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Full AI-assisted workflow for long-form SEO content (2,000+ words). Covers JTBD strategy, E-E-A-T quality standards, and 5 production phases: research, outline, draft, editing, and SEO/publishing. Built for B2B marketing agency context. Use when: user wants to write or optimize an SEO article, mentions "SEO content", "blog post", "GTM article", "long-form content", or asks to create content with keyword strategy.

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。

抓取微信公众号文章并转换为 Markdown 文档。使用脚本自动获取文章标题、作者、正文内容，保留格式并保存为 .md 文件，支持下载图片到本地。适用场景：用户要求抓取/获取/下载/保存微信公众号文章内容为 Markdown 格式，或给出 mp.weixin.qq.com 链接需要提取内容。支持 Obsidian 模式（使用 -obsidian 参数）。

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。

Read tweets, threads, bookmarks, articles, and user timelines on X (formerly Twitter) using an authenticated GraphQL flow. Use this skill whenever the user mentions a tweet URL, a Twitter thread, an X bookmark, a Twitter article, reading their X bookmarks, pulling a thread, or grabbing X content — even if they don't explicitly say 'use mktg-x'. Requires MKTG_X_AUTH_TOKEN and MKTG_X_CT0 env vars. Do NOT firecrawl x.com URLs — Twitter serves a degraded logged-out page and firecrawl will return the login wall. mktg-x is the only path for auth-walled X 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。

Take one piece of long-form content and atomize it into 10-20 platform-specific posts across 8 platforms (Twitter/X, LinkedIn, Instagram, Reddit, TikTok, YouTube, Threads, Bluesky). Turns blog posts, podcasts, videos, and newsletters into native social content for each platform. Use this skill whenever someone has existing content they want to distribute — even if they don't say 'atomize' explicitly. Triggers include: 'repurpose this', 'turn this into posts', 'social content from my blog', 'I wrote an article and want to promote it', 'cross-post this', 'content distribution', 'break this down for social', 'I have a podcast episode', 'turn this video into clips', 'make social posts from this', 'content calendar from this article', or any request to get more mileage from existing 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。

Web search with full page content extraction. Use this skill whenever the user asks to search the web, find articles, research a topic, look something up, find recent news, discover sources, or says "search for", "find me", "look up", "what are people saying about", or "find articles about". Returns real search results with optional full-page markdown — not just snippets. Provides capabilities beyond default agent web search tools.

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 you need to know where a piece of reporting actually came from — the user gives an article URL and asks "where does this come from?", "who is the original source?", "is this just a rewrite?", or another skill needs the provenance chain before grading or citing a report. Follows an article's links and named attributions back to the originating vendor, government, victim or actor document and returns the chain, the primary's stated access and confidence language, publication dates, and every hop it could not resolve. Deterministic — a script or the Liberty91 platform resolves the chain; the model does not reconstruct it from memory. Invoked by /quality-of-information-check, /threat-actor-profiling, /campaign-tracking and /intelligence-writing. Works on any URL with no API key.

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 as the default entry point for any CTI request that doesn't name a specific skill. Activates when a user asks to investigate an indicator, profile a threat actor, write an assessment, enrich IOCs, or build detection rules, and when a user asks where a report or article comes from, how reliable it is, to grade a source, check a report, or split a report into claims, or pastes a bare article URL. Routes to the right investigation, provenance, grading or analysis skill, then auto-applies rigor skills (source rating, TLP, confidence, likelihood) on the output.

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。

Remove AI-generated tone and make writing sound more human in Japanese and English. Use when reviewing drafts for AIっぽさ, humanizing article/blog text, 文体チェック, AI感を消したい, or ChatGPT tells.

Turn any context (blog URL, pasted article, PR diff, meeting notes, release notes, raw prompt) into a narrated explainer mp4 with slides, subtitles, and optionally a talking-head avatar. Uses a free local stack (Pillow + edge-tts + ffmpeg) with optional SadTalker for the avatar. Use when the user says "動画化", "explainer video", "解説動画作って", "ブログを動画に", "記事を動画化", "PRを動画で説明", "議事録を動画化", "context to video", or wants an mp4 from arbitrary text/source. Output is mp4 + srt under the user's chosen project workspace.

Export the current chat session as a structured Markdown work timeline or blog-draft seed. Use when the user asks for a session log, work log, timeline, 作業ログ, ブログネタ, blog draft, article seed, Zenn, Qiita, or はてな. Do not use for verbatim dialogue export or distilled reusable knowledge only.

Audits whether a machine-readable regelrecht law model is faithful to the LETTER of the wettekst — and strictly separates the wettekst (leading) from the toelichting (Nota/Memorie van Toelichting; explanatory, not a norm). Runs in critical passes per article/lid and detects four deviation classes: toelichting-bleed (a modeled criterion that the letter only states as an open norm, or not at all), missing verbatim elements (a word/condition such as a "tenzij" or a qualifier dropped from the model), wrong legal_basis anchoring (an endpoint hung on a "kapstok"-article while the operative norm lives elsewhere), and verbatim-drift in text: (an ingetrokken/older redaction). Also detects positief-lid vs uitsluitings-lid conflicts and checks the chapeau for an explicit derogation rule ("zo nodig in afwijking van het eerste lid") before judging precedence. Classifies every finding as modelfout (fix), wettekst-gevolg (report, do not fix), or letter-vs-toelichting-question (jurist decides what is leading + revision signal). Use when the user wants to know if a model is "dicht bij de letter", mentions letter vs toelichting/bedoeling, fidelity, "natuurgetrouw", or before trusting feature outcomes.

