2 226 document skills from 443 authors. They read and assemble documents, spreadsheets and slide decks. Half of them fit into 1 834 tokens or less — that is what one costs your context window when the agent loads it. 567 ship runnable scripts rather than instructions alone. 24 of them cannot work without an MCP server, most often rube. We also found 384 copies of these same skills sitting in other people's repositories — counted once here, not 384 times.
2 226 unique 443 authors 1 278 updated this month 156 from vendors
| Before designing, training, or auditing ANY model that replicates human-annotated labels, audit the annotation protocol's INPUT — the exact document/evidence the human labelers consulted — and give the model that is a hand-coded label set, (2) a label-replication model shows low recall concentrated in a label subset and the diagnosis on offer is "the label's information is not in the features", (3) reviewers propose construct splits (e.g. "designation vs record-evident"), adjudication sittings, or per-domain stop rules to explain residual disagreement with gold, (4) validating an extraction pipeline against labels transcribed from a source document. why gold is "partially unpredictable" when the model was simply never shown the document the annotators read.
> Master controller for the lit-review pipeline, driven by a document. Give it a .tex or .docx file describing an article — a full manuscript, an abstract, or a proposal — and it extracts a search plan, runs Undermind (an automated Playwright driver in Classic mode) and Google Scholar (SearchAPI.io), then merges, deduplicates, and screens the results. Only use this skill when explicitly requested — e.g., the user says "run lit-review-orchestrator", "lit-review-orchestrator", or "/lit-review-orchestrator". Do NOT auto-trigger on general literature review requests.
>- Convert between LaTeX and Microsoft Word for academic manuscripts in either .tex/LaTeX to Word/.docx ("tex to docx", "latex to word", "tex2docx", "convert to word", "pandoc convert"); converting .docx manuscripts to LaTeX for editing and back ("convert to latex", "manuscript", "footnotes", "reference doc", the docx-to-tex-to-docx round-trip / academic paper editing pipeline); high-fidelity delivery where plain pandoc loses tables, mangles cross-references, or fails on custom macros — booktabs/regression tables, OMML equations, cleveref, \estauto, \@@input, \thanks, TikZ, native Word tables, longtable, siunitx; and building .tex from mixed PDF/docx/LLM-generated sources. Replaces and reroutes the retired skills manuscript-editing-template-latex, latex-to-docx-fidelity, tex2docx, and latex-from-mixed-sources.
>- Convert Markdown (.md) files to a polished PDF with ALL images preserved and scaled to the page. Use whenever the user asks to "save this markdown as a PDF", "convert README.md to pdf", "export the .md as a pdf", "turn these notes/docs into a PDF", or wants a PDF deliverable of any GitHub-flavored Markdown document (README, design doc, report, notes) — especially when it contains images or diagrams (remote or local), tables, code blocks, or a headless Chrome print-to-pdf with a GitHub-like print stylesheet → pypdf verification that every referenced image is embedded. Do NOT use for .tex → PDF (use a LaTeX toolchain) or for .docx work (use word-docx / tex2docx).
| 当用户说「深度研究 X」「深入研究 X」或要求生成某产品、公司、概念、人物、产业链、政策、趋势的深度研究/发展研究报告时触发，自动进行联网搜索和研究，产出排版后的 PDF 文档，总字数通常 1-3 万字。
> 把内容生成为「真正设计过」的可编辑飞书白板——强视觉层级、有意图的构图、留白，而不是 一堆等大方块。先定设计简报（构图原型 + 配色策略 + 字号角色 + 反套路检查）再画，按坐标 骨架施工，渲染后跑设计评审（层级/平衡/密度/对比/对齐）补到最弱项达标，最后写进你自己的 飞书文档成为可编辑白板。飞书走扣子「平台授权」，一键授权即用（见 CREDENTIALS.md）。
小红书笔记素材创作技能。当用户需要创建小红书笔记素材时使用这个技能。技能包含：根据用户的需求和提供的资料，撰写小红书笔记内容（标题+正文），生成图片卡片（封面+正文卡片），以及发布小红书笔记。
Transform conversations and discussions into structured Notion documentation
Parse specifications and create implementation plans with task tracking in Notion
Research topics and document findings in Notion with organized structure and sources
> and rendered preview PDFs. Use when the user needs a cohesive design system before building a deck, presentation, document, or any visual project. Works for consulting engagements, product pitches, personal brands, or any context requiring a unified visual language. Produces a markdown design brief, a JS config, and a preview PDF.
> Create presentation decks as high-fidelity PDF files using web technology (HTML, CSS, Tailwind, Font Awesome). Use when the user wants a polished, pixel-perfect deck with responsive layouts, rich typography, icons, and modern design. For editable PPTX output, use deck-design-ppt instead.
> Create new presentation decks as native editable PPTX files with consulting-quality design. Use when the user wants to build a new slide deck, presentation, or pitch deck from a brief or topic. For editing existing .pptx files, use the pptx skill instead.
