「modi」の検索結果

Apply a Content Driven Development process to AEM Edge Delivery Services development. Use for ALL code changes - new blocks, block modifications, CSS styling, bug fixes, core functionality (scripts.js, styles, etc.), or any JavaScript/CSS work that needs validation.

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。

Guide for creating and writing Expo native modules and views using the Expo Modules API (Swift, Kotlin, TypeScript). Covers module definition DSL, native views, shared objects, config plugins, lifecycle hooks, autolinking, and type system. Use when building or modifying native modules for Expo.

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。

`@expo/ui/swift-ui` package lets you use SwiftUI Views and modifiers in your app.

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。

`@expo/ui/jetpack-compose` package lets you use Jetpack Compose Views and modifiers in your app.

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。

Validate process decomposition and architecture design quality before execution begins. Load when the setup-evaluator agent fires (automatic for agent-chain tasks), or when user says "evaluate this setup", "check the decomposition", "validate the architecture", "is this plan sound", "review the agent design". Catches structural errors, missing knowledge, unrealistic step ordering, and topology mismatches. Does NOT modify — only evaluates.

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。

Bootstrap full agent infrastructure for an existing, already-coded project without modifying a single line of source code. Surveys the repo, infers conventions from manifests, README, git history, and source samples, then generates the missing AGENTS.md, docs/architecture.md, docs/product-soul.md, ADR backfill, and seeded docs/memory/ files. Asks the user only about gaps the repo cannot reveal. Load when the user asks to "retroactive project setup", "backfill agent infrastructure", "bootstrap agents for this existing repo", "onboard agents to a legacy codebase", "set up agents without touching code", or "fill in missing agent context for this project".

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。

Required reference for Prisma ORM 7 SQL driver adapter work. Use when implementing or modifying adapters, adding database drivers, or touching SqlDriverAdapter, Transaction, savepoint, result mapping, or DriverAdapterError behavior. Covers current transaction lifecycle, optional savepoint hooks, original database-error preservation, and verification.

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。

Execute comprehensive web application security testing using Burp Suite's integrated toolset, including HTTP traffic interception and modification, request analysis and replay, automated vulnerability scanning, and manual testing 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。

Access 20+ years of global financial data: equities, options, forex, crypto, commodities, economic indicators, and 50+ technical indicators.

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。

Map N6-methyladenosine (m6A) RNA modifications at single-nucleotide resolution using miCLIP (Linder 2015), miCLIP2 + m6Aboost machine learning (Kortel 2021), GLORI (Liu 2023, antibody-free chemical conversion), DART-seq (Meyer 2019, APOBEC1-YTH fusion), m6Anet (nanopore direct RNA), or MeRIP-seq with calibration. Use when distinguishing antibody-based from antibody-free m6A detection methods, applying the DRACH motif constraint, reconciling cross-method disagreements (DART 44% in DRACH vs GLORI), or detecting m6Am at the cap.

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。

Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.

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。

Performs logistic regression for clinical trial outcomes (binary, ordinal, multinomial) with marginal-vs-conditional estimand reporting per FDA 2023 covariate adjustment guidance, g-computation/standardisation for marginal effects, modified Poisson for RR, Brant test for proportional odds, Firth penalty for separation, and Hauck-Donner detection. Use when modeling binary or ordinal endpoints in confirmatory or exploratory clinical trials.

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。

Detects allele-specific transcription factor or histone modification binding from heterozygous-variant ChIP-seq using WASP (reference-bias filter; mandatory upstream), RASQUAL (joint QTL + bias-corrected testing), BaalChIP (Bayesian beta-binomial with copy-number-aware overdispersion), and AlleleSeq (personalized diploid genome). Handles imprinted-locus awareness, X-inactivation artifacts, cancer copy-number imbalance, and integration with downstream caQTL / bQTL mapping. Use when identifying variants with allelic effects on TF binding, fine-mapping causal regulatory variants, validating deep-learning variant predictions, or characterizing cis-acting regulatory effects.

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。

Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2025 Nat Genet; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.

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。

Sequence-based deep learning for ATAC-seq using chromBPNet, BPNet, scBasset, or EnFormer. Use when correcting Tn5 bias with neural networks beyond k-mer models, predicting per-base accessibility profiles, scoring in silico variant effects at GWAS or rare-variant SNPs, discovering motifs via DeepLIFT/TF-MoDISco from a trained model, or generating cell-type-specific accessibility predictions for unobserved cell 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。

Use this skill when the user asks about refrigerated (reefer) trucking — cold chain compliance, FDA temperature recording requirements, Sanitary Transportation Rule (STR / 21 CFR 1.900-1.934), commodity-specific temperature ranges, refrigeration unit operation, temperature recorder calibration, and how a load is challenged if temperature deviates. Cite 21 CFR Part 1 Subpart Q.

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 this skill when the user asks about workers compensation for trucking — required by state law, classification of drivers, premium calculation (rate × experience modifier × payroll), Common Claim types in trucking, returning injured drivers to work, claims management, premium reduction strategies, and the difference between WC and Occupational Accident insurance for owner-operators. Reference state WC laws + IFTA / fleet insurance.

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 this skill when the user asks about the agricultural exemption from Hours of Service (HOS) rules under 49 CFR 395.1(k) — what qualifies as agricultural commodity, the 150-air-mile radius rule, state-declared planting/harvest seasons, livestock hauling specifics, and how to document operations under the exemption. Cite 49 CFR 395.1(k) and 49 USC 5117.

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 this skill when the user asks about cargo securement requirements under 49 CFR 393 Subpart I — Working Load Limit (WLL), tiedown counts, tiedown types, commodity-specific securement rules (lumber, metal coils, paper rolls, concrete pipe, intermodal containers, vehicles, large boulders, dressed lumber, building materials), CVSA out-of-service criteria, and how to calculate aggregate WLL. Cite 49 CFR 393.100-136.

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 modifying anything under features/files/, app/(core)/files/, app/(public)/share/, or any caller that uploads, downloads, lists, moves, renames, shares, or previews user files.

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。

Find and patch the water jump velocity inside CCSPlayer_MovementServices_CheckJumpButton in CS2 binary using IDA Pro MCP. This patch changes the water jump velocity from 100.0f to 145.0f by modifying the immediate operand of a mov instruction. Use this skill when reverse engineering CS2 server.dll or libserver.so to locate and patch the water jump height value. Trigger: water jump patch, CheckJumpButton water velocity, jump height patch, water jump 145

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。

Three.js shaders - GLSL, ShaderMaterial, uniforms, custom effects. Use when creating custom visual effects, modifying vertices, writing fragment shaders, or extending built-in materials.

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 and generate Convex database schemas with proper validation, indexes, and relationships. Use when creating schema.ts or modifying table definitions.

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 CQRS Commands and Handlers for PHP 8.5. Creates immutable command DTOs with handlers that modify state. Includes unit tests.

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。