Document Claude Skills
2 253 document skills from 443 authors. They read and assemble documents, spreadsheets and slide decks. Half of them fit into 1 829 tokens or less — that is what one costs your context window when the agent loads it. 571 ship runnable scripts rather than instructions alone. 24 of them cannot work without an MCP server, most often rube. We also found 422 copies of these same skills sitting in other people's repositories — counted once here, not 422 times.
2 253 unique 443 authors 2 updated this month 183 from vendors
Iteratively author and test a custom Azure AI Content Understanding analyzer for a folder of **document** files (PDFs, page images) of a single type. Walks layout extraction → schema authoring → validation → batch test → agent review → refine cycle using the typed ContentUnderstandingClient. Document modality only — audio, video, and image analyzers are planned for a later update. Use when the user wants to author a custom analyzer for invoices, contracts, forms, or any other single-type document set.
Lay out and export data-rich reports and documents. Use when the user needs report structure, figure packaging, PDFs, PowerPoint or Google Slides automation, or programmatic insertion of visualizations, UML-like diagrams, or architecture diagrams into documents.
> Document Quality Check skill for Datasite deal rooms. Use this skill whenever a deal team wants to audit document quality before going live to buyers. Triggers files", "check for blank documents", "PII check", "redaction review", "find corrupted files", "document audit", "quality check the data room", "are there any blank or broken files", "check for unredacted personal data", or any request to verify that documents in the data room are complete, accessible, and safe to share. Use this skill proactively before a data room goes live. Do not use for renaming files (use smart-file-renaming) or for identifying missing sections (use gap-analysis).
> Data Room Gap Analysis skill for Datasite deal rooms. Use this skill whenever a sell-side deal team wants to audit what is missing, sparse, or incomplete in their "what's missing from the data room", "check the data room coverage", "flag empty folders", "what haven't we uploaded yet", "data room readiness check", "find gaps before we go live", "are all the contracts in there", "check we have everything", or any request to assess completeness of the data room by section. Use this skill proactively whenever a deal team is preparing to launch a data room and wants to know what still needs to be uploaded or organised. Do not use for document quality issues such as PII or redaction (use document-quality-check), or for drafting Q&A responses (use bulk-qa-answers).
> Information Request List (IRL) Tracker skill for Datasite deal rooms. Use this skill whenever a deal team wants to compare VDR content against a buyer's information request list, track document delivery status, or build a due diligence "check the information request list", "information gathering list", "IGL", "what have we delivered", "DD tracker", "due diligence tracker", "compare VDR against the request list", "what's still outstanding", "build a diligence dashboard", or any request to track document delivery against buyer requests. Use proactively whenever a buyer has submitted a request list and the deal team needs to manage and track responses. Do not use for overall data room structural gap analysis — use gap-analysis for that.
> Launch Readiness Orchestrator skill for Datasite deal rooms. Use this skill whenever a deal team wants a single pre-go-live readiness check across their data room — combining gap analysis, document quality audit, and risk review ready to go live", "launch readiness check", "pre-launch audit", "data room readiness", "can we launch", "is the data room ready", "run a full readiness check", "go-live checklist", "pre-launch checklist", or any request to get a single overall assessment before opening the data room to buyers. Use proactively whenever a deal team is approaching their go-live date and wants a structured sign-off view. Do not use other individual audit skills (gap-analysis, document-quality-check, risk-analysis-audit) when this skill is active — this skill orchestrates all three in one pass.
> Risk Analysis Audit skill for Datasite deal rooms. Use this skill whenever a sell-side deal team wants to audit, review, or flag risks across a data room before going live. "what are the risks in this deal", "audit the data room", "risk analysis", "flag issues before we go live", "what should we fix before launch", or any request to analyse deal risk by workstream (Tax, Finance, Legal, HR, IP, Commercial, Regulatory, ESG). Use this skill proactively whenever the user is preparing a data room for launch and wants a structured view of what might concern a buyer. Do not use for document quality issues like PII or redaction (use document-quality-check), or for identifying missing sections (use gap-analysis).
> Smart File Renaming skill for Datasite deal rooms. Use this skill whenever a deal team wants to standardise document names, clean up scanned file names, normalise naming across similar document types, or improve the professionalism of the data names", "standardise naming", "the file names are a mess", "fix the document names", "rename scanned documents", "make the naming consistent", "tidy up the data room", or any request to improve, clean, or normalise document naming across a Datasite project. Never apply any rename without explicit user confirmation. Do not use for document quality or PII checks — use document-quality-check for that. Never rename files without explicit user confirmation.
