Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
日本語の概要は準備中です。原文の説明を表示しています。
Electronic lab notebook API integration. Access notebooks, manage entries/attachments, backup notebooks, integrate with Protocols.io/Jupyter/REDCap, for programmatic ELN workflows.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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 user@example.edu --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 support@labarchives.com.
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 casesまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
日本語の概要は準備中です。原文の説明を表示しています。
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
日本語の概要は準備中です。原文の説明を表示しています。
Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
日本語の概要は準備中です。原文の説明を表示しています。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
日本語の概要は準備中です。原文の説明を表示しています。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
日本語の概要は準備中です。原文の説明を表示しています。