「ids」の検索結果

Find Amazon Ads search terms ready for exact-match harvesting from auto, broad, phrase, or other discovery campaigns. Use when Codex needs standalone or Rocketcart MCP-aware search-term harvesting: classify terms by harvest readiness, resolve live IDs, choose safe destination campaigns/ad groups, preflight duplicates/current negatives/destination feasibility, decide whether source negatives are justified, execute only explicitly approved rows, and read back/monitor Sponsored Products or Sponsored Brands actions without unsafe negatives, duplicate routing, stale approvals, brand-defense cuts, own-ASIN-defense cuts, launch/rank mistakes, or low-sample overfitting.

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 distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

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-level statics covering equilibrium of rigid bodies, free body diagrams, trusses, frames, friction, centroids, and moments of inertia.

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-level condensed matter physics knowledge. Use when working with crystal structure, band theory, semiconductors, superconductivity, magnetism, phase transitions, Fermi liquids, topological materials, or strongly correlated systems. Also use when the user mentions 'band gap', 'Fermi energy', 'semiconductor', 'superconductor', 'phonon', 'crystal lattice', 'Brillouin zone', 'Bloch theorem', 'Hall effect', 'magnetism', 'phase transition', or 'topological insulator'.

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 distinctive, production-grade frontend interfaces with high design quality. Generates creative, polished code that avoids generic AI aesthetics. Use when the user asks to build web components, pages, artifacts, posters, or applications, or when any design skill requires project context. Call with 'craft' to run the full shape-then-build flow, or 'teach' for design context setup.

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 layout, spacing, and visual rhythm. Fixes monotonous grids, inconsistent spacing, and weak visual hierarchy. Use when the user mentions layout feeling off, spacing issues, visual hierarchy, crowded UI, alignment problems, or wanting better composition.

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 distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.

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。

Read the project's knowledge record before answering from memory or from the files. Use before answering about the project's state, a number, a date, a decision, a source, a deadline or what was agreed - even a casual 'what's the situation with X' or 'is that quote still current'; before changing a recorded value; and whenever the opener or a grounding line names entries. After finding relevant IDs, prefer kpop context to read them with their declared dependencies and checks. Uses pull for concise readings or when the checked reader is unavailable, affects for changed inputs, and search when no ID is known. A missing id is not absence. Not for showing the record's page (the hub skill). Requests come 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。

Design relational database schemas (and choose when to go NoSQL) that stay maintainable and fast. Use this skill whenever the user mentions tables, DDL, entities and relationships, normalization (1NF/2NF/3NF), primary and foreign keys, UUID vs bigint IDs, indexes (B-tree, composite, covering, partial), EXPLAIN, constraints (CHECK, UNIQUE, exclusion), transactions and isolation levels, migrations (Alembic, Prisma, Flyway, expand-contract, backfilling), or SQL vs NoSQL (MongoDB, DynamoDB, Cassandra, graph databases). Also trigger for "design the database", "model this domain", "which database should I use", or writing ORM models and migration files for PostgreSQL, MySQL, SQLite, or SQL Server.

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 o servidor Figma MCP para buscar contexto de design, screenshots, variáveis e assets do Figma, além de traduzir nós do Figma em código de produção. Ative quando uma tarefa envolver URLs do Figma, IDs de nós, implementação design-to-code ou configuração e troubleshooting do Figma MCP.

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。

Traduza nós do Figma em código pronto para produção com fidelidade visual 1:1 usando o workflow MCP do Figma (contexto de design, screenshots, assets e tradução de convenções de projeto). Ative quando o usuário fornecer URLs do Figma ou IDs de nó, ou solicitar implementar designs ou componentes que devem corresponder às especificações do Figma. Requer uma conexão funcional com servidor MCP do Figma.

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 distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

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 when configuring axe-core scans, reviewing automated accessibility test results, or writing tests that use @axe-core/playwright, @axe-core/react, or similar integrations. Provides a quick reference to axe 4.x rule IDs, their WCAG mapping, and default severity levels.

