「values」の検索結果

Scan, connect, and communicate with Bluetooth Low Energy peripherals and publish local peripheral services using Core Bluetooth. Use when implementing BLE central or peripheral roles, discovering services and characteristics, reading and writing characteristic values, subscribing to notifications, configuring background BLE modes, restoring state after app relaunch, or working with CBCentralManager, CBPeripheral, CBPeripheralManager, CBService, CBCharacteristic, CBUUID, or Bluetooth Low Energy 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。

Implement, review, or improve accessibility in iOS/macOS apps with SwiftUI and UIKit. Use when adding VoiceOver support with accessibility labels, hints, values, and traits; when grouping or reordering accessibility elements; when managing focus with @AccessibilityFocusState; when supporting Dynamic Type with @ScaledMetric; when building custom rotors or accessibility actions; when auditing a11y compliance; or when adapting UI for assistive technologies and system accessibility preferences.

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。

Implement, review, or improve CloudKit and iCloud sync in iOS/macOS apps. Use when working with CKContainer, CKRecord, CKQuery, CKSubscription, CKSyncEngine, CKShare, NSUbiquitousKeyValueStore, or iCloud Drive file coordination; when syncing SwiftData models via ModelConfiguration with cloudKitDatabase; when handling CKError codes for conflict resolution, network failures, or quota limits; or when checking iCloud account status before performing sync 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。

Measure ad effectiveness with privacy-preserving attribution using AdAttributionKit. Use when registering ad impressions, handling attribution postbacks, updating conversion values, implementing re-engagement attribution, configuring publisher or advertiser apps, or replacing SKAdNetwork with AdAttributionKit for ad measurement.

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 query Apple Health data using HealthKit. Covers HKHealthStore authorization, sample queries, statistics queries, statistics collection queries for charts, saving HKQuantitySample data, background delivery, workout sessions with HKWorkoutSession and HKLiveWorkoutBuilder, HKUnit, and HKQuantityTypeIdentifier values. Use when integrating with Apple Health, displaying health metrics, recording workouts, or enabling background health data delivery.

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。

Expert guidance on iOS accessibility best practices, patterns, and implementation. Use when developers mention: (1) iOS accessibility, VoiceOver, Dynamic Type, or assistive technologies, (2) accessibility labels, traits, hints, or values, (3) automated accessibility testing, auditing, or manual testing, (4) Switch Control, Voice Control, or Full Keyboard Access, (5) inclusive design or accessibility culture, (6) making apps work for users with disabilities.

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。

Create a company-specific job application package from a JD, resume.md, cover letter drafts, HR review, and optional company values input. Use when the user wants 지원 패키지, submission checklist, application folder structure, final answer snippets, email draft support, or safe pre-submission preparation without automatic final submission.

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。

Write Korean job application cover letter drafts from a question, JD, resume.md, and optional company values input. Use when the user asks for 자기소개서, 자소서, 지원동기, 입사 후 포부, 필살기, 3C4P experience decomposition, experience-based application answers, or tailored cover letter text grounded in resume.md evidence.

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。

Mobile-first design thinking and decision-making for iOS and Android apps. Touch interaction, performance patterns, platform conventions. Teaches principles, not fixed values. Use when building React Native, Flutter, or native mobile apps.

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。

Manage secrets using Azure Key Vault Secrets SDK for JavaScript (@azure/keyvault-secrets). Use when storing and retrieving application secrets or configuration values.

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。

Investigates distributed application performance using PostHog APM (OpenTelemetry span) data via MCP. Use when the user asks about service traces, slow HTTP/database spans, error spans, trace IDs, or span attributes — not AI observability traces or product logs. Uses posthog:query-apm-spans, posthog:apm-trace-get, posthog:apm-services-list, posthog:apm-attributes-list, and posthog:apm-attribute-values-list.

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。

Extract campaign or business KPIs from report documents — metrics, values, periods, and targets.

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。

Extract HS codes, declared values, and duty amounts from customs declaration documents, then generate an XLSX import duty log.

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。

Measure ad effectiveness with privacy-preserving attribution using AdAttributionKit. Use when registering ad impressions, handling attribution postbacks, updating conversion values, implementing re-engagement attribution, configuring publisher or advertiser apps, or replacing SKAdNetwork with AdAttributionKit for ad measurement.

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。

Philosophy-first writing partner for Medium with tactical depth. Combines human storytelling values with production-ready technical content tools. Six content modes based on staff pick analysis, mode-specific quality gates, evidence requirements, annotated examples, and comprehensive scripts integration. Designed for sustainable practice, not algorithm gaming.

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。

Reads, queries, and writes bigWig indexed binary signal tracks (coverage, fold-change, conservation, methylation-rate) with pyBigWig (Python) and the UCSC Kent tools (bedGraphToBigWig, bigWigToBedGraph, bigWigInfo, bigWigSummary, bigWigAverageOverBed) and deepTools (multiBigwigSummary, computeMatrix, bigwigCompare). Covers the central trap that a wide query returns a precomputed zoom-level summary (by default the mean, which annihilates narrow peaks) not per-base data, when exact=True/values() is mandatory, the NaN-not-zero gap-handling fork, choosing mean vs max vs sum vs coverage by biological question, and the sorted-bedGraph plus chrom.sizes build requirement. Use when extracting signal at regions, computing mean signal per gene/peak, building a browser track from bedGraph, comparing tracks, or building TSS/gene-body metaprofiles.

