labarchive-integration▌
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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.
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 davila7/claude-code-templates 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▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★28 reviews- ★★★★★Ira White· Dec 28, 2024
Useful defaults in labarchive-integration — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Dhruvi Jain· Dec 4, 2024
I recommend labarchive-integration for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Hassan Desai· Dec 4, 2024
labarchive-integration has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Oshnikdeep· Nov 23, 2024
labarchive-integration fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Mei Park· Nov 23, 2024
Solid pick for teams standardizing on skills: labarchive-integration is focused, and the summary matches what you get after install.
- ★★★★★Nia Kim· Nov 19, 2024
labarchive-integration is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Ganesh Mohane· Oct 14, 2024
labarchive-integration has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Hassan Khanna· Oct 14, 2024
I recommend labarchive-integration for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ishan Verma· Oct 10, 2024
labarchive-integration reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Sakshi Patil· Sep 21, 2024
labarchive-integration reduced setup friction for our internal harness; good balance of opinion and flexibility.
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