### Labarchive Integration
Works with
name: "labarchive-integration"
description: "Electronic lab notebook API integration. Access notebooks, manage entries/attachments, backup notebooks, integrate with Protocols.io/Jupyter/REDCap, for programmatic ELN workflows."
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionlabarchive-integrationExecute the skills CLI command in your project's root directory to begin installation:
Fetches labarchive-integration from K-Dense-AI/scientific-agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate labarchive-integration. Access via /labarchive-integration in your agent's command palette.
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| name | labarchive-integration |
| description | Electronic lab notebook API integration. Access notebooks, manage entries/attachments, backup notebooks, integrate with Protocols.io/Jupyter/REDCap, for programmatic ELN workflows. |
| license | Unknown |
| metadata | version: "1.0" skill-author: K-Dense Inc. |
LabArchives is an electronic lab notebook platform for research documentation and data management. Access notebooks, manage entries and attachments, generate reports, and integrate with third-party tools programmatically via REST API.
This skill should be used when:
Set up API access credentials and regional endpoints for LabArchives API integration.
Prerequisites:
Configuration setup:
Use the scripts/setup_config.py script to create a configuration file:
python3 scripts/setup_config.py
This creates a config.yaml file with the following structure:
api_url: https://api.labarchives.com/api # or regional endpoint
access_key_id: YOUR_ACCESS_KEY_ID
access_password: YOUR_ACCESS_PASSWORD
Regional API endpoints:
https://api.labarchives.com/apihttps://auapi.labarchives.com/apihttps://ukapi.labarchives.com/apiFor detailed authentication instructions and troubleshooting, refer to references/authentication_guide.md.
Obtain user ID (UID) and access information required for subsequent API operations.
Workflow:
users/user_access_info API method with login credentialsusers/user_info_via_idExample using Python wrapper:
from labarchivespy.client import Client
# Initialize client
client = Client(api_url, access_key_id, access_password)
# Get user access info
login_params = {'login_or_email': user_email, 'password': auth_token}
response = client.make_call('users', 'user_access_info', params=login_params)
# Extract UID from response
import xml.etree.ElementTree as ET
uid = ET.fromstring(response.content)[0].text
# Get detailed user info
params = {'uid': uid}
user_info = client.make_call('users', 'user_info_via_id', params=params)
Manage notebook access, backup, and metadata retrieval.
Key operations:
Notebook backup example:
Use the scripts/notebook_operations.py script:
# Backup with attachments (default, creates 7z archive)
python3 scripts/notebook_operations.py backup --uid USER_ID --nbid NOTEBOOK_ID
# Backup without attachments, JSON format
python3 scripts/notebook_operations.py backup --uid USER_ID --nbid NOTEBOOK_ID --json --no-attachments
API endpoint format:
https://<api_url>/notebooks/notebook_backup?uid=<UID>&nbid=<NOTEBOOK_ID>&json=true&no_attachments=false
For comprehensive API method documentation, refer to references/api_reference.md.
Create, modify, and manage notebook entries and file attachments.
Entry operations:
Attachment workflow:
Use the scripts/entry_operations.py script:
# Upload attachment to an entry
python3 scripts/entry_operations.py upload --uid USER_ID --nbid NOTEBOOK_ID --entry-id ENTRY_ID --file /path/to/file.pdf
# Create a new entry with text content
python3 scripts/entry_operations.py create --uid USER_ID --nbid NOTEBOOK_ID --title "Experiment Results" --content "Results from today's experiment..."
Supported file types:
Generate institutional reports on notebook usage, activity, and compliance (Enterprise feature).
Available reports:
Report generation:
# Generate detailed usage report
response = client.make_call('site_reports', 'detailed_usage_report',
params={'start_date': '2025-01-01', 'end_date': '2025-10-20'})
LabArchives integrates with numerous scientific software platforms. This skill provides guidance on leveraging these integrations programmatically.
Supported integrations:
OAuth authentication: LabArchives now uses OAuth for all new integrations. Legacy integrations may use API key authentication.
For detailed integration setup instructions and use cases, refer to references/integrations.md.
# Complete backup script
python3 scripts/notebook_operations.py backup-all --email [email protected] --password AUTH_TOKEN
Install the labarchives-py wrapper for simplified API access:
git clone https://github.com/mcmero/labarchives-py
cd labarchives-py
uv pip install .
Alternatively, use direct HTTP requests via Python's requests library for custom implementations.
Common issues:
For additional support, contact LabArchives at [email protected].
This skill includes bundled resources to support LabArchives API integration:
setup_config.py: Interactive configuration file generator for API credentialsnotebook_operations.py: Utilities for listing, backing up, and managing notebooksentry_operations.py: Tools for creating entries and uploading attachmentsapi_reference.md: Comprehensive API endpoint documentation with parameters and examplesauthentication_guide.md: Detailed authentication setup and configuration instructionsintegrations.md: Third-party integration setup guides and use casesPrerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
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💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
K-Dense-AI/scientific-agent-skills
K-Dense-AI/scientific-agent-skills
K-Dense-AI/scientific-agent-skills
K-Dense-AI/scientific-agent-skills
google-deepmind/science-skills
google-deepmind/science-skills
labarchive-integration fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
labarchive-integration has been reliable in day-to-day use. Documentation quality is above average for community skills.
labarchive-integration fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for labarchive-integration matched our evaluation — installs cleanly and behaves as described in the markdown.
labarchive-integration is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in labarchive-integration — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
labarchive-integration is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: labarchive-integration is the kind of skill you can hand to a new teammate without a long onboarding doc.
Keeps context tight: labarchive-integration is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend labarchive-integration for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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