labarchive-integration

K-Dense-AI/scientific-agent-skills · updated Jun 4, 2026

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$npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill labarchive-integration
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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."
skill.md
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:

  1. Call the users/user_access_info API method with login credentials
  2. Parse the XML/JSON response to extract the user ID (UID)
  3. 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

  1. Authenticate and obtain user ID
  2. List all accessible notebooks
  3. Iterate through notebooks and backup each one
  4. 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

  1. Authenticate with LabArchives API
  2. Identify target notebook and entry
  3. Upload experimental data files
  4. Add metadata comments to entries
  5. Generate activity report

Integration workflow example (Jupyter → LabArchives)

  1. Export Jupyter notebook to HTML or PDF
  2. Use entry_operations.py to upload to LabArchives
  3. Add comment with execution timestamp and environment info
  4. 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

  1. Rate limiting: Implement appropriate delays between API calls to avoid throttling
  2. Error handling: Always wrap API calls in try-except blocks with appropriate logging
  3. Authentication security: Store credentials in environment variables or secure config files (never in code)
  4. Backup verification: After notebook backup, verify file integrity and completeness
  5. Incremental operations: For large notebooks, use pagination and batch processing
  6. 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 credentials
  • notebook_operations.py: Utilities for listing, backing up, and managing notebooks
  • entry_operations.py: Tools for creating entries and uploading attachments

references/

  • api_reference.md: Comprehensive API endpoint documentation with parameters and examples
  • authentication_guide.md: Detailed authentication setup and configuration instructions
  • integrations.md: Third-party integration setup guides and use cases
how to use labarchive-integration

How to use labarchive-integration on Cursor

AI-first code editor with Composer

1

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
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill labarchive-integration

The skills CLI fetches labarchive-integration from GitHub repository K-Dense-AI/scientific-agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/labarchive-integration

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.

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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. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 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

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.645 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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