benchling-integration
Benchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries.
Skill by K-Dense-AI
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About
Use cases: backend, data
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/benchling-integration
Tools it pre-approves 4
From allowed-tools in SKILL.md: the agent may use these without asking you each time.
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What's inside
- Skill name
benchling-integration- Description
- 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.
- License
- MIT
- Compatibility
- Requires Python 3.9+, benchling-sdk 1.25.0, network access, a Benchling tenant with API access, and API key or OAuth app credentials.
- Allowed tools
- Read, Write, Edit, Bash
- Metadata
- version: 1.7 · skill-author: K-Dense Inc. · last-reviewed: 2026-09-30
- Instructions
- 156 lines in SKILL.md · view on GitHub
Before installing
- Pre-approves any shell command
Files (6)
- SKILL.md
- api_endpoints.md · references/
- authentication.md · references/
- core_capabilities.md · references/
- eventbridge.md · references/
- sdk_reference.md · references/
SKILL.md (first lines)
# Benchling Integration
## When to use
Use this skill for Benchling registry entities, sequence imports, inventory, ELN entries,
workflow tasks, apps, event-driven integrations, and warehouse analytics.
**Reviewed 2026-09-30:** examples target the released **benchling-sdk 1.25.0** and its
stable **v2** API models. The [current authentication guide](https://docs.benchling.com/docs/authentication)
recommends V3 for new development, while the [V3 guide](https://docs.benchling.com/docs/v3-api-overview)
still describes endpoint-specific early access. Confirm your tenant's V3 availability and
stability before migrating; these v2 SDK examples must not be mechanically rewritten to V3.
SDK imports, model serialization, and request construction were checked locally against
1.25.0. Tenant-dependent examples are **illustrative**: no authenticated requests,
mutations, AWS deployment, or warehouse connection were run.
## Workflow
1. Identify the tenant, API version, identity, and permissions. Use OAuth app credentials
for background integrations; use delegated authorization when acting as an individual
user. See [authentication](references/authentication.md).
2. Read the relevant schema and resolve actual folder, registry, status, and dropdown IDs.
Preserve sequence alphabet/topology and sample units. A valid Python model does not
establish biological correctness or satisfaction of a tenant's required fields.
3. Read a small filtered page before writing. Use typed SDK methods and check their
actual parameter names; not all services share the same CRUD naming convention.
4. Construct and serialize a representative payload. For imports, retain external IDs
and returned Benchling IDs so a retry can reconcile a partial run without duplicates.
5. Perform the requested operation and read back the result. Check terminal async status;
completed polling can still mean `FAILED`.
## Setup and a read-only query
```bash
uv pip install "benchling-sdk==1.25.0"
```
```python
import os
from benchling_sdk.benchling import Benchling
from benchling_sdk.auth.client_credentials_oauth2 import ClientCredentialsOAuth2
tenant_url = os.environ["BENCHLING_TENANT_URL"].rstrip("/")
benchling = Benchling(
url=tenant_url,
auth_method=ClientCredentialsOAuth2(
client_id=os.environ["BENCHLING_CLIENT_ID"],
client_secret=os.environ["BENCHLING_CLIENT_SECRET"],
token_url=f"{tenant_url}/oauth/token",
),
)
for page in benchling.dna_sequences.list(page_size=10, name_includes="plasmid"):
for sequence in page:
print(sequence.id, sequence.name)
break # deliberate first-page connectivity/permission check
```
A successful empty page is a valid connectivity result. It does not imply access to
all projects. There is no documented v2 `users/me` route or SDK `users.get_me()`.
## Important SDK conventions
- Import `fields` from `benchling_sdk.helpers.serialization_helpers`. Its input is
`{"field_name": {"value": value}}`, including the inner `value` mapping.
- Use `dna_sequence_id` for DNA updates and `workflow_task_id` for workflow updates.
Workflow task creation requires a `workflow_task_group_id`.
- Entry methods are `create_entry`, `get_entry_by_id`, `list_entries`, and `update_entry`.
- `list()` usually returns pages; iterate twice to reach the objects. `estimated_count`
is a property that can raise `NotImplementedError`, not a method or guaranteed count.
- Moving a tube uses `ContainerUpdate(parent_storage_id=...)`. Material transfer is a
separate operation; it changes contents and quantities.
- Register on creation with `registry_id` plus **either** `entity_registry_id` (a human
registry identifier) **or** `naming_strategy`. Do not confuse those with the registry's ID.
## Common use cases
### Import FASTA sequences
Install Biopython separately (`uv pip install biopython`). This illustrative import
creates unregistered linear DNA; choose topology and resolve collisions before running.
```python
from Bio import SeqIO
from benchling_sdk.models import DnaSequenceCreate
for record in SeqIO.parse("sequences.fasta", "fasta"):
payload = DnaSequenceCreate(
name=record.id,
bases=str(record.seq),
is_circular=False,
folder_id="lib_example",
)
created = benchling.dna_sequences.create(payload)
print(record.id, created.id) # persist this mapping for restart/reconciliation
```
### Audit inventory under a location
```python
for page in benchling.containers.list(ancestor_storage_id="box_example"):
for container in page:
print(container.id, container.name, container.barcode)
```
`ancestor_storage_id` includes descendants. For immediate children only, inspect the
returned parent storage or use the documented storage-contents service.
### Export sequences for one schema
```python
import csv
with open("sequences.csv", "w", newline="", encoding="utf-8") as handle:
writer = csv.DictWriter(handle, fieldnames=["id", "name", "bases", "length"])
writer.writeheader()
for page in benchling.dna_sequences.list(schema_id="ts_example"):
for seq in page:
writer.writerow({From K-Dense-AI/scientific-agent-skills, MIT.
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Signals
- Stars
- 54,411
- Forks
- 4,797
- Open issues
- 8
- Last commit (this folder)
- Oct 1, 2026 (27 days ago)
- Last release
- Oct 1, 2026 (27 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- Version
- 1.7
- License
- MIT
- Website
- arxiv.org/abs/2609.00065
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
benchling-integration skill. Agent Mart. https://agentmart.dev/skills/k-dense-ai-benchling-integration (last updated 2026-10-24)