pylabrobot▌
K-Dense-AI/scientific-agent-skills · updated Jun 4, 2026
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### Pylabrobot
- ›name: "pylabrobot"
- ›description: "Vendor-agnostic lab automation framework. Use when controlling multiple equipment types (Hamilton, Tecan, Opentrons, plate readers, pumps) or needing unified programming across different vendors. Best..."
| name | pylabrobot |
| description | Vendor-agnostic lab automation framework. Use when controlling multiple equipment types (Hamilton, Tecan, Opentrons, plate readers, pumps) or needing unified programming across different vendors. Best for complex workflows, multi-vendor setups, simulation. For Opentrons-only protocols with official API, opentrons-integration may be simpler. |
| license | MIT license |
| metadata | version: "1.0" skill-author: K-Dense Inc. |
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, 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.
How to use pylabrobot on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add pylabrobot
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches pylabrobot from GitHub repository K-Dense-AI/scientific-agent-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate pylabrobot. Access the skill through slash commands (e.g., /pylabrobot) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★60 reviews- ★★★★★Charlotte Nasser· Dec 28, 2024
pylabrobot reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Ren Mehta· Dec 24, 2024
Solid pick for teams standardizing on skills: pylabrobot is focused, and the summary matches what you get after install.
- ★★★★★Chaitanya Patil· Dec 20, 2024
Registry listing for pylabrobot matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Amelia Mensah· Dec 20, 2024
Useful defaults in pylabrobot — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Ama Jain· Dec 12, 2024
We added pylabrobot from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Daniel Gupta· Nov 23, 2024
pylabrobot is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Anika Choi· Nov 19, 2024
Registry listing for pylabrobot matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Kiara Flores· Nov 15, 2024
pylabrobot has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Piyush G· Nov 11, 2024
pylabrobot reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Daniel Choi· Nov 11, 2024
We added pylabrobot from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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