Skill UI

A comprehensive guide for optimizing operational spending within the Juicebox platform. This skill identifies key cost drivers—including search queries, data enrichment, contact lookups, and outreach messages—and provides actionable strategies. Users can learn techniques like caching search results, utilizing filters, and monitoring quota usage to ensure maximum efficiency and budget adherence.

Master the creation of complex, event-driven, and scheduled automation workflows. This guide covers setting up various triggers (email, chat, webhook, calendar), implementing conditional filters, managing multi-step processes, and orchestrating specialized AI agents in a multi-agent 'Society' for advanced delegation and collaboration.

This guide provides comprehensive strategies for managing AI agent resource consumption and credit usage. It details optimizing task steps, setting trigger filters, implementing webhook rate limiting, and setting up budget alerts. Use this guide to ensure efficient, cost-effective AI operations and prevent unexpected billing overruns.

LlamaGuard is Meta’s 7–8B safety-specialized LLM that filters both prompts and responses by classifying six threat categories, enabling fast inference via vLLM/SageMaker and integration into NeMo Guardrails for end-to-end moderation.

A comprehensive guide to minimizing API usage costs by implementing advanced integration patterns. Learn to reduce request volume through response caching with TTL, replacing expensive polling with webhook-driven sync, and utilizing smart polling with change detection filters. Also covers usage tracking and request deduplication for highly efficient system architecture.

NeMo Curator streams GPU-accelerated curation for LLM training, with super-fast fuzzy deduplication, multimodal quality filters, semantic dedupl, PII redaction, NSFW checks, and RAPIDS-scaled pipelines for prepping massive corpora.

Runs build, test, lint, serve, and other tasks in Nx workspaces via run, run-many, and affected flows, honoring the detected package manager and target metadata while exposing filters, parallelism, and caching controls for CI or local workflows.

Build and tune Obsidian Base files to define database-like views, filters, formulas, and summaries for notes, so you can craft tables, cards, lists, and dashboards inside your vault.

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 skill provides comprehensive, multi-layered diagnostic scripts for resolving complex and intermittent issues with the Perplexity Sonar API. It allows users to test connectivity (DNS, TLS, TCP), profile API latency, validate citation stability, and verify search recency filters. It is crucial when standard troubleshooting methods prove insufficient for diagnosing hard-to-replicate API bugs or performance regressions.

This guide provides advanced strategies for developers to significantly reduce Perplexity Sonar API billing costs. It covers key techniques including model routing (selecting the right model by query type), setting maximum output tokens, implementing robust query caching, and utilizing domain filters to minimize per-request charges. Essential for building cost-effective, high-volume AI 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.