「ada」の検索結果

Phase C of a WinForms-to-Avalonia migration: implement platform and infrastructure adapters for dependencies discovered in Phase A/B, such as persistence, process execution, dialogs, notifications, or environment capabilities. Use only in an explicit migration when such dependencies leak into Forms/core or when the orchestrator routes here. Do not invent adapters for capabilities the product does not have, and do not implement main-window XAML.

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。

Professional translation and localization for 8 languages (ES, JA, FR, DE, PT, ZH, KO, AR). Covers tone matrix, formality levels, i18n patterns (react-intl, i18next, ICU), RTL layout, cultural adaptation, and locale formatting. Use when user asks to translate text, localize an app, adapt tone, review translations, add i18n, or set up RTL.

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。

Generate, validate, and explain `auth.md` files — the open protocol for AI agent registration. Use when making apps agent-ready, generating Protected Resource Metadata (RFC 9728), validating auth.md files, or implementing agent registration endpoints. Triggers on "auth.md", "agent registration", "agent auth", "make my app agent-ready", "ID-JAG", "identity_assertion", "service_auth", "protected resource metadata", "agentic registration".

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 an agent must stand up the OpenClaw↔Cognee integration with FalkorDB as the vector + graph store. This skill is SELF-CONTAINED — it tells you to generate every file (Dockerfile, sitecustomize.py, docker-compose.yaml) from the exact contents below, build a custom Cognee image that loads the FalkorDB adapter, run Cognee + FalkorDB together, self-verify the adapter registered, and point the OpenClaw cognee plugin at it (including per-agent graphs). No other files in this directory are required.

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 guide for Monte Carlo's push ingestion model. Use this skill whenever a customer or engineer mentions: pushing data to Monte Carlo, the IngestionService, pycarlo push APIs, build me a collection script, push metadata/lineage/query logs, invocation_id tracing, custom lineage nodes or edges, deleting push tables, or any question about why pushed data is not showing up. Also trigger when they ask to generate code that collects metadata, table schema, row counts, freshness, lineage, or query history from any data warehouse or data source and sends it to Monte Carlo. If the user mentions any warehouse, database, or data platform alongside any Monte Carlo topic, this skill is almost certainly relevant.

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 patent simple bibliographic (catalog) data from the Nexscope patent database. Triggered when users mention patent bibliographic information queries, patent basic info retrieval, patent catalog data, patent publication details, searching by patent number for inventors, patent applicant information, patent abstract retrieval, patent classification codes (IPC/CPC), patent citation queries, or any request to search for structured metadata by patent ID or publication number, patent brief bibliography, patent basic info, patent number lookup, patent abstract, Nexscope, or patent metadata. Even if the user does not explicitly mention "Nexscope" or "bibliographic info," this skill should be triggered whenever their need involves querying core bibliographic fields of specific patents.

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 patent bibliographic (catalog) information from the Nexscope patent database by patent ID or publication number. Triggered when users mention patent bibliographic info queries, patent catalog information, patent applicant queries, patent inventor queries, patent classification codes, patent abstract retrieval, patent citation analysis, patent priority claims, patent application citations, patent examiner information, patent bibliographic data, inventor lookup, applicant lookup, patent classification, patent metadata, Nexscope, or patent citations. Even if the user does not explicitly mention "bibliographic info," this skill should be triggered whenever their need involves querying detailed metadata for a specific patent by patent ID or publication number.

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 an adaptive assessment loop where each student response triggers the next instructional move. Use when building technology-enhanced formative assessment cycles.

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。

Generate a cascading hint sequence for a problem type, revealing progressively without giving answers. Use when designing tutoring dialogues or scaffolded worksheets.

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 knowledge components and skill hierarchies for a cognitive tutoring system or adaptive learning platform. Use when designing intelligent tutoring software or skill-based mastery systems.

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。

Trigger.dev client/frontend surface: subscribe to runs in realtime (runs.subscribeToRun and the @trigger.dev/react-hooks hook useRealtimeRun), consume metadata and AI/text streams in React (useRealtimeStream), trigger tasks from the browser (useTaskTrigger, useRealtimeTaskTrigger), and mint scoped frontend credentials with auth.createPublicToken / auth.createTriggerPublicToken. Load when wiring a frontend (React/Next.js/Remix) or backend-for-frontend to show live run progress, status badges, token streams, trigger buttons, or wait-token approval UIs. NOT for writing the backend task itself (streams.define / metadata.set is trigger-authoring-tasks territory); this is the consumer side.

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 wants full metadata for a paper — from a DOI, PMID, arXiv id, OpenAlex id, or title. Returns title, author list, first author, corresponding author(s), publication date, journal name + ISO-4 abbreviation, impact factor, volume/issue/pages, DOI/PMID, citation count, and abstract in one record. Triggers on "paper metadata", "who is the corresponding author", "first author of", "what journal / impact factor for this DOI/PMID", "cite this paper", "文献元数据", "通讯作者", "第一作者", "影响因子". PROACTIVELY USE when the user pastes a DOI/PMID/arXiv id or paper title and asks about its authors, venue, or impact.

