「labels」の検索結果

Use when writing, editing, reviewing, or replacing UI copy, UX writing, microcopy, button labels, link text, form labels, helper text, empty states, error messages, confirmation dialogs, onboarding text, notifications, or user-facing product wording.

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。

Interact with Gmail - search emails, read messages, send emails, create drafts, and manage labels. Use when user asks to: search email, read email, send email, create email draft, mark as read, archive email, star email, or manage Gmail labels. Lightweight alternative to full Google Workspace MCP server with standalone OAuth authentication.

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。

Automated scRNA-seq cell type annotation via pre-trained logistic regression. 45+ models: immune, gut, lung, brain, fetal, cancer microenvironments. Input normalized AnnData; outputs per-cell labels, majority-vote cluster labels, confidence scores. Use for fast, reference-backed annotation without manual marker inspection.

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。

Consensus cell type annotation: runs 10+ algorithms (KNN-Harmony/BBKNN/Scanorama/scVI, CellTypist, ONCLASS, Random Forest, SCANVI, SVM, XGBoost) on a labeled reference and transfers labels via majority voting. Outputs per-method labels, consensus, agreement score. Use when single-method annotation is insufficient or you need ensemble uncertainty for novel 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。

Query FDA drug labels (DailyMed) via REST API. Search structured product labels (SPLs) by name, NDC, set ID, or RxCUI; get indications, dosage, warnings, adverse reactions, packaging. No auth. For adverse events use fda-database; for DDIs use ddinter-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。

Use this skill when an agent needs production image or video understanding with Amazon Rekognition: labels, objects, scenes, OCR, moderation, image properties, Custom Labels or moderation adapters, stored-video analysis, conditional streaming-video workflows for existing eligible accounts, searchable media libraries, confidence evaluation, S3/IAM/event architecture, privacy, biometric consent, cost control, lifecycle management, and QA.

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 writing or editing Pixi'VN story content — defining labels (scenes) with newLabel, writing dialogue steps, adding player choices with newChoiceOption/newCloseChoiceOption, conditional/branching labels, or wiring up narration.call/jump/continue and Game.start to progress the story.

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。

Route HugeGraph MCP schema design, validation, and dry-run preview tasks to stable public tools. Use when the user asks to design vertex labels, edge labels, properties, primary keys, indexes, or preview schema 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。

Design, review, and refactor Neo4j graph data models. Use when choosing node labels vs relationship types vs properties, migrating relational/document schemas to graph, detecting anti-patterns (generic labels, supernodes, missing constraints), designing intermediate nodes for n-ary relationships, enforcing schema with constraints and indexes, or assessing an existing model against graph modeling best practices. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT handle Spring Data Neo4j entity mapping — use neo4j-spring-data-skill. Does NOT handle GraphQL type definitions — use neo4j-graphql-skill. Does NOT handle data import — use neo4j-import-skill.

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 and sync GitHub labels and milestones idempotently. Use when setting up a new repository or syncing label 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。

Reads model uncertainty from token log-probabilities. Covers collecting logprobs and aggregating multi-token labels, confidence tiers and margins for triage, calibration assessment with ECE, Brier scores, and reliability diagrams against human labels, responsible downstream use of confidence, and reproducibility archives. Use when the user asks about classifier confidence by item, logprobs, top-k tokens, calibration, ECE, Brier, reliability diagrams, or routing low-confidence cases to human review. Codebook and validation design go to text-classification.

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。

Improve UX writing, marketing copy, labels, microcopy, instructions, and error messages so interfaces and product messaging are easier to understand and act on. Use when the user mentions confusing copy, unclear labels, bad error text, weak CTAs, hard-to-follow instructions, or wanting better UX writing.

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 reproducible animated infographic GIFs from structured data or measured results, with audience-language labels, explicit timing semantics, representative-frame QA, and a static fallback. Use when asked for GIF生成, animated infographic, 動的インフォグラフィック, 結果が流れる図, animated benchmark, or a blog/SNS animation whose displayed metrics must remain traceable to source data.

