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概要と使いどころ
Provides delayed computation of a matrix of residuals after fitting a linear model to each column of an input matrix. Also supports partial computation of residuals where selected factors are to be preserved in the output matrix. Implements
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。
Universal LaTeX document skill: create, compile, and convert any document to professional PDF with PNG previews. Supports resumes, reports, cover letters, invoices, academic papers, theses/dissertations, academic CVs, presentations (Beamer), scientific posters, formal letters, exams/quizzes, books, cheat sheets, reference cards, exam formula sheets, fillable PDF forms (hyperref form fields), conditional content (etoolbox toggles), mail merge from CSV/JSON (Jinja2 templates), version diffing (latexdiff), charts (pgfplots + matplotlib), tables (booktabs + CSV import), images (TikZ), Mermaid diagrams, AI-generated images, watermarks, landscape pages, bibliography/citations (BibTeX/biblatex), multi-language/CJK (auto XeLaTeX), algorithms/pseudocode, colored boxes (tcolorbox), SI units (siunitx), Pandoc format conversion (Markdown/DOCX/HTML ↔ LaTeX), and PDF-to-LaTeX conversion of handwritten or printed documents (math, business, legal, general). Compile script supports pdflatex, xelatex, lualatex with auto-detection, latexmk backend, texfot log filtering, PDF/A output, and verbosity control (--verbose/--quiet). Empirically optimized scaling: single agent 1-10 pages, split 11-20, batch-7 pipeline 21+. Use when user asks to: (1) create a resume/CV/cover letter, (2) write a LaTeX document, (3) create PDF with tables/charts/images, (4) compile a .tex file, (5) make a report/invoice/presentation, (6) anything involving LaTeX or pdflatex, (7) convert/OCR a PDF to LaTeX, (8) convert handwritten notes, (9) create charts/graphs/diagrams, (10) create slides, (11) write a thesis or dissertation, (12) create an academic CV, (13) create a poster, (14) create an exam/quiz, (15) create a book, (16) convert between document formats (Markdown, DOCX, HTML to/from LaTeX), (17) generate Mermaid diagrams for LaTeX, (18) create a formal business letter, (19) create a cheat sheet or reference card, (20) create an exam formula sheet or crib sheet, (21) condense lecture notes/PDFs into a cheat sheet, (22) create a fillable PDF form with text fields/checkboxes/dropdowns, (23) create a document with conditional content/toggles (show/hide sections), (24) generate batch/mail-merge documents from CSV/JSON data, (25) create a version diff PDF (latexdiff) highlighting changes between documents, (26) create a homework or assignment submission with problems and solutions, (27) create a lab report with data tables, graphs, and error analysis, (28) encrypt or password-protect a PDF, (29) merge multiple PDFs into one, (30) optimize/compress a PDF for web or email, (31) lint or check a LaTeX document for common issues, (32) count words in a LaTeX document, (33) analyze document statistics (figures, tables, citations), (34) fetch BibTeX from a DOI, (35) convert a Graphviz .dot file to PDF/PNG, (36) convert a PlantUML .puml file to PDF/PNG, (37) create a one-pager/fact sheet/executive summary, (38) create a datasheet or product specification sheet, (39) extract pages from a PDF (page ranges, odd/even), (40) check LaTeX package availability before compiling, (41) analyze citations and cross-reference with .bib files, (42) debug LaTeX compilation errors, (43) make a document accessible (PDF/A, tagged PDF), (44) create lecture notes or course handouts, (45) fill an existing PDF form (fillable fields or non-fillable with annotations), (46) extract text or tables from a PDF (pdfplumber, pypdf), (47) OCR a scanned PDF to text (pytesseract), (48) create a PDF programmatically with reportlab (Canvas, Platypus), (49) rotate or crop PDF pages (pypdf), (50) add a watermark to an existing PDF, (51) extract metadata from a PDF (title, author, subject).
