Skill UI

A persistent, structured knowledge base designed to accumulate and connect all research artifacts—including papers, ideas, experiments, and claims—across the entire research lifecycle. Inspired by LLM Wiki patterns, it builds a comprehensive, evolving graph of knowledge by tracking typed relationships (e.g., supports, contradicts, extends), ensuring that findings are compounded and never re-derived. Ideal for complex, multi-stage scientific projects.

Orchestrate Segment customer-data actions through Rube MCP and Composio, covering event tracking, user identification, grouping, page views, batch dispatches, alias merges, and source management while always refreshing schema info first.

This skill guides the systematic process of identifying and verifying candidate football entities (players or clubs). Use it when initial search results suggest a lead, or when the question provides enough detailed constraints (e.g., nationality, era, league) to hypothesize a candidate. The technique involves constructing targeted name-based searches and cross-checking every constraint (birth year, career timeline, etc.) against the found profile to ensure accuracy.

This skill provides direct command-line access to the Paperzilla platform, allowing users to manage, summarize, and retrieve academic research data. Users can fetch canonical papers in markdown format, get the latest project recommendations, analyze paper relevance, export data (JSON/Atom), and provide feedback on recommendations. It is an essential tool for researchers managing a large volume of academic literature.

This guide details strategies for finding the full legal birth names of European actors, particularly those from French and Eastern European backgrounds. When English sources or IMDb only list stage names, this technique emphasizes using regional resources like French Wikipedia and specific cultural identifiers (e.g., "People's Artist" USSR) to perform deep, targeted searches, ensuring the retrieval of accurate birth details.

This technique guides users to use precise chemical compound names, specific concentration values (e.g., "1.5 mg/L"), and taxonomic synonym counts as exact-match search anchors when querying biological literature. By leveraging quotation marks and combining multiple specific terms, it uniquely identifies research papers or species details, greatly improving search precision in biochemistry and botany.

This skill guides users on how to search for physical monuments, memorials, or landmarks by linking them to their originating historical events. The core technique involves treating the historical event (e.g., a battle, a protest, a significant date) as the primary search anchor, and then locating the associated memorial or structure. It provides structured query templates and anti-patterns to ensure accurate, context-aware geographical and historical information retrieval.

This technique guides users on how to construct highly specific search queries for academic papers. Instead of relying solely on broad topic keywords, users should prioritize combining concrete numerical identifiers—such as exact sample sizes, population percentages, concentration levels, or specific year ranges—with methodological terms and discipline keywords. Using these numerical anchors significantly increases the uniqueness and precision of the search results, helping to pinpoint the exact study required.

This technique guides the identification of musicians by prioritizing unique personal life events over discography details (albums or songs). It instructs users to use highly distinctive biographical information—such as career turning points, specific personal relationships, or unusual life incidents—as the primary search anchor. This method significantly improves search accuracy when typical musical clues are insufficient.

This guide teaches advanced methodology for identifying target species using multiple biological features. Users learn to combine distinct clues such as habitat, morphology, behavior, and geographic range. A key technique involves first identifying the country through demographic clues (e.g., population growth, endemic status) to significantly narrow the search scope before searching for the specific species.

Aeon is a comprehensive, scikit-learn compatible Python toolkit designed for advanced time series analysis. It supports a wide range of temporal machine learning tasks, including classification, regression, clustering, forecasting, anomaly detection, and similarity search. Use this skill when working with sequential patterns, time-indexed observations, or complex temporal data that requires specialized algorithms.

AnnData is a specialized Python package designed for managing and analyzing annotated matrices, particularly crucial in single-cell genomics and bioinformatics. It efficiently stores experimental measurements (X) alongside comprehensive metadata (obs, var, obsm, etc.). This skill allows users to read, write, subset, and concatenate large, complex datasets in formats like .h5ad and .zarr, making it essential for scRNA-seq analysis workflows and integration with the scverse ecosystem.