「documents」の検索結果

Extract text and tables from PDF files, fill forms, merge documents. Use when working with PDF files or when the user mentions PDFs, forms, or document extraction.

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。

Extract and analyze data from invoices, receipts, bank statements, and financial documents. Categorize expenses, track recurring charges, and generate expense reports. Use when user provides financial PDFs or images.

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。

Document toolkit (.docx). Create/edit documents, tracked changes, comments, formatting preservation, text extraction, for professional document processing.

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。

Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.

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。

Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.

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。

This skill should be used when the user asks about "plugin settings", "store plugin configuration", "user-configurable plugin", ".local.md files", "plugin state files", "read YAML frontmatter", "per-project plugin settings", or wants to make plugin behavior configurable. Documents the .claude/plugin-name.local.md pattern for storing plugin-specific configuration with YAML frontmatter and markdown 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。

This skill applies consistent corporate branding and styling to all generated documents including colors, fonts, layouts, and messaging

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。

Backend-neutral image generation: create new raster images and looping GIF/WebP animations through the local one-file image-use CLI (formerly chatgpt-imagegen), using the user's ChatGPT subscription by default, the Codex backend as fallback, or an optional Gemini subscription — no API key or daemon. Triggers: image generation, generate an image, draw a picture, 画图, 画一张, 生成图片, 生图, 配图. Use for photos, illustrations, icons, hero banners, mockups, sprites, concept art, animation loops, and figures for documents, proposals, blog posts, or READMEs; save outputs in the workspace. Auto mode uses the logged-in ChatGPT browser via chrome-use. Gemini users can pass --backend gemini or agy. Proactively propose useful figures while authoring long-form content. Do not use for editing existing images, SVG/vector work, code-native graphics, established icon systems, explicit high-quality or transparent API output, or end-user image-generation services.

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。

Create style-driven slide images strictly with the Image 2 model, assemble those images into image-only PPTX decks, and manage reusable visual style libraries from documents or visual references. Use when the user asks for a "PPT Skill", "风格驱动 PPT", "提炼风格做 PPT", "调用某个风格做 PPT", "图片版 PPT", "保存 PPT 风格", "列出 PPT 风格", "文档生成 PPT", "文章生成 PPT", "把文档做成演示文稿", or wants to extract, save, reuse, and apply visual style keywords specifically for visual slide/image deck creation.

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。

Compare scoped quantity snapshots and explain matches, missing rows and conflicts. Use to reconcile drawing, report or schedule counts; not to extract source documents or update adopted records.

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Creates new Architecture Decision Record (ADR) documents for significant architectural changes using a consistent template and repository-aware naming and storage guidance. Use when a user or agent decides on an architectural change, needs to document technical rationale, or wants to add a new ADR to the project history.

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。

Provides Retrieval-Augmented Generation (RAG) implementation patterns with LangChain4j for Java. Generates document ingestion pipelines, embedding stores, vector search, and semantic search capabilities. Use when building chat-with-documents systems, document Q&A over PDFs or text files, AI assistants with knowledge bases, semantic search over document repositories, or knowledge-enhanced AI applications with source attribution.

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。

Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.

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。

Work with fonts in the Shift font editor. Use for questions about the font, glyph, source, selection, or view open in the running Shift app (through its local MCP server), for reading, authoring, or compiling saved .shift documents and other font files with shift-cli, and for font concepts such as contours, components, masters, and variable fonts.

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When the user wants to prepare a due diligence data room for fundraising, or when an investor has requested additional materials after a pitch. Also activates for "data room", "due diligence", "DD checklist", or "what documents do investors need?".

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。

PDF processing for construction documents: RFIs, submittals, specifications, drawing packages. Extract data, merge packages, fill forms.

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。

Extract structured data from construction documents using LLMs. Process RFIs, submittals, contracts, specifications. Convert unstructured PDFs to structured JSON/Excel.

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。

Extract structured data from construction specifications. Parse CSI sections, requirements, submittals, and product data from spec documents.

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。

Extract structured data from construction PDFs. Convert specifications, BOMs, schedules, and reports from PDF to Excel/CSV/JSON. Use OCR for scanned documents and pdfplumber for native PDFs.

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。

Extract data from construction images using AI Vision. Analyze site photos, scanned documents, drawings.

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。

Implement semantic vector search for construction data. Build AI-powered search using embeddings and vector databases (Qdrant, ChromaDB) for intelligent querying of specifications, standards, and project documents.

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。

Automate construction data processing using LLM (ChatGPT, Claude, LLaMA). Generate Python/Pandas scripts, extract data from documents, and create automated pipelines without deep programming knowledge.

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。

Automatically classify and extract information from construction documents using NLP. Categorize RFIs, submittals, change orders, specifications, and contracts.

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 RAG / unstructured-document evaluation datasets and demo documents (e.g. for Knowledge Assistant) on Databricks: generate synthetic PDFs locally, upload to Unity Catalog volumes, and pair each document with test questions for retrieval evaluation.

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。