K-Dense-AI/scientific-agent-skills/skills/pylabrobot/SKILL.md
pylabrobot
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.
- Source repository stars
- 31,966
- Declared platforms
- 0
- Static risk flags
- 1
- Last source update
- 2026-07-28
- Source checked
- 2026-07-28
Decision brief
What it does—and where it fits
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.
Not for
- Tasks that require unconfirmed production actions or broad system permissions.
- Environments where the pinned source and install steps cannot be inspected.
Compatibility matrix
Platform support, with evidence labels
| Platform | Status | Evidence | What to check |
|---|---|---|---|
| Codex | Not declared | No explicit evidence | Portability before use |
| Claude Code | Not declared | No explicit evidence | Portability before use |
| Cursor | Not declared | No explicit evidence | Portability before use |
| Gemini CLI | Not declared | No explicit evidence | Portability before use |
Installation
Inspect first. Install second.
The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill "skills/pylabrobot"Inspect the Agent Skill "pylabrobot" from https://github.com/K-Dense-AI/scientific-agent-skills/blob/e7ac42510774624f327003c95b6650e2883bc01d/skills/pylabrobot/SKILL.md at commit e7ac42510774624f327003c95b6650e2883bc01d. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.
Workflow
What the source asks the agent to do
- 01
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.
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.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,… - 02
Verified snapshot
PyPI stable: PyLabRobot==0.2.1, released 2026-03-23.
PyPI stable: PyLabRobot==0.2.1, released 2026-03-23.Upstream requirement: Python =3.9. This skill uses Python 3.11 for its/stable/ documentation identifies itself as 0.2.1. /dev/ and repository - 03
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.
Explicitly confirm the exact backend, device identity, firmware, transport,Reconcile the physical deck against the resource tree, including carriers,Verify calibration, teaching, motion envelopes, collision risks, gripper or - 04
Required intake
Do not guess any of these:
Exact device model, installed options, firmware, computer/OS, and transport.Stable PyLabRobot version and required extras.Deck/deck origin, carriers, adapters, resource definitions, dimensions, - 05
Reproducible install
For offline API inspection and chatterbox simulation:
For offline API inspection and chatterbox simulation: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…
Permission review
Static risk signals and limitations
Runs scripts
The documentation asks the agent to run terminal commands or scripts.
python3 skills/pylabrobot/scripts/validate_manifest.py \Runs scripts
The documentation asks the agent to run terminal commands or scripts.
python3 skills/pylabrobot/scripts/check_deck_geometry.py \Evidence record
Why each signal appears
| Signal | Value | Evidence type | Meaning |
|---|---|---|---|
| Quality score | 87/100 | Computed | Documentation, specificity, maintenance, and trust rules |
| Repository stars | 31,966 | Source | Repository attention, not individual Skill quality |
| Compatibility | 0 platforms | Source | Declared in the catalog source record |
| Usage guide | automated source guide | Editorial | Generated or reviewed according to the visible evidence level |
Pinned source
Provenance and original SKILL.md
- Repository
- K-Dense-AI/scientific-agent-skills
- Skill path
- skills/pylabrobot/SKILL.md
- Commit
- e7ac42510774624f327003c95b6650e2883bc01d
- License
- MIT
- Collected
- 2026-07-28
- Default branch
- main
View the original SKILL.md
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
- PyPI stable:
PyLabRobot==0.2.1, released 2026-03-23. - Upstream requirement: Python >=3.9. This skill uses Python 3.11 for its reproducible smoke tests.
/stable/documentation identifies itself as 0.2.1./dev/and repositorymaindescribe unreleased work and must not be assumed available in 0.2.1.- Stable liquid-handler backends include
STARBackend,VantageBackend,EVOBackend,OpentronsOT2Backend, and the offlineLiquidHandlerChatterboxBackend. - PyLabRobot's GitHub Releases page has no 0.2.x software release entry; use
the PyPI history,
v0.2.1tag, and changelog as release evidence.
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:
- Explicitly confirm the exact backend, device identity, firmware, transport, deck, and protocol revision.
- Reconcile the physical deck against the resource tree, including carriers, adapters, lids, plates, tip racks, waste, labware orientation, barcodes, and every occupied coordinate.
