824 件 ・ 関連度順
概要と使いどころ
Writing and executing C# scripts and macros against Power BI semantic models using Tabular Editor 2/3. Automatically invoke when the user mentions "C# script", "Tabular Editor script", "TOM scripting", "MacroActions.json", "XMLA", or asks to "automate model changes", "bulk update measures", "create calculation groups", "write a macro", "format DAX expressions", "manage model metadata".
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。
Interactive BPA rule generation for Power BI semantic models; guided discovery, model investigation, and expert rule authoring. Automatically invoke when the user mentions "BPA rule", "Best Practice Analyzer", or asks to "create a BPA rule", "audit BPA rules", "recommend BPA rules", "set up BPA for my team", "check model for best practices", "validate BPA rules", "improve a BPA expression".
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 sculpting in Blender through Python: sculpt a head, face or bust, a stylized or cartoon character, a creature or a realistic portrait; block out from primitives; cut planes, a jaw or gonial angle, cheekbones; fix a jaw or face that looks like a ball or balloon; stylized hair clumps; eyelids; an expression or smirk; plan remesh or multires resolution; reproduce Clay Strips, Crease, Draw Sharp, Scrape, Grab or mask moves without a tablet; final clay renders; or when a sculpt operator crashes headless.
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。
Adversarial "perfection" review — hold a change to an *ideal* bar, not just a correct one, assuming eternal time, unlimited energy, and no ship pressure. Use when the user asks to review "for perfection", to make a defect "impossible to express", or to hunt where a defect "relocates" across review rounds. Grounds every claim in the diff, fans out adversarial verifiers via Workflow, and reports residual surfaces with a structural fix for each. ONLY invoke when the user explicitly asks for a perfection / ideal-bar review.
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。
Optimize content and site structure for AI-driven search experiences including AI overviews, large language model citations, generative answer engines, and AI assistants. Use this skill whenever the user wants to optimize for AI search, get cited by language models, appear in AI overviews, build llms.txt, structure content for AI extraction, or future-proof their SEO for the shift from blue links to AI answers. Triggers on AEO, GEO, AI search, AI SEO, AI overview, generative search, LLM optimization, llms.txt, AI citation, ChatGPT search, Perplexity, Gemini, Claude search, AI assistant optimization, answer engine. Also triggers when the user expresses concern about AI eating their organic traffic or wants to understand how to remain visible as search shifts.
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。
Design or evaluate a brand visual identity system covering logo, color, typography, imagery direction, iconography, and motion principles. Use this skill whenever the user wants to design a logo, build a visual identity, define brand colors, choose brand typography, develop iconography, plan brand imagery, or evaluate an existing identity for cohesion. Triggers on logo design, brand identity, visual identity, brand mark, wordmark, monogram, color palette, brand colors, brand typography, type system, iconography, brand imagery, motion design, brand system, identity system. Also triggers when the user has a brand direction approved and now needs the visual artifacts that express 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。
Query GTEx tissue-specific gene expression database
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。
Query NCBI Gene Expression Omnibus for transcriptomics datasets
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。
Backend development with Node.js/Express/Fastify and PostgreSQL. Use when designing REST or GraphQL APIs, optimizing database queries, implementing authentication, building microservices, handling migrations, or reviewing backend code.
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。
Evaluate completed mediation-competition problems consisting of general information and confidential packets for both sides. Use when asked to validate, audit, score, quality-control, or assess competition readiness; identify factual, numerical, chronological, authority, commercial-logic, or confidentiality defects; test mediation usefulness, BATNAs, settlement corridors, information asymmetry, and opportunities for firmness, cooperation, imagination, and clarification; or produce an author-only validation report. Perform a qualified partial review when packets are missing. Diagnose by default; do not generate a new problem or edit source packets unless repairs are expressly requested. Requires access to all supplied files and a host capable of reading their formats; otherwise disclose the unreviewed material and resulting limits.
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。
Design a RULER emotional literacy sequence for recognising, understanding, labelling, expressing, and regulating emotions. Use when students struggle with emotional regulation, conflict, or anxiety.
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。
Generates multiple means of engagement, representation, and action/expression for a given learning goal. Produces specific, practical alternatives — not generic options — and recommends the highest-impact single change.
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。
Audits an existing lesson against UDL's three principles — engagement, representation, and action/expression. Identifies specific access barriers and suggests concrete modifications ranked by impact.
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。
Godot Expert Auditor: Aurelius. Exhaustive never-list enforcement and architectural slap-down for Godot 4.7 projects. Use when auditing signal decay, ObjectDB orphans, typed Array/Dictionary slop, material.duplicate batch breaks, export case-sensitivity, Expression.execute risks, or sector never-lists. Keywords: auditor, Aurelius, never-list, signal decay, ObjectDB orphans, typed Array, export case-sensitivity, instance uniforms, PackedScene.get_state.
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。
Godot Expert Auditor: Aurelius. Exhaustive never-list enforcement and architectural slap-down for Godot 4.7 projects. Use when auditing signal decay, ObjectDB orphans, typed Array/Dictionary slop, material.duplicate batch breaks, export case-sensitivity, Expression.execute risks, or sector never-lists. Keywords: auditor, Aurelius, never-list, signal decay, ObjectDB orphans, typed Array, export case-sensitivity, instance uniforms, PackedScene.get_state.
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。
Swap an OUTFIT or transfer CLOTHING onto a person LOCALLY, keeping their face, pose and background. Use for "put this jacket on her", "change his shirt", "clothes swap", "try on", "outfit transfer", "virtual try-on", "dress her in", "wear this outfit". Prefer this over API/cloud edit nodes and over generic img2img/inpainting. NOT for identity-preserving edits that are not about clothing (background swaps, relighting, age/expression changes, object removal). Use a general image-edit workflow for those.
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。
Image segmentation is the process of identifying the borders of individual objects (in this case cells) within an image. This allows for the features of cells such as marker expression and morphology to be extracted, stored and analysed. simpleSeg provides functionality for user friendly, watershed based segmentation on multiplexed cellular images in R based on the intensity of user specified protein marker channels. simpleSeg can also be used for the normalization of single cell data obtained f
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。
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.
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 package implements a suite of methods to preprocess data from PTR-TOF-MS instruments (HDF5 format) and generates the 'sample by features' table of peak intensities in addition to the sample and feature metadata (as a single ExpressionSet object for subsequent statistical analysis). This package also permit usefull tools for cohorts management as analyzing data progressively, visualization tools and quality control. The steps include calibration, expiration detection, peak detection and qua
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。
SimBu can be used to simulate bulk RNA-seq datasets with known cell type fractions. You can either use your own single-cell study for the simulation or the sfaira database. Different pre-defined simulation scenarios exist, as are options to run custom simulations. Additionally, expression values can be adapted by adding an mRNA bias, which produces more biologically relevant simulations.
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。
Identification of differentially expressed genes and estimation of the False Discovery Rate (FDR) using both the Significance Analysis of Microarrays (SAM) and the Empirical Bayes Analyses of Microarrays (EBAM).
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。
SGC is a semi-supervised pipeline for gene clustering in gene co-expression networks. SGC consists of multiple novel steps that enable the computation of highly enriched modules in an unsupervised manner. But unlike all existing frameworks, it further incorporates a novel step that leverages Gene Ontology information in a semi-supervised clustering method that further improves the quality of the computed modules.
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。
A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control and visualization.
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。
SpotClean is a computational method to adjust for spot swapping in spatial transcriptomics data. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind mRNA. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case due to bleed from nearby spots, an artifact we refer to as spot swapping. SpotClean is able to estimate the contamination rate in observed data and decontamin
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。