skills/benchling-integration/SKILL.md
Benchling 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.
npx skillsauth add K-Dense-AI/claude-scientific-skills benchling-integrationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
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. Platform guide: docs.benchling.com.
This skill should be used when:
Python SDK installation:
uv pip install "benchling-sdk==1.25.0"
Preview builds (alpha; not for production):
uv pip install "benchling-sdk" --prerelease allow
Environment variables (scoped reads only):
Read only the named keys you need — never dump or iterate over the full environment:
import os
tenant_url = os.environ.get("BENCHLING_TENANT_URL") # e.g. https://your-tenant.benchling.com
api_key = os.environ.get("BENCHLING_API_KEY")
if not tenant_url or not api_key:
raise ValueError("Set BENCHLING_TENANT_URL and BENCHLING_API_KEY")
Obtain an API key from Profile Settings in Benchling. For OAuth apps, use the Developer Console and store BENCHLING_CLIENT_ID / BENCHLING_CLIENT_SECRET separately.
Authentication methods:
API key (scripts and personal automation):
from benchling_sdk.benchling import Benchling
from benchling_sdk.auth.api_key_auth import ApiKeyAuth
benchling = Benchling(
url=tenant_url,
auth_method=ApiKeyAuth(api_key),
)
OAuth client credentials (multi-user apps and production integrations):
from benchling_sdk.benchling import Benchling
from benchling_sdk.auth.client_credentials_oauth2 import ClientCredentialsOAuth2
benchling = Benchling(
url=tenant_url,
auth_method=ClientCredentialsOAuth2(
client_id=os.environ["BENCHLING_CLIENT_ID"],
client_secret=os.environ["BENCHLING_CLIENT_SECRET"],
),
)
Key points:
benchling.users.get_me() before bulk operationsFor detailed authentication information including OIDC and security best practices, refer to references/authentication.md.
Registry entities include DNA sequences, RNA sequences, AA sequences, custom entities, and mixtures. The SDK provides typed classes for creating and managing these entities.
Creating DNA Sequences:
from benchling_sdk.models import DnaSequenceCreate
sequence = benchling.dna_sequences.create(
DnaSequenceCreate(
name="My Plasmid",
bases="ATCGATCG",
is_circular=True,
folder_id="fld_abc123",
schema_id="ts_abc123", # optional
fields=benchling.models.fields({"gene_name": "GFP"})
)
)
Registry Registration:
To register an entity directly upon creation:
sequence = benchling.dna_sequences.create(
DnaSequenceCreate(
name="My Plasmid",
bases="ATCGATCG",
is_circular=True,
folder_id="fld_abc123",
entity_registry_id="src_abc123", # Registry to register in
naming_strategy="NEW_IDS" # or "IDS_FROM_NAMES"
)
)
Important: Use either entity_registry_id OR naming_strategy, never both.
Updating Entities:
from benchling_sdk.models import DnaSequenceUpdate
updated = benchling.dna_sequences.update(
sequence_id="seq_abc123",
dna_sequence=DnaSequenceUpdate(
name="Updated Plasmid Name",
fields=benchling.models.fields({"gene_name": "mCherry"})
)
)
Unspecified fields remain unchanged, allowing partial updates.
Listing and Pagination:
# List all DNA sequences (returns a generator)
sequences = benchling.dna_sequences.list()
for page in sequences:
for seq in page:
print(f"{seq.name} ({seq.id})")
# Check total count
total = sequences.estimated_count()
Key Operations:
benchling.<entity_type>.create()benchling.<entity_type>.get_by_id(id) or .list()benchling.<entity_type>.update(id, update_object)benchling.<entity_type>.archive(id)Entity types: dna_sequences, rna_sequences, aa_sequences, custom_entities, mixtures
For comprehensive SDK reference and advanced patterns, refer to references/sdk_reference.md.
Manage physical samples, containers, boxes, and locations within the Benchling inventory system.
Creating Containers:
from benchling_sdk.models import ContainerCreate
container = benchling.containers.create(
ContainerCreate(
name="Sample Tube 001",
schema_id="cont_schema_abc123",
parent_storage_id="box_abc123", # optional
fields=benchling.models.fields({"concentration": "100 ng/μL"})
)
)
Managing Boxes:
from benchling_sdk.models import BoxCreate
box = benchling.boxes.create(
BoxCreate(
name="Freezer Box A1",
schema_id="box_schema_abc123",
parent_storage_id="loc_abc123"
)
)
Transferring Items:
# Transfer a container to a new location
transfer = benchling.containers.transfer(
container_id="cont_abc123",
destination_id="box_xyz789"
)
Key Inventory Operations:
Interact with electronic lab notebook (ELN) entries, protocols, and templates.
