Skills
96,770 packaged capabilities and playbooks for agents. Install any of them with one command.
Documents
Polishes academic English paragraph by paragraph, reviewing grammar, word choice, voice, coherence, and sentence structure. Outputs revision suggestions alongside polished text. Triggered by phrases like 'polish this paragraph,' 'check the grammar,' 'rewrite in academic English,' or keywords like manuscript editing, SCI polishing, and journal submission editing.
Build slide decks and presentations for research talks. Use this for making PowerPoint slides, conference presentations, seminar talks, research presentations, thesis defense slides, or any scientific talk. Provides slide structure, design templates, timing guidance, and visual validation. Works with PowerPoint and LaTeX Beamer.
Create stunning, animation-rich HTML presentations from scratch or by converting PowerPoint files, with optional PPTX export via PptxGenJS. Features 12 curated style presets, visual style discovery, viewport-perfect slides, inline editing, and end-to-end content-to-PowerPoint workflow.
Verifies that an extracted statement is internally consistent by checking that opening_balance + sum(transactions) = closing_balance within a small tolerance, and produces a reconciliation report flagging missing rows, double-counted rows, sign errors, and rounding diffs. Use as the gate between PDF extraction and committing transactions to the data store, when reconciling a statement that does not balance, or when user mentions reconcile, balance check, or statement does not tie out.
The Pelosi Tracker. Watches the stock trades members of Congress must disclose under the STOCK Act: reads the House Clerk's public Periodic Transaction Reports (PTRs) as soon as they are posted, alerts once per new filing, and turns each PDF into clean rows (who, owner, stock, ticker, buy or sell, options strike and expiry, trade date, amount range, how many days it took to disclose). Flags trades in stocks you hold. Senate reports are covered by a documented manual route. Use when the user says "what is Pelosi buying", "track Nancy Pelosi", "Congress trades", "politician stock trades", "did any congressman buy X", "alert me when Pelosi files", or "run the Pelosi skill".
Write the tutorial content (`output/TUTORIAL.md`) from an approved module plan, including exercises and answer outlines. **Trigger**: write tutorial, tutorial modules, 教程写作, TUTORIAL.md. **Use when**: the writing stage of the tutorial pipeline (C3), and `DECISIONS.md` has recorded HUMAN approval of the scope/running example (C2). **Skip if**: the module plan is incomplete/unapproved (first run `module-planner`/`exercise-builder` and pass Approve C2). **Network**: none. **Guardrail**: only write the approved scope; keep the running example consistent; each module includes exercises and answer outlines.
Encyclopedic reference documentation for fictional worlds — wiki pages, character profiles, lore entries. Use when creating or updating reader-facing reference pages. Covers link discipline, citation conventions, and mermaid relationship diagrams. Distinct from the knowledge base, which is author/agent-facing knowledge.
Write or rewrite package README files in the style used by the Remix repository. Use when drafting a new package README, revising an existing README, or reviewing README structure, examples, installation instructions, and section ordering for Remix packages.
Generate lightweight technical specification documents with PlantUML diagrams. Use this skill whenever the user asks to design a feature, plan an implementation, document architecture changes, specify database schema changes, describe API endpoints, or needs to capture WHAT changes across services - even if they don't explicitly say "tech spec" or "specification".
Extract text, tables, metadata, and images from 107 document formats (PDF, Office, images, HTML, email, archives, academic) using Xberg. Use when writing code that calls Xberg APIs in Python, Node.js/TypeScript, Rust, or CLI. Covers installation, extraction (sync/async), configuration (OCR, chunking, output format), batch processing, error handling, and plugins.
Submit an 8D to a customer, format a Ford 8D report, BMW G8D, VW QMSS, or OEM complaint response for formal submission. Covers OEM-specific format requirements, evidence standards, and discipline-by-discipline writing rules for D0–D8. Use when preparing an 8D report for Ford, BMW, VW/Audi, Stellantis, or any customer requiring formal 8D closure.
12-agent academic paper writing pipeline. 11 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure/rebuttal-audit). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns with IRON RULE markers. Triggers: write paper, academic paper, guide my paper, parse reviews, audit my rebuttal, check my response draft, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見, 評估回覆.
Comprehensive AI-generated text detection analysis. Use when asked to evaluate whether a paper, essay, or document is AI-generated, human-authored, or collaboratively produced. Applies a 9-category research-backed framework with weighted scoring to produce a detailed report with actionable revision recommendations.
Render Mermaid diagrams as readable ASCII/Unicode art in the terminal (from .mmd/.mermaid files, stdin, or Markdown ```mermaid fences). Use when installing or using lukilabs/beautiful-mermaid, creating a CLI renderer for Mermaid-to-ASCII output, previewing Mermaid diagrams in terminal, or extracting/rendering Mermaid blocks from Markdown files.
Generate Mermaid diagrams (.mmd) and export to PNG/SVG/PDF using mmdc CLI or Kroki API. USE THIS SKILL when user mentions diagram, flowchart, sequence diagram, class diagram, ER diagram, state machine, architecture, visualize, git graph, 画图, 架构图, 流程图, 时序图. PROACTIVELY USE when explaining ANY system with 3+ components, API flows, authentication sequences, class hierarchies, database schemas, or state machines. Supports 11+ diagram types with fully automatic layout.
