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Transform a user-uploaded photograph into one horizontal collectible memory card with a large quiet editorial illustration, six integrated journaling-sticker motifs, three small English keywords beneath the left illustration, tactile paper grain, and deliberately clumsy flat color fields. Use when Codex needs to P图, edit, redraw, stylize, or turn a travel, street, landscape, lifestyle, portrait, or pet photo into a minimalist postcard-like card or sticker-card in this visual language. Preserve recognition through one same-medium identification anchor and optionally one exact place-defining landmark text shown once; never retain an unintended photorealistic patch, signature, or watermark.

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Coordinate nontrivial work in the ctx repository when a task has independent investigation, implementation, or test lanes; compute-heavy checks; or repetitive changes suited to scripts or codemods. Use for decomposition and concurrency decisions, not simple edits or questions.

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。

Propose transparent state updates for a virtual pet game. Use for this Bot Generator Bot template; creates drafts or simulations within its declared scope.

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。

单词重复比例过滤器。过滤器将单词级n-gram重复比率的样本保持在特定范围内。 本SKILL使用依赖data_juicer，请在调用前安装好python环境并安装data_juicer，你可用以下指令进行安装： pip install py-data-juicer 当用户提到单词重复过滤、文本重复比例过滤、n-gram重复过滤、清理重复内容等需求时使用此skill。

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。

字符重复比例过滤器。过滤文本中字符级n-gram重复比例不在指定范围内的样本。 当用户提到字符重复、重复比例过滤、文本重复过滤、清理重复内容等需求时使用此skill。 即使用户没有明确说出"字符重复"，只要任务涉及根据文本中字符重复的程度来筛选数据， 就应该使用此skill。

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。

Product Requirements Document writing - PRD templates, MoSCoW prioritization, user personas, competitive analysis, feature specs, acceptance criteria, risk assessment

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。

Terminal pet that lives in your statusline. 12 species, 5 stats (DEBUGGING, PATIENCE, CHAOS, WISDOM, SPEED). Reacts to your workflow - happy when tests pass, sad when builds fail, excited during swarm mode. Deterministic species from user ID.

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。

薪资谈判教练。模拟前 FAANG 招聘官 + 薪酬策略师，把「拿到 offer → 签字」这一步拆成 6 个动作：Offer 诊断（Base / RSU / Sign-on / Bonus 每项标 LOW/MED/HIGH leverage）→ 谈判杠杆图（我的力量 / 招聘官的力量 / 弱点）→ 策略排序（Base → RSU → Sign-on → Bonus，按 context 调整）→ 脚本生成（电话 / 邮件 / 招聘官回话模拟）→ Counter 模拟（"这是最好的了" / "预算固定" / "会考虑其他候选人" 三种硬回答）→ 最终建议（要什么 / 别推什么 / 何时停）。三步：贴 offer → 说 context（有没有竞争 offer / deadline / 级别 / 风险偏好 / 最看重什么）→ 自动生成并打开一份 HTML Negotiation Playbook。关键词：salary negotiation, offer negotiation, comp negotiation, RSU, sign-on, base salary, counter offer, competing offer, TC, total comp, 薪资谈判, offer 谈判, 谈薪, 反 offer, 加薪, 谈 package, levels.fyi, 招聘官话术, recruiter script。

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。

Conduct multi-round deep research on any GitHub Repo. Use when users request comprehensive analysis, timeline reconstruction, competitive analysis, or in-depth investigation of GitHub. Produces structured markdown reports with executive summaries, chronological timelines, metrics analysis, and Mermaid diagrams. Triggers on Github repository URL or open source projects.

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Use this skill when the user requests to generate, create, or write professional research reports including but not limited to market analysis, consumer insights, brand analysis, financial analysis, industry research, competitive intelligence, investment due diligence, or any consulting-grade analytical report. This skill operates in two phases — (1) generating a structured analysis framework with chapter skeleton, data query requirements, and analysis logic, and (2) after data collection by other skills, producing the final consulting-grade report with structured narratives, embedded charts, and strategic insights.

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/regression checks for the evidence-first survey pipeline: citation health, per-section coverage, placeholder leakage, and template repetition.

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 per-subsection evidence packs (NO PROSE): claim candidates, concrete comparisons, evaluation protocol, limitations, plus citation-backed evidence snippets with provenance.

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Train strict project ownership from repo-local docs/ai memory and central LLM Wiki project entities. Use when Codex or Claude Code should initialize a learning question bank, quiz or grade the user, run code tracing or failure drills, record misconceptions, schedule spaced repetition, or generate Markdown/JSON mastery dashboards without replacing llm-wiki or ai-project-memory.

