1.3万 件 ・ 関連度順
概要と使いどころ
Design UI, build components, write Playwright E2E tests, run WCAG accessibility audits, and do visual review. Use when building or testing frontend/UI work. Trigger on "build a component", "frontend", "E2E test", "accessibility audit", "a11y", "visual review", "make this look good".
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。
Phase 0 validation gate — problem framing, competitive analysis, feasibility, and a go/no-go checkpoint before committing to a feature. Use when evaluating whether an idea is worth building, before /story or /design. Trigger on "is this worth building", "validate this idea", "discovery", "should we build".
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。
Guides building and deploying Atlassian Forge Teamwork Graph connector apps that ingest external data into Atlassian's Teamwork Graph, making it searchable in Rovo Search and surfaced in Rovo Chat. Use when the user wants to build a Forge connector, ingest external data into Atlassian, connect a third-party tool (e.g. Google Drive, ServiceNow, Salesforce) to Atlassian, make external content searchable in Rovo, build a graph:connector module, use the @forge/teamwork-graph SDK, or implement onConnectionChange / validateConnection functions.
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 production-ready analytical SQL queries using CTEs, window functions, aggregations, and data transformations. Translates business questions into well-documented, performant queries. Use when writing reporting queries, building data pipelines, or answering ad-hoc business questions with SQL.
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。
One-line-prompt → cinematic, gorgeous, functional, well-tested, deployed website. Pre-hydrated SPA + full PWA kit + JSON-LD rich snippets + third-party integrations. React 19+Vite default (Angular optional). 100 concrete improvements grouped into 10 categories that EVERY single-prompt site build must satisfy before being marked done. Trigger whenever the user says 'make a website for X', 'build a site for X', 'rebuild X.com', or any equivalent one-liner.
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。
Publish finished creatives as live Meta (Facebook/Instagram) ads via the Meta Marketing API, plus research and ad-copy support. Uploads an image or video, builds a multi-variant creative (5 bodies / 5 titles / 3 descriptions in an asset feed), and creates a PAUSED ad in an existing ad set. Also pulls top-performing ads (ranked by ROAS) and competitor ads from the Ad Library to inform copy. Use when the user asks to deploy / publish / launch a creative as a Meta or Facebook ad, build a Meta ad, push a video or image into an ad set, pull their top ads, or research competitor ads. Not for generating creative (use the image/video skills) and not for writing ad-table rows.
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。
Builds reusable malware configuration extractors by applying a declarative JSON spec (offsets, decode layers, field definitions) to a sample to produce structured, defanged config. Activates for requests to build a config extractor, write a reusable family config parser, or run a spec-driven configuration extraction.
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。
Builds Zeek-based network hunting analytics by writing scripts and analyzing Zeek logs (conn, dns, http, ssl, files) to surface long connections, rare JA3s, suspicious downloads, and beaconing. Activates for requests to build Zeek analytics, write a Zeek hunting script, or analyze Zeek logs for threats.
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。
iOS platform development for Pulp — iPhone/iPad AUv3 app extensions, iOS Simulator builds, UIKit window host, CoreAudio IO audio, touch & Apple Pencil input, XcodeBuildMCP automation. Covers configure, build, deploy to simulator/device, and the gotchas discovered during iOS bringup.
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。
Step-by-step guide for building Web Audio processing pipelines — AudioWorklet processors, custom nodes, parameter automation, and WebMIDI integration. Use when the user asks how to build or wire up Web Audio code from scratch. Trigger on phrases like "build an AudioWorklet", "create a custom Web Audio effect", "how do I use WebMIDI", "how do I write an AudioWorkletProcessor", or "how do I connect AudioNodes".
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。
Unity 6 platform targeting and build guide. Use when building for specific platforms, configuring build profiles, using platform scripting defines (#if UNITY_IOS, #if UNITY_ANDROID), optimizing for mobile, WebGL, or consoles, working with Addressables/asset bundles, or choosing between IL2CPP and Mono. Based on Unity 6.3 LTS documentation.
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 Costume Designer — deciding build vs. rent vs. buy for a specific garment, budgeting a production's costume-shop spend across principals and ensemble, engineering a quick-change to hit a scripted time window, building or reading a costume plot to track pieces across a run, or diagnosing why a build is behind schedule going into tech week.
