyTorch Lightning, transformers, graph neural networks, active learning, SHAP/explainability, and leakage checks. Use when applying ML to biomedical, materials, chemistry, imaging, or other scientific datasets.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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.
| name | scientific-machine-learning |
| description | Plan scientific ML workflows with scikit-learn, PyTorch Lightning, transformers, graph neural networks, active learning, SHAP/explainability, and leakage checks. Use when applying ML to biomedical, materials, chemistry, imaging, or other scientific datasets. |
| user-invocable | true |
| license | Apache-2.0 |
Source adaptation: BioRouter-original synthesis inspired by broad Scientific Agent Skills and Auto-Empirical Research Skills capability patterns, rewritten under Apache-2.0. Do not copy upstream text; use this as a native BioRouter routing and quality-control checklist.
Use this skill when the user asks for Scientific ML. Load more specific BioSkills, BRXT extensions, or language skills when the task touches a concrete dataset, file format, package, or platform.
Collect the minimum context before acting:
Prefer these BioRouter-native routes before writing new glue code:
scientific-research for broad research orchestration.citation-temporal-integrity and claim-evidence-integrity for evidence-heavy deliverables.replication-package-audit when outputs must be reproducible.python-scripting, r-scripting, or BioSkills for package-specific code style.Before presenting results as final:
Return a compact work plan first, then the implementation or review. For each result, include: