「frame」の検索結果

Plans, creates, and optimizes digital marketing campaigns including content strategy, social media, email marketing, SEO, and AI visibility (GEO). Generates UTM parameters and tracking URLs. Helps develop go-to-market strategies, campaign messaging, content calendars, and performance measurement frameworks. Use when asked to "create a marketing campaign", "plan content strategy", "build email sequences", "set up UTM tracking", "analyze marketing metrics", "launch a product", "optimize for SEO", "improve AI visibility", "optimize for voice search", "set up IndexNow", "check accessibility for SEO", or "create social media previews". Supports both marketing beginners and experienced marketers needing templates.

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 high-converting landing page copy using proven frameworks like PAS (Problem-Agitate-Solution), AIDA, and StoryBrand. Creates headlines, value propositions, CTAs, and full page sections optimized for conversion. Use when users need landing page copy, sales page content, or marketing website text.

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。

A/B test subject line variations using proven copywriting frameworks. Predict open rates based on historical 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。

Evaluates and sharpens content hooks using The Hook Stack™ framework. Use when scoring headlines, refining hooks for video/social/newsletter, or when asked to "evaluate this hook", "run through hook stack", or "score my headline".

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 10+ ad copy variations for any major ad platform with A/B testing, character-count compliance, and four proven copy frameworks (PAS, AIDA, BAB, 4Ps)

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 complete video ad scripts for 15s, 30s, and 60s formats with shot-by-shot breakdowns, voiceover scripts, on-screen text, B-roll suggestions, music direction, storyboard outlines, and three proven video frameworks

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Design, derive, optimize, explain, and diagnose cinematic Eastern xianxia camera direction, storyboards, motion choreography, pacing, continuity locks, keyframes, and per-shot image-to-video prompts. Use for 仙侠运镜、镜头语言、分镜、推拉摇移、跟拍、环绕、航拍、打斗追逐、情绪镜头、首尾帧、视频提示词、参考视频诊断, when users ask “怎么运镜/不会运镜/给案例/帮我改运镜”, or when an input contains invalid, contradictory, physically implausible, non-camera, or off-topic instructions that need actionable feedback. Support 16:9, 21:9, 4:3, 3:2, 4:5, and 9:16; default to 30 seconds, 16:9, six shots, narrative-adaptive pacing, epic scale, and selective high saturation when unspecified.

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。

Turn ONE topic, talking-head video, or photo into a finished Vox-style paper-collage explainer / ad video on the MuAPI platform (api.muapi.ai) + local ffmpeg — script, collage keyframes, motion, voice-over, music, captions, all automated. Three input modalities: a topic (B-roll), a talking-head video (A-roll mode), or a single photo of a person/product anchored into the collage (C-roll mode).

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。

Fix silent data loss when mutating Nostr replaceable events (Kind 0 profile, Kind 3 contact/follow list, Kind 10002 relay list, etc.) in client apps. Use when: (1) Following someone wipes the user's entire follow list, (2) Updating profile metadata loses existing fields, (3) Fresh browser session or mobile login causes data loss on first action, (4) Replaceable event mutation uses stale or null cached state. Root cause: Nostr replaceable events are full-replace (no partial update), so publishing based on stale/unloaded cache overwrites the canonical version. Applies to any Nostr client using React, Flutter, or similar reactive frameworks where query state may not be loaded when a mutation fires.

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Fix "ref.listen can only be used within the build method of a ConsumerWidget" assertion error in Flutter Riverpod 3.x. Use when: (1) ConsumerStatefulWidget crashes with "Failed assertion: line 492 pos 7: 'debugDoingBuild'" in flutter_riverpod consumer.dart, (2) ref.listen is called inside initState, addPostFrameCallback, or any lifecycle method other than build(), (3) All widget tests fail with this assertion from scheduler callback. Applies to flutter_riverpod 3.0+ with ConsumerStatefulWidget.

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。

Fix slow Flutter app startup caused by blocking network operations during initialization. Use when: (1) App takes several seconds to show first frame, (2) Startup logs show sequential network operations (WebSocket connections, API calls), (3) Services initialize during startup that aren't needed until user authenticates. Covers: converting sequential network ops to parallel with Future.wait(), deferring service initialization until actually needed (lazy init), and identifying blocking operations in Riverpod/provider initialization chains.

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Fix "setState() or markNeedsBuild() called during build" error triggered from deactivate(). Use when: (1) Error occurs during widget disposal or navigation away, (2) Stack trace shows deactivate -> some notifier -> ValueListenableBuilder._valueChanged, (3) Video/audio player pause() or stop() calls in deactivate() cause the error. The fix is deferring state-modifying operations to addPostFrameCallback.

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 video clips on the user's own GPU through Guaardvark: text-to-video, image-to-video, first+last frame animation, clips with their own soundtrack and dialogue (MiniMax H3), short looping animations, and batch runs. Use when the user asks to make, render, animate, or batch-generate video locally.

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 Guaardvark's five-role Film Crew locally: screenwriter, casting (Cast Library LoRAs), cinematographer storyboards, keyframe renders, editor. Turns a logline or screenplay into a finished short video with human gates at casting and storyboard. Use when the user says "film this", "make a short film", "storyboard this script", or "start the film crew".

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。

Turn a song into a beat-synced music video with Guaardvark's Director: song analysis, per-cut shot prompts, storyboard keyframes, image-to-video renders, cuts on the beat. Use when the user says "music video", "video for this track", or wants visuals timed to a song. Has an approval gate before GPU spend.

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 SEO pages that rank on Google AND get cited by LLMs. Uses live SERP data, 500-token chunk architecture, RAG optimization for Gemini 3.5 Flash, the Two-Gate AEO framework (retrieval-pool entry + selected-citation extraction), the Anti-NLP Stuffing Protocol (structural entity placement, no keyword-density stuffing), strict single-service local isolation, and the Reddit Test quality gate. Triggers on: "write an SEO page", "seo-agi", "seo page for [keyword]", "rank for [keyword]", "rewrite this page for SEO", "GEO", "AEO", "write a page that ranks".

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 image references into spatial hypotheses, camera constraints, construction routes, material evidence, and measurable Blender reconstruction gates. Use whenever a user asks to replicate, recreate, match, clone, or reconstruct a scene, environment, object, material, lighting setup, or shot from one or more images, especially when room connectivity, hidden space, perspective, visible-frame fidelity, material identity, aging, wetness, or photographic treatment matters.

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 generates comprehensive unit tests for functions, classes, and modules. Use when you need to create tests for Python (pytest, unittest) or Java (JUnit, TestNG) code. Generates tests with comprehensive coverage including happy paths, edge cases, and error conditions. Analyzes existing test patterns in the codebase to match style and conventions. Supports mocking, parameterized tests, fixtures, and follows best practices for each 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。

Generate comprehensive test case documentation from test code, test framework output, existing test docs, and source code context. Use when documenting test suites, creating test specifications, generating test coverage reports, onboarding developers to testing practices, or preparing QA documentation. Analyzes test functions (pytest, unittest) to extract test names, docstrings, assertions, and organization, then produces structured markdown with both overview-level summaries and detailed test case specifications including purpose, preconditions, steps, expected results, and test data. Triggers when users ask to document tests, generate test specifications, create test reports, summarize test coverage, or explain what tests do.

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 generates regression tests for Python codebases by analyzing changes between old and new code versions and their existing tests. Migrates tests to work with new code, generates tests for new functionality, and creates mocks for external dependencies. Supports unittest and pytest frameworks. Use when refactoring code, adding features, or ensuring backward compatibility.

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 test suites using mutation testing to maximize mutation kill rate with minimal tests. Use when you need to reduce test suite size while maintaining quality, identify redundant tests, improve mutation coverage, or validate test effectiveness. The skill analyzes test coverage, execution intervals, and redundancy using mutation operators, selects or generates a minimal subset of tests that maximizes mutation kill rate, and outputs an optimized test suite with detailed reports showing killed and surviving mutants. Supports multiple programming languages and mutation testing frameworks.

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。

Identifies non-deterministic or unreliable tests through static code analysis and test result analysis. Use when Claude needs to find flaky tests, analyze test reliability, or investigate intermittent test failures. Supports Python (pytest, unittest) and Java (JUnit, TestNG) test frameworks. Trigger when users mention "flaky tests", "intermittent failures", "non-deterministic tests", "unreliable tests", or ask to "find flaky tests", "analyze test stability", or "why tests fail randomly".

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 updates regression tests based on interval analysis to maintain coverage of key program intervals. Use when code changes affect value ranges, conditionals, or control flow, and existing tests need updating to maintain interval coverage. Analyzes interval information from updated code, identifies coverage gaps, adjusts test inputs and assertions, removes redundant tests, and generates new tests for uncovered intervals. Supports Python, Java, JavaScript, and C/C++ with various test frameworks (pytest, JUnit, Jest, Google Test).

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。

Identify and replace deprecated API usage in source code with modern alternatives. Use when: (1) Modernizing legacy codebases, (2) Upgrading framework versions (React, Django, Spring, etc.), (3) Fixing deprecation warnings in build output, (4) Preparing for major version upgrades, (5) Ensuring code uses current best practices. Supports Python, JavaScript/TypeScript, Java, and other major languages with both AST-based detection and pattern matching for accurate identification and automated replacement with validation.

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。