Skill UI

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.

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 occupational health data to assess work-related risks, ergonomic issues, symptoms, and trends. Use it to generate personalized workplace health advice, screen for occupational hazards, and relate work factors to sleep, exercise, and mental health.

This comprehensive skill analyzes sleep data to identify patterns, assess quality, and detect potential sleep disorders. It tracks trends in sleep duration, efficiency, and consistency, and performs advanced correlation analysis with other health metrics like exercise, diet, and mood. The output provides actionable, personalized recommendations based on scientific data, helping users improve their overall sleep health.

Analyzes TCM constitution data to identify primary and mixed constitution types, assess traits and risks, and generate personalized wellness advice with links to nutrition, exercise, sleep, and trend tracking.

Parses Cobalt Strike malleable C2 profiles via pyMalleableC2 to pull beacon configs, HTTP communication patterns, sleep/jitter settings, and combines JARM scans for C2 detection when hunting suspected infrastructure or building IDS signatures.

An evidence-based coach providing guidance on fundamental wellness pillars: sleep hygiene, nutrition, exercise, and stress management. It uses behavior change science and motivational interviewing principles to help users build sustainable, lasting health habits. This tool is educational and motivational, designed to support general wellness journeys, and always emphasizes the critical need to consult healthcare professionals for diagnosis or treatment.

PyHealth is a comprehensive Python toolkit designed for building deep-learning pipelines in the clinical and healthcare domains. It supports diverse data modalities, including EHR records, physiological signals, and medical images. The framework follows a stable 5-stage pipeline (Dataset → Task → Model → Trainer → Metrics), enabling users to perform complex tasks like predicting mortality, optimizing drug recommendations, staging sleep, or cross-mapping medical codes (ICD/ATC/RxNorm) on datasets like MIMIC-III/IV and OMOP.

SkillOpt-Sleep simulates an agent's nightly 'sleep' cycle to continuously improve performance without requiring model retraining. It harvests historical sessions, mines recurring tasks, and replays them offline. Learning and insights are then consolidated into the agent's memory and skills (CLAUDE.md/SKILL.md), proposing bounded, validated edits that the user must explicitly adopt, ensuring measurable, sustained improvement over time.

This skill guides the deployment and operation of the Havoc C2 framework, enabling advanced adversary emulation. It utilizes Yaotl profiles to generate highly evasive agents (Demon) that employ sophisticated techniques like indirect syscalls and sleep obfuscation. The scope covers the full red-teaming lifecycle, including C2 setup, payload generation, post-exploitation, and lateral movement, making it ideal for authorized security testing and validating EDR detection capabilities.

This skill utilizes the built-in macOS `caffeinate` command to prevent the Mac from entering sleep mode. It is essential for guaranteeing uninterrupted workflow during long-running tasks, such as code builds, large downloads, or supervised automation sessions. Users can specify a fixed duration or attach the function to a specific process ID for continuous wakefulness.