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概要と使いどころ
Call protein-RNA binding sites from CLIP-seq BAM with CLIPper, PureCLIP, Skipper, Piranha, omniCLIP, CTK, CLAM, or Paraclu. Use when choosing between coverage-based, HMM-based, beta-binomial window-based, and crosslink-site-based peak callers; applying ENCODE eCLIP thresholds (log2 IP/SMInput >= 3, -log10 p >= 3); deciding when SMInput is mandatory; or reconciling peak-set discordance between callers for the same RBP.
日本語の概要は準備中です。原文の説明を表示しています。
Preprocess CLIP-seq reads (eCLIP, iCLIP, iCLIP2, iCLIP3, irCLIP, PAR-CLIP, FLASH) with protocol-specific UMI extraction, adapter trimming, length filtering, and post-alignment PCR-duplicate collapse. Use when raw CLIP FASTQ must be turned into deduplicated, crosslink-preserving BAM input for peak calling; choosing between two-pass and single-pass adapter trimming; deciding minimum read length; or mapping UMI patterns to specific eCLIP/iCLIP/iCLIP2/iCLIP3 library preps.
日本語の概要は準備中です。原文の説明を表示しています。
Profiles RNA-binding protein targets without antibody or UV crosslinking using STAMP (APOBEC1-RBP fusion, C-to-U editing), scSTAMP (single-cell), TRIBE/HyperTRIBE (ADAR-RBP, A-to-I editing), DART-seq (APOBEC1-YTH for m6A), or Bullseye/SAILOR edit-site detection pipelines. Use when antibody is unavailable or specificity is doubtful, when single-cell RBP profiling is needed (scSTAMP), or when in vivo RBP profiling without UV is preferred.
日本語の概要は準備中です。原文の説明を表示しています。
Annotate CLIP-seq peaks or crosslink sites to RNA features (5'UTR, CDS, 3'UTR, intron, splice junction, snoRNA, tRNA, ncRNA, repeat elements) with ChIPseeker, RCAS, RBP-Maps (Yeo splicing regulatory maps), and bedtools, applying feature-priority hierarchies, transcript-context resolution, and metagene aggregation. Use when characterizing where in transcripts an RBP binds, comparing peak distribution across regions, generating splicing-regulatory maps relative to alternative-splicing events, or distinguishing exonic vs intronic vs UTR binding.
日本語の概要は準備中です。原文の説明を表示しています。
Identifies differentially bound ChIP-seq regions between conditions using DiffBind, csaw (sliding windows), DESeq2/edgeR/PyDESeq2 on count matrices, NormR (control-aware), or MAnorm2. Distinguishes three distinct normalization problems (composition bias, trended bias, global shifts) and matches each to its appropriate fix including spike-in scaling. Use when comparing ChIP-seq binding between experimental conditions, choosing normalization for global vs local changes, integrating spike-in data, or reconciling DiffBind/DESeq2 disagreement.
日本語の概要は準備中です。原文の説明を表示しています。
Identify differentially accessible chromatin regions across conditions using DiffBind, csaw, DESeq2, or edgeR. Use when comparing ATAC-seq accessibility between treatment groups, choosing between consensus-peak vs sliding-window approaches, picking the correct normalization (full library vs reads-in-peaks), correcting batch with SVA/RUVseq, or interpreting log2FC and FDR thresholds in a chromatin context.
日本語の概要は準備中です。原文の説明を表示しています。
Check that the UI you actually rendered uses the design system, by reading computed styles in the running app against the project's design tokens. Catches hardcoded hex colors, off-palette backgrounds, invisible or unusable controls, and animations that never ran. Use after building or restyling a component, when a design review is wanted, when a UI looks slightly off but nobody can say why, or when a design system exists and nothing checks whether the code follows it.
日本語の概要は準備中です。原文の説明を表示しています。
Build SwiftUI layouts using stacks, grids, lists, scroll views, forms, and controls. Covers VStack/HStack/ZStack, LazyVGrid/LazyHGrid, List with sections and swipe actions, ScrollView with ScrollPosition and scroll-driven reveal surfaces, Form with validation, Toggle/Picker/Slider, .searchable, and overlay patterns. Use when building data-driven layouts, collection views, paged detail reveals, settings screens, search interfaces, or transient overlay UI.
日本語の概要は準備中です。原文の説明を表示しています。
Integrate Apple Pay payments and Wallet passes using PassKit. Use when adding Apple Pay buttons, creating payment requests, handling payment authorization, adding passes to Wallet, configuring merchant capabilities, managing shipping/contact fields, or working with PKPaymentRequest, PKPaymentAuthorizationController, PKPaymentButton, AddPassToWalletButton, PKPass, PKAddPassesViewController, PKPassLibrary, Wallet pass distribution, or Apple Pay checkout flows for physical goods, real-world services, donations, and eligible recurring payments.
