원클릭으로 Manus에서 모든 스킬 실행

scientific-data-engineering

스타1

포크0

업데이트2026년 7월 1일 23:09

Plan and review scalable scientific data workflows with Polars, Dask, Zarr, TileDB, Arrow/Parquet, HDF5, and dataset versioning. Use when scientific data is too large for naive pandas/R data frames or when the user asks about chunking, columnar storage, lazy execution, cloud/object storage, or reproducible data pipelines.

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

Manus에서 실행

출처

BaranziniLab

BaranziniLab/biorouter-skills

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

Scientific Data Engineering

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.

When to Use

Use this skill when the user asks for Data Engineering. Load more specific BioSkills, BRXT extensions, or language skills when the task touches a concrete dataset, file format, package, or platform.

Intake

Collect the minimum context before acting:

Research question or operational goal.
Data source, file format, platform, package, or API involved.
Unit of analysis and expected output.
Privacy, credential, licensing, and compute constraints.
Whether the user wants a plan, executable code, review, or installed-tool workflow.

Native BioRouter Routing

Prefer these BioRouter-native routes before writing new glue code:

Use relevant BRXT extensions when platform/API access is needed.
Use scientific-research for broad research orchestration.
Use citation-temporal-integrity and claim-evidence-integrity for evidence-heavy deliverables.
Use replication-package-audit when outputs must be reproducible.
Use python-scripting, r-scripting, or BioSkills for package-specific code style.

Quality Gates

Before presenting results as final:

State the data provenance and tool/package versions.
Separate exploratory output from confirmatory evidence.
Check that identifiers, coordinates, sample ids, citations, or run ids are preserved.
Identify assumptions that could change the conclusion.
Provide a rerunnable command, notebook cell, or workflow outline when execution is involved.
Mark anything requiring credentials, proprietary data, or external services as unverified unless it was actually checked.

Output Pattern

Return a compact work plan first, then the implementation or review. For each result, include:

Decision made.
Evidence or file/API source.
Failure modes.
Next verification step.

이 저장소의 다른 Skills

같은 저장소

computational-fluid-dynamics

BaranziniLab/biorouter-skills

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.

2026-07-011

arbor-optimization

BaranziniLab/biorouter-skills

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.

2026-07-011

autoskill-workflow-discovery

BaranziniLab/biorouter-skills

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.

2026-07-011

bids-neuroimaging

BaranziniLab/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.

2026-07-011

clinical-ai-modeling

BaranziniLab/biorouter-skills

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.

2026-07-011

general-time-series-forecasting

BaranziniLab/biorouter-skills

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.

2026-07-011

name	scientific-data-engineering
description	Plan and review scalable scientific data workflows with Polars, Dask, Zarr, TileDB, Arrow/Parquet, HDF5, and dataset versioning. Use when scientific data is too large for naive pandas/R data frames or when the user asks about chunking, columnar storage, lazy execution, cloud/object storage, or reproducible data pipelines.
user-invocable	true
license	Apache-2.0

Scientific Data Engineering

When to Use

Use this skill when the user asks for Data Engineering. Load more specific BioSkills, BRXT extensions, or language skills when the task touches a concrete dataset, file format, package, or platform.

Intake

Collect the minimum context before acting:

Research question or operational goal.
Data source, file format, platform, package, or API involved.
Unit of analysis and expected output.
Privacy, credential, licensing, and compute constraints.
Whether the user wants a plan, executable code, review, or installed-tool workflow.

Native BioRouter Routing

Prefer these BioRouter-native routes before writing new glue code:

Use relevant BRXT extensions when platform/API access is needed.
Use scientific-research for broad research orchestration.
Use citation-temporal-integrity and claim-evidence-integrity for evidence-heavy deliverables.
Use replication-package-audit when outputs must be reproducible.
Use python-scripting, r-scripting, or BioSkills for package-specific code style.

Quality Gates

Before presenting results as final:

State the data provenance and tool/package versions.
Separate exploratory output from confirmatory evidence.
Check that identifiers, coordinates, sample ids, citations, or run ids are preserved.
Identify assumptions that could change the conclusion.
Provide a rerunnable command, notebook cell, or workflow outline when execution is involved.
Mark anything requiring credentials, proprietary data, or external services as unverified unless it was actually checked.

Output Pattern

Return a compact work plan first, then the implementation or review. For each result, include:

Decision made.
Evidence or file/API source.
Failure modes.
Next verification step.