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 set operations on genomic intervals - intersect (-wa/-wb/-wo/-wao/-loj/-c/-v/-u), subtract (-A), merge (-d, -c/-o), complement, cluster, multiinter, unionbedg, map, and groupby - with bedtools (CLI) and pybedtools/pyranges/bioframe (Python). Covers the sorted-input contract and the -sorted chromosome-order footgun, reciprocal/fractional overlap (-f/-F/-r/-e) and the A-vs-B asymmetry, -split for spliced/BED12/BAM features, and jaccard/fisher as mechanics only. Use when finding overlapping or unique regions between BED/peak/feature files, building consensus peaksets, removing blacklisted regions, transferring annotation values onto intervals, or computing interval-set similarity; route overlap-significance testing to overlap-significance.

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。

Creates DE-specific diagnostic and result visualizations using DESeq2/edgeR built-in functions and lightweight ggplot2 wrappers. Covers MA plot (with the shrunken-LFC compression effect), volcano (with the apeglm caveat that p-values are unchanged), PCA on VST/rlog (never raw counts), sample distance heatmaps, top-DE-gene heatmaps with the row-scaling trap, dispersion / BCV plot interpretation, p-value histogram diagnostics, plotCounts for individual genes, blind=TRUE vs FALSE rationale, and the n=3 visualization stake. Use when generating DE diagnostic plots, choosing VST vs rlog for visualization, troubleshooting suspicious plot patterns (shifted MA cloud, batch-dominated PCA, anti-conservative p-value histogram), or building a standard QC figure panel.

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。

Analyzes pooled CRISPR screens with MAGeCK (Li et al 2014), covering count generation (mageck count), the RRA two-condition workflow (mageck test using alpha-RRA over per-sgRNA negative-binomial p-values), the MLE multi-condition workflow (mageck mle with explicit design matrix and beta-score output), normalization choice (median vs total vs control-sgRNA vs spike-in), sgRNA efficiency injection, paired-sample testing, time-course design, drug-screen versus dropout-screen design matrices, MAGeCKFlute and MAGeCK-VISPR downstream visualization, and decision logic for when to use MAGeCK vs JACKS / BAGEL2 / drugZ / Chronos. Use when running a fresh CRISPR screen analysis, picking RRA vs MLE for the experimental design, choosing a normalization method from QC signatures, debugging MLE convergence failure or NaN beta scores, comparing MAGeCK output across tools, or building a batch-aware multi-cell-line / multi-condition MLE design matrix.

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。

Run remote BLAST searches against NCBI servers using Biopython Bio.Blast.NCBIWWW. Use when identifying unknown sequences, finding homologs, picking the correct BLAST program (blastn/blastp/blastx/tblastn/tblastx/psiblast/megablast/dc-megablast), interpreting Karlin-Altschul E-values, avoiding the max_target_seqs trap (Shah 2019), choosing composition-based statistics, or limiting searches by organism. Covers RID lifecycle, database choice (nt/nr/refseq_select/swissprot), word-size and CBS taxonomy.

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 recurrent and driver copy number alterations across a tumor cohort with GISTIC2 (G-score, Ziggurat deconstruction, focal vs broad/arm-level analysis, q-values from permutation) and quantify copy-number signatures with the Steele 2022 COSMIC framework and the Drews 2022 CINSignatures framework. Covers driver-gene localization from recurrence peaks, distinguishing focal drivers from arm-level passengers, and the caller-sensitivity caveats of copy-number signatures. Use when finding recurrently amplified or deleted regions in a cohort, localizing driver genes, separating focal from broad events, running GISTIC2, or extracting copy-number mutational signatures.

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。

Manage secrets using Azure Key Vault Secrets SDK for JavaScript (@azure/keyvault-secrets). Use when storing and retrieving application secrets or configuration values.

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 observed site vegetation, species lists, land-cover descriptions, or plant management PFTs to EcoSIM vegetation codes using templates/ecosim_pftpar_20260303.nc.cdl. Use when choosing or validating EcoSIM pft_type values, pfts entries, pfts_short categories, pfts_long plant type meanings, or the combined short-code plus Koppen climate numerical code used by plant management inputs.

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。

Edit EcoSIM PFT-parameter NetCDF files such as ecosim_pftpar_20260303.nc with the ParEditor class from applications/notebooks/scripts/ParamEditor.py. Use when asked to modify, scale, copy, compare, or delete PFT parameter records for a plant functional type (six-character pfts code such as gr3s43 or ndlf34), update microbe parameter NetCDF values, or maintain the JSONL change log that records past parameter changes. Never modify plant_trait.*.desc files; they are read-only references.

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。