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。

[Process] Use when the developer wants something explained or taught, or wants to know HOW TO REVIEW a change — by default the current working tasks + changes in context, or whatever the prompt names (a plan, a subsystem, a decision, a concept, a bug). AI derives WHAT to explain from the prompt and ALWAYS delivers a teacher-and-coach review guide, high level first then detail: what was done, a VISUAL MAP (system flowchart, domain ERD, sequence diagrams), the user stories and business rules with their REAL test-case IDs, a REVIEW PATH naming which files to open first and in what order with an exit criterion per stage, the technical concepts needed to follow it, how it works, why THIS solution, every alternative option with pros/cons, the trade-offs accepted, the blast radius, HOW TO TEST AND DEMO the change, the developer's decision levers, and written challenge prompts that provoke the reader to pressure-test the work. Works at ANY scope — from one line of code to the whole project: it SIZES the target into a scope tier, DECOMPOSES a large one into understanding groups, BREAKS the work into tasks before the first deep read, and ACCUMULATES the report group by group on disk so a huge explanation stays complete instead of thinning out. Writes the full report to a single combined markdown file (plans/reports/understand-*.md) that opens with a detailed summary section — or delivers it in full in chat when Step 3 finds no git-ignored directory for it — and always summarizes it in chat. Regardless of coding level. Never interrogates, never quizzes, never blocks.

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。

Analyzes article structure, identifies positions requiring visual aids, generates illustrations with Type × Style × Palette three-dimension approach. Use when user asks to "illustrate article", "add images", "generate images for article", or "为文章配图".

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。

Evidence-grounded AI research workflow for turning supplied economics, finance, management, and social-science papers or structured records into versioned PaperReading artifacts. Use when Codex must ingest text, Markdown, or a text-based PDF; separate source-grounded claims from researcher analysis; bind findings to evidence IDs and locators; review extraction candidates; migrate v0.2 PaperRecord JSON; validate or verify a v0.3 PaperPackage; generate research extensions; export reviewable JSON or Markdown; or safely update a compatible 13-field literature workbook.

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 a crouton app to Cloudflare Workers STAGING (auto-provisioning) — the DEFAULT deploy, staging only, never production. Handles the staging bootstrap (auto-creates D1+KV, syncs ids, migrates), wiring CI, routine staging deploys, and Pages→Workers migration. For production use the separate /deploy-production skill. Use when deploying any app in apps/.

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。

Author a complete both-seats coaching lesson for a BBA-NATIVE bidding scenario and produce all served artifacts in one consistent pass: curate the pool → tokenized coaching-curated/<scn>.pbn → gated promote to served coaching/<scn>.pbn → resolve to South=student coaching-non-rotated/<scn>.pbn (bridge-classroom) → update both toc.json files. Invoke as `/coach-lesson <Scenario>` (e.g. `/coach-lesson Fourth_Suit_Forcing`). Use ONLY for conventions BBA bids natively (its bba/<scn>.pbn [Auction] is the answer key — Basics, New_Minor_Forcing, Fourth_Suit_Forcing). For conventions BBA CANNOT bid (Spiral Raises), use the py/spiral_auction.py substitution path instead — not this 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。

Use this skill for any website, web application, frontend UI, component, layout, styling, refactor, or review. It enforces a self-contained editorial output standard: image-first composition, oversized typography, visible hairline grids, hard-edged rectangular framing, restrained copy, and review.

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。

Distributed machine learning, data mining, and iterative HPC with Exasol. Covers end-to-end ML pipelines (DISTRIBUTE BY + SET scripts + BucketFS), per-entity federated training with partial_fit and ctx.reset(), batch inference, map-reduce ensemble training, distributed ensemble and SON algorithm for frequent itemset mining (Apriori, FP-Growth, association rules, market-basket analysis), Lua execute script orchestration for iterative algorithms (k-means, SGD, gradient descent), scikit-learn model training, parallel hyperparameter search, per-entity forecasting, anomaly detection, model lifecycle in BucketFS (pickle/joblib/ONNX versioning), GPU acceleration via CUDA SLCs (PyTorch/TensorFlow/RAPIDS), and ML-specific performance tuning (skew, OOM, multi-pass chunking).

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 distinctive, production-grade frontend interfaces with high design quality. Generates creative, polished code that avoids generic AI aesthetics. Use when the user asks to build web components, pages, artifacts, posters, or applications, or when any design skill requires project context.

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。

Raw mechanical interfaces fusing Swiss typographic print with military terminal aesthetics. Rigid grids, extreme type scale contrast, utilitarian color, analog degradation effects. For data-heavy dashboards, portfolios, or editorial sites that need to feel like declassified blueprints.

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。

Elite UX/UI & Advanced GSAP Motion Engineer. Enforces Python-driven true randomization for layout variance, strict AIDA page structure, wide editorial typography (bans 6-line wraps), gapless bento grids, strict GSAP ScrollTriggers (pinning, stacking, scrubbing), inline micro-images, and massive section spacing.

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。

Clean editorial-style interfaces. Warm monochrome palette, typographic contrast, flat bento grids, muted pastels. No gradients, no heavy shadows.

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。