「language」の検索結果

Expert SwiftUI guidance for macOS apps — covers writing and reviewing code with runtime-level understanding of observation, concurrency, performance, and platform integration. Use when user asks to "build a SwiftUI view", "review my SwiftUI code", "fix observation issues", "bridge AppKit", "architect state management", "debug view updates", or works on any macOS SwiftUI project — building features, reviewing existing code, debugging view updates or performance, fixing concurrency issues, architecting state management, bridging AppKit, or making design decisions. Do NOT use for iOS/iPadOS-only SwiftUI, UIKit bridging, or basic Swift language questions. Focuses on non-obvious traps and deep patterns, not basics.

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。

Enforce traceability from natural-language intent to machine predicates, final observables, evidence, and owner-readable acceptance. Use when conducting any audit, review, QA pass, parity or compliance check, security assessment, code or architecture review, visual or behavioral comparison, data validation, system verification, completion gate, or claim that an implementation satisfies a human request.

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 Pull Request creation specialist. Analyzes user requirements to create PRs with structured titles and bodies matching the user's query language. Handles git change analysis, PR draft creation, user confirmation, and final PR creation via gh CLI.

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。

When the user says 'refactor this', 'rename class', or 'move method'. Safely refactors code in any language — finds all callers, updates downstream dependencies, verifies via quality tools.

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。

Modelo da OpenAI que conecta visão e linguagem. Permite classificação de imagens com zero-shot, correspondência imagem-texto e recuperação cross-modal. Treinado em 400M pares imagem-texto. Use para busca de imagens, moderação de conteúdo ou tarefas visão-linguagem sem fine-tuning. Melhor para compreensão geral de imagens.

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。

Assistente de Linguagem Grande e Visão. Habilita ajuste de instrução visual e conversas baseadas em imagem. Combina encoder de visão CLIP com modelos de linguagem Vicuna/LLaMA. Suporta chat multi-turno com imagem, resposta a perguntas visuais e seguimento de instruções. Use para chatbots visão-linguagem ou tarefas de compreensão de imagem. Melhor para análise conversacional de imagem.

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。

Load this skill whenever the project involves writing, editing, or reviewing content for a general audience, including UI copy, help text, error messages, documentation, and instructions. Under no circumstances use jargon, complex sentence structures, or unexplained acronyms without providing plain alternatives. Absolutely always target a reading level accessible to the broadest possible audience. Load alongside content-design/SKILL.md for all content work.

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 returning to a repo after a break, asking what changed, what's new for this stack, or invoking project-catch-up after explain-new-project. Scans the stack for important updates, deprecations, new helper tools, agent skills, and MCP servers online—plain-language brief saved as PROJECT_CATCH_UP.md.

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 adds a project to the workspace, opens an unfamiliar repo, asks "what is this project", "explain this codebase", or invokes explain-new-project. Explores a newly opened workspace project and writes a plain-language report for a self-taught, non-technical user—quick start (what it is + how to run) first, then optional depth—and always saves PROJECT_GUIDE.md in the project root.

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。

Rewrite text that did not land, with clear context, plain language, and my writing 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。

Create an implementation plan and a GitHub Issue based on the task description provided as an argument. Use this when the user supplies a natural-language task description (not an issue number) and wants a new implementation-ready Issue. If the input is an existing issue number, use create-issue-from-issue-number (re-analyze) or update-issue (reflect comments) 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。

Re-analyze an existing GitHub Issue using its current title and body as input, refresh the implementation plan against the latest code state, and update the Issue in place. Use this when the user provides an Issue number (numeric, `#`-prefixed, or Issue URL) and wants to regenerate the Explore-based analysis. For reflecting comment-driven updates instead, use update-issue. For creating a brand-new Issue from a natural-language task description, use create-issue.

