541 件 ・ 関連度順
概要と使いどころ
Generate web, mobile, and desktop prototypes, slides, dashboards, and editorial layouts from a single prompt using brand-grade design systems. Use when the user wants /web-prototype, /mobile-app, or /dashboard interactive HTML previews exportable to HTML, PDF, PPTX, or MP4 without 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。
Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI 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。
UI design and implementation for real product surfaces with taste-first critique, reference routing, project-specific design briefs, variation comparison, and implementation verification. Use when Codex should redesign, build, polish, or critique frontend UI by reading project context, exploring taste, routing through `references/catalog.md`, synthesizing `PROJECT_DESIGN.md` or `design.md`, using investment gates to avoid wrong taste/reference/scope, implementing in the existing stack, and evaluating whether the result is actually better. Best for app screens, dashboards, landing pages, query tools, product surfaces, and design explorations; not for backend-only work or tiny cosmetic edits.
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。
Build UIs with @nuxt/ui v4 — 125+ accessible Vue components with Tailwind CSS theming. Use when creating interfaces, customizing themes to match a brand, building forms, or composing layouts like dashboards, docs sites, and chat 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。
Client reporting, quality assurance, and financial operations management. Handles KPI frameworks, compliance audits, budget tracking, and launch checklists. Use when: managing client dashboards, ensuring brand compliance, conducting QA reviews, tracking marketing spend, ensuring GDPR/CAN-SPAM compliance, handling a request to delete someone's data across the marketing stack, deciding whether an AI voice or AI presenter has to be disclosed, proofreading deliverables. Also triggers on: client reporting, QA, quality assurance, brand compliance, marketing budget, financial tracking, legal compliance, GDPR, CAN-SPAM, proofreading, launch checklist, right to erasure, right to be forgotten, data deletion request, DSAR, someone asked us to delete their data, do we delete or suppress, suppression list, they unsubscribed and we emailed them again, we deleted them and they came back, restored a backup and re-emailed people, custom audience still has deleted contacts, California Delete Act, DROP, data broker deletion, enrichment records disappearing
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。
Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.
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 a task needs the judgment of a Business Intelligence Analyst — reconciling a metric discrepancy between reports, designing a dimensional model or semantic layer for self-service dashboards, setting a data freshness/latency SLA, or diagnosing why a dashboard's aggregate number is misleading.
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 a task needs the judgment of a Chief Data Officer — resolving conflicting metric definitions across dashboards, deciding whether a dataset should be retained or purged, assigning data ownership for a system feeding regulatory or board reporting, sizing a privacy/breach risk against a proposed data collection, or auditing data readiness before an AI/ML initiative kicks off.
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。
Build rich visualizations with Apache ECharts: option config, dataset transforms, large-data performance, and custom rendering. Use for complex dashboards and big datasets.
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 clear, actionable dashboards with KPI hierarchy, data visualization best practices, and layout patterns.
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。
Datadog guidance — APM, logs, metrics, monitors, SLOs, dashboards, and cost control on the Datadog platform.
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 data visualizations with D3 for the web: dashboards, interactive explorers, maps, and storytelling pieces. Use for data-viz products and features.
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。
Orchestrate agents at runtime — task routing, scheduling, parallel execution, result aggregation, and supervision dashboards. Use when running agents as a managed fleet rather than one-off calls.
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。
Set up application observability with Prometheus, Grafana, Datadog, or CloudWatch — instrument metrics endpoints, configure Golden Signal dashboards, define alert rules with burn-rate SLOs, and add structured logging
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。
Discover shadcn-compatible components and registries across the entire ecosystem before writing any custom UI. Use PROACTIVELY before building tables, forms, modals, animations, dashboards, auth pages, or any UI component. Use when the user says "find a component for...", "is there something for...", "search registries", "what exists for...", or asks about shadcn blocks, Magic UI, Aceternity, ReUI, Animate UI, DiceUI, Tailark, AI Elements, or any other shadcn-compatible registry. Complements the official shadcn skill by surfacing registries beyond the user's configured `components.json` — use when exploring what's available to add, not just what's already installed.
