opentrons-integration

K-Dense-AI/scientific-agent-skills · updated Jun 4, 2026

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$npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill opentrons-integration
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### Opentrons Integration

  • name: "opentrons-integration"
  • description: "Official Opentrons Protocol API for OT-2 and Flex robots. Use when writing protocols specifically for Opentrons hardware with full access to Protocol API v2 features. Best for production Opentrons pro..."
skill.md
name
opentrons-integration
description
Official Opentrons Protocol API for OT-2 and Flex robots. Use when writing protocols specifically for Opentrons hardware with full access to Protocol API v2 features. Best for production Opentrons protocols, official API compatibility. For multi-vendor automation or broader equipment control use pylabrobot.
license
Unknown
metadata
version: "1.0" skill-author: K-Dense Inc.

Opentrons Integration

Overview

Opentrons is a Python-based lab automation platform for Flex and OT-2 robots. Write Protocol API v2 protocols for liquid handling, control hardware modules (heater-shaker, thermocycler), manage labware, for automated pipetting workflows.

When to Use This Skill

This skill should be used when:

  • Writing Opentrons Protocol API v2 protocols in Python
  • Automating liquid handling workflows on Flex or OT-2 robots
  • Controlling hardware modules (temperature, magnetic, heater-shaker, thermocycler)
  • Setting up labware configurations and deck layouts
  • Implementing complex pipetting operations (serial dilutions, plate replication, PCR setup)
  • Managing tip usage and optimizing protocol efficiency
  • Working with multi-channel pipettes for 96-well plate operations
  • Simulating and testing protocols before robot execution

Core Capabilities

1. Protocol Structure and Metadata

Every Opentrons protocol follows a standard structure:

from opentrons import protocol_api

# Metadata
metadata = {
    'protocolName': 'My Protocol',
    'author': 'Name <[email protected]>',
    'description': 'Protocol description',
    'apiLevel': '2.19'  # Use latest available API version
}

# Requirements (optional)
requirements = {
    'robotType': 'Flex',  # or 'OT-2'
    'apiLevel': '2.19'
}

# Run function
def run(protocol: protocol_api.ProtocolContext):
    # Protocol commands go here
    pass

Key elements:

  • Import protocol_api from opentrons
  • Define metadata dict with protocolName, author, description, apiLevel
  • Optional requirements dict for robot type and API version
  • Implement run() function receiving ProtocolContext as parameter
  • All protocol logic goes inside the run() function

2. Loading Hardware

Loading Instruments (Pipettes):

def run(protocol: protocol_api.ProtocolContext):
    # Load pipette on specific mount
    left_pipette = protocol.load_instrument(
        'p1000_single_flex',  # Instrument name
        'left',               # Mount: 'left' or 'right'
        tip_racks=[tip_rack]  # List of tip rack labware objects
    )

Common pipette names:

  • Flex: p50_single_flex, p1000_single_flex, p50_multi_flex, p1000_multi_flex
  • OT-2: p20_single_gen2, p300_single_gen2, p1000_single_gen2, p20_multi_gen2, p300_multi_gen2

Loading Labware:

# Load labware directly on deck
plate = protocol.load_labware(
    'corning_96_wellplate_360ul_flat',  # Labware API name
    'D1',                                # Deck slot (Flex: A1-D3, OT-2: 1-11)
    label='Sample Plate'                 # Optional display label
)

# Load tip rack
tip_rack = protocol.load_labware('opentrons_flex_96_tiprack_1000ul', 'C1')

# Load labware on adapter
adapter = protocol.load_adapter('opentrons_flex_96_tiprack_adapter', 'B1')
tips = adapter.load_labware('opentrons_flex_96_tiprack_200ul')

Loading Modules:

# Temperature module
temp_module = protocol.load_module('temperature module gen2', 'D3')
temp_plate = temp_module.load_labware('corning_96_wellplate_360ul_flat')

# Magnetic module
mag_module = protocol.load_module('magnetic module gen2', 'C2')
mag_plate = mag_module.load_labware('nest_96_wellplate_100ul_pcr_full_skirt')

# Heater-Shaker module
hs_module = protocol.load_module('heaterShakerModuleV1', 'D1')
hs_plate = hs_module.load_labware('corning_96_wellplate_360ul_flat')

