Skill UI

This skill provides deep technical expertise regarding the DWARF debugging format (v3-v5) and the DWARF standard. It assists in analyzing, parsing, verifying, and generating debug information from compiled binaries. Use cases include answering questions about the DWARF specification, writing code that interacts with DWARF data, and utilizing tools like `dwarfdump` or `readelf` for structural validation and quality metric extraction.

This skill provides deep analytical insights into aggregated family health data. It assesses genetic predisposition, identifies patterns of familial diseases, calculates weighted risk scores based on historical data, and generates personalized, actionable prevention recommendations. Ideal for generating comprehensive family health reports and trend analysis.

This skill provides comprehensive functionality for querying, comparing, and calculating nutritional data from a structured food database. Users can perform precise searches by food name or nutritional criteria (e.g., high protein, low GI). It supports calculating total nutrient intake based on specific portions, suggesting foods for various health conditions (like diabetes or high blood pressure), and performing detailed comparative analysis between multiple food items.

This comprehensive tool helps users set, track, and optimize personal health goals using deep data analysis. It validates goals against the SMART criteria (Specific, Measurable, Achievable, Relevant, Time-bound) and tracks progress by correlating diverse health data, including nutrition, exercise, and sleep patterns. Key features include habit formation analysis, motivation assessment, identifying roadblocks, and generating interactive reports with ECharts visualizations, providing a holistic view of wellness journeys.

This skill provides comprehensive tools for documenting and managing model performance on Hugging Face. It supports multiple methods for adding structured evaluation data, including extracting tables from README content, importing benchmark scores via APIs (like Artificial Analysis), and executing high-performance custom evaluations using advanced backends such as vLLM or lighteval. It ensures that all metadata is structured and compatible with model leaderboards and industry standards.

Provides guidance for organizing and navigating Jupyter Notebook workflows, focusing on structuring analysis, experiments, and reports within interactive Python sessions.

Literature Review Tool helps researchers gather and synthesize key insights from scholarly texts, highlighting patterns and critical arguments to accelerate literature surveying workflows even when structured content is initially unavailable.

Analyze mental health data to track mood, anxiety, depression scores, therapy progress, and crisis risk. Supports correlation analysis with sleep, exercise, nutrition, and chronic conditions to generate personalized insights and recommendations.

Analyze dietary and nutrient data, identify nutrition patterns, assess nutritional status, and generate personalized improvement advice. Also supports correlation analysis with exercise, sleep, weight, blood pressure, and blood sugar data.

Analyzes oral health records to detect trends, assess caries and periodontal risks, and generate personalized care advice. Supports linked analysis with nutrition, chronic disease, medication, and eye health data for comprehensive oral care insights.

Osint Evals provides a structured way to assess open-source intelligence collection and analysis workflows, helping security teams benchmark their OSINT tooling and methodologies against predefined scenarios.

Pandas is a Python data analysis library that simplifies loading, cleaning, transforming, and analyzing structured datasets, making it ideal for data wrangling in analytics or engineering workflows.