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 QA quality dashboards, calculate defect metrics, analyze escape rates, and produce quality trend reports. Use this skill whenever a QA lead or manager needs to measure and communicate quality outcomes. Trigger on phrases like "quality metrics", "defect density", "escape rate", "bug trends", "quality dashboard", "sprint quality report", "test coverage metrics", "QA KPIs", "how is quality trending", "defect leakage", "production bugs this quarter", "mean time to detect", "quality scorecard", or any request to quantify, visualize, or report on the quality of a product or testing process. Also trigger when a manager asks "how is QA performing?" or an engineering lead asks "what does our defect data tell us?".
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。
Run exploratory data analysis on a dataset and produce a structured report. Use when the user says "explore this dataset", "EDA on X", "analyze this data", "what's in this dataset", "summarize this data", "first look at the data", "understand this dataset before modeling", "data quality check", "describe this dataframe", or wants to understand a new dataset before building models or dashboards.
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。
FALLBACK ONLY — do not invoke unless you have already ruled out (1) a native API (Gmail API, GitHub API, Slack API …), (2) a CLI (git, gh, aws, npm, curl …), (3) direct file editing, and (4) existing browser automation (Playwright, Puppeteer). Only when all four are unavailable or have already failed should you use this skill. It gives AI agents a cursor and a keyboard on a real desktop — the last mile when the only remaining surface is a GUI. Concretely: use it when an earlier attempt via API, CLI, or direct file edit has failed and the user says things like "open X", "click Send", "type this in Word", "read what is on my screen", "do this in Outlook", "drive the Figma UI", "control my desktop", "automate this workflow", "fill out this form", or "copy text between apps". Works on Windows, macOS, and Linux with any LLM that can call functions (Claude, GPT, Gemini, Llama, Kimi, Ollama) over MCP — stdio for editor hosts (Claude Code, Cursor, Windsurf, Zed) or HTTP for daemons and dashboards.
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。
Generates Google Cloud Monitoring Server-Driven UI (SDUI) Widget and XyChart Protocol Buffer textprotos from resolved PromQL or ListTimeSeries queries. Use when: - Generating valid google.monitoring.dashboard.v1.Widget textprotos, containing PrometheusQuery or TimeSeriesFilter datasets, for use with the Cloud Monitoring Dashboards API, gcloud CLI, or declarative dashboard definitions. - Synthesizing Server-Driven UI (SDUI) widget titles, axis labels, and plot types for Prometheus or ListTimeSeries queries. Don't use for: - Metric discovery or PromQL query generation. For those tasks, use the cloud-monitoring-metric-selection or cloud-monitoring-promql-query skills.
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 Salesforce-anchored personas and journey maps where every persona ties to a Permission Set Group, primary record types, list views, dashboards, automation, and a measured mobile/desktop posture, and journeys walk each task end-to-end with friction tags that drive UX decisions. NOT for stakeholder authority / RACI (use admin/stakeholder-raci-for-sf-projects), NOT for system-side process flows (use admin/process-flow-as-is-to-be), NOT for Lightning page design itself (use admin/lightning-app-builder-advanced or the lightning-record-page-auditor agent), NOT for pure design / UX research disconnected from org artifacts.
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。
Custom Report Type design — when to create a CRT vs use the standard, A-with-B-without and A-without-B joins, primary/secondary/related-via-lookup objects, the 60-field display limit, and field-set vs cross-join layouts. NOT for building the report or dashboard itself once the report type exists — use admin/reports-and-dashboards-fundamentals. Keywords: reportType metadata, baseObject, outerJoin, ObjectRelationship, ReportLayoutSection, checkedByDefault, displayNameOverride, deployed false, custom report type sprawl, report type inventory.
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。