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.
Fetches opentrons-integration from davila7/claude-code-templates and configures it for Cursor.
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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
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
# Metadatametadata ={'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 functiondefrun(protocol: protocol_api.ProtocolContext):# Protocol commands go herepass
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):
defrun(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)
# Load labware directly on deckplate = 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 racktip_rack = protocol.load_labware('opentrons_flex_96_tiprack_1000ul','C1')# Load labware on adapteradapter = protocol.load_adapter('opentrons_flex_96_tiprack_adapter','B1')tips = adapter.load_labware('opentrons_flex_96_tiprack_200ul')
Loading Modules:
# Temperature moduletemp_module = protocol.load_module('temperature module gen2','D3')temp_plate = temp_module.load_labware('corning_96_wellplate_360ul_flat')# Magnetic modulemag_module = protocol.load_module('magnetic module gen2','C2')mag_plate = mag_module.load_labware('nest_96_wellplate_100ul_pcr_full_skirt')# Heater-Shaker modulehs_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 tippipette.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 tippipette.drop_tip()# Return tip to rackpipette.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 gappipette.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 =150pipette.flow_rate.dispense =300pipette.flow_rate.blow_out =400
4. Accessing Wells and Locations
Well Access Methods:
# By namewell_a1 = plate['A1']# By indexfirst_well = plate.wells()[0]# All wellsall_wells = plate.wells()# Returns list# By rowsrows = plate.rows(
✓
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
›Access to product documentation and roadmap tools (Jira, Notion, etc.)
›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
⚠Not validating competitive research—verify facts before sharing
⚠Accepting user stories without involving engineering team
⚠Over-relying on frameworks without qualitative judgment
⚠Not customizing outputs to company culture and communication style
⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
✗ Don't
−Don't publish competitive analysis without fact-checking
−Don't finalize user stories without engineering review
−Don't make prioritization decisions solely on AI scoring
−Don't skip customer validation of generated requirements
−Don't ignore company-specific context and culture
💡 Pro Tips
★Provide context: company goals, constraints, customer feedback
★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
1Basic: user stories, feature specs, status updates
