「orr」の検索結果

Detects differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease states.

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。

Analyze TF motif accessibility variability across samples or single cells using chromVAR. Use when identifying TF motifs whose accessibility correlates with conditions, computing per-sample motif z-scores after matched background correction, comparing to ArchR / Signac equivalents, or distinguishing motif-accessibility signal from per-site footprinting.

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。

Estimates bivariate genetic correlation (rg) between traits from GWAS summary statistics or individual-level genotypes using cross-trait LDSC, HDL, LAVA, rho-HESS, GREML-bivariate, Popcorn, and HDL-L. Use when quantifying shared genetic architecture between two traits, screening MR validity before causal inference, distinguishing global from locus-level rg, estimating trans-ancestry rg, separating partial from full causation via LCV gcp, or producing a STROBE-MR-compliant cross-trait sensitivity battery. Cross-trait LDSC intercept absorbs sample overlap and is NOT a bias; HDL is biased under sample overlap above ~5%. High rg between exposure and outcome motivates CHP-aware MR sensitivity (CAUSE, LHC-MR).

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 BEFORE writing any code that adds Additive White Gaussian Noise (AWGN) to signals and converts between SNR, Eb/No, Es/No, and per-subcarrier SNR for communications simulations, using awgn(), convertSNR(), berawgn(). The default MATLAB patterns for AWGN (e.g., 'measured' option, manual SNR formulas) produce subtly incorrect results. This skill specifies the correct calling conventions, required function usage, and critical anti-patterns that must be avoided.

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。

Smart triggered RF capture on NI USRP radios with Wireless Testbench — record only when a signal of interest appears, not continuously (triggered spectrum sensing). Use energy detection to capture when signal power rises above the noise floor, or preamble detection to cross-correlate against a known sequence (WLAN L-LTF, 5G NR PSS/SSS, LTE PSS, Zadoff-Chu, custom protocols) and capture only when that protocol's preamble is detected. Use when implementing triggered (wake-on-signal) capture or spectrum sensing, capturing protocol-based signals via cross-correlation, calibrating detection thresholds (plotDetectionSignals / plotThreshold), scanning frequency bands for activity, or building transmit-then-detect workflows.

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 BEFORE writing any code that builds or simulates OFDM systems. The default MATLAB patterns for OFDM (e.g., direct ifft/fft, awgn with 0 dBW power, missing symOffset) produce subtly incorrect results — always use ofdmmod/ofdmdemod instead of direct IFFT/FFT. This skill specifies the correct calling conventions for ofdmmod, ofdmdemod, ofdmChannelResponse, ofdmEqualize, and critical anti-patterns that must be avoided. Use when building OFDM transmitters or receivers, allocating subcarriers and guard bands, inserting pilots, computing SNR for OFDM, configuring fading channels (Rayleigh/Rician), estimating and equalizing channels, implementing timing and frequency synchronization, adding LDPC coding, designing resource grids, or setting up OFDM link 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。

Guides the agent to reference official MathWorks Documentation and Help. Determine correct function syntax and workflows from user guides when deeper context is needed. Minimize iterations and repetitive trial and error. Use this skill to: Identify correct syntax and configuration details. Retrieve relevant, version-specific (or release-specific) information from official documentation. Consult user guides when conceptual or workflow context is needed. Apply best practices.

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 auditing CloudBase cloud API wrappers, MCP tools, generated action metadata, or related docs for outdated or incorrect action names, parameters, casing, request shapes, or missing contract tests, especially during periodic quality review or before preparing corrective PRs.

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 running or scheduling periodic repository review cycles that must dispatch the correct reviewer, aggregate findings, and escalate outcomes into reports, issues, or corrective PRs across API contracts, documentation freshness, code quality, and CloudBase skill quality.

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。

Audit code for correctness and rigor — logic errors, silent error swallowing, wishful thinking, and unjustified fallbacks. This is not a style review; it's a logic review. Use when you want a focused correctness audit separate from the full code-police pass.

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。

RAID log methodology with decision extraction from emails and meeting notes. Use when asked to identify risks, log assumptions, track issues, extract decisions from correspondence, create or update a RAID log, escalate a risk to an issue, assess project risks, validate or challenge assumptions, or capture scope-relevant decisions. Also triggers when the user pastes email chains and asks what decisions were made, or needs to find buried decisions and untested assumptions in correspondence. Trigger on: 'risk report', 'RAID log', 'what are the risks', 'what decisions were made', 'update the risk register', 'what assumptions are we making', 'what could go wrong', 'flag any issues', 'extract decisions from these emails'.

