Seo Specialist

Automated hypothesis generation and testing using large language models. Use this skill when generating scientific hypotheses from datasets, combining literature insights with empirical data, testing hypotheses against observational data, or conducting systematic hypothesis exploration for research discovery in domains like deception detection, AI content detection, mental health analysis, or other empirical research tasks.

Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch/JAX/TensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.

Data journalism workflows for analysis, visualization, and storytelling. Use when analyzing datasets, creating charts and maps, cleaning messy data, calculating statistics or building data-driven stories. Essential for reporters, newsrooms and researchers working with quantitative information.

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that don't fit in memory.

Electronic lab notebook API integration. Access notebooks, manage entries/attachments, backup notebooks, integrate with Protocols.io/Jupyter/REDCap, for programmatic ELN workflows.

Automate TikTok + Instagram slideshow marketing for any app or product. Researches competitors, generates AI images, adds text overlays, posts via Upload-Post (multi-platform in one API call), tracks analytics, and iterates on what works. Use when setting up TikTok marketing automation, creating slideshow posts, analyzing post performance, optimizing app marketing funnels, or when a user mentions TikTok growth, slideshow ads, or social media marketing for their app. Covers competitor research (browser-based), image generation, text overlays, multi-platform posting (Upload-Post API — TikTok + Instagram + more simultaneously), analytics tracking, hook testing, CTA optimization, conversion tracking with RevenueCat, and a full feedback loop that adjusts hooks and CTAs based on views vs conversions.