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概要と使いどころ
Use when the user asks to "find influencers", "build an influencer list", or "discover creators in [niche]"; produces a multi-platform candidate pool, per-influencer profiles, authenticity red-flag screening, and a tiered shortlist with preliminary triage signals. Not for STAR scoring or ranking a known shortlist — use fit-scorer. 达人挖掘/找达人/创作者名单
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.
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。
AI-driven Flutter E2E testing via VM Service CLI. Control running Flutter apps in debug mode for smoke tests, regression checks, exploratory testing, and UI automation. Trigger when user mentions E2E testing, UI automation, smoke tests, integration testing, test automation, controlling Flutter apps, VM Service interaction, debugging UI flows, automated regression testing, CI sanity checks, or testing native dialogs/permissions/WebViews. Supports stateful named instances or stateless direct URI connections. Ideal for rapid test feedback, AI agent workflows, element discovery, screenshot capture, log collection, and scriptable UI interactions (tap, scroll, enterText). Use for troubleshooting flaky tests, automating repetitive manual tests, verifying app behavior after merges, or setting up automated QA 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。
Generative UI (GenUI) for Flutter using AI models like Google Gemini, Anthropic Claude, or OpenAI via the genui package. Use this skill when building dynamic AI-driven interfaces, conversational UI flows, server-rendered screen layouts, LLM-powered dashboards, chat-based UI generation, or implementing prompt-to-UI systems. Supports AI-generated widgets, reactive data binding, ContentGenerator integration, and A2uiMessageProcessor for real-time UI updates from AI responses. Ideal for apps where UI structure is determined by AI/LLM output rather than hardcoded layouts.
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。
Inspect, debug, and inject custom code into a running Steam client — desktop (macOS, Linux, Windows) or Steam Deck over the network — through its Chromium Embedded Framework (CEF) Chrome DevTools Protocol endpoint. Use when developing Steam UI plugins, mods, themes, or integrations — injecting CSS/JS to try a change, locating webpack modules and obfuscated class names, reading React or SteamUIStore state, inspecting Big Picture / Quick Access Menu windows, screenshotting what actually rendered, streaming console logs, or diagnosing runtime errors in Steam's UI. The same commands run against either device, or both at once. Injection is per-session and for development only. Not for building a plugin loader or making changes persist across restarts, Steamworks game SDK work, game development, or scraping the public Steam web store.
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 native iOS apps in Swift — SwiftUI and UIKit screens, state and data flow, NavigationStack, lists, forms and accessibility; networking with URLSession and Codable, persistence with SwiftData, Core Data, files and the Keychain; permissions, push notifications, universal links, background tasks, widgets, App Intents and StoreKit 2 purchases; performance with Instruments and MetricKit; and release with signing, TestFlight and App Store Connect. Use when writing or changing native iOS code, adding a platform feature, preparing a build for TestFlight or the App Store, or when the user asks how to do something on iOS, even if they do not name the framework.
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 Flutter apps with guides for everything specific to Flutter and Dart — layouts, responsive UI, forms, animations, theming, declarative routing with go_router, widget previews and home screen widgets; HTTP, JSON serialization, local databases and caching; plugins, platform channels and native views; isolates and app size; accessibility and localization; Effective Dart, Dart 3 features and pattern matching; and macOS setup. Use when writing or changing Flutter UI, data or native integration code, or when the user asks how to do something in Flutter, even if they do not name the topic.
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。
Designs and reviews mobile architecture across Flutter, native Android, native iOS, React Native and Kotlin Multiplatform. Covers state ownership, data flow, modules, dependency boundaries, navigation, offline data and KMP shared source sets versus host apps. Use when structuring a feature, choosing state management, refactoring layers or deciding where a real class or screen belongs.
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。
Audits and fixes mobile accessibility across Flutter, native Android, native iOS, React Native and Kotlin Multiplatform, including shared Compose UI and native hosts. Covers TalkBack/VoiceOver labels, roles, state, reading order, touch targets, text scaling, contrast and focus. Use for a11y audits, screen-reader bugs or accessible UI implementation.
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。
Spawn a new Claude Code session with remote control enabled (default: a detached background session — via screen on macOS/Linux, via a minimized PowerShell Start-Process on Windows — fully remote-safe, no approvals/windows), so it appears in the mobile Claude Code session picker. Takes an optional directory path; defaults to the current working directory. Use when the user runs /rc-new.
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 AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Use, OpenAI's Operator/CUA, and open-so...
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 or design FriendlyBet user experience, visual direction, mobile-first flows, premium sports interface, Hebrew/English copy fit, RTL behavior, accessibility, interaction patterns, and product UI polish. Use when a task touches screens, flows, layout, visual hierarchy, onboarding, prediction UX, share UX, or design quality.
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。
Audit and improve web accessibility following WCAG 2.1 guidelines. Use when asked to "improve accessibility", "a11y audit", "WCAG compliance", "screen reader support", "keyboard navigation", or "make accessible".
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 build workflow: branch, delegate to coding agent, test, screenshot, and commit. Triggers on: "$devprocess", "build this properly", "use the coding agent workflow", or any feature/refactor/infrastructure change that is too large for a direct edit. NOT for: one-line fixes, doc edits, prompt tweaks, or simple config changes — do those directly.
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 working with Cypress Deep — cypress end-to-end testing management and analysis. Covers test execution monitoring, dashboard integration, screenshot and video analysis, test report parsing, flaky test detection, and configuration auditing. Use when investigating test failures, reviewing Cypress Cloud results, or managing Cypress test suites.
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 working with Budibase — budibase low-code platform management covering application inventory, table and datasource health, screen layout analysis, automation monitoring, and user role auditing. Use when reviewing internal app configurations, investigating data connectivity issues, monitoring automation runs, or auditing user access controls.
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。
vision-audio: screenshot, describe, transcribe, diagram.
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。
Browse the web for any task — research topics, read articles, interact with web apps, fill forms, take screenshots, extract data, and test web pages. Use whenever a browser would be useful, not just when the user explicitly asks.
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。
How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
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。
Molecular docking with AutoDock Vina (Python API). Receptor/ligand prep (Meeko + RDKit), grid box, docking, pose and binding energy analysis, and batch virtual screening.
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。
Playwright automation, Chrome DevTools debugging, and browser interaction testing. Use for E2E/unit tests, capturing screenshots, inspecting network/console logs, or validating user flows in web applications.
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 WCAG 2.2 compliant interfaces with mobile accessibility, inclusive design patterns, and assistive technology support. Use when auditing accessibility, implementing ARIA patterns, building for screen readers, or ensuring inclusive user experiences.
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。
Convert Stitch HTML and screenshots into modular Vite/React/TypeScript components, or sync existing components to updated Stitch designs, with local architecture and validation checks.
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。
Convert Stitch HTML designs into React Native screens, or sync existing native components to updated Stitch designs, using native primitives, StyleSheet rules, and mobile platform checks.
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。