> VDR Index Setup skill for Datasite deal rooms. Use this skill whenever a user wants to create, propose, design, or set up a Virtual Data Room (VDR) index VDR index", "build a deal room structure", "prepare the index", "set up the fileroom", "I need a data room for [deal/company]", or any request to organise or structure documents for due diligence. Also triggers when a user wants to replicate an existing deal room structure or import an index from a spreadsheet or reference deal. This skill MUST be used whenever the user is starting a new deal room or wants to customise the folder hierarchy before documents are uploaded. Do not use to audit or review an existing data room — use gap-analysis, document-quality-check, or risk-analysis-audit for that.
Connector-first Google Docs creation and editing in local Codex plugin sessions, with direct native create and batchUpdate workflows for simple docs, DOCX-first import for polished deliverables, target-document checks, smart chip and building-block reconstruction, connector-readback verification, and reference routing for formatting, citations, tables, and write-safety.
Submit compact eQTL Catalogue API requests for association retrieval and documented metadata endpoints. Use when a user wants concise public eQTL Catalogue summaries
发布内容和文章到 X (Twitter)。支持常规推文(文字/图片/视频)和 X Articles(长文 Markdown)。使用真实 Chrome 浏览器绕过反机器人检测。当用户说"发推"、"发到 X"、"发到 twitter"、"分享到 X"、"分享到 twitter"、"发 tweet"、"同步到 X"、"发布到 X"、提到"X Articles"、想从 Obsidian 笔记发布长文内容、或需要转换 Obsidian Markdown 到 X 格式时使用。适用于所有 X/Twitter 发布任务。
Search the ExecuTorch tribal knowledge base covering QNN, XNNPACK, Vulkan, CoreML, Arm, and Cadence backends, quantization recipes, export pitfalls, runtime errors, and SoC compatibility. Use when debugging ExecuTorch errors, choosing quantization configs, checking backend op support, or answering questions about Qualcomm HTP / Snapdragon / Apple Neural Engine behavior.
Word / PDF / PPT 文档解析与数据分析引擎。覆盖三类文件格式的全量提取、表格数值化、图表理解与跨文档汇总分析。**遇到以下任一情况就主动使用本 skill**：①用户上传或指定了 .docx / .doc / .pdf / .pptx / .ppt 文件并要求分析、提取或统计其中内容；②用户出现触发词：Word分析 / PDF解析 / PPT提取 / 文档分析 / 报告解析 / 幻灯片分析 / 发票提取 / 合同分析 / 文档统计 / 错别字 / 语病 / 字号检查 / 简历分析 / 多文档对比；③任务涉及从文档中提取表格、数值、图表、格式（颜色/高亮/字号）、组织架构、时间线等结构化信息。仅不用于：Excel/CSV 数据分析（使用 sn-da-excel-workflow）、纯图片分析（使用 sn-da-image-caption）。
PDF 文档解析。自动区分文字型 PDF 与扫描型 PDF，覆盖：文本/表格提取、多页全量扫描、嵌入图表 caption、单位感知数值计算。
PPT (.pptx/.ppt) 全量解析。覆盖：所有 slide 文本/表格/图表提取、嵌入图片 caption、纯图片 slide 渲染识别、数据标签提取。
Word (.docx/.doc) 文档全量解析。覆盖：正文/段落文本提取、表格数据提取、高亮/颜色格式读取、多文件汇总对比、嵌入图片转 caption。
将 Markdown 报告、研究笔记、行业分析、战略备忘录、技术白皮书、复盘、周报等长文档，重组并创作为有编辑判断、网页美感和证据秩序的自包含 HTML 专题页。用户要求“转成 HTML”“网页化报告”“美化报告”“做成专题页”“便于分享”“提升可读性/设计感”“把报告做成网页”时使用。必须先写 plan.md 再写 HTML，保留原文事实断言和结论强度，不套模板，不做机械 Markdown 转换。
| Entry point for PPT generation. Asks the user to choose a mode (fast, standard, or creative), then collects role / audience / scene / page_count as needed. For standard mode, also asks how images should be sourced (AI generation, web search, or none), whether charts should use AI-generated infographics or ECharts, and whether the final deliverable should be PPTX or PDF. Parses uploaded pdf/docx/md/txt files, produces task_pack.json + info_pack.json in a new deck_dir, then dispatches to sn-ppt-creative or sn-ppt-standard. Fast mode skips optional questions and gets straight to building. Use when the user asks to make a PPT / presentation / 演示 / PPT. If the user asks to open, preview, inspect, or edit previously generated HTML slides in the WebUI/workbench without regenerating, dispatch to sn-ppt-workbench instead of this generation entry.
| Standard and fast PPT pipeline. All LLM / VLM / T2I calls are wrapped in a emit ONE shell command per stage, never write loops, never write prompts. Standard mode plans thoroughly with a three-sample deck preview checkpoint (three concatenated deck images plus a preview URL), web research, image search, and user-selected final output format (PPTX or PDF) for polished, delivery-ready presentations. Fast mode builds a complete draft immediately with autonomous decisions, then provides structured refinement suggestions so the user can iterate quickly. Supports AI-generated infographics (U1) for diagrams and flowcharts, web image search (Serper) for real photos, and ECharts for data charts.
