Skill UI

Automates detection and deep exploratory analysis across 200+ scientific data formats, assessing quality, extracting metadata, and recommending visualizations while generating markdown reports that guide downstream workflows and highlight structure and distributions.

End-to-end Neuropixels recording analysis covering SpikeGLX/OpenEphys/NWB loading, preprocessing, motion correction, Kilosort4 sorting, quality metrics, Allen/IBL curation, and AI-assisted visualization for extracellular electrophysiology.

Plotly is a Python graphing library for interactive, publication-quality visuals that bring hover, zoom, animation, and dashboard-ready exports to exploratory analysis and presentations.

Seaborn is a powerful Python library for creating publication-quality statistical graphics. It is designed for dataset-oriented plotting, making it ideal for exploratory data analysis, multivariate analysis, and visualizing complex relationships, distributions, and comparisons with minimal code. It builds upon Matplotlib, offering high-level interfaces for functions like scatterplots, heatmaps, and pair plots.

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.

A comprehensive, all-in-one skill for conducting deep SEO audits across any industry (E-commerce, SaaS, Local). It orchestrates multiple sub-agents to analyze technical SEO, content quality (E-E-A-T), schema markup, Core Web Vitals, local presence, and AI readiness (AI Overviews). It culminates in a synthesized, actionable strategy report.

This tool provides a comprehensive deep dive into a single webpage's SEO health. It evaluates on-page elements, content quality metrics, technical metadata (Open Graph, Schema, etc.), image optimization, and Core Web Vitals potential issues. Use this when you need to thoroughly audit a URL for search engine optimization compliance and actionable improvements.

This skill provides comprehensive tracking and visualization for Security Operations Center (SOC) performance. It calculates critical metrics such as Mean Time to Detect (MTTD), Mean Time to Respond (MTTR), and alert quality ratios using SIEM data. Ideal for SOC leadership, compliance auditing, and continuous process improvement initiatives needing quantifiable security posture reporting.

This skill guides the creation of high-quality, finished cyber threat intelligence reports. It tails content for specific audiences—from C-suite executives (strategic risk) to SOC analysts (tactical IOCs). Use it when synthesizing raw data into actionable intelligence products, generating threat briefings, or conducting post-incident assessments, ensuring adherence to industry standards like TLP and NIST.

This skill provides a structured methodology for mitigating alert fatigue within Security Operations Centers (SOCs). It guides users through advanced techniques, including implementing Risk-Based Alerting (RBA) to replace high-volume, low-fidelity alerts with consolidated risk scores. It emphasizes quantifying alert quality by analyzing True Positive (TP) and False Positive (FP) rates, and systematically tuning detection rules to maintain analyst effectiveness.

This skill automates the generation of high-quality, publication-ready figures and comparison tables from raw experimental data (CSV, JSON, etc.). It supports various plot types, including line plots, bar charts, scatter plots, heatmaps, and box plots, ensuring consistent style (DPI 300, PDF format) suitable for academic submissions. Users provide a figure plan and raw data, and the system generates the necessary code to visualize results.

This skill provides a systematic framework for managing Indicators of Compromise (IOCs) throughout their entire life cycle—from initial discovery and validation to enrichment, deployment, monitoring, and eventual retirement. It emphasizes implementing confidence decay functions, tracking hit rates, and managing false positives to maintain a high-quality, actionable threat intelligence database, thereby minimizing analyst fatigue and maximizing detection efficacy.