「documents」の検索結果

Parse and extract data from HTML with Cheerio. Use when a user asks to scrape static web pages, parse HTML files, extract data from HTML, build a web scraper for server-rendered pages, extract text or links from HTML documents, parse RSS/XML feeds, transform HTML content, or process HTML emails. Covers jQuery-style selectors, DOM traversal, text extraction, attribute parsing, and integration with HTTP clients for web scraping pipelines.

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 polished HTML slide decks and PDF-ready documents for consulting deliverables. Uses the RRBC design system with warm light mode, dark mode cover pages, Lora/Inter/Roboto Mono typography, and data visualization palette. Trigger on 'deck', 'slides', 'presentation', 'pitch deck', 'keynote', 'report', or 'PDF'.

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 polished HTML technical reports and multi-page documents for consulting deliverables. Features a professional design system with dark cover pages, warm cream content pages, Switzer + Cartograph CF typography, and a curated component library (KPI cards, data tables, callout boxes, recommendation cards, priority banners). Outputs standalone HTML that prints cleanly to PDF.

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 polished HTML technical reports and multi-page documents for consulting deliverables. Includes a customizable design system with dark cover page, warm cream content pages (8.5x11 portrait), professional typography, and a curated component library (KPI cards, data tables with severity coloring, callout boxes, recommendation cards, priority banners). Trigger on report, audit report, technical document, written report, assessment, or multi-page document. Outputs standalone HTML files exported to PDF via Chrome headless.

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。

Performs a fast 6-point quality check for technical documents (specs, design docs, READMEs). Use after editing any specification or design document.

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。

Keeps specification documents and code in agreement. Audit mode finds every divergence when an implementation is claimed complete; sync mode detects spec drift in staged changes and updates the spec after user approval, so each commit is a reconciled snapshot of spec, tests, and code. Use when an implementation is claimed complete against a spec, before committing changes that may drift from the spec, or after approving decisions that must flow back into it.

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。

Documents rollback procedures before making risky changes. Covers what's changing, verification steps, rollback procedures, and blast radius assessment. Use before database migrations, breaking API changes, infrastructure or DNS changes, large dependency upgrades, or any change where undo is not obvious.

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。

Azure AI Content Understanding SDK for Python workflow skill. Use this skill when the user needs Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Azure Document Intelligence REST SDK for TypeScript workflow skill. Use this skill when the user needs Extract text, tables, and structured data from documents using prebuilt and custom models and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Azure AI Content Understanding SDK for Python workflow skill. Use this skill when the user needs Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Azure.AI.DocumentIntelligence (.NET) workflow skill. Use this skill when the user needs Azure AI Document Intelligence SDK for .NET. Extract text, tables, and structured data from documents using prebuilt and custom models and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Azure.AI.DocumentIntelligence (.NET) workflow skill. Use this skill when the user needs Azure AI Document Intelligence SDK for .NET. Extract text, tables, and structured data from documents using prebuilt and custom models and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Assign items to a CLOSED label vocabulary that is too large to put in a prompt — product taxonomies, category hierarchies, tag vocabularies, routing tables, ICD/SIC-style code lists. A cheap model writes the label it thinks the vocabulary would use, and an embedder snaps that writing onto the nearest legal value, so the schema is never transmitted and the output is always in-vocabulary. Use for "classify these into our taxonomy", "tag these against the existing tag list", "map these queries to categories", "the enum is too big to send", or a Literal/enum that hits a provider cap. NOT for a vocabulary that fits in a prompt — structured output measured 0.701 acc@1 there against this pattern's 0.564. NOT for open-ended labelling with no fixed vocabulary, and not for ranked retrieval over documents (bm25).

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。

Ranked content search over a text corpus you point it at, using BM25 (via xhluca/bm25s). Corpus-agnostic: cloned repos, project knowledge stores, uploaded files and archives, any local directory. In-memory BM25 index per invocation, with a session-local disk cache for repeat runs against the same corpus. Use for "rank these documents", "search this corpus", "which files are most about X", "find content about Y", or any multi-word concept query against a known body of text where grep would return everything or nothing. Needs a corpus on disk. Not for searching stored memories or prior-session decisions (remembering), not for a named symbol or a file's structure (tree-sitting), and not for a literal string you could grep.

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。

Convert a file from one format to another inside the container — documents, images, audio, video. Routes to the right engine (pandoc, LibreOffice, ImageMagick, ffmpeg) by format pair. Triggers on "convert X to Y", "turn this docx into a pdf", "make a gif from this mp4", "md to docx", "batch-convert these images", or any single-file or batch format change where the source and target extensions differ. NOT for editing content (use docx/pptx/xlsx/pdf skills), creating files from scratch, or reading a file you already have in context.

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 keywords from documents using YAKE algorithm with support for 34 languages (Arabic to Chinese). Use when users request keyword extraction, key terms, topic identification, content summarization, or document analysis. Includes domain-specific stopwords for AI/ML and life sciences. Optional deeper extraction mode (n=2+n=3 combined) for comprehensive coverage.

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。

Writing documents for agents. Use when creating or editing skills, or modifying AGENTS.md or CLAUDE.md.

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。

Query Google NotebookLM notebooks for source-grounded, citation-backed answers from user-uploaded documents. Manages notebook library, handles Google authentication, and supports smart discovery. Works standalone via /blog notebooklm or internally from blog-write and blog-researcher for Tier 1 research data. Falls back gracefully when not configured. Use when user says "notebooklm", "notebook", "query notebook", "ask notebook", "notebook research", "source grounded research", "document query", "notebook library".

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。

Generate Product Requirements Documents from `idea.md` and `validate.md` files. Use when asked to create or update a PRD. Don't use for TAD, sprint tasks, or raw idea validation.

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。

Redact text from PDF documents for blind review anonymization

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。

CRE Document Ingestion suite — 4 specialist skills for classifying and extracting structured data from deal documents including rent rolls, T-12 financials, and offering memoranda.

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。

Ingests unstructured and semi-structured documents into Neo4j as a knowledge graph. Use when chunking PDFs, HTML, plain text, or Markdown; extracting entities and relationships from text with an LLM (SimpleKGPipeline, neo4j-graphrag); loading JSON via apoc.load.json; building Document→Chunk→Entity graph structures; or connecting LangChain/LlamaIndex document loaders to Neo4j. Covers neo4j-graphrag SimpleKGPipeline, LLM Graph Builder web UI, entity resolution, chunking strategies, and graph schema design for RAG pipelines. Does NOT handle structured CSV/relational import — use neo4j-import-skill. Does NOT handle GraphRAG retrieval after ingestion — use neo4j-graphrag-skill. Does NOT handle vector index creation — use neo4j-vector-search-skill.

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。

Guide for navigating and maintaining Kimix project documentation. Use when: (1) user asks about the docs structure, (2) user wants to add, update, or append content to 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。

Use when you draw a screen as HTML mockup documents (one per variant) - the sanitizer-safe recipe, phone and wide frames, the states grid, static control states, and the loopback self-check with screenshots at 375, 768 and 1440 attached to the task

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。