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Find your next superpower If you are an agent, refer to our llms.txt for full access.Collects preferences for script type, tone, audience, length, hooks, humor, and storytelling. Generates complete YouTube scripts and stores preferences and past scripts to maintain a consistent style.
Automates batch reverse engineering by generating Ghidra analyzeHeadless commands and post-analysis script skeletons to import, auto-analyze, and export results for many samples without the GUI. Activates for requests to script Ghidra headless analysis, batch-analyze samples with Ghidra, or generate an analyzeHeadless command and post-script.
Automates IDA Pro analysis with IDAPython by generating scripts that enumerate functions, decode strings, rename by signature, and export analysis results, and by documenting the headless idat batch invocation. Activates for requests to script IDA Pro, write IDAPython automation, or run IDA in batch/headless mode.
Focuses on scroll-driven web experiences using parallax storytelling, animation, and interactive narratives. Supports cinematic website experiences and scroll-based product presentations.
Focuses on scroll-driven web experiences, including parallax storytelling, scroll animations, and interactive narratives. Uses cinematic web experiences and product pages as stylistic references.
Uses Animate On Scroll for basic reveal effects on marketing pages, landing pages, and content-heavy websites. Focuses on straightforward scroll animations without complex orchestration.
Collects the topic, story sections, and brand assets, then generates cohesive scenes and seamless camera clips with Higgsfield. Connects them to a portable, framework-agnostic scroll engine, using Monid rendering by default and Higgsfield credits as a fallback.
Provides an outsider review of plans, pull requests, design documents, and code changes. Evaluates whether a simpler approach meets the goal and traces the full code path to produce concise, actionable findings with rationale.
Provides SCSS/Sassy CSS coding guidelines and best practices focused on stylesheet maintainability and scalability.
Performs RNA velocity analysis with scVelo from spliced and unspliced single-cell RNA counts. Fits deterministic or dynamical models, examines gene phase portraits, builds velocity graphs, estimates relative latent time, and ranks velocity-associated genes. Use for directional trajectory hypotheses and kinetic-model diagnostics alongside Scanpy; velocity alone does not establish cell fate or causal drivers.
Supports single-cell data integration, batch correction, ATAC-seq, CITE-seq, RNA+ATAC multiome analysis, and spatial transcriptomics deconvolution. Also covers label transfer, reference mapping, and RNA velocity using methods such as scVI, totalVI, MultiVI, DestVI, and veloVI.
Fits probabilistic models for single-cell omics, including scVI batch integration, scANVI annotation, totalVI CITE-seq, MultiVI RNA/ATAC integration, and posterior differential expression. Use for generative modeling, reference mapping, multimodal analysis, or model-based uncertainty; use scanpy for standard preprocessing and exploratory analysis.
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