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。

Virtual QA Lead that turns requirements into review-ready, TestRail-importable test cases in one conversation. Use it whenever the user has requirements, a spec, a user story, or acceptance criteria (PDF, DOCX, XLSX, TXT, MD, or pasted text) and wants test cases, a test plan, a checklist, test coverage, a regression suite, or a TestRail/Qase/Zephyr export, even without saying "test cases" ("write tests for this spec", "what should we check here?"). Especially useful when they attach their team's existing test cases to match. When the spec describes an API (endpoints, status codes, schemas, auth, OpenAPI/Swagger), Casely also builds a runnable Postman collection, so use it for "API tests", "Postman collection", or "tests I can run in Newman/CI". Triggers on the same request in any language.

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。

Retrieves scientific papers from PubMed and creates plain-language research summaries. Use when users ask about medical research, scientific studies, clinical trials, disease treatments, or want to understand recent scientific literature on any biomedical topic.

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。

Benchmark CodeGraph retrieval quality on a real codebase by comparing agent behavior with vs without CodeGraph. Use when the user runs /agent-eval or asks to test, benchmark, audit, or validate a codegraph version (the local dev build or a published npm version) against a language's repo.

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。

Core technical, safety, and maintainability rules for codebase edits. Trigger when: - Writing code, refactoring, modifying files, or creating unit tests. - Proposing commits, reviewing git history, or auditing code quality. - Modifying files in any programming language (e.g. *.go, *.rs, *.py, *.ts, *.js, *.c, *.cpp). - Prompt contains keywords: refactor, build, compile, lint, test, git, commit, rule priority, type safety, error handling, defensive programming.

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, editing, debugging, or reviewing dealer language code in btn, dlr, or script files. Provides the complete dealer DSL syntax reference for bridge hand generation constraints.

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。

Control and interact with a live browser session on any scraped page — click buttons, fill forms, navigate flows, and extract data using natural language prompts or code. Use when the user needs to interact with a webpage beyond simple scraping: logging into a site, submitting forms, clicking through pagination, handling infinite scroll, navigating multi-step checkout or wizard flows, or when a regular scrape failed because content is behind JavaScript interaction. Also useful for authenticated scraping via profiles. Triggers on "interact", "click", "fill out the form", "log in to", "sign in", "submit", "paginated", "next page", "infinite scroll", "interact with the page", "navigate to", "open a session", or "scrape failed".

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。

Research and stress-test open-source project names, from collision checks to launch-ready decisions. Use when the user asks to name or rename a project, compare candidate names, screen GitHub or package namespace risk, verify language or mythology claims, or improve SEO/GEO clarity. Perform live web and registry research, separate brand from category descriptor, classify collisions, and return one evidence-backed recommendation.

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。

Deploy and use the Exasol Text AI Extension (TXAIE) with notebook-connector. Covers `deploy_license`, `initialize_text_ai_extension`, the `PYTHON3_TXAIE` language container, the Extraction API classes `StandardExtractor`, `NamedEntityExtractor`, `TopicClassifierExtractor`, `PipelineExtractor`, and `BranchExtractor`, named-entity, zero-shot classification, and feature extraction runs, default-model installation, and result-table querying such as `TXAIE_AUDIT_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。

Deploy and use the Exasol Transformers Extension (TE) for NLP inference inside Exasol with notebook-connector. Covers `initialize_te_extension`, `deploy_scripts`, `PYTHON3_TE` language-container activation SQL, Hugging Face model-installation workflows, and the current TE SQL UDF surface.

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。

Top-level router for Exasol work. Use for any Exasol database, exapump, SQL, BucketFS, extension, integration, UDF, Script Language Container, or Exasol Personal setup task, then route to the narrowest specialized Exasol 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。

Exasol BucketFS file system management via the `exapump bucketfs` CLI. Covers listing, uploading, downloading, and deleting files and directories in BucketFS, the `bfsdefault` service and other bucket services, bucket structure, `bfs_*` profile settings, the `/buckets/<service>/<bucket>/<path>` UDF path, and staging JARs, models, and Script Language Containers for UDFs.

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。

Restate the last message in plain human language, with no jargon.

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。