Skill UI

This tool automatically converts design screenshots or visual exports into structured, functional Vue 3 Composition API components. It supports popular UI libraries like Vant, Element Plus, and Ant Design Vue. By batch converting screenshots, it streamlines the development process from raw design assets to reusable, production-ready code, significantly boosting frontend development efficiency.

Configures a comprehensive competitor intelligence pipeline to transform raw change signals into actionable insights. The system monitors multiple dimensions (pricing, content, ads, social, ranking, SERP) across specified rivals. It manages alert fatigue by applying tiered urgency rules (critical, warning, info) and batching lower-priority changes into digestible real-time alerts, daily digests, or weekly briefings.

This tool provides a holistic, portfolio-level quality assessment by evaluating multiple content pieces (assets, libraries, or deliverables) simultaneously. Instead of reviewing content one by one, it processes the entire set to generate comprehensive rankings, dimension breakdowns (e.g., brand voice, claim verification), systemic issue reports, and a prioritized revision list. Ideal for content audits, pre-launch checks, and large campaign reviews.

This tool batches syncs all accumulated session learnings, campaign insights, and performance data to a persistent memory layer. It ensures valuable knowledge is automatically retained across multiple work sessions without manual saving. It supports incremental syncing based on checkpoints, making it fast and safe for continuous knowledge capture.

A three-tier model team orchestrator that decomposes oversized tasks into parallel subtasks, dispatches cheap workers in batches, gets plan review from a stronger advisor model, verifies each result, and synthesizes the final deliverable.

This skill automatically converts structured data from Excel (.xlsx) workbooks into a clean, readable Markdown format. It is essential for enabling accurate analysis, summarization, and data extraction of spreadsheet contents, especially when the user asks to 'read', 'analyze', or 'compare' data without specifying a conversion method. It supports both single files and batch processing of entire folders.

This skill converts Portable Document Format (.pdf) files into Markdown format, making the content easily analyzable, summarized, searchable, or extracted. It is designed to handle various user requests involving PDFs, such as summarizing reports, extracting data, or processing folders of documents. It can also be invoked alongside sibling skills for batch processing mixed file types (PDF, DOCX, XLSX).

This skill converts Microsoft Word documents (.docx) into Markdown format. It is essential for accurately analyzing, summarizing, or extracting data from proprietary Word files, as direct parsing is unreliable. It supports single files, batch processing of folders, and is designed to coordinate with sibling skills (PDF and Excel) when handling mixed document types to ensure no data is skipped.

This guide provides a comprehensive overview of the VLDB submission and review pipeline. It details the rolling monthly structure, the decision outcomes (Accept, Revise, Reject), and the specific expectations for authors at each stage. Use this to set realistic expectations regarding review timelines, the nature of expert feedback, and optimizing your revision strategy, contrasting it with traditional batch venues.

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.

This skill facilitates the extraction of population-scale (cohort-level) clinical text from Electronic Health Records (EHR) or FHIR data warehouses using the FHIR Bulk Data Access ($export) operation. It manages the asynchronous job kickoff, polling, and downloading of NDJSON files. The resulting data stream is designed to be fed in batches into OpenMed for mandatory de-identification and Named Entity Recognition (NER) analysis.

A comprehensive skill for AI agents to perform advanced filesystem operations. It provides features for intelligent file listing with deep filtering, powerful content and pattern searching, safe batch file copying, and detailed directory analysis (statistics, tree visualization). Ideal for code analysis, system auditing, and managing project assets.