Skill UI

This skill facilitates the computation of Electronic Clinical Quality Measures (eCQMs) by leveraging CQL (Clinical Quality Language) logic against the QDM (Quality Data Model). It is designed to extract crucial facts—such as counseling provided, symptoms, or documented exclusions—from unstructured clinical notes. By lifting these facts, it supplements structured EHR data, ensuring that numerators and valid exclusions are accurately captured for quality measure evaluation. Note that this tool augments data capture and does not replace certified measure engines.

Generates high-quality, structured technical documentation for READMEs, APIs, and architecture guides, adhering to progressive disclosure principles. It also specializes in creating formal, evidence-backed security reports for penetration testing, CTF writeups, and reverse engineering tasks, ensuring a professional, reusable format.

This skill automatically detects and extracts tabular laboratory panels from diverse sources, including scanned images, photos, and delimited files (CSV/TSV). It converts complex visual data into clean, structured rows, capturing the test name, measured value, unit, reference range, and abnormal flag. It is essential for preparing medical data for subsequent standardization using LOINC and UCUM, and for mapping into FHIR resources.

This skill generates a comprehensive, structured handoff document from the current conversation session. It captures all critical project details, including scope, relevant files with specific line numbers, key discoveries, achieved work, current status, and actionable next steps. This is essential for knowledge transfer, ensuring that work can be seamlessly resumed by another agent or team member.

This skill loads and parses a structured handoff document, enabling the user to resume work from a previous session. It systematically extracts the project's scope, files involved, completed tasks, key discoveries, and next steps. Crucially, it summarizes its understanding and mandates user confirmation before taking any action, ensuring accurate context and continuity.

Automatically updates an existing handoff document by merging the current session’s progress. It intelligently applies specific merge rules to different sections: appending new history to 'Work Done' and 'Discoveries', while replacing volatile sections like 'Status' and 'Next Steps' to reflect the most current state. This ensures a comprehensive, chronological record of project advancement.

This module converts diverse clinical inputs—including scanned images, faxes, CSV/TSV exports, and C-CDA XML—into a normalized, clean text format (`ExtractedDocument`). It supports on-device OCR (Tesseract/PaddleOCR) and structured redaction for tables. It is the crucial first stage for feeding de-identification and Named Entity Recognition (NER) pipelines.

This comprehensive pipeline automates the entire software release cycle, from merging a Pull Request (PR) to verifying the final deployment. It performs critical steps including CI status checks, waiting for build completion, executing pre-merge readiness gates (staleness, testing, documentation), and finally merging and detecting the target deployment platform for a controlled, reliable release.

This skill generates complete, deploy-ready static landing pages by analyzing a source code repository. It reads key files (README.md, CHANGELOG.md, etc.), extracts structured content (features, installation guides, FAQ), and compiles it into a professional, responsive HTML/CSS/JS site. Ideal for standardizing open-source project showcases, converting technical documentation into marketing pages, or quickly building a project homepage.

A powerful, dependency-free CLI tool designed for programmatically creating, reading, analyzing, and modifying Microsoft Office files (.docx, .xlsx, .pptx). It supports deep DOM editing (L2) and raw XML manipulation (L3), allowing users to extract structured data, validate schemas, and perform complex document automation tasks from a single binary.

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.

A comprehensive multi-step pipeline tool that executes a fully validated development plan. It handles worktree isolation, TDD scaffolding, parallel task execution across architectural layers, quality gating (linting, testing, smoke tests), documentation reconciliation, PR creation, and final merge, ensuring the feature is fully documented and integrated.