Skill UI

Build a production-ready, AI-powered data collection agent for any public source (job boards, prices, news, etc.). This agent automatically scrapes data, enriches it using free LLMs (like Gemini Flash), and stores the results in databases such as Notion, Sheets, or Supabase. It runs on a free schedule using GitHub Actions and learns from user feedback, making it ideal for continuous data monitoring and tracking.

Provides a structured workflow for systematically accessing and analyzing official intellectual property data from the USPTO. This skill covers patent search (PatentSearch API), trademark status checks (TSDR), ownership transfer tracking, and building reproducible IP landscape logs. It is designed for data collection and record verification, emphasizing the use of official APIs and rigorous logging practices. (Warning: Not for legal advice.)

This skill helps discover natural groupings within a large collection of semantic embeddings using the `ruvector` tool. It employs graph-based algorithms (Spectral/Louvain) to cluster code or data, revealing underlying themes, identifying outliers, and organizing vast vector sets. Ideal for analyzing codebases or large datasets to understand relationships between entries.

A comprehensive guide for designing and auditing empirical research in CSCW. It emphasizes methodological pluralism, defining distinct standards of rigor for various techniques like ethnography, trace analysis, surveys, and mixed methods. The guide mandates pre-design planning, explicitly addressing ethical concerns, unit of analysis, and aggregation logic before data collection.

A comprehensive guide for planning rigorous scientific studies in Earth and Planetary Sciences, covering sampling, analytical campaigns, experimental runs, and modeling. It ensures that research designs are robust, addressing critical elements like sample context, traceability to reference materials, replication levels, and falsifiability before data collection begins. Essential for high-impact scientific publishing.

A comprehensive guide for authors responding to revise-and-resubmit reports in Experimental Economics (ExpEcon). It details how to triage complex reviewer comments, plan for necessary new data collection (new treatments, more sessions), address critical methodological concerns like deception or power deficits, and structure a highly persuasive, point-by-point response letter.

This guide helps researchers elevate local, sub-field specific findings into global-scale scientific contributions. It teaches how to structure a manuscript by anchoring the work on driver-response literature, speaking across different Earth systems (e.g., terrestrial and marine), and engaging with major synthesis meta-analyses. Use this when your work needs to prove broader applicability beyond its initial data collection site.

This guide provides a comprehensive backward planning workflow for submitting a full paper to the ACM/IEEE HRI conference. It warns researchers that the most critical and time-consuming steps—such as IRB approval, participant recruitment, and data collection—must be planned months in advance, often taking longer than the writing process. It maps out the entire academic cycle, from initial abstract submission through to camera-ready preparation, including parallel tracks like alt.HRI and Late-Breaking Reports.

Comprehensive guide for planning an end-to-end academic campaign submission to IMC. This workflow details backward planning from target cutoffs, managing ethics approvals, handling two annual deadlines, and navigating the One-Shot-Revision path, ensuring a smooth research cycle from data collection to final publication.

This tool guides researchers through the critical initial phase of academic manuscript preparation. It tests whether a research question has genuine accounting relevance, a credible source of identifying variation (e.g., policy shock, rule change), clear economic stakes, and sufficient marginal contribution for top-tier accounting journals like JAR. It serves as a methodological fit check before any data collection begins.

A comprehensive academic guide for refining research questions intended for the Journal of Economic Geography (JEG). It implements a rigorous "two-community test," ensuring the topic successfully bridges geographical economics (quantitative models) and human geography (place-based studies). Use this checklist before major data collection to validate the mechanism and justify the spatial role of the research.

This guide outlines a systematic methodology for gathering, reading, and synthesizing large bodies of academic research for comprehensive economic surveys (JEL style). It moves beyond simple summarization by enforcing structured data collection into a detailed evidence matrix. The process ensures maximum coverage through advanced search techniques (e.g., snowballing, JEL code sweeps) and focuses on synthesizing connections, identifying consensus, and articulating scholarly tensions across different studies.