Skill UI

A 13-agent pipeline for rigorous academic research on any topic, with 8 modes including full research, quick brief, paper review, literature review, fact-check, three-way scan, Socratic guided dialogue, and systematic review with meta-analysis. Handles question formulation, source verification, synthesis, bias assessment, and APA 7.0 reports.

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.

Perform comprehensive, granular SEO audits on a single URL. This tool examines title tags, meta descriptions, content depth, E-E-A-T signals, technical health (Core Web Vitals, canonical tags), and AI search readiness. Ideal for landing page optimization, content auditing, and ensuring compliance with the latest Google guidelines.

This comprehensive preflight checklist is designed for authors submitting a commissioned review article to Annual Reviews of Psychology (ARPsych). It guides users through critical compliance checks, ensuring adherence to assigned length, proper reference formatting, securing figure permissions, completing conflict-of-interest disclosures, and verifying the transparency and reproducibility of any embedded meta-analysis or literature search. It serves as the final quality assurance step before formal publishing submission.

A methodological guide for documenting the rigor of literature reviews, especially in sociology. It details how to build a comprehensive 'coverage account' (a near-PRISMA record of the search process) and how to ensure any quantitative synthesis (meta-analysis, bibliometrics) conducted within the review is fully reproducible by depositing data and code.