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Plan safe lab-automation workflows for Opentrons, cloud labs, Ginkgo-style APIs, plate maps, run metadata, and protocol simulation. Use when a request involves robots, wet-lab run planning, samples, plates, liquid handling, or cloud-lab execution.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | laboratory-automation-planning |
| description | Plan safe lab-automation workflows for Opentrons, cloud labs, Ginkgo-style APIs, plate maps, run metadata, and protocol simulation. Use when a request involves robots, wet-lab run planning, samples, plates, liquid handling, or cloud-lab execution. |
| 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 Lab Automation. 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:
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.