Skill UI

Acts as a collaborative writing partner, helping you build outlines, research with citations, improve hooks, and review each section so blogs, tutorials, thought leadership, and docs get polished without losing your voice.

This skill manages and refines search results obtained from Exa's neural search API. It provides comprehensive control over content extraction scope, allowing users to selectively retrieve metadata, highlights, full text, or structured summaries. Key features include implementing result caching with TTL, deduplicating citations, and crucial token budget management, ensuring the retrieved context fits within LLM context window limits for robust RAG pipelines.

Searches across your Notion workspace, gathers page content, synthesizes findings, and generates structured research docs with citations and actionable conclusions.

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.

A comprehensive workflow designed for clinical professionals to search, retrieve, and synthesize medical evidence. It allows users to query clinical literature with specialized filters (e.g., specialty, evidence level), check potential drug interactions against patient profiles, and look up the latest specialty guidelines from major health organizations (like ACC/AHA, NICE). The output provides structured citations, confidence scores, and evidence grading to support informed clinical decision-making.

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.

This workflow executes a single-query search using the Perplexity Sonar model, ensuring all returned answers are web-grounded and accompanied by inline citations. It is essential for building high-trust applications, fact-checking tools, or any system requiring verifiable, real-time information retrieval. The process includes API interaction, detailed response parsing (answer, citations, search results), and formatting the output into a usable, source-linked format.

A comprehensive workflow utilizing the Perplexity Sonar API for advanced, multi-turn research. It systematically decomposes broad topics into focused sub-queries, executes batch queries while maintaining context continuity, deduplicates citations, and synthesizes all findings into a structured, comprehensive research document. Ideal for academic research, market analysis, and generating detailed investment or industry briefs.

A comprehensive toolkit designed to manage the complex data flow associated with Perplexity search workflows. This skill implements critical security measures, including PII sanitization in user queries, validates external citations for accuracy, optimizes performance via result caching with freshness policies, and manages conversation context to maintain coherence and reliability. Essential for building robust, compliance-aware AI applications.

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.