Skill UI

Self-hosted open-source alternative to NotebookLM for organizing research, ingesting diverse sources, generating AI notes, running context-aware chats, performing vector search, producing multi-speaker podcasts, and automating workflows via a REST API.

This agent enhances AI's auto-memory by analyzing raw learned patterns (from MEMORY.md). It allows users to promote proven knowledge into structured project rules (CLAUDE.md) or extract them into reusable skills. Use it to audit memory health, enforce best practices, and graduate temporary notes into permanent, actionable standards.

This utility compresses natural language documents (notes, preferences, memory files) into a highly condensed, 'caveman-speak' format. Its primary function is drastically reducing input tokens for efficiency while meticulously preserving all technical substance, including code blocks, URLs, file paths, and markdown structure. Ideal for optimizing memory consumption when interacting with AI models.

Napkin gives Copilot CLI users a browser whiteboard to draw, sketch, and add sticky notes; Copilot reads snapshots and JSON data, then replies with analysis, suggestions, and next steps for business-oriented thinkers.

Self-hosted open-source alternative to Google NotebookLM. Organize research sources, generate AI notes and summaries, create multi-speaker podcasts, chat with documents, and search materials via full-text or vector search with 16+ AI providers.

This workflow implements Abridge's end-to-end clinical documentation pipeline. It captures ambient audio from patient encounters, performs real-time transcription, utilizes generative AI to structure the medical notes (e.g., SOAP, H&P), generates required medical codes (ICD-10, CPT), and finally inserts the structured data directly into the EHR system. Designed for primary clinical encounters, minimizing manual charting time.

This workflow converts complex clinical encounter notes into plain-language, patient-facing After-Visit Summaries (AVS). It structures key information such as diagnosis, next steps, and medications. The system supports multi-language translation and facilitates pushing the final, readable summary directly to patient portals using FHIR standards.

This minimal working example demonstrates Abridge's ambient AI clinical documentation capabilities. It simulates a complete patient encounter session, allowing users to submit transcripts and receive a highly structured clinical note. Ideal for testing EHR connectivity, verifying AI workflow, or learning how structured medical data (SOAP notes, ICD/CPT codes) is automatically generated from free-text conversation.

This powerful compiler ingests heterogeneous research inputs, including PDF papers, code repositories, experiment logs, and raw notes. It transforms this diverse data into a comprehensive, structured Agent-Native Research Artifact (ARA). The system extracts core knowledge atoms, builds cognitive maps (claims, concepts, heuristics), and generates a verifiable, executable knowledge package for advanced AI consumption and research iteration.

A self-hosted, semantic memory service designed for AI agents. It allows agents to save worklogs, decisions, and notes, and recall this knowledge across sessions based on meaning rather than exact keywords. Using pgvector and structured workspaces, it maintains persistent, context-aware knowledge bases, making it ideal for collaborative AI projects.

This advanced AI skill converts raw study materials—such as syllabi, past exam papers, or personal notes—into a structured, prioritized, and difficulty-ranked High Score Roadmap. It covers all key question types (Theory, Numerical, MCQ, Coding, Lab) and optimizes study flow by ordering topics from Easy to Hard. Use it for targeted revision, gap analysis, and predicting high-yield exam questions.

A robust, on-device solution for processing large-scale clinical text corpora. Supports batch NER, PII extraction, and de-identification over thousands of notes. Features include sharding, checkpointing, resumable functionality, and streaming results to JSONL, ensuring PHI safety and constant memory usage for massive datasets.