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.

Guides CEOs and C-suite teams through an eight-dimension health check, scoring metrics on a traffic-light scale and surfacing risks before board reviews or health-dashboard deep dives.

This advanced agent simulates the deep investment philosophy of Warren Buffett, blending Benjamin Graham's quantitative rigor with Charlie Munger's qualitative insight. It helps users perform disciplined analyses by applying frameworks like 'Margin of Safety' and identifying durable business 'moats,' ensuring recommendations are free from emotional speculation. Ideal for deep dives into value investing and long-term capital allocation.

This skill provides a comprehensive methodology for simulating privilege escalation attacks on compromised Linux and Windows environments. It guides the user through identifying vectors—including SUID abuse, misconfigured services, kernel vulnerabilities, cron job exploitation, and credential harvesting—to elevate initial low-privilege access to root or SYSTEM control. It is crucial for deep-dive penetration testing and assessing system security hardening.

This powerful tool facilitates comprehensive academic literature reviews. It connects to multiple advanced data sources—including Zotero, local PDFs, Semantic Scholar, and the general web—to search for, retrieve, and summarize key ideas from scholarly papers on any given topic. It is essential for students, researchers, or professionals conducting deep dives into complex academic material.

This skill provides interactive, periodic monitoring of deep learning training runs. It continuously checks critical metrics—such as loss trends, evaluation scores, and gradient norms—against defined baselines to detect signs of divergence, NaN values, plateaus, or general instability. It delivers a structured report and recommends whether to continue, wait, or stop the training process, ensuring computational resources are not wasted on failing models.

Guides multi-round investigation of any GitHub repository, combining API queries, discovery searches, and deep dives to produce structured markdown reports with summaries, timelines, metrics tables, and Mermaid diagrams for executive-ready briefs.

Defines a systematic, multi-phase research methodology that prioritizes broad exploration, targeted deep dives, and validation before initiating any content generation, ensuring reliable, multi-angle web intelligence.

Satori is a sophisticated AI wisdom companion that merges advanced clinical psychology frameworks (such as IFS, DBT, and Schema Therapy) with diverse philosophical traditions (like Stoicism, Jungian depth psychology, and Buddhism). It functions as a structured thinking partner, guiding users through deep self-exploration, internal conflicts, and existential questions to foster profound personal insight.

Perform a comprehensive, deep-dive audit on a single URL, covering all critical aspects of Search Engine Optimization. Analysis includes on-page elements (title, headings, meta tags), content quality (readability, E-E-A-T), technical structure (schema, canonical, OG tags), image optimization, and core web vitals potential issues. Ideal for SEO audits and content quality checks.

This advanced research technique is designed for deep dives into football topics. Instead of relying solely on the original query, it mandates tracking every discovered entity—such as player names, club names, or coaches—from search results. By treating these entities as new leads, the user must formulate follow-up searches using the discovered names and remaining constraints, ensuring nothing relevant is overlooked and building a comprehensive knowledge chain.

An autonomous research agent that plans complex investigations using Directed Acyclic Graphs (DAGs). It executes sub-questions in parallel, performs gap analysis, and iteratively refines findings before synthesizing a comprehensive, structured report. Ideal for deep market analysis, academic literature reviews, or complex problem-solving requiring multiple data sources.