Skill UI

Generates Python code and workflows for collaborative, content-based, or hybrid recommendation models, handling data prep, training, evaluation, and scalability so Claude can deliver tailored suggestions across domains.

This skill models the expertise of a senior data scientist, covering the entire data science lifecycle. It provides advanced capabilities in statistical modeling, machine learning (deep learning, XGBoost), predictive analytics, and business intelligence. From raw data exploration (EDA) to deploying production-ready models and creating actionable dashboards, it covers technical depth across Python, R, SQL, and cloud platforms, while applying domain knowledge in finance, marketing, and operations.

A comprehensive workflow for executing advanced, semantic web searches using Exa. This tool moves beyond simple keyword matching, enabling deep context retrieval by understanding the intent behind the query. Key features include RAG context retrieval, content extraction (summaries, highlights, full text), and robust filtering by date range, specific domains, and content categories. Ideal for building knowledge base search features and complex research pipelines.

This skill functions as an expert-level web researcher, utilizing advanced search strategies (query optimization, domain filtering) to gather, synthesize, and validate information from multiple web sources. It is ideal for complex research tasks requiring fact verification, identifying trends, and producing structured, actionable insights, ensuring high credibility for the final output.

Conduct rigorous, multi-database literature reviews with systematic workflows, verified citations in styles like APA or Nature, and AI-enhanced schematics for publication-ready markdown and PDF outputs in biomedical and technical domains.

Query InterPro for protein family, domain, and GO annotations via a REST API with Python helpers, enabling function prediction, domain architecture mapping, and evolutionary classification insights for proteomic workflows.

bdistill transforms AI interactions into a structured, queryable knowledge base. It extracts, structures, and quality-scores domain knowledge from both closed-source AI models (in-session) and local open-source models (via Ollama). Users can build comprehensive reference datasets, Q&A pairs, or generate structured training data for traditional machine learning, ensuring validated and exportable domain expertise.

Access and query 78 public databases covering diverse fields including biomedical, physical sciences, earth science, and economics. This skill identifies the relevant database(s) for user queries (e.g., genes, compounds, patents, climate data, financial indicators), executes structured REST API calls, and returns raw JSON results, providing a comprehensive data retrieval pipeline.

This advanced technique allows users to identify an obscure actor by linking indirect clues across three domains: the director, the film's characteristics, and the actor's biographical details. It is essential when direct searches fail, using the director's work or a film's context as the primary entry point to resolve the actor's identity.

Conduct comprehensive, systematic literature reviews across biomedical, scientific, and technical domains. This skill facilitates meta-analyses, scoping reviews, and research syntheses by searching multiple academic databases (e.g., PubMed, arXiv). It generates professionally formatted reports (Markdown/PDF) with verified citations and mandates the inclusion of AI-generated scientific schematics (e.g., PRISMA flow diagrams). Ideal for academic research and thesis writing.