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。
系统化学习材料生成器。给一个新领域/技术/概念，AI 自主调研、搭体系、产出 HTML 学习材料（含骨架/案例/工程化/争议+盲区）。当用户说"我要学 X / 帮我系统拆解 X / 我想吃透 X 这个领域 / 给我整理 X 的全貌 / 深度调研 X 给我系统讲讲 / 把 X 搞透"时触发。不适用于"X 行不行/为什么 Y"（使用可用的调研工具直接研究）、"X 有哪些好玩案例"（用 case-radar 给散点）、"把这堆素材整理成 HTML"（用 readable-output 处理已有素材）、文章写作（用 writing-assistant）、整页界面方案（用 page-solution-design）。
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。
产出给人读的 HTML 长文——把已有的素材、经历、资料想清楚再写出来。每次触发先用 AskUserQuestion 一次性问清「给谁看 / 读完拿什么 / 多长 / 风格 + 侧重」4 个关键参数，再按 6 阶段框架输出。**产出只有 HTML 一种格式**——用户要 markdown 或要在聊天里直接说，就不该用本 skill。当用户说"做个复盘""汇总一下""总结这堆""整理成 HTML""做个教程/学习指南""把 X 讲清楚"等需要超过 500 字的结构化阅读内容时触发。不适用于：项目的计划报告/框架计划/版本路线（用 issue-pool，它自带 md2html）、PRD/需求文档/测试用例（用 prd-test-writer）、整页界面方案（用 page-solution-design）、起名（用 product-naming）、写代码/修 bug/改文件、一两句话就能答完的问题。
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。
带用户精读一篇文章/文档并真正学透（不是出摘要）。五步：抽干货 → 切讲次 → 每讲跑「学-考-讲」循环 → 对号入座 → 实操+测验+讲错题+蒸馏。每讲产出 HTML 课件 + 笔记落盘；讲完必考一次，用户复述后必须挑不精确处拧紧；抽象概念上可交互演示（能点能跑）；全程用用户自己的业务场景当案例；学完出多题型自动判分测验卷，最后把收获蒸馏回用户的工具。核心触发条件是用户要的是"学会"而不是"要一份结果"，例如"我们一起学这篇文章/这个链接"、"带我学"、"精读"、"我想学会 X"、"这篇我看不懂你给我讲讲"。有具体材料（链接、本地文件、PDF，或用户自己的 skill/文档/代码）时直接开跑；只有学习意图而没材料时仍走本 skill，但开工第一件事是和用户一起把材料定下来，禁止凭记忆开讲。不适用于：只要一份总结/摘要/教程长文，用户读完就完、不需要答题（用 readable-output）、只是搜集资料做调研（使用可用的网页/平台取材工具）、帮我写 PRD/测试用例（用 prd-test-writer）、以及用户其实是想让你直接把活干了（那就直接做）。
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 knowledge for Azure Stream Analytics development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building ASA jobs with Event Hubs/Kafka, Cosmos/SQL outputs, ML/UDF queries, autoscale, or IoT Edge clusters, and other Azure Stream Analytics related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure Data Factory (use azure-data-factory), Azure Event Hubs (use azure-event-hubs), Azure Functions (use azure-functions).
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 RightStack as a read-only Web3 stack advisor for architecture recommendations, workflow inspection, tool comparisons, and package-migration checks. Use for planning; do not treat corpus output as implementation proof.
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 prospect lists where the prospect is already one hop from the client, and write the line that says so. Seven members, each with its own source. Current-customer alumni is the priority member - everyone who ever worked at one of the client's customers or case-study companies and has since moved on. Triggers on "warm intro", "who do we know at that account", "they used to work at our customer", "alumni of our case study company", "shared investor angle", "who is in the same city as our rep", "did they visit our site". Outputs warm_intro_line plus a per-member evidence column.
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。
Turns one customer case study into the tightest list of companies that would read that case study and see themselves. Decomposes WHY the case-study company resonates into concrete attributes, translates those attributes into database filters, intersects them with a lookalike anchor, then widens with a model judge. Triggers on "find more companies like our best customer", "who would our case study land with", "lookalikes of X", "build a list around this case study", "companies like our happiest client", "expand this list, it is too small". Outputs a filter set plus one merge field naming which case study to cite.
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 the people who like and comment on a source account's LinkedIn posts (a competitor, or one of the client's own customers) into ICP-filtered prospect rows, with an optional copy-ready personalization line when the campaign asks for one. Triggers on "who engages with our competitors", "likers and commenters", "LinkedIn engagement list", "people commenting on competitor posts", "who engages with our customers' posts", "warm LinkedIn audience", "competitor engagers". Outputs one clean prospect row per engager; engagement_line only on request.