# Thermocycler module (takes up specific slots automatically)
tc_module = protocol.load_module('thermocyclerModuleV2')
tc_plate = tc_module.load_labware('nest_96_wellplate_100ul_pcr_full_skirt')

3. Liquid Handling Operations

Basic Operations:

# Pick up tip
pipette.pick_up_tip()

# Aspirate (draw liquid in)
pipette.aspirate(
    volume=100,           # Volume in µL
    location=source['A1'] # Well or location object
)

# Dispense (expel liquid)
pipette.dispense(
    volume=100,
    location=dest['B1']
)

# Drop tip
pipette.drop_tip()

# Return tip to rack
pipette.return_tip()

Complex Operations:

# Transfer (combines pick_up, aspirate, dispense, drop_tip)
pipette.transfer(
    volume=100,
    source=source_plate['A1'],
    dest=dest_plate['B1'],
    new_tip='always'  # 'always', 'once', or 'never'
)

# Distribute (one source to multiple destinations)
pipette.distribute(
    volume=50,
    source=reservoir['A1'],
    dest=[plate['A1'], plate['A2'], plate['A3']],
    new_tip='once'
)

# Consolidate (multiple sources to one destination)
pipette.consolidate(
    volume=50,
    source=[plate['A1'], plate['A2'], plate['A3']],
    dest=reservoir['A1'],
    new_tip='once'
)

Advanced Techniques:

# Mix (aspirate and dispense in same location)
pipette.mix(
    repetitions=3,
    volume=50,
    location=plate['A1']
)

# Air gap (prevent dripping)
pipette.aspirate(100, source['A1'])
pipette.air_gap(20)  # 20µL air gap
pipette.dispense(120, dest['A1'])

# Blow out (expel remaining liquid)
pipette.blow_out(location=dest['A1'].top())

# Touch tip (remove droplets on tip exterior)
pipette.touch_tip(location=plate['A1'])

Flow Rate Control:

# Set flow rates (µL/s)
pipette.flow_rate.aspirate = 150
pipette.flow_rate.dispense = 300
pipette.flow_rate.blow_out = 400

4. Accessing Wells and Locations

Well Access Methods:

# By name
well_a1 = plate['A1']

# By index
first_well = plate.wells()[0]

# All wells
all_wells = plate.wells()  # Returns list

# By rows
rows = plate.rows()  # Returns list of lists
row_a = plate.rows()[0]  # All wells in row A

# By columns
columns = plate.columns()  # Returns list of lists
column_1 = plate.columns()[0]  # All wells in column 1

# Wells by name (dictionary)
wells_dict = plate.wells_by_name()  # {'A1': Well, 'A2': Well, ...}

Location Methods:

# Top of well (default: 1mm below top)
pipette.aspirate(100, well.top())
pipette.aspirate(100, well.top(z=5))  # 5mm above top

# Bottom of well (default: 1mm above bottom)
pipette.aspirate(100, well.bottom())
pipette.aspirate(100, well.bottom(z=2))  # 2mm above bottom

# Center of well
pipette.aspirate(100, well.center())

5. Hardware Module Control

Temperature Module:

# Set temperature
temp_module.set_temperature(celsius=4)

# Wait for temperature
temp_module.await_temperature(celsius=4)

# Deactivate
temp_module.deactivate()

# Check status
current_temp = temp_module.temperature  # Current temperature
target_temp = temp_module.target  # Target temperature

Magnetic Module:

# Engage (raise magnets)
mag_module.engage(height_from_base=10)  # mm from labware base

# Disengage (lower magnets)
mag_module.disengage()

# Check status
is_engaged = mag_module.status  # 'engaged' or 'disengaged'

Heater-Shaker Module:

# Set temperature
hs_module.set_target_temperature(celsius=37)

# Wait for temperature
hs_module.wait_for_temperature()

# Set shake speed
hs_module.set_and_wait_for_shake_speed(rpm=500)

# Close labware latch
hs_module.close_labware_latch()

# Open labware latch
hs_module.open_labware_latch()

# Deactivate heater
hs_module.deactivate_heater()

# Deactivate shaker
hs_module.deactivate_shaker()

Thermocycler Module:

