Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/magic3007/dotfiles/opentrons-integrationnpx skills add magic3007/dotfiles --skill opentrons-integrationgit clone --depth 1 https://github.com/magic3007/dotfilesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/magic3007/dotfiles/opentrons-integration)<a href="https://agentmods.dev/skills/magic3007/dotfiles/opentrons-integration"><img src="https://agentmods.dev/badge/skills/magic3007/dotfiles/opentrons-integration.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00066 | $0.03937 |
| Opus 5 | $0.00033 | $0.01969 |
| Sonnet 5 | $0.00013 | $0.00787 |
| Haiku 4.5 | $0.00007 | $0.00394 |
Grade A, and why
opentrons-integration scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to opentrons-integration — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 573 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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_apifromopentrons - Define
metadatadict with protocolName, author, description, apiLevel - Optional
requirementsdict for robot type and API version - Implement
run()function receivingProtocolContextas 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
)
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 573 lines · 66 tokens per session scan A eae6bcf8fb74
opentrons-integration is a skill published in the GitHub repository magic3007/dotfiles (11 stars, last pushed yesterday), licensed MIT. It adds 66 tokens to every session and 3,937 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to opentrons-integration, differing in 4 lines, and is treated as a copy.
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