Mmcp.market

pylabrobot skill

by K-Dense-AI·K-Dense-AI/scientific-agent-skills·47k stars·MIT

Develop and review PyLabRobot lab-automation resources, liquid-handling plans, offline simulations, and supported-device integrations. Use for PyLabRobot protocols or API questions; keep physical execution behind an explicit operator safety gate.

A100/100content scan

Is the pylabrobot skill safe?

Clean: nothing in its files matched our rules. We read 15 files in the folder on 2026-09-28.

No findings.

Install the pylabrobot skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p ~/.claude/skills
cp -r /tmp/scientific-agent-skills/skills/pylabrobot ~/.claude/skills/pylabrobot
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

PyLabRobot

Use PyLabRobot's hardware-agnostic frontends, resource tree, trackers, and device-specific backends to develop laboratory automation. Default to local manifest validation, bookkeeping, and the software-only chatterbox backend.

Verified snapshot

reproducible smoke tests.

  • PyPI stable: PyLabRobot==0.2.1, released 2026-03-23.
  • Upstream requirement: Python >=3.9. This skill uses Python 3.11 for its

main describe unreleased work and must not be assumed available in 0.2.1.

  • /stable/ documentation identifies itself as 0.2.1. /dev/ and repository

EVOBackend, OpentronsOT2Backend, and the offline LiquidHandlerChatterboxBackend.

  • Stable liquid-handler backends include STARBackend, VantageBackend,

the PyPI history, v0.2.1 tag, and changelog as release evidence.

  • PyLabRobot's GitHub Releases page has no 0.2.x software release entry; use

Non-negotiable hardware boundary

Never connect to, initialize, home, move, heat, shake, spin, pump, open/close, or otherwise command physical equipment automatically. Do not turn a simulation plan into a live backend merely by changing an environment variable, config value, or import.

Before any separately authorized live run, require a trained human to:

deck, and protocol revision.

  1. Explicitly confirm the exact backend, device identity, firmware, transport,

adapters, lids, plates, tip racks, waste, labware orientation, barcodes, and every occupied coordinate.

  1. Reconcile the physical deck against the resource tree, including carriers,

channel clearances, and all aspiration/dispense coordinates.

  1. Verify calibration, teaching, motion envelopes, collision risks, gripper or

capacity, tip type/capacity/filter compatibility, channel mapping, units, heights, rates, liquid class, blowout/mixing, and contamination boundaries.

  1. Review source identity and actual fill volume, dead volume, destination

PPE, biosafety/chemical controls, and a safe abort/recovery procedure.

  1. Confirm guards, doors, waste capacity, containment, emergency stop readiness,

new or changed.

  1. Approve a slow dry run or nonhazardous commissioning run when anything is

Tracker state is bookkeeping, not sensing. It cannot prove that liquid or a tip is physically present. The Visualizer renders resource/tracker events; it does not model physics. Chatterbox prints planned operations; it does not prove calibration, reachability, collision freedom, liquid behavior, or device state.

Required intake

Do not guess any of these:

coordinates, orientations, and motion clearances.

  • Exact device model, installed options, firmware, computer/OS, and transport.
  • Stable PyLabRobot version and required extras.
  • Deck/deck origin, carriers, adapters, resource definitions, dimensions,

mapping.

  • Plate/tube/reservoir capacities and dead volumes; initial physical volumes.
  • Tip model, filter, fitting, capacity, rack state, channel count, and channel

blowout, liquid properties, and validated vendor liquid class.

  • Transfer units (uL, mm, uL/s, s), heights, rates, mixing, air gaps,

acceptance criteria, and recovery procedure.

  • Contamination policy, controls, waste handling, operator interventions,

If information is missing, produce an assumptions/blockers list and an offline draft only.

Reproducible install

For offline API inspection and chatterbox simulation:

uv venv --python 3.11 .venv-pylabrobot
uv pip install --python .venv-pylabrobot/bin/python "PyLabRobot==0.2.1"

On Windows, use .venv-pylabrobot\Scripts\python.exe. Do not install hardware extras until the user names the device and explicitly approves its transport dependencies. Then inspect the matching stable device page before considering a pin such as "PyLabRobot[serial]==0.2.1" or "PyLabRobot[usb]==0.2.1".