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。

後方互換性がゴミコードを量産する構造を検出し、互換性を「契約と撤去計画」として管理する ガバナンスを支援するスキル。公開API境界の明確化、非推奨化サイクル（deprecation cycle）の 制度化、互換層の局所化（Adapter/Strangler Fig）、契約テスト（CDC）による互換性検証、 AI生成コードの互換性ゲート設計を含む。コードレビュー、API設計、リファクタリング、 レガシー移行時に互換性起因の技術的負債を防ぐために使用。 対象言語: 言語非依存（Java, TypeScript, Go, Python, Rust等すべて）。 トリガー：「後方互換性を保ちたい」「非推奨APIをどうする」「互換層が増えてきた」 「レガシー移行の戦略」「API設計レビュー」「互換性のためのコードが多い」 「deprecation policyを作りたい」「破壊的変更の管理」といった互換性管理関連リクエストで起動。

Expert guide for error handling patterns, resilience engineering, retry strategies, circuit breakers, and graceful degradation across React, Next.js, and Node.js / Panduan ahli pola penanganan error, rekayasa ketahanan, strategi retry, circuit breaker, dan degradasi anggun di React, Next.js, dan Node.js.

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 guide for autonomous API schema drift detection, OpenAPI / gRPC breaking change monitoring, self-healing SDK adapter generation, and zero-downtime client-server synchronization / Panduan ahli deteksi drift skema API otonom, pemantauan breaking change OpenAPI / gRPC, pembuatan adapter SDK self-healing, dan sinkronisasi client-server tanpa downtime.

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。

麦麦活动雷达——查「附近有什么麦当劳活动」「周末去哪家麦麦主题活动」「某城市的生日派对/主题派对/体验营/品鉴会/读书会及可预约场次」，并可把结果生成卡通活动地图（门店位置取自官方经纬度）。首次使用自动引导申请官方 Token 并一键配置连接器，支持城市级位置检测；区分「已核实可预约场次」与「活动线索待核实」，用户确认后才创建主题活动订单。

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。

Searches and discovers academic papers across multiple sources (Semantic Scholar, arXiv, Tavily, Exa, Gemini deep research, AMiner, Google Scholar) with adaptive engine selection based on query type. Returns ranked, deduplicated results with metadata (authors, venue, year, citations, abstract, PDF link). Use when the user asks to find papers / literature / publications / preprints / references on a topic, search for related work, look up a specific paper by title or DOI or arXiv ID, find papers by an author, find recent SOTA / state-of-the-art work, survey a research area, or run a deep / comprehensive literature search with synthesis.

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。

Translation and interpretation as language meta-skills -- equivalence theory (Nida's formal vs. dynamic equivalence), translation strategies (borrowing, calque, transposition, modulation, adaptation), interpretation modes (simultaneous, consecutive, sight), source text analysis, register preservation, cultural adaptation, untranslatability and compensatory strategies, machine translation literacy, and back-translation for verification. Covers literary, technical, legal, and community interpreting contexts. Use when translating between languages, evaluating translation quality, teaching translation skills, or understanding the limits of cross-linguistic transfer.

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。

Human modification of Earth's systems and the geographic dimensions of environmental change. Covers climate change science (greenhouse effect, feedback loops, projections), deforestation and land use change, desertification and soil degradation, water resource management, biodiversity loss and conservation geography, pollution and environmental justice, and sustainability frameworks. Use when reasoning about human impacts on the environment, environmental policy, conservation planning, climate adaptation, or the spatial distribution of environmental risks.

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。

Provides Hatchling build backend guidance for Python packaging — use when configuring pyproject.toml metadata, build targets (wheel, sdist, binary), file selection with git-style globs, build hooks, metadata hooks, version management (code/regex/env sources), editable installs, the hatch-vcs plugin, plugin development, build environment setup (UV/pip/Cython), setuptools migration, or troubleshooting Hatchling errors. Covers PEP 517/518/621/660 standards and context variable interpolation.

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。

Validates and corrects Python shebangs and PEP 723 inline script metadata by applying four shebang-selection rules. Use when auditing or fixing shebangs in Python files — choosing between plain python3 and the uv shebang for standalone scripts with external dependencies, adding or removing PEP 723 metadata blocks to match actual import requirements, checking execute bit presence, or avoiding redundant transitive dependencies when typer is declared.

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。

Walks a user from one rough content idea to a finished post, platform-adapted variants, and repurposed follow-on assets using a 7-step sequential prompting workflow. Use when the user wants to turn a half-formed idea, frustration, story, or client situation into publishable content quickly and consistently — covering idea extraction, hook generation, outline, draft, humanizer pass, platform adaptation (LinkedIn, Reddit, X/Twitter, newsletter, Telegram), and repurpose engine.

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。

Personal research-paper radar. Fetches the latest papers and code repos for the user's keyword subscriptions across arXiv, Crossref (IEEE/ACM), Semantic Scholar, GitHub, HuggingFace, Papers with Code and RSS; deduplicates against history; then YOU (the assistant) expand the queries, filter for computer-science relevance, write Chinese summaries, detect hot sub-fields, and emit a Markdown + HTML digest (optionally emailed). Use when the user asks to "run my paper radar / 文献雷达", "check for new papers", "what's new in my topics", "今天有什么新论文 / 最新文献", "today's research digest", or to configure their keyword subscriptions / sources / schedule.

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。

Branch skill for building and improving agents. Use when creating new agents, adapting marketplace agents, validating agent structure, writing system prompts, or improving existing agents. Triggers: 'create agent', 'improve agent', 'validate agent', 'fix agent', 'agent frontmatter', 'system prompt', 'adapt agent', 'customize agent', 'agent examples', 'agent 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。