「nda」の検索結果

Generate content calendars with AI. TRIGGERS - Use when user needs help with ai-content-calendar-gen related tasks.

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。

Build AI calendar optimization systems. TRIGGERS - Use when user needs help with ai-calendar-optimizer related tasks.

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。

Add Google Calendar as an MCP tool (list calendars, list/search/create events, free/busy queries) using OneCLI-managed OAuth. Multi-calendar and multi-account supported. Mirrors /add-gmail-tool's stub pattern — no raw credentials ever reach the container; OneCLI injects real tokens at request time.

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。

指定されたPR番号のDependabot PRを確認し、依存ライブラリのバージョンアップ内容をCHANGELOGとcontext7から取得して、コード修正が必要かを判定します。修正が必要な場合は修正を行い、pushまで実施します。

Quantifies biodiversity from species abundance/incidence tables using Hill numbers (iNEXT) with coverage-based rarefaction-extrapolation (Chao & Jost 2012), asymptotic richness via Chao1/ACE/jackknife as a lower bound, Baselga turnover/nestedness partition with the Podani alternative as sensitivity check, mandatory Hellinger transformation before ordination (Legendre & Gallagher 2001), Faith PD and SES_MPD/SES_MNTD with explicit null-model choice, and Maire 2015 functional-diversity dimensionality optimization. Use when comparing diversity across sites with unequal sampling effort, picking the right richness estimator for singleton-heavy amplicon data, partitioning beta diversity into turnover vs nestedness, reporting Hill-number effective species counts rather than raw entropies, computing SES_MPD with explicit null-model justification, or deciding whether to apply standard metrics to compositional amplicon data. Not for clinical 16S microbiome diversity (see microbiome/diversity-analysis).

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 ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.

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。

Orientación legal y guía de procesos contractuales para el equipo de Vambe (Chile/MX). Usar SIEMPRE ante preguntas legales o de contratos: 'quiero enviar un contrato', 'cómo mando el NDA', 'el cliente quiere cambios', 'cliente M vs L', '¿me pueden demandar?', 'el cliente no paga', 'cómo finiquito a un empleado', 'protección de datos', 'qué le digo al cliente sobre sus datos', 'nos acusan de incumplimiento', 'me llegó carta de abogados', 'quién puede firmar', 'quién es dueño del código', o cualquier consulta sobre contratos, NDAs, reclamos, laboral, RRHH, PI o cumplimiento normativo.

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。

旅行会社の予約メールなどの生テキストから、旅程（交通機関・宿泊）を抽出して Google Calendar primary にイベント登録する。フォーマットは問わない。登録前にユーザー承認を必須化し、開始 30 分前のポップアップ通知をデフォルトで付ける運用。

Establish and enforce coding standards, best practices, and architectural patterns across the codebase using linters, formatters, and code review processes. Use when setting up ESLint/Prettier, configuring linting rules, creating code style guides, implementing pre-commit hooks, establishing naming conventions, enforcing TypeScript strict mode, maintaining consistency, conducting architecture reviews, or defining team coding standards.

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。

Three-axis orchestrator: (1) clean section boundaries — naming, routes, views, ViewModels, DB ownership, cross-section access; (2) internal cohesion — no EF in service layer, caching in service only, proper interfaces, reusable ViewComponents, architecture-test coverage; (3) focused tests — grouped under the section, covering invariants/negatives/triggers, pruned of redundancy. Push-and-bot-review loop. Use when a sizable PR landed, a section shows arch drift, or /pr-review returned a non-trivial violation list. Camps is the gold-standard reference.

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 stable APIs and module boundaries — contract-first types, consistent errors, boundary validation, additive changes. Load when designing REST or GraphQL endpoints, public module interfaces, component props, or FE/BE contracts. Also triggers on "API design", "interface design", "design the API", "module boundary", "API contract", "define the interface". Complements feature-spec (product layer). Routes breaking retirement to api-deprecation-and-migration when it exists.

