opentrons-integration
by K-Dense-AIopentrons-integration helps write Opentrons Protocol API v2 Python protocols for OT-2 and Flex robots. Use it for production liquid handling, deck layout, module control, serial dilutions, PCR setup, and backend-style automation. It is best when you need a reliable opentrons-integration guide for exact protocol scripting, not multi-vendor orchestration.
This skill scores 78/100, which means it is a solid listing candidate for directory users who need Opentrons-specific protocol help. It gives agents a clear trigger, real workflow templates, and API reference support, so it should reduce guesswork compared with a generic prompt, though it is still somewhat template-oriented rather than a complete end-to-end workflow pack.
- Explicitly scoped to Opentrons Protocol API v2 for OT-2 and Flex, with clear use cases like liquid handling, thermocycler control, serial dilution, and PCR setup.
- Substantial body content plus supporting scripts and an API reference make it easier for an agent to follow concrete patterns instead of inventing protocol structure.
- Includes practical guidance on when not to use it, such as deferring to pylabrobot for multi-vendor automation, which helps install decisions.
- No install command is provided in SKILL.md, so users must wire it into their environment manually.
- The included scripts are templates/examples, so users with highly custom hardware or uncommon workflows may still need additional adaptation.
Overview of opentrons-integration skill
What opentrons-integration does
The opentrons-integration skill helps you write Opentrons Protocol API v2 Python protocols for OT-2 and Flex robots. It is a good fit when you need the opentrons-integration skill for production-ready liquid handling, labware setup, and module control rather than a generic lab automation prompt.
Best fit and boundaries
Use this skill for backend-style protocol work: deck layout, pipette actions, thermocycler or heater-shaker steps, serial dilutions, PCR setup, and other structured workflows. It is less suitable if you need multi-vendor orchestration or broader instrument control; in that case, a more general automation stack like pylabrobot is usually a better fit.
What makes it useful
The main value is that it centers the official Opentrons API and practical protocol patterns, so you spend less time guessing method names, labware placement, or module usage. For users evaluating opentrons-integration for Backend Development, the skill is strongest when the deliverable is a reliable protocol script, not a high-level experiment plan.
How to Use opentrons-integration skill
Install and confirm the skill
Use the opentrons-integration install flow from your directory or agent workflow, then confirm the skill files are available before writing code. Start by reading SKILL.md, then inspect references/api_reference.md and the templates in scripts/ so you understand the API surface and the expected protocol shape.
Turn a rough request into a good prompt
The best opentrons-integration usage starts with concrete experiment details. Include robot type, API level, labware names, pipette models, liquid volumes, source and destination wells, module requirements, and any constraints such as tip reuse, mixing, or whether the run must be simulation-safe.
Example of a strong request:
- “Write a Flex Protocol API v2 script for a 96-well serial dilution using
p300_single_flex, one 200 µL tip rack,nest_12_reservoir_15ml, and acorning_96_wellplate_360ul_flatplate. Include comments and minimize tip usage.”
Weak input:
- “Make a dilution protocol.”
Read the files that matter first
For this opentrons-integration guide, prioritize:
SKILL.mdfor scope and workflow rulesreferences/api_reference.mdfor method names and context objectsscripts/basic_protocol_template.pyfor the minimal structurescripts/pcr_setup_template.pyandscripts/serial_dilution_template.pyfor common patterns
Workflow that usually produces the best output
Start with a template, replace placeholder metadata, then verify the deck layout and labware compatibility before adding complex liquid handling. If your task includes modules or custom liquids, define those early so the protocol logic stays readable and simulation-friendly.
opentrons-integration skill FAQ
Is opentrons-integration only for Opentrons robots?
Yes. The opentrons-integration skill is specifically for OT-2 and Flex workflows built on the Opentrons Protocol API v2. If your environment includes other robot brands or a mixed fleet, this skill will feel too narrow.
Do I need programming experience to use it?
Basic Python familiarity helps, but you do not need to be an API expert if you provide exact experimental details. Beginners usually get better results when they ask for one protocol step at a time and reuse the included templates instead of starting from scratch.
How is this different from a normal prompt?
A normal prompt may describe the science, but opentrons-integration gives you a more execution-oriented structure: protocol metadata, load steps, module calls, and concrete helper references. That reduces guesswork when you need a script that can be simulated, reviewed, and adapted for lab use.
When should I not use this skill?
Do not use it when you need vendor-neutral automation, scheduling across instruments, or a system that goes beyond Opentrons protocol authoring. It is also a poor fit if you cannot yet specify robot model, deck contents, or target wells, because those details determine whether the protocol is valid.
How to Improve opentrons-integration skill
Give the inputs that affect protocol validity
The biggest improvements come from specifying robot model, API level, labware names, mount positions, volumes, and whether the run is single-channel or multi-channel. For opentrons-integration for Backend Development, the more exact your constraints are, the less cleanup the generated script will need.
Avoid the common failure modes
The most common problems are vague labware names, missing deck positions, unclear source-destination mapping, and assuming a pipette can handle every transfer efficiently. If a step depends on mixing, settling, slow aspiration, or module timing, say so explicitly; otherwise the output may be syntactically correct but operationally weak.
Iterate from a simulation-first draft
Ask for a first-pass script that is easy to simulate, then refine based on what breaks in the robot context. Useful follow-up edits include changing tip strategy, reducing waste, adding comments for the wet lab team, or converting a prototype into a cleaner production protocol.
Use the templates as patterns, not templates to copy
The provided examples are strongest as structural references for metadata, run(protocol), labware loading, and command ordering. Adapt them to your own deck plan and reagent logic, then re-check the API reference when you add modules, custom labware, or less common methods.