opentrons-integration
Lab automation platform for Flex/OT-2 robots. Write Protocol API v2 protocols, liquid handling, hardware modules (heater-shaker, thermocycler), labware management, for automated pipetting workflows.
What this skill does
# 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:
```python
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):**
```python
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:**
```python
# 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:**
```python
# 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:**
```python
# 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:**
```python
# 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:**
```python
# 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:**
```python
# 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:**
```python
# 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:**
```python
# 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:**
```python
# 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:**
```python
# 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:**
```python
# 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:**
```python
# 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:**
```python
#Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.