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。

Quantifies biodiversity from species abundance/incidence tables using Hill numbers (iNEXT) with coverage-based rarefaction-extrapolation (Chao & Jost 2012), asymptotic richness via Chao1/ACE/jackknife as a lower bound, Baselga turnover/nestedness partition with the Podani alternative as sensitivity check, mandatory Hellinger transformation before ordination (Legendre & Gallagher 2001), Faith PD and SES_MPD/SES_MNTD with explicit null-model choice, and Maire 2015 functional-diversity dimensionality optimization. Use when comparing diversity across sites with unequal sampling effort, picking the right richness estimator for singleton-heavy amplicon data, partitioning beta diversity into turnover vs nestedness, reporting Hill-number effective species counts rather than raw entropies, computing SES_MPD with explicit null-model justification, or deciding whether to apply standard metrics to compositional amplicon data. Not for clinical 16S microbiome diversity (see microbiome/diversity-analysis).

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 ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.

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。

No-loss protocol for changing an existing mandate's output contract. Use when changing what an agent or mandate emits or how it is typed: binding a kind, declaring output_kind, kind-backfill batches, provision reconciliation, or any make-this-contract-honest sweep. NOT for creating a new agent (use create-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 an Agent SKILL.md fallback for an existing find-XXXX finder that relies on a fragile discovery foundation — above all LLM_DECOMPILE (patterns C/D/E), which matches the decompiled shape of a predecessor function against a stored reference and breaks when a symbol is inlined or de-inlined in a way the reference does not cover. The generated fallback coexists with the preprocessor and runs only when it returns failure, recovering every target robustly by decompiling the predecessor and following the inline/de-inline boundary with semantic anchors. Use when a finder broke on a game update, or you want to durably backstop one before it does. The recipe generalizes to any finder foundation. Triggers: create agent skill fallback, add SKILL.md fallback, robust fallback for finder, backstop LLM_DECOMPILE finder, final guarantee 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。

Apple FoundationModelsフレームワークによるオンデバイスLLM — テキスト生成、@Generableによるガイド生成、ツール呼び出し、iOS 26+でのスナップショットストリーミング。

Analyzes bounded context boundaries in DDD projects. Detects cross-context coupling, shared kernel violations, context mapping issues, and ubiquitous language inconsistencies. Generates context map diagrams and boundary recommendations.

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。

四国めたんとずんだもん (VOICEVOX の声と立ち絵) の掛け合いで解説動画を作る。台本 JSON からセリフを合成して 1 本の音声にまとめ、字幕・立ち絵 (場面に合わせた表情)・口パク付きで、HTML プレイヤー 1 ファイル (書き起こし・チャプター付き) か mp4 動画、またはその両方に書き出す。「ずんだもん解説を作って」「ずんだもん動画を作って」「VOICEVOX で掛け合い動画」「キャラの音声で解説を読み上げさせたい」「zundamon-kaisetsu」で発火。ブラウザ内のリアルタイム合成 (Web Speech API) が要るときには使わない。

Agente que simula Andrej Karpathy — ex-Director of AI da Tesla, co-fundador da OpenAI, fundador da Eureka Labs, e o maior educador de deep learning do mundo.

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。

在对具有多层索引的Pandas DataFrame进行分组计算时，确保返回的结果保留原始的完整索引结构，而不是仅保留分组键。

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。

Legal contract analysis using CUAD dataset (41 risk categories). Supports NDA, SaaS, M&A, employment, payment/merchant, and finder/broker agreements. Identifies red flags, suggests redlines, compares to market standards.

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。

Calendar management and scheduling. Create events, manage meetings, and sync across calendar providers.

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。

.NET testing fundamentals overview and navigation center. Triggered when users ask about "how to write .NET tests", ".NET testing getting started", "required testing tools", "testing best practices", "learn testing from scratch", and other general testing needs. Recommends appropriate sub-skills based on specific requirements, covering testing fundamentals, test data, assertions, mocking, special scenarios, and other 19 fundamental skills. Keywords: dotnet testing, .NET testing, testing getting started, how to write tests, testing best practices, unit test, unit test, xunit, 3A pattern, FIRST principle, assertion, assertion, mock, stub, NSubstitute, test data, AutoFixture, Bogus, validator, FluentValidation, TimeProvider, IFileSystem, code coverage, ITestOutputHelper, test naming

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Standardizes UX states for data fetching flows (loading, error, empty, success) and complex multi-view UI patterns. Provides state machine approach recommendations, loading skeletons, error boundaries, and empty state components. Use when implementing "loading states", "error handling", "empty states", or "state machines".

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。