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used when creating Ultralytics-branded content of any kind, including PDF, PPTX, Canva, or Google Slides presentations, DOCX documents, marketing or newsletter HTML emails, social media post copy or visuals, website or landing page designs, and dataset or model result visuals. Provides official colors, typography, logo rules, naming rules, and tone of voice from ultralytics.com/brand.
日本語の概要は準備中です。原文の説明を表示しています。
Diagnose and fix parfor errors in MATLAB. Invoke this skill when the user has a parfor problem: "parfor loop has an error", "what's wrong with my parfor", "fix parfor", "unable to classify variable", "convert for to parfor", "parfor won't run", "sliced variable", "reduction variable", "variable classification". Also invoke when you read a .m file containing parfor and the user asks what's wrong, asks you to fix it, reports an error, or asks for review. Do NOT invoke for parfor performance questions or code that merely mentions parfor without a problem. ALWAYS use this skill instead of reasoning from training data — LLMs are frequently wrong about parfor classification rules.
日本語の概要は準備中です。原文の説明を表示しています。
Generate C/C++ or CUDA code from an AI model (PyTorch, LiteRT) using MATLAB Coder or GPU Coder. Use when the user wants to integrate an AI model into an application with code generation as the end goal — generating MEX, CUDA MEX, static library, dynamic library, or executable — or using the model in Simulink for simulation and code generation. Covers PyTorch ExportedProgram (.pt2) via loadPyTorchExportedProgram and LiteRT (.tflite) via loadLiteRTModel (R2026a+). Keywords: PyTorch, torch, .pt2, ExportedProgram, loadPyTorchExportedProgram, invoke, codegen, MEX, CUDA, GPU, C, C++, deploy, AI model, deep learning model, LiteRT, TFLite, TensorFlow Lite, Simulink, slbuild, PyTorch ExportedProgram block, MATLAB Function block, dlosslib, loadLiteRTModel.
日本語の概要は準備中です。原文の説明を表示しています。
Read, write, and manipulate medical imaging data (DICOM, NIfTI, NRRD) in MATLAB. Covers Image Processing Toolbox functions (dicomreadVolume, niftiread, dicomContours, dicomanon) and Medical Imaging Toolbox enhanced APIs (medicalVolume, medicalImage, medicalref3d, extractSlice, updateOrientation). Use when reading medical files, listing DICOM series, extracting spatial referencing, changing orientation, working with RT structures, or anonymizing DICOM data. Some features require Medical Imaging Toolbox — see skill body and references for details.
日本語の概要は準備中です。原文の説明を表示しています。
Verify claims, quotations, citations and references against live primary sources, and produce a reproducible audit record instead of an assertion. Use when asked to: check whether a quotation, verse, statistic, date, attribution or citation is accurate; verify references before publishing a report, thesis, article, slide deck or legal filing; confirm a text against an authoritative database; cross-check a claim across independent sources; build a citation register or verification appendix; find out whether two datasets agree; detect a misattributed or fabricated quotation; or audit a document's existing citations. Source-agnostic by design, with working providers for REST/JSON APIs, scraped HTML, bulk corpora, bibliographic catalogues (OpenLibrary), DOI resolution (Crossref) and reference works (Wikipedia) -- including scriptural tiers (Qur'an.com, sunnah.com and an independent hadith corpus) with full diacritic handling for Arabic, Hebrew, Greek and any script with optional marks. Covers content-addressed matching when sources disagree on numbering, graded match verdicts, byte-level integrity checks, published-string verification, and a catalogue of silent failure modes (identifier divergence, mojibake, scraper blocking, false-positive keyword rules, missing glyphs, unreliable PDF text layers).
日本語の概要は準備中です。原文の説明を表示しています。
Simple scroll-triggered reveal animations using AOS (Animate On Scroll). Use this skill when building marketing pages, landing pages, or content-heavy sites requiring basic fade/slide effects without complex animation orchestration. Triggers on tasks involving scroll animations, scroll-triggered reveals, AOS, simple animations, or basic scroll effects. Alternative to GSAP ScrollTrigger and Locomotive Scroll for simpler use cases. Compare with motion-framer for React-specific animations.
日本語の概要は準備中です。原文の説明を表示しています。
Multi-agent orchestrator for Claude Code. Use when user mentions gastown, gas town, gt commands, bd commands, convoys, polecats, crew, rigs, slinging work, multi-agent coordination, beads, hooks, molecules, workflows, the witness, the mayor, the refinery, the deacon, dogs, escalation, or wants to run multiple AI agents on projects simultaneously. Handles installation, workspace setup, work tracking, agent lifecycle, crash recovery, and all gt/bd CLI operations.
