「documents」の検索結果

Use when you need MongoDB persistence in Quarkus — including Panache Mongo entities/repositories, document design, indexes, transactions where applicable, and error handling. This should trigger for requests such as Add MongoDB in Quarkus; Review Quarkus Mongo Panache design; Improve Mongo error handling in Quarkus services; Model MongoDB documents for a Quarkus service; Configure Quarkus MongoDB clients codecs or transactions. Part of Plinth Toolkit

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Use when you need to generate Architecture Decision Records (ADRs) for a Java project through an interactive, conversational process that systematically gathers context, stakeholders, options, and outcomes to produce well-structured ADR documents. This should trigger for requests such as Generate ADR; Create Architecture Decision Record; Document architecture decision; Architecture Decision Record for Java. Part of Plinth Toolkit

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Convert documents and text to audio using Google Cloud Text-to-Speech. Use this skill when the user wants to: narrate a document, read aloud text, generate audio from a file, convert text to speech, create a recording of documentation or analysis, create a podcast from a document, or use Google TTS/text-to-speech. Trigger phrases: "read this aloud", "narrate this", "create a recording", "text to speech", "TTS", "convert to audio", "audio from document", "listen to this", "generate audio", "google tts", "create a podcast".

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Convert documents and text to audio using ElevenLabs text-to-speech. Use this skill when the user wants to create a podcast, narrate a document, read aloud text, generate audio from a file, or convert text to speech.

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Interact with Google Docs - create documents, search by title, read content, and edit text. Use when user asks to: create a Google Doc, find a document, read doc content, add text to a doc, or replace text in a document. Lightweight alternative to full Google Workspace MCP server with standalone OAuth authentication.

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This skill handles file format conversions across documents (PDF, DOCX, Markdown, HTML, TXT), data files (JSON, CSV, YAML, XML, TOML), and images (PNG, JPG, WebP, SVG, GIF). Use when the user requests converting, transforming, or exporting files between formats. Generates conversion code dynamically based on the specific request.

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Comprehensive toolkit for developing with the CocoIndex library. Use when users need to create data transformation pipelines (flows), write custom functions, or operate flows via CLI or API. Covers building ETL workflows for AI data processing, including embedding documents into vector databases, building knowledge graphs, creating search indexes, or processing data streams with incremental updates.

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Systematic framework for extracting actionable items from documents and organizing them into prioritized, trackable checklists. Use when converting reports, meeting notes, audits, or any document with embedded action items into structured TODO lists.

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Markdown linting and automated fixing using markdownlint-cli2. Use when Claude needs to: (1) Check markdown files for style issues, (2) Fix markdown formatting problems, (3) Ensure markdown follows best practices, (4) Validate markdown documents, or (5) Apply consistent markdown styling

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Trigger for: explaining current FAR, DFARS, or agency-supplement text; determining documented acquisition-policy status for an agency and FAR part; comparing codified text, RFO model text, and agency deviations; tracing acquisition rulemaking; finding procurement comment periods; analyzing public comments; refreshing prior policy analysis; or producing an Acquisition Policy Impact Brief. Use published federal sources and supplied policy documents without making legal or procurement-specific applicability determinations. Do not use for clause selection, market research execution, opportunities, pricing, grants, or cooperative agreements.

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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.

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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.

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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.

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Adds and tunes a reranker stage for RAG using the retrieve-wide, rerank-narrow pattern. Use when relevant documents appear in the initial top-N but are buried by noise, top-1 precision or MRR is low despite adequate recall, or too many marginal chunks consume context. Covers cross-encoder, API, and local rerankers; N-to-k selection; and latency/cost tradeoffs. Do not use when retrieval recall itself is failing.

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Operating-system distillation of LlamaIndex — the leading RAG / document-agent framework. Activate when the calling agent must build, debug, harden, or evaluate a Retrieval-Augmented Generation pipeline over unstructured/private data, decide between RAG primitives (Index types, retrievers, query engines, routers, agents), or pick LlamaIndex vs LangChain / Haystack / raw vector store for a coding task. Encodes the 5-layer mental model (Documents → Nodes → Indices → Retrievers → Query Engines / Response Synthesizers), the canonical RAG bootstrap SOP from baseline `VectorStoreIndex` through hybrid + reranker + eval-loop hardening, the official 13-failure-mode checklist, and 5 dilemma cases distilled from docs, GitHub issues, and 2025 production post-mortems.

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Universal discipline for any LM-driven loop — agent retries, plan-act-observe, multi-agent handoffs, optimiser passes, test-fix cycles. Encodes the one rule every framework documents quietly and every team relearns expensively: the LM in the loop is NEVER a reliable terminator. Termination must be provided by an explicit counter + exit predicate + stagnation signal + escalation path that live OUTSIDE the LM's control. This is a tool- level, framework-agnostic skill. It maps onto LangGraph (recursion_limit + state counter + interrupt), CrewAI (max_iter + max_rpm + human_input), Claude / OpenAI SDKs (max_iterations + tool_use_budget), DSPy (declared evaluation budget), Aider (REPL + explicit retry cap), and AutoGen (max_consecutive_auto_reply). Search keywords: infinite loop, recursion limit, recursion_limit, GraphRecursionError, max iterations, max_iter, agent stuck, agent won't stop, runaway agent, ReAct loop not terminating, agent repeating itself.

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Designs multiscale chunking for RAG by embedding small units for retrieval precision and returning larger context for synthesis. Use when fixed-size chunks either lose surrounding context or dilute relevance in long documents, manuals, filings, or codebases. Covers sentence-window and parent-child or auto-merging strategies, base chunk sizing, and evaluation. Do not use when a single chunk scale already meets retrieval and generation needs.

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Answer a question across a corpus of contract documents with verified citations. Use when the user asks what a contract says, which contracts have a clause, what changed between amendments, or any question that needs reading and citing across a set of contract files. The corpus must be on the local filesystem (see README).

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Drive the contracts engine's runtime surface (the stdio MCP server) to verify changes end-to-end. Scoped to public-plugins/plugins/healthcare/skills/contracts/ and ../../servers/documents/.

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Extract plain text from a document file - PDF, DOCX, XLSX, PPTX, RTF, or plain text/markdown/HTML. Use when a binary document needs to be turned into text, for example a contract PDF or an EHR DocumentReference attachment. Other skills (fhir) invoke scripts/extract.ts directly; the contracts MCP server bundles its own copy (servers/documents/src/extract.mjs) so its bundle stays self-contained — port fixes to both.

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Verify sources, claims, images, video, documents, interviews, and synthetic media with SIFT and a durable evidence trail. Use before relying on uncertain material.

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Create or refine readable Mermaid diagrams for changes, architecture, lifecycles, and dependencies in Markdown documents and PR descriptions.

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Handles files in Convex: upload URLs, storing blobs from actions, serving with getUrl or an HTTP action, metadata from the _storage table, and deletion. Use when users upload images or documents, when an action fetches a file from a third party, or when a file URL expires unexpectedly.

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Changes a live Convex schema without downtime: make a field optional, backfill in batches, flip the validator, then clean up. Uses the @convex-dev/migrations component where it fits. Use when renaming or removing a field, changing a type, splitting a table, or when deploy fails with a schema validation error on existing 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。