Skill UI

A comprehensive guide for researchers determining if their Human-Computer Interaction (HCI) project contribution is an 'interface artifact' suitable for UIST, or if it belongs to a different venue like CHI or CSCW. It employs the 'artifact-subtraction test' to distinguish between technical system novelty and empirical user findings, ensuring proper conference submission routing.

A guide for scholars on structuring academic essays to stake a clear, significant contribution within a broad critical conversation. It teaches how to move beyond simple citation dumps by identifying the core scholarly problem, engaging diverse critical traditions, and pre-empting counter-arguments, ensuring the work resonates with both specialists and generalists in the field.

This skill maps chronic conditions extracted from clinical notes to CMS-HCC V28 categories. It estimates the Risk Adjustment Factor (RAF) score, which is crucial for Medicare Advantage payments. It serves as decision support for coders by identifying candidate HCCs, applying complex coding hierarchies, and checking for MEAT documentation evidence for human validation.

Provides decision support for mapping diagnoses and procedures extracted by OpenMed to standardized ICD-10-CM and ICD-10-PCS codes. This skill suggests candidate codes along with detailed clinical rationale, helping human coders narrow down options, route findings to specific chapters, and pre-fill encounter records. Note that the output is advisory and requires mandatory validation by a certified medical coder.

This skill replaces detected Protected Health Information (PHI) in clinical notes with realistic, type-matched synthetic surrogates. Unlike simple redaction, surrogates maintain the natural readability and structure of the text, ensuring the data remains parseable for downstream NLP tasks, QA, or human review. It supports consistent, reproducible fake identities and allows custom identifier generation for proprietary formats.

This mode defines an autonomous, senior-level AI agent capable of resolving complex tasks from start to finish with minimal human supervision. It operates by strictly verifying every claim against real evidence (live web search, codebase, available tools). The agent prioritizes action over questioning, only interrupting the user for irreversible actions, genuine dead-ends, or critical ambiguities, ensuring maximum efficiency in hands-on engineering and operational workflows.

This skill parses standardized C-CDA/CCD XML clinical documents exported from EHRs. It extracts both human-readable section narrative text and coded entries, mapping them by LOINC codes and template IDs. It is essential for preparing clinical free-text data for subsequent NLP analysis and supports XML-aware de-identification while preserving document structure.

Performs comprehensive, two-layer validation on generated plans. Layer 1 executes instant structural checks (format, dependencies, compliance) while Layer 2 triggers specialist agents (e.g., security, DB, performance) based on plan content. The system forces resolution of all identified issues before allowing execution, supporting auto-fixes and guided human input.

This orchestrator manages the entire end-to-end pipeline for preparing technical talks, transforming raw materials—whether code commits (REX mode) or conceptual articles—into polished revision sheets. It sequentially runs through multiple stages (Research, Concepts, Scripting, Revision) with human-in-the-loop checkpoints, ensuring a structured and comprehensive content creation workflow.

This skill processes public Shopify App Store reviews (especially low-star ones) to generate a structured, actionable brief. It categorizes feedback into incident risk, repeated friction, pricing confusion, and feature requests, providing a prioritized P0-P3 assessment for product and support teams. It ensures every piece of feedback maintains its public source link and labeling for human review.

Creates an Agent Authority Charter to define permissions, delegation, approvals, escalation, and audit requirements for AI agents before enterprise or regulated deployment. Helps legal, compliance, risk, and product teams document what an agent may and may not do, prepare for human review, and ensure auditable governance across the agent lifecycle.

Builds a four-tier agent memory store where facts earn promotion through recurrence across sessions and days, with human adoption before anything reaches committed project files.