Skill UI

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.

Provides a comprehensive architectural blueprint for integrating ambient AI solutions (Abridge) into multi-site health systems. It guides the full process from ambient audio capture to structured note generation, covering data flow, component design, FHIR R4 standards, HIPAA compliance, and integration patterns with major EHRs like Epic and Athena.

A persistent memory kit designed for Claude AI agents. It maintains deep context across multiple sessions and projects using a two-layer architecture (hot cache and knowledge wiki). Features include safety hooks, multi-project context isolation, and a /close-day command for daily synthesis, ensuring agents remember complex information and continuously learn.

A comprehensive evaluation suite designed to benchmark the performance of Large Language Models (LLM) and Vision-Language Models (VLM) specifically for home security applications. It covers 143 diverse tests across 16 critical domains, including tool use, prompt injection resistance, contextual reasoning, alert triage, and detailed scene analysis, ensuring robustness for smart home AI systems.

Lambda is a compact, machine-native communication protocol specifically designed for agent-to-agent (A2A) messaging. It functions as a shared, unambiguous vocabulary built from 'atoms' (2-character codes), completely bypassing the need for human natural language interpretation. It is ideal for orchestrators, task delegation, and logging structured coordination signals (like heartbeats or state changes) in multi-agent systems. Crucially, Lambda is intended only for machine communication, not for user-facing dialogue.

A systematic auditor designed to evaluate the resilience and quality of complex AI workflows. It scans across five critical dimensions—Prompt Quality, Context Efficiency, Tool Health, Architecture Fitness, and Safety—providing a scored report and prioritized, actionable remediation steps. Use this tool to find hidden flaws and ensure robustness before deploying any AI agent or system.

This CLI alias serves as the primary interface for the Codex /pua:pro protocol, designed for advanced code operations within the Claude Code environment. It manages complex workflows related to self-evolution, platform telemetry monitoring, key performance indicators (KPIs), and system configuration updates, ensuring robust and consistent protocol adherence.

A specialist tool designed to pressure-test AI Management Systems (AIMS) against the ISO 42001 standard. It executes six critical compliance checks, covering scope definition, risk mapping, policy adherence, and framework integration. Essential for pre-certification readiness, annual internal audits, and new system onboarding.

This system creates and registers high-quality, reusable synthetic voices (voice clones) from local audio samples. It operates under a strict, consent-first workflow, requiring explicit speaker consent, detailed use permissions, and source verification before cloning. It is designed for professional Text-to-Speech (TTS) applications, IVR systems, or training content development where a consistent, branded voice is essential.

Automated system designed to monitor paste sites (e.g., Pastebin, GitHub Gists) for leaked credentials, API keys, and sensitive data dumps. It uses advanced scraping, keyword matching, and regex patterns to provide early detection of data breaches and security incidents.

This skill provides a hybrid, persistent, and searchable knowledge memory system specifically designed for enhancing AI agents' capabilities. It allows developers to record, retrieve, and search key knowledge, architectural decisions, and operational memories automatically. Use it to build agents with long-term context and reliable knowledge management, ensuring findings are synced back into project documentation.

This comprehensive guide outlines best practices for building reliable and robust AI agent harnesses. It covers optimizing action spaces, defining structured tools, managing observation formats, and implementing essential error recovery contracts. Use these principles to dramatically improve agent completion rates, manage context budgets effectively, and transition AI prototypes into stable, production-grade autonomous systems.