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。

将文章内容自动拆分为多张信息图海报（2400x3600竖版），支持多种风格模板和6种页面布局。 Use when: (1) 用户说"生成海报", (2) 用户发文章要求做图, (3) 用户说"做成卡片/信息图" (4) 用户发文章并希望转成可分享的图片, (5) 用户要求制作信息图/infographic

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 maintain a persistent, interlinked markdown wiki (second brain / research notebook / personal knowledge base) from raw sources. Use this skill when the user wants to ingest an article / paper / note / URL / PDF into their wiki, ask a question against their wiki, lint/health-check their wiki, or set up a new wiki from a folder. Triggers include phrases like "add this to my wiki", "ingest this", "file this article", "update my notes with this", "what does my wiki say about X", "health-check my wiki", "find stale/orphan pages", or any workflow involving a `raw/` source folder, `wiki/` markdown folder, `index.md`, or `log.md`. Compatible with Obsidian vaults.

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。

Inject juice and game feel into gameplay code using the project's chosen middleware. Use when adding screen shake, tweens, particles, audio feedback, haptics, hitstop, or any form of "juice" to gameplay events. Triggers on requests like "add juice", "make this feel better", "add screen shake", "polish this feature", "add feedback to this action", "make the hit feel impactful", "add particles when", "screen shake on damage", "tween this", "add camera shake", "make this snappy", or any game feel, polish, or feedback work. Always reads ProjectConfig.yaml -> feel_tools and docs/GFD.md Feedback Matrix before generating code.

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。

Master orchestrator for B2B SaaS content creation and the editorial production system behind it. Use this skill to produce blog articles, whitepapers, case studies, video scripts, newsletters and ghostwritten thought leadership, to develop content strategy, and to run the publishing line itself — content briefs, editorial calendars and production capacity, review and approval workflow, subject-matter-expert access, the style guide, an AI-drafting policy, and a content inventory with owners. Also triggers on: blog, article, case study, whitepaper, newsletter, video script, ghostwriting, content calendar, editorial, thought leadership, content strategy, content brief, content operations, content ops, editorial workflow, managing editor, who reviews this, our content pipeline is stuck, content is stuck in review, content audit, content inventory, content governance, style guide, house style, SME interview, expert interview, freelance writers, AI content policy, how much content can we actually produce. Also owns the customer academy and customer education: online courses, onboarding curriculum, tutorials, certification programs, digital credentials and badges, exam and pass-mark design, course freshness when the product UI changes, and whether the academy should sit behind a login. Triggers on: customer academy, customer education, online course, course library, learning path, curriculum, tutorial, certification program, certified users, digital badge, Open Badges, recertification, exam, pass mark, LMS, Skilljar, our tutorial shows the old UI, our course is out of date, should the academy be gated, do certified customers renew better.

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。

为他山学科交叉公众号撰写微信文章，包括选择文章类型、套用排版模板、生成配图、转换HTML、添加底部模板。当用户说"写一篇公众号文章""帮我写他山文章""写公众号推文""帮我把这篇写完""补全这篇文章""继续写这篇"时使用。写完后自动触发 article-proofreading 审稿。

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。

撰写通用长文（技术分析/产品思考/对外分享/个人随笔），区别于微信公众号文章（公众号请用 wechat-article-writer）。读取写作习惯手册后起草，支持大纲确认、正文撰写、挑战者自检、审稿闭环。当用户说「写一篇技术文章」「写一篇对外分享」「写一篇产品分析/产品思考」「帮我写一篇通用文章/长文」「写一篇个人随笔」时使用。

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。

Teach a topic, video, article, codebase change, spec, or transcript until the user demonstrably understands it, using staged instruction, a running checklist doc, and quizzes. Use whenever the user says "teach me", "explain this in detail", "help me understand X", "/teach-me", "walk me through this", "I want to learn X", "understand this deeply", or pastes a YouTube URL, article, paper, PR, or spec and asks to understand it rather than summarize it. Also use when the user wants study notes or raw material from a source they are learning. Do NOT use for a quick factual lookup, a summary request, or when the user just wants the answer.

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 long-form thought leadership articles, opinion pieces, industry POV essays, and CEO/founder bylines using the Made to Stick SUCCESs framework (Chip and Dan Heath). Use when the user asks for a long-form article, executive byline, opinion piece, industry POV, manifesto, "explain our point of view on X", or wants to publish an authority-building piece (1200-2500 words). Reads brand voice and positioning from knowledge/. For a short-form post, see linkedin-post. For the newsletter, see newsletter-writer. For improving an existing draft, see copy-review.

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。

Fetch any URL and convert to markdown using Chrome CDP, with automatic image downloading. Supports auto-capture and wait-for-user modes. Downloads article images to local directory and rewrites image references to relative paths. Use when user wants to save a webpage as markdown with images. Keywords: url to markdown, save webpage, download article, 网页转markdown

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 articles, guides, blog posts, tutorials, newsletter issues, and other long-form content in a distinctive voice derived from supplied examples or brand guidance. Use when the user wants polished written content longer than a paragraph, especially when voice consistency, structure, and credibility matter.

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 or replace OneQuery blog thumbnail images and article diagram images for apps/landing. Use when Codex needs to create blog list thumbnails, social share thumbnails, in-article architecture diagrams, update blog imageSrc references, follow the OneQuery diagram design system, or use the bundled assets/diagram-design-system.png visual reference.

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。