문서·Office 스킬

Category and brand ungating — requirements, documentation, appeal process, restricted categories guide

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This packages provides C++ header files for developers wishing to create R packages that processes BAM files. ompBAM automates file access, memory management, and handling of multiple threads 'behind the scenes', so developers can focus on creating domain-specific functionality. The included vignette contains detailed documentation of this API, including quick-start instructions to create a new ompBAM-based package, and step-by-step explanation of the functionality behind the example packaged in

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

A set of tools for working with miRNA affinity models (KdModels), efficiently scanning for miRNA binding sites, and predicting target repression. It supports scanning using miRNA seeds, full miRNA sequences (enabling 3' alignment) and KdModels, and includes the prediction of slicing and TDMD sites. Finally, it includes utility and plotting functions (e.g. for the visual representation of miRNA-target alignment).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

The BloodGen3Module package provides functions for R user performing module repertoire analyses and generating fingerprint representations. Functions can perform group comparison or individual sample analysis and visualization by fingerprint grid plot or fingerprint heatmap. Module repertoire analyses typically involve determining the percentage of the constitutive genes for each module that are significantly increased or decreased. As we describe in details;https://www.biorxiv.org/content/10.11

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when the user asks for a slideshow, presentation, pitch deck, or interactive deck, or to convert an existing page into a deck. Authors a HyperFrames deck with fragments, branching and presenter mode; unclear -> /hyperframes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when the user asks to generate, update, refresh or verify project documentation, or to check that docs still match the codebase. Writes up to 9 doc files, each verified against real paths; supports --force and --verify-only.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, passive voice, negative parallelisms, and filler phrases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate an Architecture Review Board (ARB) presentation in Coco Inc's standard 11-slide format. Use when someone asks to "create an ARB deck", "architecture review presentation", "prepare for architecture review board", "ARB slides", or needs to present a product/platform architecture for sign-off. Reads PRD, architecture maps, and project memory to pre-fill technical content. Produces a self-contained HTML slide deck.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Always-on spam and deliverability guardrails for cold email copy. Apply these rules whenever writing, reviewing, or QA-ing subject lines, openers, follow-ups, CTAs, or any cold outreach copy. Trigger on phrases like "check this for spam", "spam word check", "deliverability review", "QA this copy", "flag banned words", "check subject line", "is this safe to send", "scan for spam triggers", or any time cold email copy is being generated or reviewed. Also trigger automatically as a background rule whenever the campaign-copywriting or cold-email-copywriting skill is active -- these guardrails apply to EVERY piece of outbound copy generated, even if the user does not explicitly ask for a check.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Decides which named technologies a company actually runs on its website, verified against the live site. Triggers on "find companies using Shopify", "they're on HubSpot", "tech stack targeting", "technographics", "who uses Klaviyo", "companies running WordPress". Outputs a per-domain verdict (tech_confirmed plus the verified stack and its evidence); the copy-ready clause is OFF by default and runs only when the operator asks for one.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Checks whether a company's own website mentions a specific keyword, using one simple Google site: query per keyword through a SERP API, and turns the hit into a copy-ready line. Triggers on "does their site mention X", "site: search", "find companies whose website says X", "filter this list by a keyword on their site", "check if they talk about X", "google search enrichment". Outputs site_keyword_hit plus a one-clause personalization line with an evidence URL.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build a near-complete company list for any ICP by learning how your real customers label themselves, pulling every industry and keyword they use, judging every single row with a cheap AI model (Jev or gpt-5-nano), and running a Claygent lookalike loop until it runs dry. The method from the SCULPT 2026 talk "Build the perfect company list". Use when a database filter feels too narrow or too noisy, when someone says "find every company like our customers", "our TAM looks too small", or "why is this list full of junk".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expand a hard-to-build niche list from ~10 known-good seed companies into a full qualified TAM. Seed fingerprinting (how do good-fit companies ACTUALLY show up in the company database's filters) → lookalike generation (Prospeo company_lookalike, Exa findSimilar, Parallel.ai entity search) → filter mining with a precision/volume scorecard → wide pull with auto-sharding → cheap-AI qualification → live-website verification → client transparency report. Use when a vertical list feels "too small" (medical groups, hedge funds, Medicare brokerages), when known good-fit companies don't show up in keyword searches, or when someone says "expand this list", "the TAM should be bigger", "find more companies like these".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Keep README files current with project changes. Use when project structure changes, features added, or setup instructions modified. Suggests README updates based on code changes. Triggers on significant project changes, new features, dependency changes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Auto-generate API documentation from code and comments. Use when API endpoints change, or user mentions API docs. Creates OpenAPI/Swagger specs from code. Triggers on API file changes, documentation requests, endpoint additions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Mention/keyword sweep on social platforms for [REPLACE: KEYWORDS] — trends, sentiment, top posts

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

One sentence that describes WHEN to invoke this skill. Write it as a trigger phrase, not a summary — include the words a user would say ("fix bug", "add feature", "review diff"). The agent routes on this field.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Get a PDF into the model without blowing the context window or losing structure. Native PDF beats OCR-then-text for most cases; extract-then-summarize beats native for very long docs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

PDF 数据提取工具。当用户提到"PDF 提取"、"PDF 转 Markdown"、"PDF 解析"、"提取 PDF 内容"、"PDF 转 JSON"、"RAG PDF"时使用。OpenDataLoader PDF 是目前基准测试第一的 PDF 解析器，支持本地模式（快速、确定）和混合 AI 模式（复杂表格、扫描件、公式），输出 Markdown、JSON（带边界框）、HTML。适用于需要从 PDF 提取结构化数据用于 RAG/LLM pipeline，或需要批量处理 PDF 文档的场景。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

文章解读助手。对用户提供的文章（URL、Markdown、PDF、粘贴文本）进行结构化解读，输出包含总览、关键要点、个人启发、批判性思考和延伸思考的 Markdown 报告。触发词：文章解读、解读文章、帮我解读、读一下这篇、分析这篇文章、article interpretation。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Map 50-200+ keywords into topical clusters for SEO domination. Build content roadmaps for topical authority. Triggers on: "keyword research", "keyword clustering", "topical authority", "keyword map", "keyword strategy", "content roadmap for SEO", "keyword grouping", "topic clusters", "SEO keyword plan", "map my keywords", "keyword cluster", "hub and spoke content", "build topical authority", "SEO content plan", "keyword universe".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Onboard a new user into a Symbiotic AI setup by interviewing them one sharp question at a time and writing their own context files (SOUL.md, USER.md, NOW.md, and AGENTS.md) as plain Markdown they keep. Use this whenever someone is setting up a personal AI agent, wants their assistant to remember or understand them across sessions, mentions Symbiotic AI / SOUL.md / USER.md / NOW.md, is staring at blank template files full of [bracketed placeholders], or asks how to give an AI persistent context they own and can move between models. Trigger even if they do not say the word "onboard".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

用 MinerU 将复杂PDF文档转换为LLM友好的Markdown/JSON格式。适用于：(1) PDF转Markdown/JSON，(2) 提取PDF中的文本、表格、公式、图像，(3) 解析学术论文、技术文档、商业报告，(4) 为RAG应用准备文档数据，(5) 批量处理PDF。触发关键词："PDF解析"、"PDF转Markdown"、"提取PDF表格/公式"、"MinerU"、"parse PDF"等。不用于：PDF的阅读/填表/签名/拆分合并（用宿主pdf工具）、Word/PPT等非PDF格式解析、只需读几页内容的场景（直接读即可，不必转换）。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.