# Benchling Integration

> Integración con el SDK de Python de Benchling y su API REST para entidades del registro, inventario, entradas del cuaderno electrónico (ELN), workflows, Benchling Apps y consultas al Data Warehouse.

Fuente: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/benchling-integration
Markdown: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/benchling-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: 53 tok instalada, 1.9k tok al activarse, 17.3k tok con todos los archivos del bundle
Bundle: 6 archivos, 68 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 benchling-integration --agent claude-code
# Cursor
npx -y skills add K-Dense-AI/scientific-agent-skills --skill benchling-integration --agent cursor
# Codex
npx -y skills add K-Dense-AI/scientific-agent-skills --skill benchling-integration --agent codex
# Gemini CLI
npx -y skills add K-Dense-AI/scientific-agent-skills --skill benchling-integration --agent gemini
# Windsurf
npx -y skills add K-Dense-AI/scientific-agent-skills --skill benchling-integration --agent windsurf
# Cline
npx -y skills add K-Dense-AI/scientific-agent-skills --skill benchling-integration --agent cline
```

## Qué hace

- Gestiona entidades del registro (secuencias de ADN, ARN, proteínas y entidades personalizadas) vía el SDK de Benchling
- Automatiza operaciones de inventario: contenedores, cajas, placas, ubicaciones y transferencias
- Crea y consulta entradas del cuaderno electrónico (ELN) y tablas estructuradas
- Construye automatizaciones de workflows, tareas y ejecuciones de ensayo (assay runs)
- Consulta el Data Warehouse de Benchling por SQL y configura integraciones con AWS EventBridge

## Cuándo usarla

- Automatizar datos de laboratorio con benchling-sdk o la API v2
- Gestionar entidades biológicas (ADN, ARN, proteínas) e inventario de forma programática
- Crear o consultar entradas de cuaderno electrónico o construir automatizaciones de workflow
- Sincronizar datos entre Benchling y sistemas externos, o configurar eventos con EventBridge

## Qué la activa

- "Importa estas secuencias FASTA al registro de Benchling"
- "Lista todos los contenedores de esta ubicación en Benchling"
- "Actualiza las tareas pendientes de este workflow en Benchling"
- "Consulta el Data Warehouse de Benchling por SQL"

## Antes de instalar

- Necesita una cuenta de Benchling, la URL del tenant y una API key o credenciales OAuth; instala benchling-sdk con uv pip.

## Archivos

- SKILL.md — 7 KB
- references/api_endpoints.md — 14 KB
- references/authentication.md — 11 KB
- references/core_capabilities.md — 10 KB
- references/eventbridge.md — 8 KB
- references/sdk_reference.md — 17 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.

# Benchling Integration

## Overview

Benchling is a cloud platform for life sciences R&D. Access registry entities (DNA, RNA, proteins), inventory, electronic lab notebooks, and workflows programmatically via the Python SDK and REST API.

**Version note:** Examples target **benchling-sdk 1.25.0** (latest stable on PyPI). Docs: [benchling.com/sdk-docs](https://benchling.com/sdk-docs/). Platform guide: [docs.benchling.com](https://docs.benchling.com/).

## When to Use This Skill

This skill should be used when:
- Working with Benchling's Python SDK or REST API
- Managing biological sequences (DNA, RNA, proteins) and registry entities
- Automating inventory operations (samples, containers, locations, transfers)
- Creating or querying electronic lab notebook entries
- Building workflow automations or Benchling Apps
- Syncing data between Benchling and external systems
- Querying the Benchling Data Warehouse for analytics
- Setting up event-driven integrations with AWS EventBridge

## Core Capabilities

Seven capability areas, each with code, are in
[references/core_capabilities.md](references/core_capabilities.md):

1. **Authentication and setup** — API key and OAuth app auth; see
   [references/authentication.md](references/authentication.md).
2. **Registry and entity management** — DNA and AA sequences, custom entities, schemas,
   and registration.
3. **Inventory management** — containers, boxes, plates, locations, and transfers.
4. **Notebook and documentation** — entries, day-to-day notes, and structured tables.
5. **Workflows and automation** — tasks, flowcharts, and assay runs.
6. **Events and integration** — EventBridge subscriptions; see
   [references/eventbridge.md](references/eventbridge.md).
7. **Data warehouse and analytics** — SQL access to the warehouse.

Endpoint and SDK detail is in
[references/api_endpoints.md](references/api_endpoints.md) and
[references/sdk_reference.md](references/sdk_reference.md).

## Best Practices

### Error Handling

The SDK automatically retries failed requests:
```python
# Automatic retry for 429, 502, 503, 504 status codes
# Up to 5 retries with exponential backoff
# Customize retry behavior if needed
from benchling_sdk.retry import RetryStrategy

