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Labarchive Integration

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jimmc414
labarchive-integration

Electronic lab notebook API integration. Access notebooks, manage entries/attachments, backup notebooks, integrate with Protocols.io/Jupyter/REDCap, for programmatic ELN workflows.

Overview

Publisherjimmc414
RepositoryKosmos
Skill namelabarchive-integration
Stars
585
Forks
105
Bundled files
6
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by jimmc414 on GitHub. Read the source before you install it.

Installation

Install the Labarchive Integration AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jimmc414/Kosmos.git /tmp/Kosmos
mkdir -p .claude/skills
cp -r /tmp/Kosmos/kosmos-claude-scientific-skills/scientific-skills/labarchive-integration .claude/skills/labarchive-integration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Labarchive Integration in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Labarchive Integration on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Labarchive Integration is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

bash
python3 scripts/setup_config.py

This creates a config.yaml file with the following structure:

yaml
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:

python
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:

bash
# 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:

bash
# 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:

python
# 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
bash
# Complete backup script
python3 scripts/notebook_operations.py backup-all --email user@example.edu --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:

bash
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 support@labarchives.com.

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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Labarchive Integration AI skill do?

Electronic lab notebook API integration. Access notebooks, manage entries/attachments, backup notebooks, integrate with Protocols.io/Jupyter/REDCap, for programmatic ELN workflows.

Why use Labarchive Integration on TypingMind?

Because you install it once and use it with any model. Labarchive Integration is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Labarchive Integration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jimmc414/Kosmos/tree/master/kosmos-claude-scientific-skills/scientific-skills/labarchive-integration. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Labarchive Integration?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Labarchive Integration?

As many as you like. As long as a model supports skills, you can use Labarchive Integration with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Labarchive Integration AI skill free?

It is published on GitHub by jimmc414. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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