Skill UI

A comprehensive skill for interacting with Weaviate vector databases. It enables advanced operations including semantic search, hybrid keyword querying, schema inspection, data exploration, collection creation, and bulk data import (CSV, JSON, PDF). Use this skill when you need to query data conceptually, retrieve specific raw objects, or manage the lifecycle of Weaviate collections.

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 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.

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.

This skill provides a rigorous framework for designing academic studies, particularly for high-impact psychological journals. It guides researchers to proactively address common methodological pitfalls such as common-method variance (CMV), unmodeled nesting, and construct validity gaps. Topics covered include temporal separation, multi-level modeling, advanced causal inference techniques (DiD, IV), and pre-registration best practices, strengthening the plan before data collection.

Provides comprehensive methodological guidelines for designing rigorous studies in public policy and marketing. The framework covers both prospective (e.g., realistic experiments) and retrospective (e.g., DiD, RDD) evaluation designs, emphasizing the necessity of establishing a strong counterfactual basis and ensuring policy relevance in data collection and analysis.

This guide provides methodological standards for defending the empirical design of a language manuscript. It covers various subfields—including fieldwork, corpus construction, phonetics, and typological comparison—ensuring that the evidence base is transparent, rigorous, and directly supports the stated theoretical claims. Use this when justifying data collection methods or addressing reviewer concerns about evidence validity.

A comprehensive guide for ensuring the reproducibility of Information Retrieval (IR) research, essential for SIGIR submissions. It details best practices for pinning data collection versions, index settings, neural model hyperparameters, and managing non-determinism. It helps researchers document artifacts—not just promises—to validate reported gains.

A comprehensive, multi-phase workflow guide for authorized reverse engineering of complex network protocols. Ideal for analyzing proprietary formats such as custom TCP/UDP binary protocols, Protobuf, gRPC, WebSocket frames, and reconstructing state machines from raw PCAP data. This guide covers data collection, field layout restoration, serialization recovery, and secure replaying.

HasData is a comprehensive cloud platform designed for extracting structured public web data. It supports three core execution modes: Web Scraping API for arbitrary URLs (including JS rendering), pre-parsed Scraper APIs for known platforms (like Google or Amazon), and asynchronous Scraper Jobs for bulk or recursive crawling. Ideal for market research, e-commerce intelligence, job data collection, and local business lead generation.