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 SLSA build-provenance attestations to existing GitHub Actions workflows. Use when the user wants to add artifact attestations, build provenance, or SLSA attestations to Docker container image builds in GitHub Actions CI/CD pipelines.
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-less ESM frontends that run directly in the browser without bundlers. Use this skill when creating static frontends, SPAs without build tools, prototypes, or when the user explicitly wants no Vite/Webpack/bundler. Covers import maps, CDN imports, cache-busting, hash routing, and performance 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。
Generate a fully customized Amazon Connect AI agent PoC bundle (Lambda handlers, OpenAPI spec, AI agent prompt, Contact Flows, CloudFormation/CDK infrastructure, FAQ knowledge base) from a ~15-minute structured interview — or from a requirements doc, a flow sketch, or an existing flow/prompt you want to improve. Use when the user wants to build, scaffold, prototype, or refine an Amazon Connect contact center, an AI voice/chat agent on Amazon Connect, or any contact-center use case that mentions Connect, Lex, Wisdom, Q in Connect, "AI agent domain", or Agentic CX Designer (ACXD, Connect Customer, the Agentic CX block — the ACXD runtime target). Also triggers on "I have a requirements document", "turn this flow sketch into a Contact Flow", or "improve my existing Contact Flow / AI prompt". Produces 6 asset packages on local disk.
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 and ship features with security baked in — runs OWASP Top 10 pre-scan, builds and ships with /ship, validates with post-build security review, then penetration tests the result. Use when shipping auth flows, payment logic, API endpoints, admin panels, or any security-sensitive code.
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 a detailed buyer persona or customer profile. Use when the user says "write a persona", "build a buyer persona", "customer persona for [product]", "who is our target customer", "ICP document", "ideal customer profile", "define our buyer", "who are we building this for", "customer profile", "audience profile", or wants to create a documented, research-backed profile of who buys the product and why. For sourcing the real research behind a persona, see customer-research. For segment and firmographic definition, see icp-research. For writing it into the knowledge base, see brand-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。
Build a customer health scorecard across usage, engagement, adoption, NPS, support load, and financial risk. Use when user says "health score", "customer health", "build a scorecard", "how healthy is this account", "customer scoring model", "green yellow red", "health dashboard", or "account health framework" - even if they don't say "scorecard" explicitly. Applies to CSMs building account health models or CS leaders standardizing health tracking across the team.
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。
scope.md をもとに Next.js アプリを projects/<name>/apps/ に実装する対話スキル。MVP を一括生成したあと「完了」で終わらず、想定ユーザー1人になりきって触る → 1サイクル1改善のチューニングを回し続けて体験を磨く。/scope-design の後、/verify-project の前に実行する。/build-app で明示起動。
End-to-end pipeline that builds a new, playable Phaser 4 browser game from a natural-language idea (design, build, verify, iterate; Startup derives routing metadata). Trigger when the user asks to make, create, build, or generate a game, describes a game concept, or describes interactive mechanics, levels, or player actions, even without the word 'game'. Do NOT trigger for editing, debugging, or adding features to an existing game codebase, or for general questions about games.
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 custom AI prompts from scratch using guided workflows and patterns from a 2,500+ prompt database. Supports image, video, and text prompts for Midjourney, Flux, Leonardo AI, DALL-E, Sora, and 12 more models. Use when user says "build a prompt", "create a prompt", "write a prompt", "make me a prompt", "new prompt", or "prompt from scratch".
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。
Ultimate AI prompt database and builder with 2,500+ curated prompts across 19 categories and 17 AI models (Midjourney, Flux, Leonardo AI, DALL-E, Sora, Imagen, Mystic, Stable Diffusion, Ideogram, Adobe Firefly, and more). Search, build, enhance, and adapt prompts for image generation, video creation, text, and meta-prompt generators. Use when user says "prompt", "find a prompt", "build a prompt", "enhance my prompt", "adapt this prompt", "prompt library", "image prompt", "video prompt", "midjourney prompt", "flux prompt", or any AI prompt engineering task. Do NOT use for terminal/CLI prompts, JavaScript prompt() dialogs, user input prompts, or bash prompt customization.
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。
Maintain Yuzu's Meson build graph, source lists, dependency wiring, and platform conditionals. Use when editing any `meson.build`, adding or removing source files, changing dependencies, updating native or cross files, or adjusting CI build behavior.
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。