Formats plain text or markdown files with frontmatter, titles, summaries, headings, bold, lists, and code blocks. Use when user asks to "format markdown", "beautify article", "add formatting", or improve article layout. Outputs to {filename}-formatted.md.
将法条/规范文件（.txt/.docx/.pdf）转为 Markdown。适用于用户要求“法条转 markdown”“pdf/docx 转 markdown”。处理 .pdf/.docx 时先检查是否已安装 mineru-ocr skill；未安装先引导安装，安装后优先用 mineru-ocr；仅在用户明确同意时再用本地回退方案。
Create and edit Obsidian Bases (.base files) with views, filters, formulas, and summaries. Use when working with .base files, creating database-like views of notes, or when the user mentions Bases, table views, card views, filters, or formulas in Obsidian.
根据arXiv论文网址自动下载PDF并进行多维度分析，包括文本提取、词频分析、语音播报、播客对话生成、交互式网页、PPT、总结图和引用分析
基于七角色协作的智能 PPT 生成与优化工具。支持主题生成、模板推荐、内容填充、AI 智能配图、文本润色和 PPTX 文件生成。适用于学术汇报、商业演示、培训课件、产品发布等多种场景。
将 JSON 格式的 PPT 内容转换为标准的 .pptx 文件。使用 python-pptx 库，支持多种布局、图表、表格和样式。与 ppt-generator Skill 完全协同，可作为独立使用或与其他 PPT Skill 配合。
Publish Markdown articles to X (Twitter) Articles editor with proper formatting. Use when user wants to publish a Markdown file/URL to X Articles, or mentions "publish to X", "post article to Twitter", "X article", or wants help with X Premium article publishing. Handles cover image upload and converts Markdown to rich text automatically.
Publish Markdown articles to X (Twitter) Articles editor with proper formatting. Use when user wants to publish a Markdown file/URL to X Articles, or mentions "publish to X", "post article to Twitter", "X article", or wants help with X Premium article publishing. Handles cover image upload and converts Markdown to rich text automatically.
小红书美妆内容创作技能，支持笔记生成、笔记优化、文生图描述创作；适用于美妆护肤产品的内容创作、测评和营销推广
制作 PPT、演示文稿、幻灯片、汇报材料时使用。Dashi PPT 基于预置视觉主题组合页面,生成可离线打开、可在浏览器编辑的 HTML 演示,支持导出 PPTX / PDF 文件。
Generate visually unified image-based PPT/PPTX decks from articles, reports, papers, notes, or outlines.
Use when adding docstrings, creating API documentation, or building documentation sites. Invoke for OpenAPI/Swagger specs, JSDoc, doc portals, tutorials, user guides.
Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.
Generate professional primary market / venture capital industry research reports, including sector deep-dives, investment memos, and market analysis. Produces detailed PDF reports covering TMT, consumer, healthcare, and industrials. Triggered by requests to analyze an industry, create a market report, write a sector deep-dive, or generate any PE/VC-style research document in Chinese or English.
Document codebase as-is with thoughts directory for historical context
> Operate any Obsidian vault as a living, self-rewriting second brain (an evolution reconcile automatically, scheduled agents maintain the vault while you sleep). Use this skill whenever the user asks Claude to read, write, update, search, or manage their Obsidian vault - including saving notes from conversation, creating daily entries, updating kanban boards, logging dev work, managing people notes, capturing decisions, tracking deals, or maintaining any vault structure. Also triggers when the user wants to bootstrap a new vault from scratch, run a vault health check, or drop a _CLAUDE.md into their vault so all Claude surfaces share the same operating rules. /notebooklm, /youtube, /podcast) for AI-powered research via Grok, Perplexity, NotebookLM, YouTube, and podcast feeds - findings save to the vault automatically following the AI-first vault rule. Use proactively whenever the conversation produces information worth preserving (decisions, people met, projects started, tasks completed, lessons learned, research findings).
Extract an Allium specification from an existing codebase. Use when the user has existing code and wants to distil behaviour into a spec, reverse engineer a specification from implementation, generate a spec from code, turn implementation into a behavioural specification, or document what a codebase does in Allium terms.
Generate hierarchical AGENTS.md knowledge base for a codebase. Creates root + complexity-scored subdirectory documentation.
