Skill UI

This advanced workflow provides comprehensive meeting intelligence by allowing users to search across stored transcripts using keywords and date filters. It integrates the AskFred AI assistant for natural language Q&A over single or cross-meeting data. Furthermore, it aggregates detailed meeting analytics, providing key metrics such as total duration, action items, and top keywords for robust reporting and knowledge retrieval.

This skill outlines the complete workflow for capturing and structuring meeting notes using Granola. It captures audio, transcribes the content, and intelligently merges live user notes with the full transcript. The output is structured into key sections like Summaries, Decisions, and Action Items. Users can also interact with Granola Chat for post-meeting Q&A, making it an essential tool for professional meeting documentation and follow-up.

LlamaIndex is a data-centric framework for building LLM applications, offering 300+ connectors, vector indices, query engines, agents, and multimodal support to power document Q&A, chatbots, knowledge retrieval, and RAG pipelines over private data.

LLaVA is an open-source vision-language assistant pairing CLIP vision encoders with Vicuna/LLaMA models to power multimodal chatbots, multi-turn image Q&A, captioning, and instruction-following tasks.

This skill provides comprehensive methods for implementing intelligent model routing using the OpenRouter API. It allows developers to dynamically select the optimal Large Language Model (LLM) based on specific criteria: task complexity (e.g., classification, code generation), required capability (e.g., deep reasoning vs. simple Q&A), cost budget, and latency tolerance. Implement cost-aware, task-based, and complexity-based routing to maximize system efficiency and minimize operational expenses when building multi-model applications.

This specialized skill guides users in formatting and structuring content to maximize its eligibility for featured snippets and rich results across Search Engine Results Pages (SERP). It focuses on best practices for Q&A format, list generation, and comparative tables, ensuring content is highly concise, direct, and easily digestible by search algorithms.

Search, import, and analyze TikTok, YouTube, and Instagram content via Memories.ai LVMM, indexing videos once for repeatable Q&A, summarization, social research, and searchable knowledge-base building backed by MAG.

bdistill transforms AI interactions into a structured, queryable knowledge base. It extracts, structures, and quality-scores domain knowledge from both closed-source AI models (in-session) and local open-source models (via Ollama). Users can build comprehensive reference datasets, Q&A pairs, or generate structured training data for traditional machine learning, ensuring validated and exportable domain expertise.

Copilot Spaces allow users to consolidate and manage project-specific knowledge, including documentation, code snippets, architectural standards, and operational instructions. By loading a Space, Copilot grounds its responses not just on general knowledge, but on the curated, authoritative context of the entire project, enabling accurate Q&A, code generation, and workflow execution based on team standards. Ideal for onboarding, large-scale projects, and maintaining consistency.

A comprehensive guide to using AssemblyAI for rich audio processing. This guide covers basic speech-to-text transcription from URLs or local files, along with advanced AI features. Capabilities include speaker diarization, sentiment analysis, key phrase extraction, entity detection, and powerful LLM-powered analysis (LeMUR) for summarization and Q&A. Ideal for integrating deep, multi-faceted audio insights into applications.

This comprehensive workflow guides the implementation of a complete Retrieval-Augmented Generation (RAG) pipeline using Cohere's core services. It demonstrates the full process: embedding documents, searching for relevant context, reranking results for precision, and finally generating a grounded answer with precise citations using the Chat API. Ideal for building knowledge-intensive, enterprise-grade Q&A systems.

This workflow demonstrates the core capabilities of the Glean Client API, allowing users to build comprehensive knowledge assistants. It covers advanced full-text search with filters and facets, implements AI-powered chat for contextual Q&A, and provides autocomplete suggestions. Ideal for integrating deep search and conversational AI into enterprise applications.