Offline-first workflow

Run from the repository root. Every bundled CLI uses strict, bounded UTF-8 JSON/CSV, local non-symlink paths, fixed allowlists, and JSON output. None can select a live backend.

python3 skills/pylabrobot/scripts/validate_manifest.py \
  --input tests/pylabrobot/fixtures/protocol_manifest.json

python3 skills/pylabrobot/scripts/check_deck_geometry.py \
  --input tests/pylabrobot/fixtures/protocol_manifest.json

python3 skills/pylabrobot/scripts/plan_transfers.py \
  --manifest tests/pylabrobot/fixtures/protocol_manifest.json \
  --transfers tests/pylabrobot/fixtures/transfers.csv

python3 skills/pylabrobot/scripts/generate_simulation_plan.py \
  --manifest tests/pylabrobot/fixtures/protocol_manifest.json \
  --transfers tests/pylabrobot/fixtures/transfers.csv

python3 skills/pylabrobot/scripts/inspect_backends.py \
  --expected-version 0.2.1 --strict

The geometry checker uses conservative static axis-aligned boxes; it is not a motion planner. The transfer planner requires one new tip per row and checks source/dead/destination volumes, tip capacity, wells, channels, heights, rates, units, and allowlists. Review assets/protocol-manifest.schema.json and the synthetic fixtures before making a project-specific manifest.

Verified software-only example

The exact backend below is software-only. Do not substitute a hardware backend.

from pylabrobot.liquid_handling import LiquidHandler
from pylabrobot.liquid_handling.backends import LiquidHandlerChatterboxBackend
from pylabrobot.resources import (
    Cor_96_wellplate_360ul_Fb,
    PLT_CAR_L5AC_A00,
    TIP_CAR_480_A00,
    hamilton_96_tiprack_1000uL_filter,
    set_tip_tracking,
    set_volume_tracking,
)
from pylabrobot.resources.hamilton import STARLetDeck

set_tip_tracking(True)
set_volume_tracking(True)

deck = STARLetDeck()
tip_carrier = TIP_CAR_480_A00(name="tip_carrier")
tips = hamilton_96_tiprack_1000uL_filter(name="tips")
tip_carrier[0] = tips
plate_carrier = PLT_CAR_L5AC_A00(name="plate_carrier")
source = Cor_96_wellplate_360ul_Fb(name="source")
destination = Cor_96_wellplate_360ul_Fb(name="destination")
plate_carrier[0] = source
plate_carrier[1] = destination
deck.assign_child_resource(tip_carrier, rails=3)
deck.assign_child_resource(plate_carrier, rails=15)
source.get_well("A1").tracker.set_volume(100.0)  # planned state, not sensing

lh = LiquidHandler(backend=LiquidHandlerChatterboxBackend(), deck=deck)
await lh.setup()  # safe here only because the backend above is software-only
try:
    await lh.pick_up_tips(tips["A1"])
    await lh.aspirate(so

API rules that prevent stale code

OpentronsOT2Backend; do not use stale STAR, TecanBackend, OpentronsBackend, or ChatterboxBackend imports.

  • Current names are STARBackend, VantageBackend, EVOBackend, and

testing. ChatterBoxBackend is a separate legacy-named export; do not conflate the two.

  • Use LiquidHandlerChatterboxBackend for generic offline liquid-handler

await vis.stop(); it starts localhost HTTP/WebSocket servers and may open a browser.

  • Visualizer(resource=...) is valid, followed by await vis.setup() and

0.2.1. Stable liquid classes are vendor-specific, for example pylabrobot.liquidhandling.liquidclasses.hamilton.HamiltonLiquidClass.

  • There is no generic from pylabrobot.liquid_handling import LiquidClass in

vendor/model specific; a shared frontend does not imply identical behavior.

  • Most frontend methods are async. Backend kwargs and capabilities are

More skills from K-Dense-AI/scientific-agent-skills

  • AadaptyvHow to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
  • AaeonThis skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
  • AalphagenomeLook up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), score variants or scan windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and build Atlas website deep links. Use when the user mentions AlphaGenome, AlphaGenome Atlas, AVI or AlphaGenome Variant Impact, DeepMind variant effect prediction, or wants to prioritise or mechanistically interpret non-coding, regulatory, splicing, enhancer, promoter, or chromatin-accessibility effects of SNVs from a VCF, credible set, or region. Research use only; not a clinical tool.
  • Aanalytical-method-validationPlan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include "method validation", "analytical method validation", "AMV", "validation protocol", "acceptance criteria", "linearity", "reportable range", "accuracy and precision", "repeatability", "intermediate precision", "recovery", "LOD", "LOQ", "detection limit", "quantitation limit", "specificity", "robustness", "method transfer", "method comparison", "Deming", "Passing-Bablok", "Bland-Altman", "equivalence testing", "OOS investigation", "ICH Q2", "Q2(R2)", "Q14", "USP 1225", "ICH M10", "incurred sample reanalysis", "ISR", "CLSI EP", and any request to show that an assay works.
  • AanndataData structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
  • AarborAutonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
  • AarboretoInfer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
  • AastropyCore Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
  • AautoskillObserve the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
  • Abenchling-integrationBenchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.
  • Abgpt-paper-searchSearch scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.
  • AbidsUse this skill when working with Brain Imaging Data Structure (BIDS) datasets: organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars, or creating BIDS derivatives.

All agent skills → · MCP servers