日本語の概要は準備中です。原文の説明を表示しています。
SVG generation via DAX measures and extension measures with ImageUrl data category for inline visualizations in PBIR reports, including ready templates for matrix and table cells, new card images and per-button images on the new button slicer. Automatically invoke when the user mentions "SVG visual", "DAX sparkline", "SVG measure", "inline graphics with DAX", "ImageUrl data category", "extension measure", or asks to create any DAX-generated chart (progress bars, bullet charts, KPI indicators, data bars, gauges, donut charts, lollipop charts, dumbbell charts, status pills, overlapping bars, boxplots, IBCS bars, jitter plots, box-and-whisker charts).
日本語の概要は準備中です。原文の説明を表示しています。
When the user wants to define, audit, or apply brand strategy—purpose, values, positioning, storytelling, voice, narrative (not only visuals). Also use when the user mentions "brand strategy," "brand story," "brand storytelling," "brand voice," "brand identity," "brand guidelines," "brand purpose," "brand values," "origin story," "brand narrative," "brand personality," "brand archetype," "slide deck branding," "PPT brand colors," or "document style guide." For typography, colors, design tokens, and frontend visuals, use brand-visual-generator.
日本語の概要は準備中です。原文の説明を表示しています。
Use when a Unity 6.3 game must run in a browser: 'export to WebGL', Web build settings or Build Profiles, gzip or Brotli, Decompression Fallback, server headers, 'Unable to parse .framework.js.br', build too big or slow to load, itch.io, CrazyGames or Poki limits, Addressables or AssetBundles on the Web, 'Could not produce class with ID', Task.Run freezing the browser, WebGL 2 vs WebGPU, .jslib and SendMessage interop, a page text field that gets no keystrokes, mobile browsers crashing, HTTPS for sensors, hosting and rollout.
日本語の概要は準備中です。原文の説明を表示しています。
Use when adding or fixing sound in Godot 4.7: audio buses and effects, volume sliders, 'too many sounds', combat audio with hundreds of enemies, sounds clipping or distorting, 3D sound too quiet or not panning, reverb zones not working, occlusion, adaptive or interactive music (AudioStreamInteractive, layers, stingers), music loops with gaps, SFX that repeat, AudioStreamRandomizer, AudioStreamGenerator, import settings, or importing AI-generated music, SFX and voice from Scenario or ElevenLabs.
日本語の概要は準備中です。原文の説明を表示しています。
Use when building or fixing game UI in Godot 4.7: main menu, settings screen, HUD (health bar, ammo, minimap frame), Control layout, containers, anchors, themes in code, 'UI scales wrong on phones', 'text too small on 4K', notch or safe area, stretch mode and aspect, gamepad or keyboard menu navigation, focus lost, input rebinding screen, localization (CSV, PO, tr_n plurals, Arabic RTL, CJK fonts, pseudolocalization), screen reader accessibility, UI scale slider, or clicks not reaching the game.
日本語の概要は準備中です。原文の説明を表示しています。
Use when animating 2D game art with Scenario: walk, idle or attack cycles in 4 or 8 directions, isometric sprite sheets, animated GIFs, frame-by-frame pixel art animation, VFX loops such as fire or smoke, looping a sprite seamlessly, animating an existing character image, a full animation sheet from one prompt, extracting animation frames from a generated video, or slicing a sprite sheet into engine-ready frames for Unity, Godot, or Unreal. Keywords: spritesheet, keyframes, frame grid, GIF.
日本語の概要は準備中です。原文の説明を表示しています。
Use when animating a character or prop in Maya through Python: a walk or run cycle, a jump or landing, a heavy lift, throw or punch, an acting or dialogue shot, lip sync, eye darts and blinks, stepped blocking, splining, polish, Graph Editor tangents, animation layers, mocap cleanup or retargeting, game clips and root motion, or reviewing a playblast. Also when motion looks floaty, weightless, mushy, poppy, posey, sliding or chattery, or feet slap or skate.
日本語の概要は準備中です。原文の説明を表示しています。
Use when modeling hard surface in ZBrush 2026 (sci-fi helmet, weapon, prop, armor plates, mechanical parts, kitbash, vents, panel lines, bolts, concept to game asset), when using ZModeler, Dynamic Subdivision and creasing, Live Boolean, Knife, Clip, Slice or Trim curves, IMM, ArrayMesh, NanoMesh or Panel Loops, or when edges look razor sharp, a Make Boolean Mesh fails or leaves holes, planes look wobbly, Mirror And Weld erased an edit, or ZRemesher ruins a hard-surface part.
日本語の概要は準備中です。原文の説明を表示しています。