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Drive Chrome via the DevTools Protocol from JavaScript. Run JS snippets through the `browser-harness-js` CLI — it auto-spawns a long-lived bun HTTP server holding a fully-typed CDP `Session`, and every call (`browser-harness-js 'await session.Page.navigate(...)'`) executes against the same persistent connection. Session, active target, and globals survive across calls. Use when the user wants to automate, script, or inspect a Chrome browser via CDP — single tab or multi-tab, attach to existing Chrome or to a new one launched with --remote-debugging-port.

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。

Search the web with LLM-optimized results via the Tavily CLI. Use this skill when the user wants to search the web, find articles, look up information, get recent news, discover sources, or says "search for", "find me", "look up", "what's the latest on", "find articles about", or needs current information from the internet. Returns relevant results with content snippets, relevance scores, and metadata — optimized for LLM consumption. Supports domain filtering, time ranges, and multiple search depths.

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 F# Interactive (`dotnet fsi`) for .NET exploration, scriptable experiments, package-backed .fsx workflows, quick data transforms, and reproducible command-line probes. USE FOR: .fsx scripts, F# REPL work, #r nuget references, #load composition, interactive type exploration, and small strongly typed experiments before moving code into a project. DO NOT USE FOR: production application code that needs compiled project structure; C# scripting; long-lived automation better expressed as a normal CLI, test, or build target. INVOKES: run dotnet fsi, edit .fsx scripts, load project or source files, and validate snippets against the target SDK.

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Detects duplicate boilerplate, copy-paste tests, and structural maintainability issues across .NET test suites. Use when the user asks to reduce repetition, consolidate similar test methods, convert copy-paste tests to data-driven parameterized tests, suggest a better test structure, or identify refactoring opportunities. Identifies repeated construction, assertion patterns, copy-paste methods convertible to DataRow/Theory/TestCase, redundant setup/teardown, and shared infrastructure. Produces an analysis report with concrete before/after suggestions. Works with MSTest, xUnit, NUnit, and TUnit. DO NOT USE FOR: writing new tests (use writing-mstest-tests), reviewing test quality or anti-patterns (use test-anti-patterns), or deep mock auditing (use exp-mock-usage-analysis).

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Read before a js eval cell that installs a package, spawns a server or PTY, or starts a long run, and before any bun -e, script, server, CLI, test, bundle, or package-management work: this session's eval js kernel runs Bun 1.4+ (this skill is present only when it does). Bun 1.4 ships builtins that replace 15+ npm deps — check here BEFORE installing sharp, puppeteer/playwright (scraping), marked, node-cron, node-pty, concurrently, serve-static, tar, json5, fast-xml-parser, string-width. Triggers: eval js, bun, Bun.serve, bun test, bun build, bun install, bun run, image resize, headless browser, markdown render, cron, PTY.

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 cinematic AI shortfilm prompts (works with Seedance 2.0, Xiaoyunque, Sora, Kling, Jimeng, Veo) using the 5-stage structure from Mx-Shell's Zombie Scavenger. Trigger when the user wants transformation sequences, multi-shot narrative shorts, weapon-charge/combat segments, emotional family/pet/farewell narratives (催泪/亲情/萌宠/离别), or any cinematic video prompt.

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 Google Business Profile visibility, geographic grid rankings, local search keywords, and nearby competitors through the standalone Google Maps CLI. Use for local SEO or location-strategy decisions, not ordinary place recommendations.

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。

Comprehensive SEO/GEO/AEO analysis toolkit for optimizing content visibility across traditional search engines (Google, Bing), AI platforms (ChatGPT, Perplexity, Claude, Gemini, Grokipedia), answer engines (Google AI Overviews, Bing Copilot, featured snippets), voice assistants (Google Assistant, Siri, Alexa), and social media (Facebook, Twitter, LinkedIn, WhatsApp, Instagram). Analyzes HTML/Markdown/JSX files for metadata completeness, schema markup, keyword optimization, entity extraction, and generates multi-format audit reports with platform-specific recommendations.

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。

Discover, analyze, and prioritize keywords for SEO and GEO content strategies. Identifies high-value opportunities based on search volume, competition, intent, and business relevance. Generates topic clusters and content calendars. Use when asked to "find keywords", "keyword research", "what should I write about", "keyword analysis", "find me topics to write", "search volume", "keyword difficulty", "content ideas", or any keyword discovery task.

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 review game sound banks, map chosen takes to real events, and prepare or verify runtime playback with timing, repetition and mix limits. Use for unit voices, foley, impacts, UI cues and ambience.

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。

Tear down a competitor's repo or product into a feature inventory and adoption choices. Use when: comparing us to another tool or asking what to steal.

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。