# Pylabrobot > Desarrolla y revisa recursos de automatización de laboratorio, planes de manejo de líquidos y simulaciones offline con PyLabRobot, manteniendo la ejecución física detrás de una puerta de seguridad del operador. Fuente: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/pylabrobot Markdown: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/pylabrobot.md Repositorio: https://github.com/K-Dense-AI/scientific-agent-skills Autor: K-Dense-AI Licencia: MIT Actualizado: el mes pasado Coste de contexto: 62 tok instalada, 2.4k tok al activarse, 27.1k tok con todos los archivos del bundle Bundle: 15 archivos, 106 KB Permisos que pide: read write edit bash ## Instalación Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina. ```bash # Claude Code npx -y skills add K-Dense-AI/scientific-agent-skills --skill pylabrobot --agent claude-code # Cursor npx -y skills add K-Dense-AI/scientific-agent-skills --skill pylabrobot --agent cursor # Codex npx -y skills add K-Dense-AI/scientific-agent-skills --skill pylabrobot --agent codex # Gemini CLI npx -y skills add K-Dense-AI/scientific-agent-skills --skill pylabrobot --agent gemini # Windsurf npx -y skills add K-Dense-AI/scientific-agent-skills --skill pylabrobot --agent windsurf # Cline npx -y skills add K-Dense-AI/scientific-agent-skills --skill pylabrobot --agent cline ``` ## Qué hace - Desarrolla y revisa recursos, planes de manejo de líquidos y simulaciones offline de PyLabRobot para automatización de laboratorio - Valida manifiestos de protocolo, geometría del deck y transferencias con CLIs locales antes de tocar hardware - Genera planes de simulación y ejecuta el backend chatterbox software-only para probar protocolos sin conectar equipos - Exige una puerta de seguridad explícita del operador antes de cualquier ejecución física en hardware real - Documenta los backends soportados (STARBackend, VantageBackend, EVOBackend, OpentronsOT2Backend) y sus nombres de API actuales ## Cuándo usarla - El usuario trabaja con protocolos o preguntas de API de PyLabRobot - Necesita validar un manifiesto de protocolo, la geometría del deck o un plan de transferencias sin conectar hardware - Quiere simular un flujo de manejo de líquidos con el backend chatterbox antes de una ejecución real - Va a ejecutar algo en un dispositivo físico y necesita la lista de verificación de seguridad del operador ## Qué la activa - "Valida este manifiesto de protocolo de PyLabRobot" - "Simula una transferencia de líquidos con el backend chatterbox" - "Revisa la geometría del deck antes de correr el protocolo" - "Qué backend uso para un Hamilton STAR con PyLabRobot" ## Antes de instalar - Verificado con PyLabRobot 0.2.1 en Python 3.9+; las CLIs de planificación solo necesitan Python 3.11+ sin red, y los dispositivos físicos requieren extras, calibración y aprobación de un operador entrenado. - Necesita en el PATH: python3 ## Archivos - SKILL.md — 10 KB - assets/protocol-manifest.schema.json — 8 KB - references/analytical-equipment.md — 7 KB - references/hardware-backends.md — 8 KB - references/liquid-handling.md — 8 KB - references/material-handling.md — 8 KB - references/resources.md — 8 KB - references/visualization.md — 6 KB - scripts/__init__.py — 72 B - scripts/_common.py — 26 KB - scripts/check_deck_geometry.py — 1 KB - scripts/generate_simulation_plan.py — 5 KB - scripts/inspect_backends.py — 7 KB - scripts/plan_transfers.py — 2 KB - scripts/validate_manifest.py — 1 KB ## SKILL.md Reproducido tal cual desde K-Dense-AI/scientific-agent-skills bajo MIT. Esta sección es el documento original y está en inglés. # 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 repository `main` describe unreleased work and must not be assumed available in 0.2.1. - Stable liquid-handler backends include `STARBackend`, `VantageBackend`, `EVOBackend`, `OpentronsOT2Backend`, and the offline `LiquidHandlerChatterboxBackend`. - PyLabRobot's GitHub Releases page has no 0.2.x software release entry; use the PyPI history, `v0.2.1` tag, 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: 1. Explicitly confirm the exact backend, device identity, firmware, transport, deck, and protocol revision. 2. Reconcile the physical deck against the resource tree, including carriers, adapters, lids, plates, tip racks, waste, labware orientation, barcodes, and every occupied coordinate. 3. Verify calibration, teaching, motion envelopes, collision risks, gripper or channel clearances, and all aspiration/dispense coordinates. 4. 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. 