「ema」の検索結果

Comprehensive R package for command-line interface styling, semantic messaging, and user communication. Use this skill when working with R code that needs to: (1) Format console output with inline markup and colors, (2) Display errors, warnings, or messages with cli_abort/cli_warn/cli_inform, (3) Show progress indicators for long-running operations, (4) Create semantic CLI elements (headers, lists, alerts, code blocks), (5) Apply themes and customize output styling, (6) Handle pluralization in user-facing text, (7) Work with ANSI strings, hyperlinks, or custom containers. Also use when migrating from base R message/warning/stop, debugging cli code, or improving existing cli usage.

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。

Verify or select a SageMaker execution role before creating models, endpoints, or training jobs. Use when a role ARN is missing or IAM access fails; inspect existing roles before proposing role creation.

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。

Implement a production SageMaker endpoint with autoscaling, CloudWatch alarms, and tags. Use after the serving image and IAM role are known; use the deployment planner first when architecture is undecided.

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。

Structured data extraction - tables, pricing, products, API endpoints with schema

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Full-stack frontend development combining premium UI design, cinematic animations, AI-generated media assets, persuasive copywriting, and visual art. Builds complete, visually striking web pages with real media, advanced motion, and compelling copy. Use when: building landing pages, marketing sites, product pages, dashboards, generating media assets (image/video/audio/music), writing conversion copy, creating generative art, or implementing cinematic scroll animations.

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。

Email delivery infrastructure - DNS authentication (SPF/DKIM/DMARC), subdomain isolation, provider abstraction, template systems, bounce handling, warmup strategy, and deliverability monitoring.

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。

Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.

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Builds dashboards, reports, and data-driven interfaces requiring charts, graphs, or visual analytics. Provides systematic framework for selecting appropriate visualizations based on data characteristics and analytical purpose. Includes 24+ visualization types organized by purpose (trends, comparisons, distributions, relationships, flows, hierarchies, geospatial), accessibility patterns (WCAG 2.1 AA compliance), colorblind-safe palettes, and performance optimization strategies. Use when creating visualizations, choosing chart types, displaying data graphically, or designing data interfaces.

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。

Optimizing vector embeddings for RAG systems through model selection, chunking strategies, caching, and performance tuning. Use when building semantic search, RAG pipelines, or document retrieval systems that require cost-effective, high-quality embeddings.

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。

Plan short-form video, vlog, travel, lifestyle, documentary, or cinematic social video scripts by negotiating duration, choosing a story arc and visual style, then splitting the film into timed scenes of 4-15 seconds. Use this skill whenever the user asks for a video plan, short video plan, scene breakdown, montage plan, travel video script, TikTok/Reels/Shorts storyboard, or cinematic short-form narrative, even if they only describe a loose theme or destination.

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。

Transform a UI screenshot into a structured Cinema4D/Octane JSON render prompt for photorealistic 3D poster art. Use this skill whenever the user uploads a UI screenshot (app, website, tool, dashboard) and wants a Cinema4D render prompt, 3D poster, AIGC image prompt, or any photorealistic visualization of an interface. The output is always a clean JSON file — never prose or Midjourney text strings.

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Execute exactly one eligible Unit in an existing research Workspace; use for stepwise or manual semantic execution when status, Attempt, Artifact, Manifest, checkpoint, and acceptance evidence must remain synchronized.

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。

Define evidence-first table schemas for a survey: what each table must answer, row unit, columns, and which evidence-pack fields are required to fill it.

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Create branded architecture, architecture delta, IT current-state, flowchart, sequence, state machine, ER/data model, timeline, swimlane, quadrant, radar/spider, polar chart (polar/radial lollipop), loop/flywheel, nested, tree, org chart, layer stack, exploded axonometric, axonometric plan, Venn, pyramid/funnel, treemap and marimekko, heatmap, bar and dumbbell, waterfall, line (slopegraph, ridgeline, streamgraph, bump), Gantt and scatter charts (bubble, beeswarm), high-level, process, medallion, data flow, DP integration, DP security matrix, Sankey, fishbone, Wardley map, kanban, user journey, deployment, dependency graph, UML class, story map, or database schema diagrams as HTML/SVG/PNG, with .drawio, Mermaid, and .excalidraw import, plus lifecycle phase maps, block decomposition trees, and onboarding guidance.

