2,974 件 ・ 関連度順
概要と使いどころ
When the user wants to create or optimize popups, modals, overlays, slide-ins, or banners for conversion purposes. Also use when the user mentions "exit intent," "popup conversions," "modal optimization," "lead capture popup," "email popup," "announcement banner," or "overlay." For forms outside of popups, see form-cro. For general page conversion optimization, see page-cro.
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。
Close the post-launch loop — turn a live app's App Store signals (reviews, analytics, sales, crashes, listing conversion) into a metric-tagged backlog for the next version, AND verify whether last cycle's changes moved the metric they promised to move. Read-only on App Store Connect; every change is surfaced and routed to another command, never auto-applied. Use before planning the next version, on a monthly cadence, or ~1-2 weeks after shipping to check if a change worked.
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 a minimum version enforcement system with hard-block and soft-prompt update flows, App Store redirect, and remote config or App Store lookup for version checks. Use when user wants force update, mandatory update, or minimum version check.
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 the optimal funnel from click to conversion. Trigger on "funnel", "conversion path", "booked 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。
Design, plan, and analyze A/B tests with statistical rigor. Use when the user asks about A/B testing, split testing, experiment design, statistical significance, sample size calculation, test duration, multivariate testing, or conversion experiments. Trigger phrases include "A/B test", "split test", "experiment", "statistical significance", "sample size", "test duration", "which version wins", "conversion experiment", "hypothesis test", "variant testing".
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 affiliate conversion tracking with UTM parameters and link tagging. Triggers on: "set up tracking", "create UTM links", "track my affiliate links", "tracking pixels", "click attribution", "organize my links", "UTM parameters", "tag my links", "campaign tracking", "link tracking setup", "prepare for launch", "debug attribution", "tracking spreadsheet".
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。
Routes Maven version questions to the right workflow by choosing project-local update report interpretation for a user’s own pom.xml, or Maven artifact discovery from maintainer-approved structured evidence. Use when interpreting dependency, plugin, or property update reports; finding Maven coordinates; verifying groupId artifactId version; browsing versions from approved evidence; or constructing artifact URLs without fetching remote content. 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。
Establishes ongoing RoPA maintenance processes including update triggers, change management integration, version control, stakeholder review cycles, and completeness verification procedures. Activate for RoPA updates, record maintenance, change management, version history, review scheduling.
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 the periodic DPIA review lifecycle including trigger identification for regulatory changes, new data categories, technology changes, and breach incidents. Covers version control, stakeholder sign-off procedures, and DPIA register management per Art. 35(11). Keywords: DPIA review, PIA update, review cadence, version control, Art. 35(11), periodic review, trigger events, stakeholder sign-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。
Manages AI model retention and machine unlearning requirements. Covers training data deletion verification, model versioning for compliance, machine unlearning techniques (SISA, gradient-based), and retraining triggers. Keywords: AI retention, machine unlearning, model versioning, training data deletion, retraining, storage limitation.
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。
Conducts privacy auditing of AI models including training data extraction testing, membership inference attacks, model inversion testing, and attribute inference assessment. Uses ML Privacy Meter and related tools to quantify privacy leakage. Keywords: model audit, membership inference, privacy meter, model inversion, training data 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。
Tag and push a prerelease from the current Cargo.toml version. Use when the user wants to cut an alpha/beta/rc prerelease. Aborts on stable versions (no `-` in version string) and if the tag already exists.
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。
Automatically generate user-facing release notes from git commits, pull requests, changelogs, and code changes. Use when preparing software releases, creating version announcements, documenting what changed between versions, or communicating updates to users. Analyzes commit messages, PR descriptions, and code diffs to produce categorized markdown release notes organized by New Features, Improvements, Bug Fixes, and Breaking Changes. Focuses on user-visible changes while filtering out internal/technical details. Triggers when users ask to generate release notes, create changelog, write version announcement, or summarize changes for a release.