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 3-bullet creative ideas email. Produces three per-company bullets that each sit in a slot the operator defined up front, so the model fills in the company detail and never invents a new idea. Triggers on "creative ideas campaign", "three ideas email", "I had a few ideas for you", "3 bullets", "use cases for their business", "what could they build with us". Outputs creative_idea_1, creative_idea_2, creative_idea_3 plus a rendered bullet block.
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。
Find lookalike companies via DiscoLike's 65M+ business domain database. Search by seed domains ("find companies like clay.com and apollo.io") or natural-language ICP text ("B2B cold email outreach"). Supports negation domains (exclude competitors/existing customers) and country filtering. Use when you already know 3-10 reference companies and want hundreds more that look like them. Outputs CSV ready for /blitz-list-builder or the email waterfall.
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。
Produces a copy-ready sentence about a company's funding round, and the list-building filters that select companies which have ever raised or raised recently. Triggers on "raised a round", "recently funded", "companies that raised", "Series A list", "VC backed", "post funding outreach", "who just raised". Outputs funding_line, a COMPLETE sentence including the leading "Saw" and the trailing period, so an empty value renders as nothing at all.
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。
Produces a line about what a company is currently advertising on Facebook and Instagram, from the Meta Ad Library. Triggers on "are they running ads", "what are they advertising", "meta ad library", "facebook ads", "pull their ad copy", "who is spending on paid social". Outputs ad_library_line, a lowercase clause that completes "Noticed <line>."
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。
Turns the raw company-name string on a lead row into the short human form a person would say out loud, so it can be dropped into email copy. Triggers on "clean these company names", "company name variable", "strip the LLC and Inc", "the company names look robotic", "normalize company names for the campaign". Outputs company_clean, one short string per row.
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。
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.
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。
Produces a copy-ready clause about a role a company is currently hiring for, filtered by language the client cares about (for example "cold call", "outbound prospecting", "Salesforce"). Triggers on "they're hiring", "job posting language", "who is hiring SDRs", "companies hiring for X", "find the hiring signal", "what roles are they hiring for", "job description mentions". Outputs job_posting_line_safe, one lowercase clause that slots into "Saw {{job_posting_line_safe}}."
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。
Produces a copy-ready clause about a company adding people to one specific department in the last 6 months, plus the department-level headcount counts that drive the segment gate. Triggers on "they're hiring", "hiring surge", "growing their sales team", "scaling the team", "headcount growth", "who added SDRs", "companies that just built a marketing team". Outputs hiring_surge_line, a lowercase second-person clause that completes "Noticed <line>."
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。
Diagnostic audit for a running cold email program. Checks domain authentication (SPF/DKIM/DMARC), inbox health/reputation from Smartlead, bounce rate by inbox type, and optionally runs a spam placement test via Smartlead's Smart Delivery API. Outputs markdown report + CSV with per-domain/per-inbox scores and concrete action items. Use when reply rates drop, when bounces spike, when onboarding someone else's account, or as a weekly/monthly health 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。
Find people actively engaging with competitor LinkedIn posts. Given a website URL, discovers competitors, fetches their company and employee posts, collects all commenters and reactors, and outputs a deduplicated CSV of engaged prospects. Use when someone wants to find leads from competitor engagement, build outreach lists from LinkedIn activity, or analyze who interacts with competitor 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。
META skill — build the largest possible qualified lead list for any request, end to end. Orchestrates discovery (Prospeo + GetLeads + Blitz + Google Maps + lookalike snowball from /list-expander), AI qualification with live-website verify, uncapped contact pull (GetLeads → Blitz → Prospeo), and provider emails (GetLeads/Blitz/Prospeo) validated with MillionVerifier before sending. Use for any "build me a list", "get me leads for X", "find everyone who...", client list request, or campaign list build. Starts with a 7-line intake brief; outputs to Postgres, Google Sheet, and/or CSV (asks which).
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。
Conversational intake for cold email campaigns. Interviews the user about their business, ICP (Ideal Customer Profile), offer, lead magnets, and hard vs soft filters. Outputs a structured `client-profile.yaml` that every other skill in the repo reads from. Use at the very start of a new campaign or when onboarding a new business. Triggers on "onboard me", "set up my ICP", "new client", "new business intake", "help me define my ICP".
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。
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.
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。
Creates cold email copy through a stepwise confirmation process. Use when writing email campaigns, given a campaign strategy document, website, or client context. Confirms direction at each step before outputting final copy.
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。