Skill UI

deepTools is a Python command-line suite for NGS workflows, converting BAM files into normalized coverage, running QC (fingerprint, correlation, PCA), comparing samples, and rendering heatmaps or profiles for ChIP-seq, RNA-seq, and ATAC-seq analyses.

A comprehensive toolkit designed for advanced data scraping and automation workflows on X/Twitter. Capabilities include deep tweet search, user profile analysis, bulk follower/media export, automated posting (replies, DMs), real-time account monitoring via webhooks, and integration with multiple AI agent platforms (Hermes, OpenClaw). Ideal for market research, social listening, and content automation.

GeoMaster is a comprehensive geospatial science skill that guides remote sensing, GIS, spatial analysis, and machine learning workflows for Earth observation and planetary data projects, covering satellite imagery, vector/raster math, point clouds, terrain, hydrology, and cloud-native data stacks.

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 suite provides comprehensive utilities for digital forensic investigations, allowing users to analyze critical Windows artifacts. Tools like KAPE, MFTECmd, PECmd, and RECmd parse registry hives, $MFT records, prefetch files, and event logs. It is essential for building a robust timeline of program execution, file access patterns, and system persistence during DFIR incident response.

This skill provides a highly rigorous framework for analyzing complex public issues, platform governance, and institutional compliance. It is designed to structure evidence boundaries, assess procedural justice, and track public commitments by synthesizing source ledgers and multiple data points. It is intended as a specialized sub-routine triggered by the main CrossFrame suite, not for standalone use. Key focus areas include source verification, identifying weak signals, and mitigating AI compliance performance risks.

This skill generates a comprehensive budget variance analysis report. It takes actual and budgeted figures, identifies variances exceeding a materiality threshold, and provides structured commentary. The output includes variance summaries, detailed root cause analysis (distinguishing between permanent and timing issues), recommended management actions, and revised full-year financial forecasts, suitable for C-suite presentations or board packs.

A comprehensive suite designed for AI agents to perform safe, deep, and multi-format web searches. It allows agents to retrieve current web information, analyze PDFs, extract YouTube transcripts, and access GitHub code files. Instructions include guidelines for varying search depths and using specialized tools for code context retrieval.

This guide details how to construct a sophisticated and rigorous robustness suite for top-tier academic econometrics journals (e.g., AEJ: Applied). It moves beyond simple regression tables, providing targeted checks against common methodological threats such as omitted variables, specification search, functional form choices, and incorrect statistical inference. The goal is to systematically demonstrate that the main point estimate is stable, reliable, and not an artifact of the research design.

This guide helps researchers determine the optimal publication venue for their econometrics research. It provides detailed rules for assessing whether a paper's contribution is best suited for The Econometrics Journal (EctJ), a general econometrics journal, a theory journal, or a specific field journal. It emphasizes distinguishing between methods with leading-case applied value, purely theoretical proofs, and general empirical findings to improve publication chances.

A comprehensive toolkit designed for international macroeconomics research, ensuring that headline estimates are stable and reliable. It systematically tests the robustness of findings against common methodological threats, such as leaving out individual countries, changing sample periods, controlling for global common shocks, or varying estimation specifications. Essential for submitting high-impact academic papers.

This guide provides a structured methodology for designing a comprehensive robustness suite for high-stakes academic papers, particularly in monetary economics and banking. Instead of running arbitrary checks, it maps diagnostic tests directly to named econometric threats (e.g., shock contamination, specification dependence, inference understatement, regime shifts), ensuring the credibility and rigor of empirical findings.