# Latchbio Integration

> Crea, registra, depura y opera workflows de bioinformática en Latch con el SDK de Python, la CLI, Latch Data y Registry, Nextflow, Snakemake, ejecución programática y Latch MCP.

Fuente: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/latchbio-integration
Markdown: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/latchbio-integration.md

Repositorio: https://github.com/K-Dense-AI/scientific-agent-skills
Autor: K-Dense-AI
Licencia: MIT
Actualizado: el mes pasado
Coste de contexto: 84 tok instalada, 2.1k tok al activarse, 21.5k tok con todos los archivos del bundle
Bundle: 11 archivos, 84 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 latchbio-integration --agent claude-code
# Cursor
npx -y skills add K-Dense-AI/scientific-agent-skills --skill latchbio-integration --agent cursor
# Codex
npx -y skills add K-Dense-AI/scientific-agent-skills --skill latchbio-integration --agent codex
# Gemini CLI
npx -y skills add K-Dense-AI/scientific-agent-skills --skill latchbio-integration --agent gemini
# Windsurf
npx -y skills add K-Dense-AI/scientific-agent-skills --skill latchbio-integration --agent windsurf
# Cline
npx -y skills add K-Dense-AI/scientific-agent-skills --skill latchbio-integration --agent cline
```

## Qué hace

- Crea workflows tipados con el SDK de Python de Latch (`@small_task`, `@workflow`) con tareas declarativas
- Empaqueta y registra pipelines Python, Nextflow o Snakemake con `latch register` y `latch develop`
- Configura recursos de tarea (CPU, memoria, almacenamiento, GPU, caché, reintentos, timeouts)
- Mueve datos con `LPath`, `LatchFile` o `LatchDir`, y lee/actualiza proyectos y tablas de Latch Registry
- Lanza y monitorea ejecuciones vía Python (`launch_v2`) o Latch MCP, pidiendo confirmación antes de cómputo de pago

## Cuándo usarla

- Necesitas crear o mantener un workflow del SDK de Python de Latch
- Vas a empaquetar y registrar un pipeline Nextflow o Snakemake en Latch
- Necesitas leer o actualizar Latch Registry, o lanzar y monitorear una ejecución

## Qué la activa

- "Crea un workflow de Latch que haga el reverso complementario de una secuencia"
- "Registra este pipeline de Nextflow en Latch"
- "Configura GPU y caché para esta tarea de Latch"
- "Lanza esta ejecución de Latch y monitorea su estado"

## Antes de instalar

- Requiere una cuenta de Latch, acceso a red, Python 3.9+ (recomendado 3.12), `uv`, y Docker solo para builds de imagen locales.

## Archivos

- SKILL.md — 8 KB
- references/data-management.md — 7 KB
- references/latch-mcp.md — 4 KB
- references/nextflow-snakemake.md — 8 KB
- references/operations-and-debugging.md — 8 KB
- references/registry.md — 7 KB
- references/resource-configuration.md — 7 KB
- references/ui-and-automation.md — 9 KB
- references/verified-workflows.md — 6 KB
- references/workflow-creation.md — 8 KB
- scripts/inspect_latch_sdk.py — 9 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.

# LatchBio Integration

## Current Baseline

This skill targets **Latch SDK 2.76.8**, released July 10, 2026. The package
metadata supports Python 3.9–3.12 and declares Python 3.9+.

Treat the installed package and its changelog as authoritative when a guide
disagrees with the SDK. Some Latch guides retain older Python ranges or
compatibility-specific pre-release pins, especially the Snakemake v2 tutorial.
Never combine commands or imports from different tracks without checking their
version requirements.

## When to Use

Use this skill to:

- Create or maintain Python SDK workflows and task graphs
- Package and register Python, Nextflow, or Snakemake pipelines
- Configure task CPU, memory, storage, GPU, caching, retries, and timeouts
- Work with Latch Data through `LPath`, `LatchFile`, `LatchDir`, or the CLI
- Read or update Latch Registry projects, tables, and records
- Design workflow forms, launch plans, samplesheets, messages, and result links
- Stage and debug workflow images with `latch register --staging` and `latch develop`
- Launch and monitor workflows through Python or Latch MCP
- Discover and use ready-to-run Latch workflows

## Route to the Right Reference

Read only the references needed for the task:

| Need | Reference |
|---|---|
| Python workflows, tasks, maps, conditions, caching | `references/workflow-creation.md` |
| `LPath`, legacy file types, Latch URLs, data CLI | `references/data-management.md` |
| Registry reads, transactions, samplesheets | `references/registry.md` |
| CPU, memory, storage, GPU, dynamic resources | `references/resource-configuration.md` |
| Nextflow and Snakemake packaging | `references/nextflow-snakemake.md` |
| Metadata, forms, launch plans, messages, automations | `references/ui-and-automation.md` |
| Registration, development, execution, monitoring | `references/operations-and-debugging.md` |
| Ready-to-use workflows and `latch.verified` | `references/verified-workflows.md` |
| Remote MCP setup and tool workflow | `references/latch-mcp.md` |

Before relying on a symbol, run `scripts/inspect_latch_sdk.py` against the
target SDK version. It performs local imports only and does not authenticate or
make network requests.

## Installation and Authentication

For a reproducible environment:

