Skill UI

This skill implements advanced 'Gatekeeper' logic to optimize AI agent performance in long-running workflows. It prevents context window bloat by sharding metadata, allocating token budgets, and enforcing Atomic Precision to deliver purely functional, filler-free responses. Ideal for complex coding, multi-step reasoning, and large document analysis where conciseness is critical.

A comprehensive playbook for digital forensic experts. It details systematic techniques and tools for detecting and extracting hidden data embedded across various media types, including images (LSB, PNG chunks, JPEG DCT, EXIF metadata), audio (spectrogram, DTMF), and files (polyglots, appended data). Essential for deep-dive forensic investigations.

This comprehensive tool performs a deep audit on App Store and Google Play listings, assessing metadata, visual assets, and market performance. It analyzes titles, descriptions, keywords, and screenshots against industry best practices, while also factoring in the app's brand maturity (Dominant, Established, Challenger). The output is a highly prioritized, actionable plan to improve search visibility, ranking, and user conversion rates. Ideal for optimizing existing apps or competitive analysis.

A comprehensive, domain-agnostic 13-agent system designed for rigorous academic research on any topic. It guides users through the entire research lifecycle, supporting deep literature reviews, systematic meta-analysis, cross-source synthesis, bias assessment, and generating professional APA 7.0 reports. Ideal for academics, students, and analysts needing structured, deep insights.

This meta-skill is designed for complex decision analysis when a user provides or references structured documents such as contracts, quotes, spreadsheets, or notices. Instead of simple summarization, it extracts key evidence, identifies risks (e.g., renewal risk, payment terms), and structures the output into decision-ready formats, such as 'sign/reject/negotiate' recommendations, evidence tables, or required next actions. It supports input from PDF, DOCX, and XLSX formats.

This meta-skill orchestrates a comprehensive workflow for analyzing PDF documents. It handles complex tasks such as summarizing, cross-document comparison, and targeted Q&A. It ensures findings are traceable by indexing evidence from various sources (full PDFs, pasted excerpts, etc.) and synthesizing structured insights across multiple files.

This meta-skill orchestrates a multi-step process to transform complex research questions, data, or deep reports into a structured, source-backed presentation deck (PPTX). It automatically handles searching, source curation, synthesizing an executive storyline, and generating a professional slide outline and talk track, making it ideal for competitive analysis, leadership briefings, or academic presentations.

This meta-skill processes an entire Excel workbook to extract deep business insights. It systematically reads all data, performs structured trend and anomaly analysis, writes the comprehensive results back into a dedicated 'Insights' sheet, and permanently stores key performance indicators (KPIs) in long-term memory. Ideal for detailed business review and data deep-dives.

This meta-skill is designed to automate the diagnosis of complex programming errors. When presented with a stack trace, traceback, or failing log, it orchestrates a multi-step investigation. It parses the trace, searches the repository history, checks relevant issues, and analyzes code diffs to provide a comprehensive brief for root cause analysis and debugging.

This skill acts as a literature-research assistant, orchestrating a Python CLI to interface with APIs like Semantic Scholar and arXiv. It enables users to search for academic papers by topic, retrieve detailed metadata, perform impact analysis using citation tracking, download PDFs, and extract text content. It streamlines the entire academic research workflow from initial discovery to deep reading.

A systematic skill designed to investigate data incidents, including freshness delays, volume anomalies, schema changes, and ETL failures. It guides the agent through a comprehensive process using Monte Carlo's observability metadata and optional direct data querying to pinpoint the root cause.

This powerful tool provides real-time tracking and deep analysis of advertising spend across multiple connected platforms (Google, Meta, TikTok, etc.). It analyzes spending pacing against set targets, projects end-of-period spending, and flags overspend or underspend risks. Furthermore, it recommends data-driven budget reallocations based on efficiency metrics (CPA, ROAS) to maximize overall ROI for media buyers and marketing managers.