labarchive-integration▌
K-Dense-AI/scientific-agent-skills · updated Jun 4, 2026
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### Labarchive Integration
- ›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."
| 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 Integration
Overview
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.
When to Use This Skill
This skill should be used when:
- Working with LabArchives REST API for notebook automation
- Backing up notebooks programmatically
- Creating or managing notebook entries and attachments
- Generating site reports and analytics
- Integrating LabArchives with third-party tools (Protocols.io, Jupyter, REDCap)
- Automating data upload to electronic lab notebooks
- Managing user access and permissions programmatically
Core Capabilities
1. Authentication and Configuration
Set up API access credentials and regional endpoints for LabArchives API integration.
Prerequisites:
- Enterprise LabArchives license with API access enabled
- API access key ID and password from LabArchives administrator
- User authentication credentials (email and external applications password)
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:
- US/International:
https://api.labarchives.com/api - Australia:
https://auapi.labarchives.com/api - UK:
https://ukapi.labarchives.com/api
For detailed authentication instructions and troubleshooting, refer to references/authentication_guide.md.
2. User Information Retrieval
Obtain user ID (UID) and access information required for subsequent API operations.
Workflow:
- Call the
users/user_access_infoAPI method with login credentials - Parse the XML/JSON response to extract the user ID (UID)
- Use the UID to retrieve detailed user information via
users/user_info_via_id
Example 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)
3. Notebook Operations
Manage notebook access, backup, and metadata retrieval.
Key operations:
- List notebooks: Retrieve all notebooks accessible to a user
- Backup notebooks: Download complete notebook data with optional attachment inclusion
- Get notebook IDs: Retrieve institution-defined notebook identifiers for integration with grants/project management systems
- Get notebook members: List all users with access to a specific notebook
- Get notebook settings: Retrieve configuration and permissions for notebooks
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.
4. Entry and Attachment Management
Create, modify, and manage notebook entries and file attachments.
Entry operations:
- Create new entries in notebooks
- Add comments to existing entries
- Create entry parts/components
- Upload file attachments to entries
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:
- Documents (PDF, DOCX, TXT)
- Images (PNG, JPG, TIFF)
- Data files (CSV, XLSX, HDF5)
- Scientific formats (CIF, MOL, PDB)
- Archives (ZIP, 7Z)
5. Site Reports and Analytics
Generate institutional reports on notebook usage, activity, and compliance (Enterprise feature).
Available reports:
- Detailed Usage Report: User activity metrics and engagement statistics
- Detailed Notebook Report: Notebook metadata, member lists, and settings
- PDF/Offline Notebook Generation Report: Export tracking for compliance
- Notebook Members Report: Access control and collaboration analytics
- Notebook Settings Report: Configuration and permission auditing
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'})
6. Third-Party Integrations
LabArchives integrates with numerous scientific software platforms. This skill provides guidance on leveraging these integrations programmatically.
Supported integrations:
- Protocols.io: Export protocols directly to LabArchives notebooks
- GraphPad Prism: Export analyses and figures (Version 8+)
- SnapGene: Direct molecular biology workflow integration
- Geneious: Bioinformatics analysis export
- Jupyter: Embed Jupyter notebooks as entries
- REDCap: Clinical data capture integration
- Qeios: Research publishing platform
- SciSpace: Literature management
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.
Common Workflows
Complete notebook backup workflow
- Authenticate and obtain user ID
- List all accessible notebooks
- Iterate through notebooks and backup each one
- Store backups with timestamp metadata
# Complete backup script
python3 scripts/notebook_operations.py backup-all --email [email protected] --password AUTH_TOKEN
Automated data upload workflow
- Authenticate with LabArchives API
- Identify target notebook and entry
- Upload experimental data files
- Add metadata comments to entries
- Generate activity report
Integration workflow example (Jupyter → LabArchives)
- Export Jupyter notebook to HTML or PDF
- Use entry_operations.py to upload to LabArchives
- Add comment with execution timestamp and environment info
- Tag entry for easy retrieval
Python Package Installation
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.
Best Practices
- Rate limiting: Implement appropriate delays between API calls to avoid throttling
- Error handling: Always wrap API calls in try-except blocks with appropriate logging
- Authentication security: Store credentials in environment variables or secure config files (never in code)
- Backup verification: After notebook backup, verify file integrity and completeness
- Incremental operations: For large notebooks, use pagination and batch processing
- Regional endpoints: Use the correct regional API endpoint for optimal performance
Troubleshooting
Common issues:
- 401 Unauthorized: Verify access key ID and password are correct; check API access is enabled for your account
- 404 Not Found: Confirm notebook ID (nbid) exists and user has access permissions
- 403 Forbidden: Check user permissions for the requested operation
- Empty response: Ensure required parameters (uid, nbid) are provided correctly
- Attachment upload failures: Verify file size limits and format compatibility
For additional support, contact LabArchives at [email protected].
Resources
This skill includes bundled resources to support LabArchives API integration:
scripts/
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 attachments
references/
api_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 cases
How to use labarchive-integration on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add labarchive-integration
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches labarchive-integration from GitHub repository K-Dense-AI/scientific-agent-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate labarchive-integration. Access the skill through slash commands (e.g., /labarchive-integration) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ 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.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★45 reviews- ★★★★★Layla Lopez· Dec 16, 2024
labarchive-integration fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Yuki Dixit· Dec 12, 2024
labarchive-integration has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Pratham Ware· Dec 4, 2024
labarchive-integration fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Kaira Thompson· Dec 4, 2024
Registry listing for labarchive-integration matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Sakshi Patil· Nov 23, 2024
labarchive-integration is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Amina Kapoor· Nov 23, 2024
Useful defaults in labarchive-integration — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Yuki Haddad· Nov 7, 2024
labarchive-integration is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Yuki Yang· Oct 26, 2024
Keeps context tight: labarchive-integration is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Chaitanya Patil· Oct 14, 2024
Keeps context tight: labarchive-integration is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Amina Dixit· Oct 10, 2024
I recommend labarchive-integration for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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