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。

Génération de sujets d'entraînement complets pour l'épreuve de NOTE DE SYNTHÈSE (5 heures, coefficient 3) de l'examen d'accès au CRFPA : choix du sujet, dossier documentaire de 16 à 20 documents assemblé à partir de sources réelles vérifiées (textes, jurisprudence, travaux parlementaires, doctrine) avec contrôle du verbatim, puis grille de notation au modèle de la Commission nationale (résumé de chaque document, problématique, thèmes cotés sur 20, plan). Livraison en deux fichiers .docx distincts, le sujet et la grille. Si l'utilisateur n'impose pas de sujet, en chercher un dans l'actualité juridique. Mode aveugle pour l'étudiant qui compose sans rien voir du corrigé. Déclencher dès que l'utilisateur demande un sujet de note de synthèse, un dossier documentaire, une note de synthèse blanche, un entraînement à l'épreuve de 5 heures du CRFPA ou de l'IEJ, ou une grille de note de synthèse. NE PAS déclencher pour les épreuves de 3 heures (sujet-crfpa-toutes-matieres) ni pour la correction de copies.

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。

Apply elite legal writing techniques drawn from Justice Kagan, Boies & Olson, and other top advocates to any legal document — briefs, submissions, letters, opinions, memos, or persuasive correspondence. This skill covers both prose craft (clarity, examples, parallel construction, voice, quoting) and architectural strategy (argument sequencing, framing, openings, endings). Use this skill whenever the user asks you to draft, edit, or improve legal writing and wants it to be genuinely persuasive — not just correct. Also trigger when the user mentions 'make it more persuasive', 'sharpen this', 'strengthen the argument', 'rewrite this section', 'punch it up', 'legal writing', 'draft a brief', 'draft a submission', 'write like a top advocate', or asks you to improve the rhetoric, flow, or force of any legal document. This skill complements the larissa-legal-voice skill (which handles tone and personal voice) by adding the persuasive technique layer on top.

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。

Assistant académique pour enseignants-chercheurs en droit. Six tâches : (1) recherche juridique approfondie avec accent doctrinal, (2) relecture de documents avec vérification des références, commentaires Word, détection d'indices de plagiat/IA et cohérence argumentative, (3) création de sujets et corrigés d'exercices juridiques universitaires, (4) mise à jour de cours, ouvrages et documents juridiques, (5) création de cours avec support docx et PPTX, (6) préparation de fiches de TD. MCP : OpenLegi, Themia, LegalDataHunter. Déclencher pour toute demande liée à l'enseignement ou la recherche en droit : cours, TD, fiches de TD, examens, sujets, corrigés, relecture, mise à jour, recherche doctrinale. NE PAS déclencher pour les QCM (utiliser qcm-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。

Build students' capacity to compare accounts across multiple historical sources — identifying agreements, contradictions, and gaps. Use when students treat individual documents as complete answers rather than partial perspectives.

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 formative assessments that make students' historical thinking visible — revealing whether they source, close-read, contextualise, and corroborate. Use when assessing historical thinking skills or planning a diagnostic.

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。

Dino normalizes single-cell, mRNA sequencing data to correct for technical variation, particularly sequencing depth, prior to downstream analysis. The approach produces a matrix of corrected expression for which the dependency between sequencing depth and the full distribution of normalized expression; many existing methods aim to remove only the dependency between sequencing depth and the mean of the normalized expression. This is particuarly useful in the context of highly sparse datasets such

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 the user wants a genre-specific scene template, starting point for a type of video (action, horror, romance, product ad, documentary, sci-fi, dance, etc.), or asks for prompt examples for a specific style of content.

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。

Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.

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。

在MATLAB中计算复信号的自相关函数向量，且不使用xcorr和toeplitz函数。

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。

Application Security Posture Management persona. Correlates findings from SAST, DAST, and SCA tools, deduplicates noise, maps vulnerabilities to specific code commits, and generates targeted remediation PRs.

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。

Help users write correct R code for async, parallel, and distributed computing using mirai. Use when users need to run R code asynchronously or in parallel, write mirai code with correct dependency passing, set up parallel workers, convert from future or parallel, use mirai_map, integrate with Shiny or promises, or configure cluster/HPC computing.

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。

.omd/preferences.md의 status:pending 항목을 Core v2 System Graph에 합쳐 DESIGN.md를 재생성하고 status를 applied로 플립. '프리퍼런스 정리해줘', 'fold preferences', 'apply all corrections', 「好みをDESIGN.mdに反映」, 「套用偏好」류의 요청에 트리거. 단발성 교정 기록은 omd:remember.