「ept」の検索結果

BUSseq R package fits an interpretable Bayesian hierarchical model---the Batch Effects Correction with Unknown Subtypes for scRNA seq Data (BUSseq)---to correct batch effects in the presence of unknown cell types. BUSseq is able to simultaneously correct batch effects, clusters cell types, and takes care of the count data nature, the overdispersion, the dropout events, and the cell-specific sequencing depth of scRNA-seq data. After correcting the batch effects with BUSseq, the corrected value ca

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。

The MsDataHub package uses the ExperimentHub infrastructure to distribute raw mass spectrometry data files, peptide spectrum matches or quantitative data from proteomics and metabolomics experiments.

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。

PhIPData defines an S4 class for phage-immunoprecipitation sequencing (PhIP-seq) experiments. Buliding upon the RangedSummarizedExperiment class, PhIPData enables users to coordinate metadata with experimental data in analyses. Additionally, PhIPData provides specialized methods to subset and identify beads-only samples, subset objects using virus aliases, and use existing peptide libraries to populate object parameters.

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。

Harmonize data management of multiple experimental assays performed on an overlapping set of specimens. It provides a familiar Bioconductor user experience by extending concepts from SummarizedExperiment, supporting an open-ended mix of st

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。

TrailSnap 需求规格、Agent 编码交付、提交与 PR 工作流。Use when implementing TrailSnap requirements, managing platform requirements and acceptance criteria, preparing commits, pushing branches, creating pull requests, or monitoring PR CI in this repository.

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。

Point Aeon at a service's /feedback endpoint - pull what agents reported (bugs, missing features), triage and cluster it, then build the best accepted request as a PR for a human to approve.

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 QA validation on a pull request — boots the local environment, tests acceptance criteria, and optionally posts the report as a PR comment. Standalone entry point for the qa-engineer agent.

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 ad concepts that stop attention in the first 3 seconds. Trigger on "ad creative", "thumbstopper", "video ideas".

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。

Lead substantial Codex builds with one configured implementation worker and lead acceptance. Use when the user requests codex-on-crack, names a model to delegate work to, asks to run subagents through Claude, or requests a lead/worker split. Keep host-only work direct; honor explicit external model choices. Workers must not invoke this orchestrator.

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。

Define KPIs, metrics, and tracking systems to measure brand health, awareness, perception, and equity over time. Use when the user says "brand measurement", "brand metrics", "how do we measure the brand", "brand KPIs", "brand health tracking", "brand awareness metrics", "brand equity measurement", "is our brand working", "brand ROI", "brand performance", "brand tracking", "brand health score", "prove brand value", "brand dashboard", or needs to show stakeholders whether brand investment is delivering results.

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。

Assess the health and consistency of an existing brand — identity, messaging, voice, positioning, and market perception. Use when the user says "brand audit", "brand review", "assess our brand", "is our brand consistent", "brand health check", "brand analysis", "something's off with our brand", "our brand feels outdated", "brand diagnosis", "review our brand", "brand gap analysis", or wants to understand where their brand is strong and where it needs work before investing in brand development.

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 the actual cause from a stack trace instead of pattern-matching the error type. Use on any crash or exception.

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 and constrain untrusted input at the boundary. Use on any handler that accepts external data.

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。

Expand a 1-4 sentence product brief into a full spec with a design language, acceptance surface, and an ordered feature 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。

Calibrate a reviewer persona with few-shot rubric examples so skepticism stays consistent and doesn't drift lenient over long runs.

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。

Spring Boot best practices and patterns. Use when creating controllers, services or repositories, or when the user asks about Spring Boot layering, wiring, configuration or exception handling. For JPA and Hibernate behaviour, use jpa-patterns instead.

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。

JPA/Hibernate patterns and common pitfalls (N+1, lazy loading, transactions, queries). Use when user has JPA performance issues, LazyInitializationException, or asks about entity relationships and fetching strategies.

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。

Systematic code review for Java with null safety, exception handling, concurrency, and performance checks. Use when user says "review code", "check this PR", "code review", or before merging changes.

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。

Force unrelated concepts together to discover emergent properties - "What if we treated X like Y?"

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。

Receive and act on code review feedback with technical rigor, not performative agreement or blind implementation

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 at every layer data passes through to make bugs impossible

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。

Pre-production story-and-character development for Higgsfield projects — the upstream layer that decides WHAT to prompt before any model runs. Use when the user wants to build a character, design a world, develop a story or premise, create a character sheet / character bible / story bible, lock a visual style or 'visual DNA', plan a multi-shot narrative with consistent characters, or says things like 'help me design a character', 'build the world', 'I need a backstory', 'make this character consistent across shots', 'develop my film/series concept', or 'I keep getting generic AI characters'. Routes the locked outputs into higgsfield-prompt + the right model. Adapted from Higgsfield's official character-design framework by @vavavinca.

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 mentions GPT Image 2.0 or GPT Image 2.5, gpt-image-2, gpt-image-2.5, gpt_image_2_5, GPT-Image-2 prompts, the Flare / Sunburst variants, a transparent-background image, or wants to generate an image with GPT Image 2.0 or 2.5. Covers when to prefer GPT Image 2.5 over 2.0 (transparent background, xhigh/max quality, extra aspect ratios — from platform and OpenAI facts), the CLI-vs-MCP disagreement on gpt_image_2, the three-format prompt taxonomy (Format A structured JSON for UI mockups and layout-dense images; Format B dense cinematic prose for single-subject scenes; Format C auto-derive meta-prompt for theme-only concepts), per-format craft patterns, output conventions, the 6-item pre-delivery checklist, and cross-surface workflow context (companion static-ads-workflow.md for ad recreation; higgsfield-marketing-studio cross-surface-workflow.md §3 for ms_image / DTC Ads Higgsfield-native alternative).

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 a full GEO/SEO client analysis for a domain via the Enception external API — start the analysis, poll its status, and download the PDF report covering AI visibility, market research, and competitors. Use when the user says 'run a client analysis', 'analyze <domain> for GEO', 'GEO analysis for <domain>', or 'check analysis status'.

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。