```bash
uv venv --python 3.12
source .venv/bin/activate
uv pip install "latch==2.76.8"
```

On Windows, use WSL for the documented Linux workflow tooling.

Authenticate through the supported OAuth flow; do not read, print, copy, or
parse `~/.latch/token` manually:

```bash
latch login
latch workspace
```

Select a workspace non-interactively when its numeric ID is already known:

```bash
latch workspace --id 12345
```

`latch login` credentials are for the SDK and CLI. Latch MCP uses a separate
OAuth authorization and its credentials cannot be reused for general SDK
access.

## Fast Path

Create and remotely register the maintained subprocess template:

```bash
latch init covid-wf --template subprocess
latch register --yes --open covid-wf
```

Remote image building is the default. Use `--no-remote` only when a local
Docker daemon is available and a local build is intentional.

## Minimal Python Workflow

Keep workflow bodies declarative: invoke tasks and return their promises.
Perform computation and side effects inside tasks.

```python
from latch import small_task, workflow

@small_task
def reverse_complement(sequence: str) -> str:
    table = str.maketrans("ACGTacgt", "TGCAtgca")
    return sequence.translate(table)[::-1]

@workflow
def reverse_complement_workflow(sequence: str) -> str:
    """Return the reverse complement of a DNA sequence."""
    return reverse_complement(sequence=sequence)
```

Use `@workflow(metadata)` when the generated interface needs custom labels,
sections, validation rules, samplesheets, or documentation links. Use `LatchFile` or
`LatchDir` for automatic task input staging and output upload; use `LPath` for
imperative remote path operations.

## Recommended Development Lifecycle

1. **Inspect compatibility**
   - Confirm the installed SDK and Python version.
   - Identify whether the project is Python, Nextflow, the legacy Snakemake
     flag path, or the separately pinned Snakemake v2 tutorial track.

2. **Define a typed interface**
   - Annotate every workflow and task input and output.
   - Keep module import time free of network calls, data mutations, and secret
     retrieval. Isolate documented exceptions such as `workflow_reference`,
     which resolves the active workspace when its decorator is evaluated.
   - Use dataclasses and enums for structured parameters.

3. **Configure metadata and resources**
   - Match metadata parameter keys to the workflow signature.
   - Start with named task decorators, then use `custom_task` only when measured
     requirements justify it.

4. **Validate in the execution image**

   Fresh Nextflow and Snakemake projects must generate their
   version-compatible Python entrypoint before staging. In SDK 2.76.8, the
   staging branch does not generate one from `--nf-script` or `--snakefile`.

   ```bash
   latch register --staging .
   latch develop .
   ```

   Re-run staging registration after changing the Dockerfile or dependencies.
   Edits made inside the development container are not synced back.

5. **Register deliberately**

   ```bash
   latch register --yes --open .
   ```

   Useful controls:

   ```bash
   latch register --workspace-id 12345 .
   latch register --mark-as-release .
   latch register --workflow-module wf.custom_entrypoint .
   ```

   Duplicate registration exits with status `2`; it is not the same as a build
   failure.

6. **Launch only after reviewing cost and parameters**
   - Prefer the Console or Latch MCP for interactive operation.
   - Prefer `latch_cli.services.launch.launch_v2` for Python automation.
   - Do not use the deprecated `latch launch` CLI as a new integration pattern.

7. **Monitor and verify**
   - Check terminal status, task logs, result links, and scientific outputs.
   - Treat successful orchestration as necessary but not sufficient scientific
     validation.

## Operational Safety

- Ask for confirmation before launching paid compute, especially GPU or large
  batch runs.
- Ask for confirmation before `LPath.rmr`, `latch rmr`, Registry deletion, or
  overwriting shared destinations.
- Never log secrets, SDK tokens, signed URLs, or secret values.
- Call `get_secret()` only inside a task, use the returned value only for its
  intended service, and never return it as workflow output.
- Do not pass untrusted strings through shell commands. Prefer argument lists
  with `subprocess.run(..., check=True)`.
- Pin the SDK and workflow dependencies for releases. Upgrade only after
  reviewing the changelog and re-running staging tests.
- Treat generated files as generated: customize the documented extension file
  rather than editing output that the CLI will overwrite.

## Inspect the Installed SDK

From this skill directory:

```bash
uv run --no-project --python 3.12 --with "latch==2.76.8" \
  python scripts/inspect_latch_sdk.py
```

Use JSON output for automated comparisons:

```bash
uv run --no-project --python 3.12 --with "latch==2.76.8" \
  python scripts/inspect_latch_sdk.py --json
```

## Authoritative Sources

- Documentation index: https://wiki.latch.bio/llms.txt
- Workflow and SDK guides: https://wiki.latch.bio/workflows/overview
- SDK API reference: https://wiki.latch.bio/reference/sdk
- PyPI package: https://pypi.org/project/latch/
- SDK 2.76.8 release source: https://github.com/latchbio/latch/tree/0faa9dcd8186444ac008f50adf95d43f0fa30e06
- SDK changelog: https://github.com/latchbio/latch/blob/0faa9dcd8186444ac008f50adf95d43f0fa30e06/CHANGELOG.md
- Latch Console: https://console.latch.bio

## Dónde encaja

- Categoría: [DevOps e infraestructura](https://skillsagentes.com/categorias/devops-infraestructura.md) — Despliegues, contenedores, IaC y flujos de gestión de incidentes.
- 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.

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