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 skills add dralkh/iktinah --skill opentrons-integrationgit clone --depth 1 https://github.com/dralkh/iktinahWrote 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/dralkh/iktinah/opentrons-integration)<a href="https://agentmods.dev/skills/dralkh/iktinah/opentrons-integration"><img src="https://agentmods.dev/badge/skills/dralkh/iktinah/opentrons-integration/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dralkh/iktinah/opentrons-integration"><img src="https://agentmods.dev/badge/skills/dralkh/iktinah/opentrons-integration.svg" alt="Reviewed on agentmods" width="80" 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 | $0.03939 |
| Opus 5 | $0.00033 | $0.01969 |
| Sonnet 5 | $0.00013 | $0.00788 |
| 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 8d 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 — 571 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.
- 8d ago First seen · 571 lines · 66 tokens per session scan A 39a9881864ef
opentrons-integration is a skill published in the GitHub repository dralkh/iktinah (77 stars, last pushed 2mo ago), licensed MIT. It adds 66 tokens to every session and 3,939 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.
Other skills, from other repositories
opentrons-integration
Official Opentrons Protocol API for OT-2 and Flex robots. Use when writing protocols specifically for Opentrons hardware with full access to Protocol API v2 features. Best for production Opentrons protocols, official API compatibility. For multi-vendor automation or broader equipment control use pylabrobot.
pylabrobot
Vendor-agnostic lab automation framework. Use when controlling multiple equipment types (Hamilton, Tecan, Opentrons, plate readers, pumps) or needing unified programming across different vendors. Best for complex workflows, multi-vendor setups, simulation. For Opentrons-only protocols with official API…
sci-extract
Read an academic paper end to end and extract professional research insights, figures, metadata, and critique. Use this skill whenever the user shares a scientific paper, review paper, survey paper, systematic review, meta-analysis, scoping review, arXiv link, DOI, PDF, or pasted paper text and asks to read…
sci-polish
Two-stage academic paper polishing skill. Stage A reduces AI detection traces (targeting GPTZero, Turnitin, Originality.ai). Stage B performs 8-dimension quality improvement (grammar, tone, coherence, conciseness, terminology, structure, argument clarity, journal compliance). Use whenever the user asks to polish…
sci-ppt
Generate professional academic PowerPoint (PPTX) presentations from paper PDFs, structured outlines, or plain text. Use for thesis defense, seminar reports, literature presentations, and graduate school applications. Supports automatic figure extraction, LaTeX formula rendering, and bilingual (Chinese/English) layouts.
econ-compass
Your compass for navigating economics literature. Find the most important, must-read papers in any economics field or research area — from broad subfields like labor economics or macroeconomics to narrow topics like 'carbon pricing' or 'digitalization and firm innovation'. This skill curates essential reading lists by…