Expert Word document design agent for macOS using python-docx and AppleScript. Creates and edits stunning, professional documents with premium design quality. Use when: (1) Creating new Word documents from scratch with python-docx, (2) Editing or redesigning existing .docx files, (3) Building documents with custom design (cover pages, styled tables, callout cards, pull quotes, sidebars, KPI panels), (4) Live editing documents via AppleScript IPC (text, fonts, paragraphs, find/replace, fields, TOC), (5) Updating fields, TOC, or exporting to PDF via AppleScript on macOS, (6) Generating AI images for document covers and content, (7) Any task requiring python-docx code generation with design best practices.
GDPR-compliant Data Processing Agreement drafting per Article 28(3). Covers all 8 mandatory provisions including subject matter, duration, nature and purpose, data types, categories of data subjects, controller and processor obligations, and sub-processor cascade requirements.
Format and submit papers to IEEE journals (Transactions, Journals, Letters, IEEE Access) and IEEE conferences using the IEEEtran LaTeX class or Word templates, covering journal vs conference modes, abstracts and index terms, numbered IEEE reference style, figures and equations, author biographies, page limits and overlength charges, IEEE PDF eXpress, copyright forms, double-blind options and ScholarOne/IEEE Author Portal submission. Use when writing for any IEEE venue, fixing IEEEtran layout issues, or checking IEEE submission compliance.
Multimodal knowledge pipeline skill — ingest documents (PDF/image/video/Office) into a persistent knowledge wiki, supporting knowledge fusion, cross-source contradiction detection, multi-source aggregate queries, and knowledge graph construction. Trigger this skill when the user mentions ingesting documents, building a knowledge base, querying the wiki, checking consistency, building a knowledge graph, document analysis, multimodal understanding, data pipelines, and similar needs. Even if the user does not explicitly say pipeline or knowledge-pipline, this skill should trigger whenever the task involves turning documents into structured knowledge, cross-document analysis and comparison, or knowledge graph visualization.
Turn complex or visual agent responses into rich, reviewable HTML artifacts the user can annotate and send feedback on, using the lavish-axi CLI. Use when about to give a plan, comparison, diagram, table, code diff, report, or anything easier to grasp visually than as prose.
This skill should be used when completing LBO (Leveraged Buyout) model templates in Excel for private equity transactions, deal materials, or investment committee presentations. The skill fills in formulas, validates calculations, and ensures professional formatting standards that adapt to any template structure.
Local document and PDF parsing that returns spatial text with bounding boxes. Use for extracting text from PDFs, DOCX, Office files, and images; running OCR on scans; producing layout-preserved JSON for RAG; batch-ingesting folders of papers; or rendering pages to PNG for multimodal agents. Distinguishing capabilities are per-token bounding boxes, page raster output, and fully local processing with no cloud API.
Paper reader. Takes an academic paper (URL, PDF, or file), runs the atom pipeline (split→squeeze→plain→feynman→博导审稿), and synthesizes a fluent analysis. Focus: what gap does it fill, and would a seasoned advisor take it seriously? Use when user shares an arxiv link, paper URL, PDF, or asks to analyze a research paper. Trigger words: '读论文', '分析论文', 'paper', or when user shares an academic paper.
Use when a user wants to convert any markdown file in their Claude project into a single-file, lightly-interactive HTML — long-form documents (specs, plans, RFCs, reports, explainers), code reviews with diffs and severity-tagged annotations, or slide decks. Triggers on "convert this markdown to HTML", "make this an HTML file", "turn this into an interactive document", "render this report as HTML", "PR writeup as HTML", "slides from this markdown". Forks context to route to one of three converter sub-skills (md-document, md-review, md-slides) based on a deterministic doctype classifier, after the user has run the design-system onboarding once. Refuses if input is under 100 lines (per Shihipar — markdown still wins below the threshold) or design-system isn't onboarded. Distinct from Anthropic's official Playground plugin (which is interactive prompt-tuning controls with sliders/knobs/prompt-copy-back) and from marketing/landing/ (which is a landing-page generator).
Convert various file formats (PDF, Office documents, images, audio, web content, structured data) to Markdown optimized for LLM processing. Use when converting documents to markdown, extracting text from PDFs/Office files, transcribing audio, performing OCR on images, extracting YouTube transcripts, or processing batches of files. Supports 20+ formats including DOCX, XLSX, PPTX, PDF, HTML, EPUB, CSV, JSON, images with OCR, and audio with transcription.
Converts documents to Markdown with Microsoft MarkItDown (Python API, markitdown CLI, markitdown-ocr plugin, markitdown-mcp server), covering PDF, Word, PowerPoint, Excel, HTML, CSV, EPUB, ZIP, and streams, plus Azure extraction. Use when preparing papers or reports for LLM/RAG ingestion; when batch-converting a literature folder to Markdown with provenance; when converting uploaded bytes or file streams; when OCR of scanned PDFs or image text is needed; when exposing conversion to a local agent through MCP. Do not use for bounding boxes or page coordinates (use LiteParse) or PDF merge/split/forms (use the pdf skill).
Convert files (PDF, DOCX, PPTX, XLSX, HTML, CSV, JSON, XML, images, audio, etc.) to Markdown using Microsoft's markitdown. Use when user says "convert to markdown", "markitdown", "read this pdf", "extract text", "转markdown", "转换文件", or wants to convert any document to readable Markdown text.
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From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.