# Open lid
tc_module.open_lid()

# Close lid
tc_module.close_lid()

# Set lid temperature
tc_module.set_lid_temperature(celsius=105)

# Set block temperature
tc_module.set_block_temperature(
    temperature=95,
    hold_time_seconds=30,
    hold_time_minutes=0.5,
    block_max_volume=50  # µL per well
)

# Execute profile (PCR cycling)
profile = [
    {'temperature': 95, 'hold_time_seconds': 30},
    {'temperature': 57, 'hold_time_seconds': 30},
    {'temperature': 72, 'hold_time_seconds': 60}
]
tc_module.execute_profile(
    steps=profile,
    repetitions=30,
    block_max_volume=50
)

# Deactivate
tc_module.deactivate_lid()
tc_module.deactivate_block()

Absorbance Plate Reader:

# Initialize and read
result = plate_reader.read(wavelengths=[450, 650])

# Access readings
absorbance_data = result  # Dict with wavelength keys

6. Liquid Tracking and Labeling

Define Liquids:

# Define liquid types
water = protocol.define_liquid(
    name='Water',
    description='Ultrapure water',
    display_color='#0000FF'  # Hex color code
)

sample = protocol.define_liquid(
    name='Sample',
    description='Cell lysate sample',
    display_color='#FF0000'
)

Load Liquids into Wells:

# Load liquid into specific wells
reservoir['A1'].load_liquid(liquid=water, volume=50000)  # µL
plate['A1'].load_liquid(liquid=sample, volume=100)

# Mark wells as empty
plate['B1'].load_empty()

7. Protocol Control and Utilities

Execution Control:

# Pause protocol
protocol.pause(msg='Replace tip box and resume')

# Delay
protocol.delay(seconds=60)
protocol.delay(minutes=5)

# Comment (appears in logs)
protocol.comment('Starting serial dilution')

# Home robot
protocol.home()

Conditional Logic:

# Check if simulating
if protocol.is_simulating():
    protocol.comment('Running in simulation mode')
else:
    protocol.comment('Running on actual robot')

Rail Lights (Flex only):

# Turn lights on
protocol.set_rail_lights(on=True)

# Turn lights off
protocol.set_rail_lights(on=False)

8. Multi-Channel and 8-Channel Pipetting

When using multi-channel pipettes:

# Load 8-channel pipette
multi_pipette = protocol.load_instrument(
    'p300_multi_gen2',
    'left',
    tip_racks=[tips]
)

# Access entire column with single well reference
multi_pipette.transfer(
    volume=100,
    source=source_plate['A1'],  # Accesses entire column 1
    dest=dest_plate['A1']       # Dispenses to entire column 1
)

# Use rows() for row-wise operations
for row in plate.rows():
    multi_pipette.transfer(100, reservoir['A1'], row[0])

9. Common Protocol Patterns

Serial Dilution:

def run(protocol: protocol_api.ProtocolContext):
    # Load labware
    tips = protocol.load_labware('opentrons_flex_96_tiprack_200ul', 'D1')
    reservoir = protocol.load_labware('nest_12_reservoir_15ml', 'D2')
    plate = protocol.load_labware('corning_96_wellplate_360ul_flat', 'D3')

    # Load pipette
    p300 = protocol.load_instrument('p300_single_flex', 'left', tip_racks=[tips])

    # Add diluent to all wells except first
    p300.transfer(100, reservoir['A1'], plate.rows()[0][1:])

    # Serial dilution across row
    p300.transfer(
        100,
        plate.rows()[0][:11],  # Source: wells 0-10
        plate.rows()[0][1:],   # Dest: wells 1-11
        mix_after=(3, 50),     # Mix 3x with 50µL after dispense
        new_tip='always'
    )

Plate Replication:

def run(protocol: protocol_api.ProtocolContext):
    # Load labware
    tips = protocol.load_labware('opentrons_flex_96_tiprack_1000ul', 'C1')
    source = protocol.load_labware('corning_96_wellplate_360ul_flat', 'D1')
    dest = protocol.load_labware('corning_96_wellplate_360ul_flat', 'D2')

    # Load pipette
    p1000 = protocol.load_instrument('p1000_single_flex', 'left', tip_racks=[tips])

    # Transfer from all wells in source to dest
    p1000.transfer(
        100,
        source.wells(),
        dest.wells(),
        new_tip='always'
    )

