pylabrobot
npx machina-cli add skill Microck/ordinary-claude-skills/pylabrobot --openclawPyLabRobot
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, each detailed in the references/ directory:
1. Liquid Handling (references/liquid-handling.md)
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 (references/resources.md)
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 (references/hardware-backends.md)
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 (references/analytical-equipment.md)
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 (references/material-handling.md)
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 (references/visualization.md)
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()
Working with References
This skill organizes detailed information across multiple reference files. Load the relevant reference when:
- Liquid Handling: Writing pipetting protocols, tip management, transfers
- Resources: Defining deck layouts, managing plates/tips, custom labware
- Hardware Backends: Connecting to specific robots, switching platforms
- Analytical Equipment: Integrating plate readers, scales, or analytical devices
- Material Handling: Using heater shakers, incubators, centrifuges, pumps
- Visualization: Simulating protocols, visualizing deck states
All reference files can be found in the references/ directory and contain comprehensive examples, API usage patterns, and best practices.
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/
For detailed usage of specific capabilities, refer to the corresponding reference file in the references/ directory.
Source
git clone https://github.com/Microck/ordinary-claude-skills/blob/main/skills_all/claude-scientific-skills/scientific-skills/pylabrobot/SKILL.mdView on GitHub Overview
PyLabRobot is a hardware-agnostic Python SDK that unifies control of lab automation devices. It enables programming liquid handlers, plate readers, pumps, heaters, incubators, centrifuges, and more through a single interface across Windows, macOS, Linux, and Raspberry Pi. It supports deck layout management, resource tracking, protocol state management, and protocol simulation for safe testing.
How This Skill Works
PyLabRobot exposes backend-agnostic APIs for controlling diverse lab equipment. It modularizes capabilities into liquid handling, resource management, hardware backends, analytical equipment, and material handling, with a simulation backend for testing. Deck layouts and resource states are serialized to JSON, enabling reproducibility and easy sharing of protocols across hardware platforms.
When to Use It
- Programming liquid handling robots (Hamilton STAR/STARlet, Opentrons OT-2, Tecan EVO) to perform pipetting and transfers
- Automating workflows that include pipetting, sample preparation, and analytical measurements
- Managing deck layouts and resources (plates, tips, containers) with state tracking
- Integrating multiple devices (liquid handlers, plate readers, heater shakers, pumps) into a single protocol
- Simulating protocols before running on physical hardware and reading plates with supported plate readers
Quick Start
- Step 1: Install PyLabRobot and your desired backends, then connect to hardware or start the simulation backend
- Step 2: Define a deck layout (plates, tips, containers) and serialize it to JSON for persistence
- Step 3: Create a simple protocol (e.g., aspirate from well A1 and dispense to B1) and run it in simulation or on hardware
Best Practices
- Start with a mock deck layout and a dry run in simulation (ChatterboxBackend) before touching hardware
- Enable and regularly save deck layouts and resource states in JSON for reproducibility
- Use the backend-agnostic API to avoid rewriting protocol logic when swapping hardware
- Leverage resource discovery to access wells, tips, and containers directly from code
- Monitor volumes and tip usage throughout the protocol to prevent interruptions
Example Use Cases
- Automated serial dilutions and plate setup on Opentrons OT-2 with a shared deck layout
- Integrated workflow moving plates between a Hamilton STAR liquid handler and a CLARIOstar plate reader
- Automated sample preparation with a heater shaker and incubator across multiple racks
- Simulation-based validation of a complex multi-device protocol using ChatterboxBackend
- Protocol deployment for automated pipetting, mixing, and data capture on a Tecan EVO setup