PyLabRobot
Research skill, available on Zeplik
PyLabRobot is a ready-to-run research skill on Zeplik. Automate lab hardware with PyLabRobot: liquid handlers (Hamilton, Opentrons, Tecan), plate readers, reproducible runs. Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
The PyLabRobot skill loads automatically when your request matches it, or you can invoke it directly by typing /pylabrobot in any chat. It works with attachments, connectors, and any model that supports the task, so you get the same expert method every time without setting anything up.
What the PyLabRobot skill can do
- Write and run liquid handling protocols on Hamilton, Opentrons, and Tecan robots
- Manage deck layouts and track tip and liquid volume states
- Integrate plate readers, scales, and other analytical equipment into workflows
- Simulate protocols with ChatterboxBackend before executing on real hardware
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How the PyLabRobot skill works
PyLabRobot
Overview
PyLabRobot is a hardware-agnostic, pure Python Software Development Kit for automated and autonomous laboratories. Use this skill to control liquid handling robots, plate readers, pumps, heater shakers, incubators, centrifuges, and other laboratory automation equipment through a unified Python interface that works across platforms (Windows, macOS, Linux).
When to Use This Skill
Use this skill when:
- Programming liquid handling robots (Hamilton STAR/STARlet, Opentrons OT-2, Tecan EVO)
- Automating laboratory workflows involving pipetting, sample preparation, or analytical measurements
- Managing deck layouts and laboratory resources (plates, tips, containers, troughs)
- Integrating multiple lab devices (liquid handlers, plate readers, heater shakers, pumps)
- Creating reproducible laboratory protocols with state management
- Simulating protocols before running on physical hardware
- Reading plates using BMG CLARIOstar or other supported plate readers
- Controlling temperature, shaking, centrifugation, or other material handling operations
- Working with laboratory automation in Python
Core Capabilities
PyLabRobot provides comprehensive laboratory automation through six main capability areas:
1. Liquid Handling
Control liquid handling robots for aspirating, dispensing, and transferring liquids. Key operations include:
- Basic Operations: Aspirate, dispense, transfer liquids between wells
- Tip Management: Pick up, drop, and track pipette tips automatically
- Advanced Techniques: Multi-channel pipetting, serial dilutions, plate replication
- Volume Tracking: Automatic tracking of liquid volumes in wells
- Hardware Support: Hamilton STAR/STARlet, Opentrons OT-2, Tecan EVO, and others
2. Resource Management
Manage laboratory resources in a hierarchical system:
- Resource Types: Plates, tip racks, troughs, tubes, carriers, and custom labware
- Deck Layout: Assign resources to deck positions with coordinate systems
- State Management: Track tip presence, liquid volumes, and resource states
- Serialization: Save and load deck layouts and states from JSON files
- Resource Discovery: Access wells, tips, and containers through intuitive APIs
3. Hardware Backends
Connect to diverse laboratory equipment through backend abstraction:
- Liquid Handlers: Hamilton STAR (full support), Opentrons OT-2, Tecan EVO
- Simulation: ChatterboxBackend for protocol testing without hardware
- Platform Support: Works on Windows, macOS, Linux, and Raspberry Pi
- Backend Switching: Change robots by swapping backend without rewriting protocols
4. Analytical Equipment
Integrate plate readers and analytical instruments:
- Plate Readers: BMG CLARIOstar for absorbance, luminescence, fluorescence
- Scales: Mettler Toledo integration for mass measurements
- Integration Patterns: Combine liquid handlers with analytical equipment
- Automated Workflows: Move plates between devices automatically
5. Material Handling
Control environmental and material handling equipment:
- Heater Shakers: Hamilton HeaterShaker, Inheco ThermoShake
- Incubators: Inheco and Thermo Fisher incubators with temperature control
- Centrifuges: Agilent VSpin with bucket positioning and spin control
- Pumps: Cole Parmer Masterflex for fluid pumping operations
- Temperature Control: Set and monitor temperatures during protocols
6. Visualization & Simulation
Visualize and simulate laboratory protocols:
- Browser Visualizer: Real-time 3D visualization of deck state
- Simulation Mode: Test protocols without physical hardware
- State Tracking: Monitor tip presence and liquid volumes visually
- Deck Editor: Graphical tool for designing deck layouts
- Protocol Validation: Verify protocols before running on hardware
Quick Start
To get started with PyLabRobot, install the package and initialize a liquid handler:
# Install PyLabRobot
# uv pip install pylabrobot
# Basic liquid handling setup
from pylabrobot.liquid_handling import LiquidHandler
from pylabrobot.liquid_handling.backends import STAR
from pylabrobot.resources import STARLetDeck
# Initialize liquid handler
lh = LiquidHandler(backend=STAR(), deck=STARLetDeck())
await lh.setup()
# Basic operations
await lh.pick_up_tips(tip_rack["A1:H1"])
await lh.aspirate(plate["A1"], vols=100)
await lh.dispense(plate["A2"], vols=100)
await lh.drop_tips()
Going deeper than this page
This skill bundles no reference files, and the API surface above is a map, not a specification. PyLabRobot moves quickly and its exact class names, backend constructors and method signatures differ between versions -- so for anything beyond the patterns shown here, read the current docs rather than extrapolating from them:
read_urlhttps://docs.pylabrobot.org for the live API and per-device guidesread_urlthe relevant module under https://github.com/PyLabRobot/pylabrobot when the docs are thin, since the source is the only authority on a signature
Say which version or page you worked from. Never invent a backend class, a labware definition name, or a method that you have not seen in the docs or the source: this code drives physical hardware, and a plausible-looking wrong call crashes a robot arm or ruins a plate.