> Think and deliver like a management consultant from McKinsey, BCG, or Bain. hypothesis-driven decomposition, (2) Run strategy analysis with professional McKinsey verdict-first, BCG framework-first, or Bain decision-first, board presentations, conference talks. Produces structured analysis and deliverable CONTENT. For visual production, hand off to a delivery skill for slides, documents, or spreadsheets.
| 通过分步访谈引导用户理清需求，最终产出完整的Skill文件包（含SKILL.md、参考文档、示例文件等）， 并打包为可直接使用的压缩包。 当用户说"我想通过访谈新建Skill"、"用访谈方式做一个Skill"、"访谈建Skill"、 "通过访谈帮我生成Skill"、"访谈式创建Skill"、"我想访谈做一个XX的技能"时触发。 触发关键词必须包含"访谈"二字，不含"访谈"的Skill创建请求不由本Skill处理。 不用于已有完整SKILL.md只需小改的情况，也不用于一次性提示词请求。
| 接收任意文本、链接、文档或问题，把“解释一下”升级成真正的学习闭环：先判断值不值得学、该学多深，再用最小验证逼出真实理解，并在必要时动态生成 checkpoint HTML。只要用户明显想“学懂”“吃透”“带我学”“帮我验证我是不是真懂了”，而不是只要摘要、改写或普通解释，就应主动使用本技能。也适用于用户想导出学习卡片、checkpoint 页面、复盘页、学习状态页的场景。
Generate structured software specifications for features, bug fixes, and products. Use when the user wants to create a spec, PRD, feature brief, requirements document, or when starting any new implementation that needs a specification first. Invoke via /spec-writer or when the user says "write a spec", "spec this out", "create a spec", "I need a spec for...", or describes a feature they want to build. Produces adaptive-complexity specs with Job Stories, Gherkin acceptance criteria, and three-tier boundaries. Output is a markdown file ready for agent execution or human review.
Performs consistency and traceability audits across documents (PRD vs Spec vs Plan) to detect missing coverage and scope creep.
Standardized workflow for discovering, reading, writing, and compacting the project's memory file (memory.instructions.md) to persist context across AI chat sessions, with a permanent Knowledge Base for cross-session decisions and lessons learned.
Generates formal, structured, and executable implementation plan documents based on specifications.
Scans, analyzes, and documents the existing repository architecture, directories, and file purposes into docs/ARCHITECTURE.md.
Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.
Generates or updates highly detailed, machine-readable technical specification documents in the /spec/ directory.
Performs consistency and traceability audits across documents (PRD vs Spec vs Plan) to detect missing coverage and scope creep.
Standardized workflow for discovering, reading, writing, and compacting the project's memory file (memory.instructions.md) to persist context across AI chat sessions, with a permanent Knowledge Base for cross-session decisions and lessons learned.
Generates formal, structured, and executable implementation plan documents based on specifications.
Scans, analyzes, and documents the existing repository architecture, directories, and file purposes into docs/ARCHITECTURE.md.
Performs consistency and traceability audits across documents (PRD vs Spec vs Plan) to detect missing coverage and scope creep.
Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.
Generates or updates highly detailed, machine-readable technical specification documents in the /spec/ directory.
Standardized workflow for discovering, reading, writing, and compacting the project's memory file (memory.instructions.md) to persist context across AI chat sessions, with a permanent Knowledge Base for cross-session decisions and lessons learned.
Generates formal, structured, and executable implementation plan documents based on specifications.
Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.
Scans, analyzes, and documents the existing repository architecture, directories, and file purposes into docs/ARCHITECTURE.md.
Generates or updates highly detailed, machine-readable technical specification documents in the /spec/ directory.
Performs consistency and traceability audits across documents (PRD vs Spec vs Plan) to detect missing coverage and scope creep.
Standardized workflow for discovering, reading, writing, and compacting the project's memory file (memory.instructions.md) to persist context across AI chat sessions, with a permanent Knowledge Base for cross-session decisions and lessons learned.
Generates formal, structured, and executable implementation plan documents based on specifications.
Scans, analyzes, and documents the existing repository architecture, directories, and file purposes into docs/ARCHITECTURE.md.
Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.
Generates or updates highly detailed, machine-readable technical specification documents in the /spec/ directory.
Performs consistency and traceability audits across documents (PRD vs Spec vs Plan) to detect missing coverage and scope creep.
Generates formal, structured, and executable implementation plan documents based on specifications.
Scans, analyzes, and documents the existing repository architecture, directories, and file purposes into docs/ARCHITECTURE.md.
Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.
Standardized workflow for discovering, reading, writing, and compacting the project's memory file (memory.instructions.md) to persist context across AI chat sessions, with a permanent Knowledge Base for cross-session decisions and lessons learned.
Performs consistency and traceability audits across documents (PRD vs Spec vs Plan) to detect missing coverage and scope creep.
Generates or updates highly detailed, machine-readable technical specification documents in the /spec/ directory.