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概要と使いどころ
Ship AI features to production — model selection, evals, latency/cost optimization, guardrails, monitoring, and iteration loops. Use when turning an LLM prototype into a reliable product feature.
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 marketing operations and automation for B2B SaaS. Use this skill for newsletter strategy, email automation sequences, lifecycle campaigns, nurture workflows, onboarding sequences, deliverability optimization, email copywriting, lead scoring, marketing automation platform setup, and cold-email sending infrastructure — secondary sending domains, SPF/DKIM/DMARC, and domain warmup — plus the contact budget: a cross-channel frequency cap across every sender (marketing, sales sequences, customer success, in-app). Also triggers on: email, newsletter, drip campaign, lifecycle, nurture, onboarding sequence, deliverability, email automation, lead scoring, marketing automation, our emails are going to spam, SPF, DKIM, DMARC, sender reputation, sending domain, cold email domains, domain warmup, mailbox setup, blacklist, bounce rate, BIMI, brand logo in inbox, VMC, Verified Mark Certificate, Common Mark Certificate, transactional email, password reset emails going to spam, DMARCbis, RFC 9989, DMARC pct tag, DMARC t=y test mode, DMARC rollout, move DMARC to reject, frequency cap, email fatigue, customers get too many emails, too many messages, sales and marketing both emailing the same person, communication limits, send frequency safeguard, global frequency capping, quiet period, message precedence.
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。
This skill provides unified expert-level guidance for terminal implementation in VS Code extensions. Covers xterm.js API and addons, VS Code terminal architecture, PTY integration, session persistence, input handling (keyboard/IME/mouse), shell integration with OSC sequences, and performance optimization. Use when implementing any terminal-related 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。
Set up GEPA prompt optimization for an ORC workflow
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.js textures - texture types, UV mapping, environment maps, texture settings. Use when working with images, UV coordinates, cubemaps, HDR environments, or texture optimization.
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 performance profiling workflow. Identifies bottlenecks, measures against budgets, and generates optimization recommendations with priority rankings.
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 UI patterns including Canvas optimization, list virtualization, and mobile-friendly 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。
Unity animation patterns including Animancer, state machines, and performance optimization.
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 NavMesh pathfinding patterns, safety checks, and performance optimization.
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 performance profiling and optimization guide. Use when profiling, optimizing frame rate, reducing memory usage, debugging performance bottlenecks, or using the Profiler, Memory Profiler, or Frame Debugger. 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。
Unity 6 audio system guide. Use when working with sound effects, music, AudioSource, AudioClip, AudioListener, Audio Mixer, spatial audio, or audio optimization. Covers 3D sound, mixer groups, snapshots, and audio import settings. 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。
用于诊断或改进前端性能、Core Web Vitals、包体积、运行时渲染成本、网络瀑布、内存泄漏、长任务、Lighthouse 问题或性能预算；中文触发词包括 性能优化、页面卡顿、首屏慢、包体积、Web Vitals。
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 optimizing or reviewing large lists, virtual scrolling, windowing, react-window, TanStack Virtual, variable-height rows, dynamic measurement, infinite scroll, grid virtualization, or scroll performance; Chinese triggers include virtual lists, large list optimization, scroll performance.
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 SEO-optimized competitor comparison and alternatives pages. Covers "X vs Y" layouts, "alternatives to X" pages, feature matrices, schema markup, and conversion optimization. Use when user says "comparison page", "vs page", "alternatives page", "competitor comparison", "X vs Y", "alternatives page", "versus page", "comparison matrix", or "feature comparison".
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。
Local SEO audit covering NAP consistency, local schema markup, Google Business Profile signals, local content, citations, review signals, and mobile optimization. No MCP required — fetches pages via WebFetch. Use when user says "local SEO", "Google Business Profile", "GBP", "NAP consistency", "local citations", "local schema", "local pack", "map pack".
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 high-quality, secure, and performance-optimized Containerfiles (Dockerfiles) following best practices for multi-architecture builds, OpenShift/Kubernetes compatibility, and BuildKit cache optimization. Use when the user wants to: (1) create a new Containerfile or Dockerfile for any project (Python, Rust, Go, Node.js, .NET, or any language), (2) containerize an application with multi-stage builds, (3) optimize an existing Containerfile for security, performance, or image size, (4) review or improve container image build practices, (5) set up BuildKit cache mounts for package managers, (6) create OpenShift-compatible container images with non-root users and arbitrary UID support, (7) write a .dockerignore file, or (8) apply OCI LABEL 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。
Use when deploying or managing Kubernetes workloads. Invoke to create deployment manifests, configure pod security policies, set up service accounts, define network isolation rules, debug pod crashes, analyze resource limits, inspect container logs, or right-size workloads. Use for Helm charts, RBAC policies, NetworkPolicies, storage configuration, performance optimization, GitOps pipelines, and multi-cluster management.
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。
Review Circuit Weaver's exhaustive placement proposal and apply approved SVG coordinates to a real electrically annotated KiCad PCB. Use after schematic generation for placement optimization, official-reference review, interactive SVG editing, strict reference reconciliation, and PCB placement handoff. Do not treat review artifacts as fabrication data.
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。
Write, audit, or improve an iOS App Store listing — app name, subtitle, keyword field, screenshots, app icon, preview video, creative assets, tags, ratings strategy, localization, and featuring nominations. Use when creating or reviewing App Store metadata, doing keyword research, planning a metadata update, or when the user mentions ASO, App Store Optimization, app store listing, keywords, subtitle, screenshot captions, preview video, app icon, Liquid Glass, creative assets, product page header, search results creative, Asset Library, featuring, ratings prompt, or Apple Ads.
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。
Optimize and stress-test a logistics system end-to-end — chains route optimization, warehouse operations review, inventory demand forecasting, supply chain risk analysis, and peak-load testing. Use for delivery platforms, warehouse management systems, fleet routing, or supply chain software.
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。
Configure the complete Google Play Store publishing pipeline — upload key generation, AAB bundle optimization, Fastlane supply lanes for internal/beta/production tracks, staged rollout strategy, store listing metadata, data safety form guidance, and content rating preparation
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 a complete mobile app pre-launch verification — chains performance audit, QA testing, OWASP mobile security review, App Store and Play Store compliance checks, and store listing optimization into a single launch-ready report. Use before submitting to TestFlight, App Store, or Google Play.
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。
Analyze franchise and multi-location unit economics including per-location P&L construction, four-wall EBITDA margins, prime cost optimization, contribution margin analysis, break-even modeling, and store-level ROI calculations.
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。