Before designing, training, or auditing ANY model that replicates human-annotated labels, audit the annotation protocol's INPUT — the exact document/evidence the human labelers consulted — and give the model that same input. Use when: (1) designing a classifier/LLM extractor whose target is a hand-coded label set, (2) a label-replication model shows low recall concentrated in a label subset and the diagnosis on offer is "the label's information is not in the features", (3) reviewers propose construct splits (e.g. "designation vs record-evident"), adjudication sittings, or per-domain stop rules to explain residual disagreement with gold, (4) validating an extraction pipeline against labels transcribed from a source document. Symptom of the underlying failure: elaborate theory accumulates to explain why gold is "partially unpredictable" when the model was simply never shown the document the annotators read.

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。

Configura el repositorio de GitHub de punta a punta con gh — descripción, topics, labels, rama develop, protección de ramas, merge settings y metadatos — a partir de la definición del producto. Úsalo al crear el repo de un proyecto nuevo o cuando haya que poner en orden uno existente (p. ej. "configura el repo", "prepara el repositorio en GitHub", "pon los labels y la protección de ramas").

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。

GitHub project management via the gh CLI — create and triage issues, manage GitHub Projects v2 boards (add items, update status fields), manage labels and milestones, and generate handoff-status summaries. Use when "create a GitHub issue", "add to project board", "update sprint status", "create label/milestone", or "give me a handoff summary of open work". NOT for code review (see mk:review-pr); NOT for PR creation (see mk:ship); NOT for git operations (see mk:ship / mk:resolving-merge-conflicts).

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。

Improve unclear UX copy, error messages, microcopy, labels, and instructions to make interfaces easier to understand. Use when the user mentions confusing text, unclear labels, bad error messages, hard-to-follow instructions, or wanting better UX writing.

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 GitHub issue digest for openai/codex by feature-area labels, all areas, and configurable time windows. Use when asked to summarize recent Codex bug reports or enhancement requests, especially for owner-specific labels such as tui, exec, app, or similar areas.

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。

Gmail CLI for searching emails, reading threads, sending messages, managing drafts, and handling labels/attachments. Triggers: Gmail, send email, search email, read email, email thread, email draft, email labels, gmcli.

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。

Improve unclear UX copy, error messages, microcopy, labels, and instructions to make interfaces easier to understand. Use when the user mentions confusing text, unclear labels, bad error messages, hard-to-follow instructions, or wanting better UX writing.

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。

Improve unclear UX copy, error messages, microcopy, labels, and instructions to make interfaces easier to understand. Use when the user mentions confusing text, unclear labels, bad error messages, hard-to-follow instructions, or wanting better UX writing.

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。

Improve unclear UX copy, error messages, microcopy, labels, and instructions to make interfaces easier to understand. Use when the user mentions confusing text, unclear labels, bad error messages, hard-to-follow instructions, or wanting better UX writing.

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。

Implements data loss prevention policies using Microsoft Purview to protect sensitive information across Exchange Online, SharePoint, OneDrive, Teams, endpoint devices, and Power BI. The analyst configures sensitivity labels with encryption and content marking, creates DLP policies using built-in and custom sensitive information types with regex patterns, Dağıt:s endpoint DLP rules to control file operations on Windows and macOS devices, and monitors policy effectiveness through Activity Expl...

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。

Maps query single-cell data onto reference atlases and transfers cell-type labels using scArches surgery (scVI/scANVI), Symphony, Azimuth, CellTypist, scPoli, popV, and foundation models, with explicit out-of-distribution and label-transfer uncertainty. Use when annotating new single-cell datasets against a pre-trained reference, deciding which mapping method fits, or judging whether transferred labels are trustworthy. For de novo clustering and manual annotation see single-cell/cell-annotation; for batch integration without a reference see single-cell/batch-integration.

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。