Creating Notebook Entries:
from benchling_sdk.models import EntryCreate
entry = benchling.entries.create(
EntryCreate(
name="Experiment 2025-10-20",
folder_id="fld_abc123",
schema_id="entry_schema_abc123",
fields=benchling.models.fields({"objective": "Test gene expression"})
)
)
Linking Entities to Entries:
# Add references to entities in an entry
entry_link = benchling.entry_links.create(
entry_id="entry_abc123",
entity_id="seq_xyz789"
)
Key Notebook Operations:
Automate laboratory processes using Benchling's workflow system.
Creating Workflow Tasks:
from benchling_sdk.models import WorkflowTaskCreate
task = benchling.workflow_tasks.create(
WorkflowTaskCreate(
name="PCR Amplification",
workflow_id="wf_abc123",
assignee_id="user_abc123",
fields=benchling.models.fields({"template": "seq_abc123"})
)
)
Updating Task Status:
from benchling_sdk.models import WorkflowTaskUpdate
updated_task = benchling.workflow_tasks.update(
task_id="task_abc123",
workflow_task=WorkflowTaskUpdate(
status_id="status_complete_abc123"
)
)
Asynchronous Operations:
Some operations are asynchronous and return tasks. The SDK default max_wait_seconds for polling is 600 seconds (since SDK 1.11.0):
from benchling_sdk.helpers.tasks import wait_for_task
result = wait_for_task(
benchling,
task_id="task_abc123",
interval_wait_seconds=2,
max_wait_seconds=300, # override for long-running serverless handlers
)
Key Workflow Operations:
Subscribe to Benchling changes via AWS EventBridge (customer-owned bus) or Webhooks (recommended for new Benchling Apps). EventBridge delivers hydrated v2 API objects; webhooks use thinner payloads.
Common EventBridge detail-type values:
v2.dnaSequence.created, v2.dnaSequence.updatedv2.entity.registeredv2.entry.created, v2.entry.updatedv2.workflowTask.updated.statusv2.request.createdMinimal EventBridge rule (filter request creation by schema name):
{
"detail-type": ["v2.request.created"],
"detail": {
"schema": {
"name": ["Validated Request"]
}
}
}
Lambda handler skeleton:
def handler(event, context):
detail_type = event["detail-type"]
detail = event["detail"]
if detail.get("deprecated"):
# Alert — migrate before Benchling removes this event type
pass
if detail.get("excludedProperties"):
# Payload exceeded 256 KB; re-fetch via detail["request"]["apiURL"]
pass
if detail_type == "v2.request.created":
request_id = (detail.get("request") or {}).get("id")
# Re-fetch authoritative state — events can be late or out of order
# request = benchling.requests.get_by_id(request_id)
return {"request_id": request_id}
return {"status": "ignored", "detail_type": detail_type}
Setup flow:
https://your-tenant.benchling.com/event-subscriptionsRecovery: EventBridge deliveries are not replayed. Use the List Events API for events up to ~2 weeks old after outages.
For payload schema, CloudFormation templates, SDK list/recovery examples, and validation steps, see references/eventbridge.md.
Query historical Benchling data using SQL through the Data Warehouse.
Access Method: The Benchling Data Warehouse provides SQL access to Benchling data for analytics and reporting. Connect using standard SQL clients with provided credentials.
Common Queries:
Integration with Analysis Tools:
The SDK automatically retries failed requests:
# 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),
)
Use generators for memory-efficient pagination:
# 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()
Use the fields() helper for custom schema fields:
# Convert dict to Fields object
custom_fields = benchling.models.fields({
"concentration": "100 ng/μL",
"date_prepared": "2025-10-20",
"notes": "High quality prep"
})
The SDK handles unknown enum values and types gracefully:
UnknownTypeBENCHLING_TENANT_URL, BENCHLING_API_KEY, etc.)Detailed reference documentation for in-depth information:
Load these references as needed for specific integration requirements.
1. Bulk Entity Import:
# 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:
# 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:
# 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:
# 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)
development
Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.
development
Convert files and office documents to Markdown. Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs and more.
development
Generate comprehensive market research reports (50+ pages) in the style of top consulting firms (McKinsey, BCG, Gartner). Features professional LaTeX formatting, extensive visual generation with scientific-schematics and generate-image, deep integration with research-lookup for data gathering, and multi-framework strategic analysis including Porter Five Forces, PESTLE, SWOT, TAM/SAM/SOM, and BCG Matrix.
testing
Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.