Skill UI

This skill generates comprehensive Entity Relationship Diagrams (ERDs) and database schemas as D2 source code from an existing Entity Framework Core codebase. It analyzes DbContexts, DbSet declarations, Fluent API mappings, and migrations to accurately visualize tables, columns, primary/foreign keys, relationships (including many-to-many joins), owned types, and constraints. The output is a structured .d2 file, suitable for rendering into SVG or PNG, helping developers document the persistence model.

A robust API-backed skill for fetching YouTube transcripts, searching video content, browsing channels, and extracting playlists. It functions reliably from any cloud server environment, bypassing limitations found in local tools like yt-dlp or reliance on Google API keys. Ideal for building research corpora, competitive intelligence monitoring, and systematic content analysis.

This tool assists researchers in structuring academic papers by mapping specific claims across different sections (Introduction, Method, Results, Discussion) to required external citations (BibTeX keys). It enforces citation density rules and ensures the correct role alignment (e.g., background, comparison, limitation) before the actual drafting process, guaranteeing that every core claim is properly supported and distributed.

Provides comprehensive, programmatic control over tmux sessions. Users can list, capture output from, send specific keystrokes, and programmatically navigate panes/windows in remote, long-running interactive CLI sessions. Essential for monitoring AI code generation tools (like Claude/Codex) or managing complex multi-terminal workflows via scripting.

An open-source, advanced tool for professional post-production of screen recordings. It automatically speeds up idle segments, applies auto-zoom on click clusters, overlays keystroke chips, and renders smooth synthetic cursors. It specializes in generating high-quality, vertical (9:16) videos optimized for social media and public demos.

A comprehensive guide to deploying cloud-native deception across AWS, Azure, and GCP. This skill utilizes decoy resources—such as canary IAM keys, honey object storage buckets, and decoy secrets—whose sole purpose is to generate high-fidelity alerts the instant they are interacted with by an attacker. It detects cloud reconnaissance, credential theft, and lateral movement by wiring detection through native logging services (CloudTrail, Sentinel, Cloud Audit Logs), ensuring near-zero false positives for the SOC.

Automated system designed to monitor paste sites (e.g., Pastebin, GitHub Gists) for leaked credentials, API keys, and sensitive data dumps. It uses advanced scraping, keyword matching, and regex patterns to provide early detection of data breaches and security incidents.

Implements HashiCorp Vault's dynamic secrets engine to automatically generate, lease, and rotate short-lived credentials for databases (e.g., Postgres, MySQL), AWS IAM keys, and PKI certificates. This eliminates the risk associated with storing static secrets, ensuring compliance with zero-trust principles and automating the entire secret lifecycle.

This guide outlines a comprehensive workflow for detecting and preventing the exposure of sensitive AWS credentials (access keys, session tokens) within source code repositories, CI/CD pipelines, and configuration files. It utilizes TruffleHog for deep historical scanning and integrates git-secrets for robust pre-commit hooks, significantly reducing the risk of credential theft and unauthorized account access.

This technique exploits the Active Directory attribute `msDS-KeyCredentialLink` by injecting attacker-controlled public keys (Shadow Credentials). Using tools like pyWhisker and Certipy, the attacker forces the target account to register a new key. Subsequently, by leveraging PKINIT (Public Key Cryptography for Initial Authentication in Kerberos), the attacker can obtain a service ticket and recover the target's NT hash, achieving a stealthy, password-less, complete account takeover.

Plaso (log2timeline) is an open-source engine used to generate forensic super-timelines. This skill guides the process of normalizing hundreds of diverse artifact types—including MACB times, registry keys, EVTX logs, and browser history—into a single, cohesive chronological view. The workflow covers extraction, filtering, and importing the timeline into Timesketch for collaborative analysis, which is crucial for incident response, correlating complex activity chains, and detecting anti-forensic techniques like timestomping.

This guide provides a structured approach to red-teaming Large Language Models (LLMs) to detect sensitive data leakage from system prompts. It simulates advanced attacks—including prompt injection, instruction override, and encoding tricks—to identify embedded secrets, API keys, and proprietary business logic. Essential for validating adherence to OWASP LLM07 and securing AI applications against data exfiltration.