Skill UI

A comprehensive pipeline for processing, validating, and optimizing scraped web content from Firecrawl. This tool handles markdown cleaning, structured data extraction using Zod validation, content deduplication, and chunking tailored for Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. It ensures crawled data is standardized, clean, and ready for downstream consumption or knowledge base indexing.

A comprehensive guide for advanced geospatial data processing using GeoPandas. This skill targets professional workflows involving planar vector data, emphasizing rigorous data provenance, coordinate reference system (CRS) management, and complex spatial operations. It details necessary safety contracts, correctness gates, and best practices for handling sensitive geographic information in reproducible Python environments.

Provides comprehensive programmatic control over Apify's core data infrastructure, including Datasets, Key-Value Stores, and Request Queues. This skill allows users to perform CRUD operations on various storage types, manage data persistence, and orchestrate complex, multi-stage data pipelines (e.g., scrape -> transform -> export). Ideal for building robust, end-to-end web scraping and data processing automation workflows.

This skill provides an end-to-end, reproducible workflow for analyzing matched tumor-normal pairs using the nf-core/pacsomatic pipeline. It handles input validation (BAM files, IDs), generates necessary samplesheets and launch artifacts, and supports execution across various environments, including local runs and cluster schedulers (Slurm, LSF). It is ideal for initiating, validating, or troubleshooting the entire analysis process.

This skill processes raw, unstructured materials (e.g., conversations, reports, controversies) into highly structured, reusable casebook entries. It focuses on identifying core facts, defining mechanism chains, and mapping responsibility links while ensuring rigorous data anonymization. It is designed for deep knowledge management and pattern recognition, helping users build a reliable, reusable knowledge base for future professional analysis, rather than generating immediate advice.

This tool automates the process of optimizing investment portfolio allocations using a mean-variance engine. It integrates neural prediction models and risk constraints to determine optimal asset weights. Users can set specific risk targets, assess comprehensive risk metrics (variance, correlation), and generate actionable, step-by-step rebalancing plans for professional portfolio management.

This comprehensive skill guides researchers through the submission process for the Chinese Journal of Management Science. It covers the journal's core academic positioning (operations research, decision analysis, complex systems), required methodological rigor (model setting, algorithms, proofs), structural guidelines, and essential formatting checks. It helps authors assess manuscript fit and identify common rejection risks before official submission.

A comprehensive project management guide for submitting database systems papers to EDBT. It details the complexities of the multiple-cycle rolling publication model, covering everything from initial evidence lock and cycle selection to the five-step review arc (Submission, Author Feedback, Revision, etc.). Use this to backward-plan your research timeline and manage the entire academic publishing process.

This skill offers a comprehensive project management framework for the end-to-end submission process of data mining research to major conferences like ICDM. It guides users through critical milestones, including topic selection, experimental design, abstract submission, and final camera-ready preparation. It emphasizes backward planning from the final deadline, addressing unique constraints such as no-rebuttal policies, short-paper contingencies, and strict page limits.

This comprehensive guide assists researchers in structuring and refining empirical studies for submission to top management journals. It addresses critical methodological pitfalls, including common-method bias, endogeneity, and multilevel structures. Learn how to correctly match theoretical claims (e.g., causal effects, temporal processes) to appropriate designs (experiments, panel data, meta-analysis) to ensure rigorous scientific validity and statistical integrity.

This guide provides a framework for generating high-quality, self-explanatory exhibits for empirical academic manuscripts, particularly for fields like Operations Management. It covers creating descriptive statistics, correlation matrices, complex regression/SEM results, interaction plots, and process figures. The focus is on adhering to rigorous standards, ensuring all evidence is readable without accompanying text, and meeting APA style guidelines for publication.

This guide details the rigorous review process for the Journal of Policy Analysis and Management (JPAM). It helps authors pre-empt potential objections—from desk screening to cross-disciplinary peer review—by detailing expected standards, article types, and the criteria for acceptance or rejection. Use this before submission to stress-test your manuscript and maximize acceptance chances.