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概要と使いどころ
Authors or reviews Know/Understand/Do charts for competency-based learning targets across developmental bands. Handles seven input types from raw curriculum documents to existing LT sets. Routes to upstream skills when stronger inputs are available.
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Professional network reconnaissance and port scanning using nmap. Supports various scan types (quick, full, UDP, stealth), service detection, vulnerability scanning, and NSE scripts. Use when you need to enumerate network services, detect versions, or perform network reconnaissance.
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Advanced file finder with type detection and filesystem extraction for analyzing firmware and extracting embedded filesystems. Use when you need to analyze firmware files, identify file types, or extract ext2/3/4 or F2FS filesystems.
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When you want to draft, update, convert, or export a slide deck for a React/Next.js slide system (${SLIDE_DECK_REPO:-$HOME/code/your-slide-deck-site}/src/app/slides/). Writes TypeScript Slide[] arrays using your primitives (Eyebrow, Heading, Accent, Body, BulletList, Divider, TwoCol, GradientText), 12 cycling brand gradients, optional sections for "where am I" context, and speaker notes. Inspired by zarazhangrui/frontend-slides — "show, don't tell" for narrative (presents 3 angles, you pick) plus density modes (speaker-led vs reading-first). Four modes — new (draft from brief), update (modify existing, with overflow guards), ppt (convert legacy PPTX → React deck), export (Playwright snapshot existing React deck to standalone HTML, PDF, or Vercel URL — keeps brand). Triggers on "/slide-deck," "/slides new," "/slides export," "/slides update," "/slides ppt," "draft a deck," "deck for [topic]," "talk on [topic]," "keynote on [topic]," "internal deck for [audience]," "convert this pptx," "export this deck."
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Expert patterns for AnimationPlayer including track types (Value, Method, Audio, Bezier), root motion extraction, animation callbacks, procedural animation generation, call mode optimization, and RESET tracks. Use for timeline-based animations, cutscenes, or UI transitions. Trigger keywords: AnimationPlayer, Animation, track_insert_key, root_motion, animation_finished, RESET_track, call_mode, animation_set_next, queue, blend_times.
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Expert patterns for AnimationPlayer including track types (Value, Method, Audio, Bezier), root motion extraction, animation callbacks, procedural animation generation, call mode optimization, and RESET tracks. Use for timeline-based animations, cutscenes, or UI transitions. Trigger keywords: AnimationPlayer, Animation, track_insert_key, root_motion, animation_finished, RESET_track, call_mode, animation_set_next, queue, blend_times.
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Core ComfyUI knowledge covering workflow format, node types, pipeline patterns, and MCP tool usage
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Core module implementation for claude-flow v3. Implements DDD domains, clean architecture patterns, dependency injection, and modular TypeScript codebase with comprehensive testing.
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When the user wants to create competitor comparison or alternative pages for SEO and sales enablement. Also use when the user mentions 'alternative page,' 'vs page,' 'competitor comparison,' 'comparison page,' '[Product] vs [Product],' '[Product] alternative,' or 'competitive landing pages.' Covers four formats: singular alternative, plural alternatives, you vs competitor, and competitor vs competitor. Emphasizes deep research, modular content architecture, and varied section types beyond feature tables.
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Use when building with the yahoo-finance2 TypeScript/Deno/npm library, using its Yahoo Finance data modules, CLI, or MCP server, or contributing to the yahoo-finance2 repository including modules, validation schemas, cached fixtures, and npm/JSR build output.
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Swift 6.2 InlineArray and Span types for zero-overhead memory access, fixed-size collections, and safe pointer alternatives. Use when optimizing performance-critical code paths.
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Generates StoreKit 2 code for all subscription offer types — introductory, promotional, offer codes, and win-back. Includes eligibility checks, offer presentation, and the preferredSubscriptionOffer modifier. Use when adding subscription offers, free trials, or promotional pricing.
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Generates In-App Event metadata templates for App Store Connect — event names, descriptions, badge types, image specs, and deep link configuration. Use when creating events for App Store visibility, engagement campaigns, or seasonal promotions.
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Write professional, empathetic responses to App Store reviews that build trust and turn critics into fans. Use when responding to negative reviews, drafting templates for common review types, or improving review management strategy.
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Variance-stabilizing transformations help with the analysis of heteroskedastic data (i.e., data where the variance is not constant, like count data). This package provide two types of variance stabilizing transformations: (1) methods based on the delta method (e.g., 'acosh', 'log(x+1)'), (2) model residual based (Pearson and randomized quantile residuals).
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Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently.
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Statial is a suite of functions for identifying changes in cell state. The functionality provided by Statial provides robust quantification of cell type localisation which are invariant to changes in tissue structure. In addition to this Statial uncovers changes in marker expression associated with varying levels of localisation. These features can be used to explore how the structure and function of different cell types may be altered by the agents they are surrounded with.
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Classes and statistical methods for large SNP association studies. This extends the earlier snpMatrix package, allowing for uncertainty in genotypes.
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This package provides a workflow for the use of EaSIeR tool, developed to assess patients' likelihood to respond to ICB therapies providing just the patients' RNA-seq data as input. We integrate RNA-seq data with different types of prior knowledge to extract quantitative descriptors of the tumor microenvironment from several points of view, including composition of the immune repertoire, and activity of intra- and extra-cellular communications. Then, we use multi-task machine learning trained in
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granulator is an R package for the cell type deconvolution of heterogeneous tissues based on bulk RNA-seq data or single cell RNA-seq expression profiles. The package provides a unified testing interface to rapidly run and benchmark multiple state-of-the-art deconvolution methods. Data for the deconvolution of peripheral blood mononuclear cells (PBMCs) into individual immune cell types is provided as well.
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This package provides a collection of functions designed for analyzing deconvolution of the bulk sample(s) using an atlas of reference omic signature profiles and a user-selected model. Users are given the option to create or extend a reference atlas and,also simulate the desired size of the bulk signature profile of the reference cell types.The package includes the cell-type-specific methylation atlas and, Illumina Epic B5 probe ids that can be used in deconvolution. Additionally,we included BS
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lisaClust provides a series of functions to identify and visualise regions of tissue where spatial associations between cell-types is similar. This package can be used to provide a high-level summary of cell-type colocalization in multiplexed imaging data that has been segmented at a single-cell resolution.
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Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualiz
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MEM, Marker Enrichment Modeling, automatically generates and displays quantitative labels for cell populations that have been identified from single-cell data. The input for MEM is a dataset that has pre-clustered or pre-gated populations with cells in rows and features in columns. Labels convey a list of measured features and the features' levels of relative enrichment on each population. MEM can be applied to a wide variety of data types and can compare between MEM labels from flow cytometry,
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