Best Practices
When creating laboratory automation protocols with PyLabRobot:
- Start with Simulation: Use ChatterboxBackend and the visualizer to test protocols before running on hardware
- Enable Tracking: Turn on tip tracking and volume tracking for accurate state management
- Resource Naming: Use clear, descriptive names for all resources (plates, tip racks, containers)
- State Serialization: Save deck layouts and states to JSON for reproducibility
- Error Handling: Implement proper async error handling for hardware operations
- Temperature Control: Set temperatures early as heating/cooling takes time
- Modular Protocols: Break complex workflows into reusable functions
- Documentation: Reference official docs at https://docs.pylabrobot.org for latest features
Common Workflows
Liquid Transfer Protocol
# Setup
lh = LiquidHandler(backend=STAR(), deck=STARLetDeck())
await lh.setup()
# Define resources
tip_rack = TIP_CAR_480_A00(name="tip_rack")
source_plate = Cos_96_DW_1mL(name="source")
dest_plate = Cos_96_DW_1mL(name="dest")
lh.deck.assign_child_resource(tip_rack, rails=1)
lh.deck.assign_child_resource(source_plate, rails=10)
lh.deck.assign_child_resource(dest_plate, rails=15)
# Transfer protocol
await lh.pick_up_tips(tip_rack["A1:H1"])
await lh.transfer(source_plate["A1:H12"], dest_plate["A1:H12"], vols=100)
await lh.drop_tips()
Plate Reading Workflow
# Setup plate reader
from pylabrobot.plate_reading import PlateReader
from pylabrobot.plate_reading.clario_star_backend import CLARIOstarBackend
pr = PlateReader(name="CLARIOstar", backend=CLARIOstarBackend())
await pr.setup()
# Set temperature and read
await pr.set_temperature(37)
await pr.open()
# (manually or robotically load plate)
await pr.close()
data = await pr.read_absorbance(wavelength=450)
Additional Resources
- Official Documentation: https://docs.pylabrobot.org
- GitHub Repository: https://github.com/PyLabRobot/pylabrobot
- Community Forum: https://discuss.pylabrobot.org
- PyPI Package: https://pypi.org/project/PyLabRobot/
How to use the PyLabRobot skill
Sign in to Zeplik
Create a free Zeplik account or sign in. New accounts start with free credits, so you can try the PyLabRobot skill right away.
Describe your research task
Ask in plain language, or type /pylabrobot to invoke the skill directly. Zeplik recognizes the PyLabRobot skill and applies its method.
Review and refine the result
Zeplik returns a clear, structured answer. Ask follow-ups in the same chat to refine it or take the next step.
Source and credit
- Author
- davila7 (D7 Class-A standalone)
- License
- MIT
Adapted from the open-source davila7/claude-code-templates project and tuned to run natively on Zeplik. View source on GitHub.
Frequently asked questions
- What is the PyLabRobot skill?
- PyLabRobot is a ready-to-run research skill on Zeplik. Automate lab hardware with PyLabRobot: liquid handlers (Hamilton, Opentrons, Tecan), plate readers, reproducible runs. Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
- How do I use PyLabRobot on Zeplik?
- Sign in to Zeplik and ask in plain language, or type /pylabrobot in any chat to invoke it directly. The skill applies its method and returns a result you can refine in the same conversation.
- Which AI model does the PyLabRobot skill use?
- Any model you choose. Zeplik works across every model in one chat, so the PyLabRobot skill runs on your preferred model for the task.
- Where does the PyLabRobot skill come from?
- The PyLabRobot skill is adapted from the open-source davila7/claude-code-templates project (MIT) and tuned to run natively on Zeplik. The original source is linked on this page.
- How much does the PyLabRobot skill cost?
- Using the skill is free to start. You only spend Zeplik credits when the assistant runs, and new accounts begin with free credits.
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