5. Confirm guards, doors, waste capacity, containment, emergency stop readiness, PPE, biosafety/chemical controls, and a safe abort/recovery procedure. 6. 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: ```bash 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. ```bash 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. ```python 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`, and `OpentronsOT2Backend`; do not use stale `STAR`, `TecanBackend`, `OpentronsBackend`, or `ChatterboxBackend` imports. - Use `LiquidHandlerChatterboxBackend` for generic offline liquid-handler testing. `ChatterBoxBackend` is a separate legacy-named export; do not conflate the two. - `Visualizer(resource=...)` is valid, followed by `await vis.setup()` and `await vis.stop()`; it starts localhost HTTP/WebSocket servers and may open a browser. - There is no generic `from pylabrobot.liquid_handling import LiquidClass` in 0.2.1. Stable liquid classes are vendor-specific, for example `pylabrobot.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](references/liquid-handling.md) — operations, tips, tracking, liquid classes, units, and validation. - [Resources](references/resources.md) — decks, coordinates, plates, tip racks, collisions, state, and serialization. - [Hardware backends](references/hardware-backends.md) — verified names, support levels, capabilities, and live-run gate. - [Analytical equipment](references/analytical-equipment.md) — plate readers and scales. - [Material handling](references/material-handling.md) — pumps, heaters, shakers, temperature control, storage, and centrifuges. - [Visualization](references/visualization.md) — chatterbox, Visualizer, localhost services, and simulation limits. ## Dated upstream sources Checked **2026-07-23**: - [PyPI 0.2.1](https://pypi.org/project/PyLabRobot/) — released 2026-03-23; Python >=3.9; extras and artifacts. - [Stable installation guide](https://docs.pylabrobot.org/stable/user_guide/_getting-started/installation.html) — stable versus source/dev install and optional transport groups. - [Stable API](https://docs.pylabrobot.org/stable/api/pylabrobot.html) and [supported machines](https://docs.pylabrobot.org/stable/user_guide/machines.html) — 0.2.1 API and model-specific support labels. - [`v0.2.1` source tag](https://github.com/PyLabRobot/pylabrobot/tree/v0.2.1) and [changelog](https://github.com/PyLabRobot/pylabrobot/blob/main/CHANGELOG.md) — tag dated 2026-03-23; `Unreleased` is development-only. ## Dónde encaja - Categoría: [Automatización](https://skillsagentes.com/categorias/automatizacion.md) — Flujos de varios pasos que se ejecutan sin supervisión. - Creador: [K-Dense-AI](https://skillsagentes.com/creators/k-dense-ai.md) — 163 skills en el directorio - [Todas las skills](https://skillsagentes.com/skills.md) - [Ranking de instalaciones](https://skillsagentes.com/ranking.md) ## Otras skills del mismo repositorio - [Citation Management](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/citation-management.md): Gestión integral de citas académicas: busca en OpenAlex, PubMed y Google Scholar, extrae metadatos precisos, valida citas y genera entradas BibTeX correctamente formateadas. - [Scientific Slides](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/scientific-slides.md): Crea decks de diapositivas y presentaciones para charlas de investigación: PowerPoint, presentaciones de conferencia, seminarios, defensas de tesis. Da estructura, plantillas, guía de tiempos y validación visual. - [Literature Review](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/literature-review.md): Realiza revisiones bibliográficas sistemáticas y completas usando varias bases académicas (PubMed, arXiv, bioRxiv, Semantic Scholar). Genera markdown y PDF con citas verificadas en varios estilos (APA, Nature, Vancouver). - [Infographics](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/infographics.md): Crea infografías profesionales con Nano Banana Pro AI y refinamiento iterativo inteligente. Usa Gemini 3.6 Flash para revisar la calidad e integra investigación con Perplexity Sonar. Soporta 10 tipos, 8 estilos y paletas para daltonismo. - [Latex Posters](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/latex-posters.md): Crea pósteres de investigación profesionales en LaTeX con beamerposter, tikzposter o baposter, para conferencias y comunicación científica: layout, colores, columnas múltiples e integración de figuras. --- Skills Agentes · [Índice de páginas en markdown](https://skillsagentes.com/sitemap.md) · [Inicio](https://skillsagentes.com/index.md)