benchling = Benchling(
    url=tenant_url,
    auth_method=ApiKeyAuth(api_key),
    retry_strategy=RetryStrategy(max_retries=3),
)
```

### Pagination Efficiency

Use generators for memory-efficient pagination:
```python
# Generator-based iteration
for page in benchling.dna_sequences.list():
    for sequence in page:
        process(sequence)

# Check estimated count without loading all pages
total = benchling.dna_sequences.list().estimated_count()
```

### Schema Fields Helper

Use the `fields()` helper for custom schema fields:
```python
# Convert dict to Fields object
custom_fields = benchling.models.fields({
    "concentration": "100 ng/μL",
    "date_prepared": "2025-10-20",
    "notes": "High quality prep"
})
```

### Forward Compatibility

The SDK handles unknown enum values and types gracefully:
- Unknown enum values are preserved
- Unrecognized polymorphic types return `UnknownType`
- Allows working with newer API versions

### Security Considerations

- Never commit API keys or OAuth secrets to version control
- Read only named environment variables (`BENCHLING_TENANT_URL`, `BENCHLING_API_KEY`, etc.)
- Route network calls exclusively to your tenant URL
- Rotate keys if compromised; use OAuth for multi-user production apps
- Grant minimal necessary permissions for apps in the Developer Console

## Resources

### references/

Detailed reference documentation for in-depth information:

- **authentication.md** - Comprehensive authentication guide including OIDC, security best practices, and credential management
- **sdk_reference.md** - Detailed Python SDK reference with advanced patterns, examples, and all entity types
- **api_endpoints.md** - REST API endpoint reference for direct HTTP calls without the SDK
- **eventbridge.md** - EventBridge setup, event payload schema, rule examples, Lambda handler, validation, and recovery

Load these references as needed for specific integration requirements.

## Common Use Cases

**1. Bulk Entity Import:**
```python
# Import multiple sequences from FASTA file
from Bio import SeqIO

for record in SeqIO.parse("sequences.fasta", "fasta"):
    benchling.dna_sequences.create(
        DnaSequenceCreate(
            name=record.id,
            bases=str(record.seq),
            is_circular=False,
            folder_id="fld_abc123"
        )
    )
```

**2. Inventory Audit:**
```python
# List all containers in a specific location
containers = benchling.containers.list(
    parent_storage_id="box_abc123"
)

for page in containers:
    for container in page:
        print(f"{container.name}: {container.barcode}")
```

**3. Workflow Automation:**
```python
# Update all pending tasks for a workflow
tasks = benchling.workflow_tasks.list(
    workflow_id="wf_abc123",
    status="pending"
)

for page in tasks:
    for task in page:
        # Perform automated checks
        if auto_validate(task):
            benchling.workflow_tasks.update(
                task_id=task.id,
                workflow_task=WorkflowTaskUpdate(
                    status_id="status_complete"
                )
            )
```

**4. Data Export:**
```python
# Export all sequences with specific properties
sequences = benchling.dna_sequences.list()
export_data = []

for page in sequences:
    for seq in page:
        if seq.schema_id == "target_schema_id":
            export_data.append({
                "id": seq.id,
                "name": seq.name,
                "bases": seq.bases,
                "length": len(seq.bases)
            })

# Save to CSV or database
import csv
with open("sequences.csv", "w") as f:
    writer = csv.DictWriter(f, fieldnames=export_data[0].keys())
    writer.writeheader()
    writer.writerows(export_data)
```

## Additional Resources

- **Official Documentation:** https://docs.benchling.com
- **Python SDK Reference:** https://benchling.com/sdk-docs/
- **API Reference:** https://benchling.com/api/reference
- **Support:** [email protected]

## Dónde encaja

- Categoría: [Investigación](https://skillsagentes.com/categorias/investigacion.md) — Investigación estructurada, búsqueda de fuentes y síntesis.
- 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)