pylabrobot

Overview of pylabrobot skill

What pylabrobot does

The pylabrobot skill helps you plan and run lab automation in Python with a hardware-agnostic interface. It is useful when you need one workflow layer for liquid handling, plate readers, pumps, heater shakers, incubators, centrifuges, and similar devices, instead of writing separate vendor-specific scripts.

Who should use it

Choose the pylabrobot skill if you are building or maintaining automated lab workflows across multiple instruments, or if you want to simulate and validate a protocol before running it on real hardware. It is a strong fit for teams that care about reproducibility, deck/resource management, and cross-platform Python control.

Why it stands out

The main advantage of pylabrobot is unified control across different lab devices. That makes it better than a one-off prompt for complex workflows with state, deck layout, and multi-step execution. If you only need a simple Opentrons-only protocol, a vendor-native approach may be easier.

How to Use pylabrobot skill

Install and inspect the right files

Use the pylabrobot install flow for this directory, then open scientific-skills/pylabrobot/SKILL.md first. Because this repo has no extra references/, resources/, or scripts/ folders, the main source of truth is the skill document itself plus any linked repo references inside it.

Turn your goal into a usable prompt

For best pylabrobot usage, state the lab task, hardware, and constraints up front. A weak request is “write a protocol.” A stronger request is: “Create a pylabrobot workflow for 96-well plate aliquoting on a Hamilton STAR with tip tracking, deck map assumptions, and a dry-run simulation path.” The more specific your device list and workflow stages, the less guesswork the model has.

Read the workflow before writing code

Start with the sections on overview, when to use, core capabilities, and liquid handling. Those are the parts that clarify whether you need resource setup, protocol simulation, device-specific integration, or just a generic Python scaffold. If your task involves plate reading or auxiliary devices, scan those sections before asking for implementation.

Prompt for decisions, not just output

The best pylabrobot guide style prompts ask for choices that affect execution quality: deck layout assumptions, labware definitions, tip handling, volume ranges, and simulation checks. For example: “Assume a 384-well destination plate, disposable tips, and a pre-validated deck layout; flag any steps that should be simulated first.” That produces more actionable output for pylabrobot for Workflow Automation.

pylabrobot skill FAQ

Is pylabrobot only for one robot brand?

No. The core value of pylabrobot is vendor-agnostic automation. It is meant for workflows that span different equipment types or need a common Python layer across devices.

Is it better than a normal prompt?

Usually yes for real lab automation work, because the pylabrobot skill gives you a clearer framework for resources, execution flow, and simulation. A plain prompt can generate code, but it is more likely to miss lab-specific constraints or skip critical setup details.

When should I not use it?

Do not reach for pylabrobot if you only need a trivial, vendor-specific protocol and the official SDK already covers it well. For narrow Opentrons-only work, a native protocol approach may be faster and simpler.

Is it beginner-friendly?

It is beginner-friendly if you can describe a workflow in stages and you are willing to define hardware assumptions. It is less friendly if you do not know your deck layout, labware, or device constraints, because those inputs matter to correct output.

How to Improve pylabrobot skill

Give the missing lab details first

The biggest quality gains come from specifying device model, labware names, volume ranges, sample count, and whether the run is real or simulated. For example, “transfer 20 µL from a 96-well source plate to a 384-well assay plate using a Hamilton STAR” is far more useful than “make a transfer script.”

Ask for failure-aware output

Common failures with pylabrobot are vague resource assumptions, incorrect tip strategy, and unclear plate geometry. Ask the model to call out assumptions, list what must be configured manually, and separate simulation-only checks from hardware-ready steps.

Iterate from simulation to execution

Use the first pass to validate layout and logic, then refine around edge cases like dead volumes, partial fills, wash steps, or multi-device handoffs. For pylabrobot usage, the best workflow is usually draft, simulate, inspect resource mapping, then harden for the actual instrument.

Reuse the same structure across requests

When you find a working prompt pattern, keep the same order: goal, instrument, labware, volumes, state constraints, then output format. That consistency makes the pylabrobot skill easier to reuse for related automation tasks and produces more reliable code from one job to the next.