Skill UI

Automates the Continuous Integration and Continuous Deployment (CI/CD) process for OpenEvidence clinical decision support integrations. This setup ensures that unit tests (using mocked evidence queries and citations) and live API connectivity tests are run automatically on every pull request and merge to main. It validates critical functions like query formatting, evidence parsing, citation extraction, and response quality scoring, maintaining high reliability for medical AI applications.

This bundle collects comprehensive diagnostic evidence from the OpenEvidence clinical decision support API. It validates API credentials, checks query endpoint health, retrieves evidence citations, and captures model metadata. Use it to diagnose complex issues like query failures, missing references, or high latency without needing direct dashboard access.

This guide provides comprehensive strategies for optimizing API interactions with OpenEvidence. It covers implementing robust caching mechanisms (TTL management), efficient batch retrieval of citations, connection pooling, and handling rate limits. By applying these techniques, users can significantly reduce query latency, ensuring smooth system performance even when handling complex multi-condition queries or large citation batches.

This guide provides comprehensive documentation and code examples for integrating OpenEvidence webhooks. Learn how to subscribe to real-time events (e.g., query completion, evidence updates, new citations) to keep clinical decision support systems fresh. Use these webhooks to trigger downstream audit workflows and ensure immediate data synchronization.

Provides a minimal working example demonstrating how to integrate Perplexity's API for web-grounded answers. Learn to perform advanced searches, retrieve cited sources, and handle features like domain filtering and streaming using both TypeScript and Python. Ideal for integrating real-time, source-backed search functionality into applications.

A comprehensive guide to identifying and avoiding common integration mistakes and anti-patterns when using the Perplexity Sonar API. It covers critical best practices such as correctly handling citations, setting maximum token limits, utilizing recency filters, and optimizing API calls to ensure cost efficiency and data accuracy. Essential for code review and developer onboarding.

This skill answers technical questions about a codebase by grounding all responses strictly within the provided source files. It is ideal for users who need to understand how a specific function works, locate definitions, or gain an overview of a component's implementation. All answers include inline citations to ensure accuracy and traceability.

Provides access to USPTO PatentSearch, PEDS, TSDR, and related APIs for patent and trademark searches, examination history, assignments, citations, and litigation data, with helper scripts for Python-based IP research and portfolio insights.

A robust Python client wrapper for interacting with the Zotero Web API v3. This skill allows users to programmatically manage academic references, including retrieving, creating, updating, and deleting items, collections, tags, and attachments. Ideal for academic researchers building automated workflows, bulk exporting citations (e.g., BibTeX), or integrating Zotero data into larger research applications.

Draft a LaTeX paper section by section from a paper plan and narrative report. Automatically applies venue-specific styles for ICLR, NeurIPS, ICML, CVPR, ACL, AAAI, ACM and IEEE formats, fetches real BibTeX from DBLP/CrossRef to avoid hallucinated citations, and runs reviewer-backed section reviews.

Manage Zotero reference libraries programmatically with the pyzotero Python client and Zotero Web API v3. Retrieve, create, update and delete items, collections, tags and attachments; search libraries and export BibTeX or CSL-JSON citations for research automation workflows.

This tool scans your entire content library to detect decay signals—such as declining traffic, falling rankings, outdated stats, and lost AI citations. It then prioritizes content refreshes by calculating the potential revenue impact, ensuring you focus on updating the pieces that will recover the most revenue first.