Skill UI

This skill provides real-time object detection capabilities by leveraging the Google Coral Edge TPU accelerator. It operates natively within Windows Subsystem for Linux (WSL), allowing high-speed, low-latency inference on live camera frames. By utilizing hardware acceleration, it achieves impressive performance (e.g., ~4ms inference at 320x320), making it ideal for edge computing projects requiring rapid visual analysis and resource efficiency.

A comprehensive methodology for exploiting Time-of-Check / Time-of-Use (TOCTOU) race conditions across diverse system layers. This covers symlink swaps, file-descriptor races, kernel double-fetch vulnerabilities, and advanced window-widening primitives like FUSE slow filesystems and userfaultfd. Essential for security researchers targeting race-based privilege escalation in binaries, kernels, and container orchestrators.

This advanced research skill synthesizes the current conversation and emerging trends of any topic across multiple sources, including Reddit, Hacker News, the open web, and X/Twitter. By defining the topic specificity, required analysis angle (e.g., trend, sentiment, problems), and time window, users receive a comprehensive briefing complete with citations and cross-platform pattern analysis. Ideal for competitor research, trend discovery, and audience sentiment analysis.

This meta-skill is designed for generating comprehensive competitive intelligence briefs for sales, business development, and strategy teams. It monitors signals across various dimensions (pricing, product, leadership, funding, etc.) for specified competitors or targets over a defined time window. It helps track changes against a baseline and provides actionable recommendations, moving beyond simple raw data dumps.

This skill analyzes your Gmail inbox over a specified time window, filtering out noise such as newsletters, receipts, and automated notifications. It surfaces only actionable items—emails requiring a direct reply, a decision, or follow-up—and even drafts suggested replies for efficiency.

Hayabusa is a specialized threat-hunting and fast-forensics tool designed to analyze Windows EVTX event logs. It applies a comprehensive library of Sigma detection rules to raw log data, generating a single, prioritized, chronological timeline. The output includes severity levels, MITRE ATT&CK mappings, and detailed metrics, enabling analysts to perform rapid, SIEM-free incident triage and export results for collaborative analysis.

This guide details the process of parsing core Windows forensic artifacts, including MFT, Prefetch, Registry hives, ShellBags, and Amcache. Utilizing Eric Zimmerman's specialized EZ Tools, raw evidence is converted into standardized CSV/JSON formats. This process is crucial for Digital Forensics and Incident Response (DFIR) investigations, enabling analysts to reconstruct program execution, file access history, and persistence mechanisms, which can then be loaded into Timeline Explorer for unified analysis.

A comprehensive guide to high-performance analytical database patterns using ClickHouse. Covers advanced topics including MergeTree engine types (Replacing, Aggregating), efficient query optimization techniques (filtering, window functions), bulk data insertion strategies, and setting up materialized views for real-time analytics. Ideal for data engineers and analysts building OLAP systems.

Monitors the acceleration trend of production spending (burn rate) over defined time windows. It compares the latest spending bucket against the historical average, providing an early warning system. This is crucial for detecting sudden, unexpected cost spikes, such as a 'hot loop,' independent of overall budget limits.

This skill consumes federation_spend events to aggregate per-peer spending across rolling time windows (1h, 24h, 7d). It implements a crucial circuit breaker mechanism by monitoring if any peer's spending exceeds a predefined threshold, ensuring the stability and resource management of the federation. Used to establish spending baselines and verify budget constraints.

This tool provides forward-looking cost projection by calculating the average daily burn rate from a specified measurement window. It linearly extrapolates future spending across multiple time horizons (7d, 30d, 90d, 365d) and alerts users to the projected date of budget exhaustion. It is essential for proactive financial planning, SRE cost monitoring, and setting CI/CD build cost gates.

This skill provides a comprehensive, end-to-end workflow guide for academic journal submissions, specifically tailored for Acta Electronica Sinica (AES). It maps the entire publishing journey—from topic selection and research design to submission, review tracking, revision, and final publication—into a structured timeline. It helps authors manage complex decision points, adhere to strict deadlines (like the 6-month response window), and avoid common pitfalls, ensuring a systematic approach to high-stakes scholarly publishing.