PCR Setup:

def run(protocol: protocol_api.ProtocolContext):
    # Load thermocycler
    tc_mod = protocol.load_module('thermocyclerModuleV2')
    tc_plate = tc_mod.load_labware('nest_96_wellplate_100ul_pcr_full_skirt')

    # Load tips and reagents
    tips = protocol.load_labware('opentrons_flex_96_tiprack_200ul', 'C1')
    reagents = protocol.load_labware('opentrons_24_tuberack_nest_1.5ml_snapcap', 'D1')

    # Load pipette
    p300 = protocol.load_instrument('p300_single_flex', 'left', tip_racks=[tips])

    # Open thermocycler lid
    tc_mod.open_lid()

    # Distribute master mix
    p300.distribute(
        20,
        reagents['A1'],
        tc_plate.wells(),
        new_tip='once'
    )

    # Add samples (example for first 8 wells)
    for i, well in enumerate(tc_plate.wells()[:8]):
        p300.transfer(5, reagents.wells()[i+1], well, new_tip='always')

    # Run PCR
    tc_mod.close_lid()
    tc_mod.set_lid_temperature(105)

    # PCR profile
    tc_mod.set_block_temperature(95, hold_time_seconds=180)

    profile = [
        {'temperature': 95, 'hold_time_seconds': 15},
        {'temperature': 60, 'hold_time_seconds': 30},
        {'temperature': 72, 'hold_time_seconds': 30}
    ]
    tc_mod.execute_profile(steps=profile, repetitions=35, block_max_volume=25)

    tc_mod.set_block_temperature(72, hold_time_minutes=5)
    tc_mod.set_block_temperature(4)

    tc_mod.deactivate_lid()
    tc_mod.open_lid()

Best Practices

  1. Always specify API level: Use the latest stable API version in metadata
  2. Use meaningful labels: Label labware for easier identification in logs
  3. Check tip availability: Ensure sufficient tips for protocol completion
  4. Add comments: Use protocol.comment() for debugging and logging
  5. Simulate first: Always test protocols in simulation before running on robot
  6. Handle errors gracefully: Add pauses for manual intervention when needed
  7. Consider timing: Use delays when protocols require incubation periods
  8. Track liquids: Use liquid tracking for better setup validation
  9. Optimize tip usage: Use new_tip='once' when appropriate to save tips
  10. Control flow rates: Adjust flow rates for viscous or volatile liquids

Troubleshooting

Common Issues:

  • Out of tips: Verify tip rack capacity matches protocol requirements
  • Labware collisions: Check deck layout for spatial conflicts
  • Volume errors: Ensure volumes don't exceed well or pipette capacities
  • Module not responding: Verify module is properly connected and firmware is updated
  • Inaccurate volumes: Calibrate pipettes and check for air bubbles
  • Protocol fails in simulation: Check API version compatibility and labware definitions

Resources

For detailed API documentation, see references/api_reference.md in this skill directory.

For example protocol templates, see scripts/ directory.

how to use opentrons-integration

How to use opentrons-integration on Cursor

AI-first code editor with Composer

1

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 opentrons-integration
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill opentrons-integration

The skills CLI fetches opentrons-integration from GitHub repository K-Dense-AI/scientific-agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/opentrons-integration

Reload or restart Cursor to activate opentrons-integration. Access the skill through slash commands (e.g., /opentrons-integration) 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

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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. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 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

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.775 reviews
  • Chaitanya Patil· Dec 28, 2024

    Solid pick for teams standardizing on skills: opentrons-integration is focused, and the summary matches what you get after install.

  • Jin Shah· Dec 20, 2024

    Keeps context tight: opentrons-integration is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Xiao Jain· Dec 16, 2024

    opentrons-integration has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Isabella Liu· Dec 4, 2024

    opentrons-integration reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Michael Flores· Dec 4, 2024

    opentrons-integration is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Isabella Kapoor· Nov 27, 2024

    Useful defaults in opentrons-integration — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Amina Desai· Nov 23, 2024

    opentrons-integration is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Xiao Desai· Nov 23, 2024

    opentrons-integration reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Piyush G· Nov 19, 2024

    We added opentrons-integration from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Jin Jackson· Nov 11, 2024

    Registry listing for opentrons-integration matched our evaluation — installs cleanly and behaves as described in the markdown.

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