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Own concrete MSBuild ItemGroup and item-expression questions. USE FOR: Include, Remove, Update, item identity and metadata, transforms, filtering, batching, duplicate or overlapping items, and reviews that verify whether those operations are correct. Generated items stay in scope when the central defect is item identity, metadata, batching, duplicate declarations, or glob/Remove/Update semantics. For a generated artifact missing from compilation or output, wrong target timing/path, or FileWrites clean tracking without a broader item-semantics defect, use including-generated-files. The item operation may be broken, suspected, or already correct. Exclude property-only issues, general incrementality with no item question, broad reviews with no concrete item concern, and non-MSBuild systems.

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USE ONLY for (1) two or more projects with repeated MSBuild policy, targets, or package versions, or (2) an existing Directory.Build.props/targets/rsp hierarchy with an import, placement, or layering defect. A one-project repo without an existing Directory.Build file is outside scope; do not load this skill. Covers centralizing shared policy and targets, Directory.Packages.props across projects, parent hierarchy/import structure, props-versus-targets placement, and folder/project exceptions. An existing hierarchy defect remains in scope when only one project is affected. For property value or condition semantics use property-patterns. Exclude legacy-to-SDK migration and non-MSBuild 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。

Create animated, explorable editorial diagrams in 45 types (architecture, flowchart, sequence, state, ER, DB schema, UML class, swimlane, timeline, gantt, journey, tree, org chart, nested, venn, quadrant, fishbone, wardley, bar, line, scatter, sankey, treemap, heatmap, 3D isometric objects, floor plans and exploded views) as standalone HTML from a short JSON spec. Use when the user asks to draw, diagram, chart or visualize a system, codebase, flow, process, API call sequence, hierarchy, data or a physical object (a chair); to convert Mermaid, Graphviz/DOT, PlantUML, D2, draw.io, Excalidraw, SQL or CSV into a clean diagram; or to export a diagram as PNG, JPEG, SVG, GIF or MP4. Also for everyday plans: trips, moves, job hunts, approvals, budgets, where money goes.

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。

Document column-level mappings between source and target schemas. Use when integrating data from multiple systems, designing ETL transformations, or documenting how raw fields become analytical assets.

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 cinematic AI shortfilm prompts (works with Seedance 2.0, Xiaoyunque, Sora, Kling, Jimeng, Veo) using the 5-stage structure from Mx-Shell's Zombie Scavenger. Trigger when the user wants transformation sequences, multi-shot narrative shorts, weapon-charge/combat segments, emotional family/pet/farewell narratives (催泪/亲情/萌宠/离别), or any cinematic video prompt.

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 genuinely beautiful, on-brand UI instead of generic "AI slop" — use whenever the user asks to build, design, style, or improve a UI, landing page, dashboard, app screen, or component, whenever a PRD/spec needs a design pass before implementation, whenever the user pastes reference images/Pinterest/Dribbble links and wants the app to look like them, or whenever the user complains the AI-generated UI looks generic, boring, cookie-cutter, or "like every other AI app." Make sure to trigger this even if the user doesn't say "design" explicitly — phrases like "make this look good", "build the frontend for X", "this looks like every other SaaS site", or "match this vibe" all qualify. Also triggers on two verbs, "study"/"extract the look of" a reference screenshot or URL, and "audit"/"review"/"why does this look AI-generated" for critiquing existing UI.

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 you need framework-agnostic SQL guidance — schema naming, relational table design, query readability, indexes, transactions, database security, migrations, testing, and monitoring — without choosing Spring Boot, Quarkus, or Micronaut. This should trigger for requests such as Review SQL schema or migrations; Improve SQL query performance and readability; Design relational tables and indexes; Review database transaction, security, or monitoring practices. Part of Plinth Toolkit

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 you need framework-agnostic OpenAPI 3.x guidance — spec structure, metadata and versioning, paths and operations, reusable schemas, security schemes, examples, documentation quality, contract validation (e.g. Spectral), breaking-change awareness, and handoffs to codegen — without choosing Spring Boot, Quarkus, or Micronaut. This should trigger for requests such as Review an OpenAPI; Improve an OpenAPI; Improve API contract; Improve API schema design. Part of Plinth Toolkit

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 personalized cold outreach sequences for LinkedIn and email. Use when someone needs to reach prospects, warm up cold leads, or build a systematic outreach engine. Covers research, connection requests, follow-ups, and conversion.

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Get one actionable improvement for your AI product based on the latest GenAI PM briefs. Fetch the last 5 days of curated AI PM insights from genaipm.com, analyze the current repo/project, find synergy between trending topics and the user's work, then research the source material and apply a concrete improvement. Use when the user wants to improve their AI product, get coaching on AI PM best practices, apply the latest industry insights to their codebase, or run "/one-step-better-ai-pm". Requires a GenAI PM subscriber email (set GENAIPM_EMAIL env var or provide when prompted).

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。