Skill UI

This skill helps you write growth log entries that extract reusable patterns from completed work, focusing on root causes and transferable rules rather than diary-style narration. It guides you to capture learnings after complex tasks, debugging, or failures using a specific template, ensuring knowledge compounds across sessions.

This skill enforces rigorous meeting discipline through a four-stage workflow: Cost Gating, Timeboxed Agenda Building, Running the Meeting, and Action Item Extraction. It calculates meeting costs, ensures every agenda topic has a desired outcome, and converts raw notes into a structured, owner-assigned, due-dated checklist, flagging any missing owners or deadlines. Ideal for transforming unproductive meetings into actionable, accountable outcomes.

A comprehensive skill to extract and transcribe subtitles from any YouTube video URL. It utilizes a two-stage approach: first, attempting fast extraction via the yt-dlp CLI tool; failing that, it falls back to advanced browser automation using Chrome DevTools MCP to scrape the transcript panel content directly from the webpage DOM. The output is always a clean, timestamped plain text file.

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 specialized skill traces the complete lifecycle of an uploaded or imported file through complex backend systems. It maps the sequence from ingress to final consumer, tracking every derived artifact, archive extraction, format conversion, parser invocation, and deserialization step. It is crucial for security analysis, vulnerability research, and understanding how a file reaches a decisive backend effect after initial sandbox assumptions are made.

This specialized downstream skill is designed for deep analysis of complex attack paths originating from Server-Side Request Forgery (SSRF). It systematically tracks the entire exploitation chain: from the initial SSRF source (e.g., webhook, URL parameter) through internal service reachability, metadata endpoint exploitation, credential extraction, and finally, demonstrating how an attacker achieves accepted access or elevated privileges. Use this when tracing the decisive path through server-side requests and credential pivoting.

This skill deploys the OpenMed Model Context Protocol (MCP) server, exposing critical clinical NLP tools such as NER, PII extraction, and de-identification. It enables AI coding agents and chat clients to interact with OpenMed's capabilities via simple tool calls, running locally on-device to ensure user data privacy and security. Supports both stdio and Streamable HTTP deployment modes.

This skill performs the crucial ETL process of mapping OpenMed-extracted, terminology-coded clinical facts (conditions, drugs, measurements) into the OMOP Common Data Model (CDM) v5.4 tables. It is used downstream after entity extraction and source coding, transforming raw NLP findings into standardized, OMOP-conformant records for OHDSI analytics.

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.

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 processes clinical text to interpret laboratory results. It parses reference ranges (e.g., "135-145", "<5") and derives structured abnormal flags (low, normal, high, critical) for a given numeric value. It is unit-agnostic and respects explicit flags provided by the originating lab, making it crucial for structured clinical data extraction and interpretation workflows.

A specialized downstream skill designed for solving encrypted ZIP/PKZIP challenges, particularly those using the legacy ZipCrypto method in CTF competitions. This workflow utilizes known-plaintext attacks (via tools like bkcrack) to recover internal encryption keys, enabling decryption and extraction without resorting to brute-force password guessing. Use this when a predictable plaintext fixture is available.