Hunt file upload bugs — RCE via webshell, XSS via SVG/HTML, SSRF via XXE in DOCX, path traversal via filename. Bypass tables (10 techniques): double extension (shell.php.jpg if server checks last ext only), magic bytes spoofing (PNG header on PHP), null byte (shell.php\0.jpg), case (PHP, .Php, .pHP), .htaccess upload to enable execution, SVG with <script>, HTML/SVG XSS, DOCX with embedded XXE, ZIP slip (../../../etc/passwd in archive), polyglot files. Detection: any /upload, /avatar, /profile-picture, /attachment, /import endpoint. Test: upload PHP/JSP/ASPX shells, request via direct URL, check response. Validate: actual code execution (whoami output) for RCE; reflected XSS in profile-photo URL. Use when testing file upload features, avatar/attachment endpoints, import/export functions, XML/DOCX/ZIP processors. Real paid examples.
Hunt server-side template injection (SSTI) across Jinja2 (Flask/Django), Twig (Symfony), Freemarker (Java), ERB (Rails), Spring, Velocity, Mako, Thymeleaf, Smarty. Detection probes use double-curly and dollar-curly math expressions evaluated server-side. Once an engine is fingerprinted, escalate to RCE via the engine-specific class-walker, callback-registrar, or Execute-utility patterns documented in disclosed reports. Detection patterns: error messages reveal engine, blank or numeric eval reveals expression mode. Targets: email templates, PDF/report generators, CMS preview features, error pages with user input. Use when hunting RCE via template rendering, when content shows engine fingerprints, when finding endpoints that compose strings with user input before render.
Hunting skill for xxe vulnerabilities. Built from 10 public bug bounty reports including SVG-upload XXE, Office-doc (PPTX/DOCX) XXE, SOAP XXE, SAML AssertionConsumer XXE, blind OOB XXE via DTD callback, parameter-entity XXE, XXE-to-LFI, XXE-to-SSRF, and XXE-to-RCE chains (Adobe Commerce CosmicSting CVE-2024-34102). Use when hunting XXE on any target — emphasis on OOB-Or-It-Didn't-Happen Gate for blind cases.
Read when the user asks what OpenKnowledge is, wants to install it on a repository, wants to open or preview a single markdown file that is not part of an OpenKnowledge project, wants to share an OpenKnowledge project with collaborators, or asks how `ok init` / `ok cowork` / OK Desktop set up a project. Do NOT load to perform OpenKnowledge reads/writes — the runtime guidance for editing markdown inside an initialized OK project ships as a separate project-local skill at `.claude/skills/open-knowledge/` whenever `ok init` runs.
How to work in a Knowledge Base project (the `knowledge-base` starter pack). Read when the project has the three-layer source-grounded layout — `external-sources/` → `research/` → `articles/` — or when asked how this project is organized. Carries the layer model, per-folder rules, status flows, and log discipline so this guidance does NOT live inside template bodies or log.md. The three procedures live elsewhere: ingest in the platform `open-knowledge` skill, research and consolidate as their own sibling skills in this pack. Complements the platform `open-knowledge` skill; does not replace it.
Promote existing research into a canonical article under `articles/` in a Knowledge Base project (the `knowledge-base` starter pack). Read when a decision has actually been made and the team wants the source-of-truth written down, or when asked to consolidate, canonicalize, promote research, or supersede an older article. Carries the decision-confirmation gate, the `supersedes:` chain that keeps the evidence trail intact, and the canonical voice. Does not conduct new research — that is the sibling `research` skill.
Investigate a topic against preserved sources and write a provisional research article under `research/` in a Knowledge Base project (the `knowledge-base` starter pack). Read when asked to research a topic, compare options, synthesize sources, gather evidence, or extend an existing research doc. Carries the full procedure: scan existing coverage, agree a research rubric, capture every source verbatim before analyzing, write the article incrementally so a crash never loses work, cite every claim, and link it back into the graph. Does not promote findings to canonical knowledge — that is the sibling `consolidate` skill, after a decision lands.
How to work in an OKF starter project (the `okf` starter pack): a knowledge base that is conformant with Google's Open Knowledge Format (OKF) from commit one — `concepts/`, `references/`, `notes/`, a reserved `index.md` navigation hub, and a reserved `log.md` change history. Read when the project has these folders plus reserved files, OR when asked whether a document is OKF-conformant, or to add a document to an OKF knowledge base. Carries the OKF conventions (non-empty `type` on every non-reserved doc; reserved files carry no frontmatter) as guidance, not enforcement. Complements the platform `open-knowledge` skill; does not replace it.
Write a blameless incident postmortem under postmortems/ following the Google SRE shape — evidence-based timeline, trigger vs root cause vs symptom, contributing factors, what went well, and owned+dated+verifiable action items. Read when asked to write a postmortem, do an incident review, run a root cause analysis, write up the outage, retro on the outage, or when the user says we had an incident and wants it documented. Do NOT read to frame a proposal (use frame-a-proposal), write a spec (use write-a-spec), record a decision (use record-a-decision), or review a design (use review-a-design) — a postmortem documents an incident that already happened, it does not propose, specify, decide, or critique future work.