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 releasing a bundle-plugin, bumping versions, fixing version drift across manifests, setting up version sync infrastructure, updating CHANGELOG, publishing to marketplaces, or checking release readiness
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。
Implements API versioning using URL paths, headers, or query parameters with backward compatibility and deprecation strategies. Use when managing multiple API versions, planning breaking changes, or designing migration paths.
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, implement, or review git workflow and versioning work in an existing codebase with compatibility, security, and verification controls. Use when the user explicitly requests git workflow and versioning work.
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。
Context7 fetches current, version-specific documentation and code examples for software libraries and hands them to an AI coding agent, so generated code matches the library version in use instead of stale training data. Use when someone says "use context7", "get the latest docs for this library", "look up the current API", "the agent keeps using a deprecated method", "set up Context7 in Claude Code or Cursor", or asks how to call a framework whose API changed recently. Covers the ctx7 CLI, the MCP server and its two tools, library IDs and version pinning, API keys and rate limits, and the REST API.
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。
World-class copywriting expertise combining the persuasive clarity of David Ogilvy, the conversational warmth of Ann Handley, and the conversion science of modern growth marketers. Copywriting is where brand voice becomes words that move people. Great copy doesn't just communicate—it persuades, connects, and converts. The best copywriters understand that every word earns its place, that benefits beat features, that clarity trumps cleverness, and that the reader's problem is the only starting point that matters. Use when "copywriting, write copy, headlines, taglines, email copy, ad copy, landing page copy, product copy, UX writing, CTAs, value proposition, microcopy, sales copy, conversion copy, writing, copy, headlines, conversion, persuasion, messaging, UX-writing, emails" mentioned.
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。
The intersection of AI generation and performance marketing. This skill covers creating ad creatives at scale using AI tools—from static images to video ads to dynamic creative optimization—while maintaining the conversion focus that performance marketing demands. Traditional creative testing is slow: create 5 variants, wait weeks for data, iterate. AI-powered ad creative is fast: generate 50 variants in an hour, test simultaneously, learn in days, iterate continuously. The constraint is no longer production capacity—it's testing velocity and creative strategy. This skill bridges AI generation capability with advertising effectiveness, ensuring that AI-created ads don't just look good—they convert. Use when "AI ads, ad creative, performance creative, ad generation, creative testing, ad variants, DCO, dynamic creative, Meta ads, Google ads, ad fatigue, conversion creative, advertising, performance-marketing, creative-testing, ai-ads, conversion, paid-media, scale" mentioned.
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。
API design specialist for REST, GraphQL, gRPC, versioning strategies, and developer experienceUse when "api design, rest, graphql, grpc, openapi, swagger, versioning, pagination, rate limiting, endpoint, api, rest, graphql, grpc, openapi, swagger, versioning, pagination, rate-limiting, ml-memory" mentioned.
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。
Expert at designing clean, consistent, and developer-friendly APIs. Covers RESTful conventions, versioning strategies, error handling, pagination, rate limiting, and OpenAPI documentation. Designs APIs that are intuitive to use and easy to evolve. Use when "API design, REST API, RESTful, API versioning, API documentation, OpenAPI, Swagger, API error handling, pagination, rate limiting, api, rest, http, versioning, pagination, openapi, swagger" mentioned.
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。
Track different resume versions, maintain master resume, manage tailored versions
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。
When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics. For page-level conversion optimization, see cro.
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 王建硕 wants to systematically improve his X (Twitter) content by iterating on the content-generation prompt (prompts/x/prompt.md, used by the every-6h tweet Action) and finding which prompt version produces the highest-reach tweets. Each prompt edit is a git-SHA-versioned, numbered experiment with a hypothesis; tweets are attributed to the version live at post time and judged on median impressions per tweet. Also mines per-tweet impression data for content-feature signals (angle / length / topic) that feed the next prompt edit. North-star = impressions per tweet. Triggers — "改 X 的 prompt", "X 内容改进", "哪版 prompt 最好", "什么内容 impression 高", "improve my tweets", "A/B test the X prompt", "/wjs-x-improving-content".
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。