- Verify calibration, teaching, motion envelopes, collision risks, gripper or channel clearances, and all aspiration/dispense coordinates.
- Review source identity and actual fill volume, dead volume, destination capacity, tip type/capacity/filter compatibility, channel mapping, units, heights, rates, liquid class, blowout/mixing, and contamination boundaries.
- Confirm guards, doors, waste capacity, containment, emergency stop readiness, PPE, biosafety/chemical controls, and a safe abort/recovery procedure.
- Approve a slow dry run or nonhazardous commissioning run when anything is new or changed.
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:
- Exact device model, installed options, firmware, computer/OS, and transport.
- Stable PyLabRobot version and required extras.
- Deck/deck origin, carriers, adapters, resource definitions, dimensions, coordinates, orientations, and motion clearances.
- Plate/tube/reservoir capacities and dead volumes; initial physical volumes.
- Tip model, filter, fitting, capacity, rack state, channel count, and channel mapping.
- Transfer units (
uL,mm,uL/s,s), heights, rates, mixing, air gaps, blowout, liquid properties, and validated vendor liquid class. - Contamination policy, controls, waste handling, operator interventions, acceptance criteria, and recovery procedure.
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(source["A1"], vols=[10.0])
await lh.dispense(destination["A1"], vols=[10.0])
await lh.return_tips()
finally:
await lh.stop()
API rules that prevent stale code
- Current names are
STARBackend,VantageBackend,EVOBackend, andOpentronsOT2Backend; do not use staleSTAR,TecanBackend,OpentronsBackend, orChatterboxBackendimports. - Use
LiquidHandlerChatterboxBackendfor generic offline liquid-handler testing.ChatterBoxBackendis a separate legacy-named export; do not conflate the two. Visualizer(resource=...)is valid, followed byawait vis.setup()andawait vis.stop(); it starts localhost HTTP/WebSocket servers and may open a browser.- There is no generic
from pylabrobot.liquid_handling import LiquidClassin 0.2.1. Stable liquid classes are vendor-specific, for examplepylabrobot.liquid_handling.liquid_classes.hamilton.HamiltonLiquidClass. - Most frontend methods are async. Backend kwargs and capabilities are vendor/model specific; a shared frontend does not imply identical behavior.
References
- Liquid handling — operations, tips, tracking, liquid classes, units, and validation.
- Resources — decks, coordinates, plates, tip racks, collisions, state, and serialization.
- Hardware backends — verified names, support levels, capabilities, and live-run gate.
- Analytical equipment — plate readers and scales.
- Material handling — pumps, heaters, shakers, temperature control, storage, and centrifuges.
- Visualization — chatterbox, Visualizer, localhost services, and simulation limits.
Dated upstream sources
Checked 2026-07-23:
- PyPI 0.2.1 — released 2026-03-23; Python >=3.9; extras and artifacts.
- Stable installation guide — stable versus source/dev install and optional transport groups.
- Stable API and supported machines — 0.2.1 API and model-specific support labels.
v0.2.1source tag and changelog — tag dated 2026-03-23;Unreleasedis development-only.
Alternatives
Compare before choosing
K-Dense-AI/scientific-agent-skills
opentrons-integration
Author, review, migrate, simulate, and troubleshoot official Opentrons Python Protocol API v2 protocols for Flex and OT-2 robots. Use for robot-specific liquid handling, deck and labware setup, pipettes, modules, runtime parameters, liquid classes, and Opentrons App analysis. Use pylabrobot instead when one workflow must support multiple robot vendors.
K-Dense-AI/scientific-agent-skills
dask
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
K-Dense-AI/scientific-agent-skills
medchem
Medicinal chemistry filters for compound triage. Apply drug-likeness rules (Lipinski, Veber, CNS), structural alert catalogs (PAINS, NIBR, ChEMBL), complexity metrics, and the medchem query language for library filtering.
K-Dense-AI/scientific-agent-skills
neurokit2
Use NeuroKit2 to build or audit reproducible research workflows for physiological time-series preprocessing, event/interval analysis, multimodal alignment, variability, and complexity. Trigger when code imports neurokit2 or needs its current APIs, schemas, and method-aware validation—not for diagnosis or device validation.