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.
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node --versionopentrons-integrationExecute the skills CLI command in your project's root directory to begin installation:
Fetches opentrons-integration from davila7/claude-code-templates and configures it for Cursor.
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Confirm successful installation by checking the skill directory location:
Restart Cursor to activate opentrons-integration. Access via /opentrons-integration in your agent's command palette.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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.
This skill should be used when:
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:
protocol_api from opentronsmetadata dict with protocolName, author, description, apiLevelrequirements dict for robot type and API versionrun() function receiving ProtocolContext as parameterrun() functionLoading 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:
p50_single_flex, p1000_single_flex, p50_multi_flex, p1000_multi_flexp20_single_gen2, p300_single_gen2, p1000_single_gen2, p20_multi_gen2, p300_multi_gen2Loading 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')
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
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(✓Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
✓Save 3-5 hours/week on communication overhead
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client
- ›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
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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4.4★★★★★51 reviews- LLuis Gonzalez★★★★★Dec 28, 2024
opentrons-integration reduced setup friction for our internal harness; good balance of opinion and flexibility.
- MMichael Rao★★★★★Nov 19, 2024
We added opentrons-integration from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- NNaina Brown★★★★★Nov 15, 2024
opentrons-integration reduced setup friction for our internal harness; good balance of opinion and flexibility.
- RRahul Santra★★★★★Nov 7, 2024
I recommend opentrons-integration for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- PPratham Ware★★★★★Oct 26, 2024
Useful defaults in opentrons-integration — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- NNeel Kapoor★★★★★Oct 10, 2024
opentrons-integration fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- YYash Thakker★★★★★Sep 25, 2024
opentrons-integration is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- LLuis Khan★★★★★Sep 25, 2024
Useful defaults in opentrons-integration — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- AAva Rao★★★★★Sep 17, 2024
Solid pick for teams standardizing on skills: opentrons-integration is focused, and the summary matches what you get after install.
- AAva Reddy★★★★★Sep 5, 2024
opentrons-integration fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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