Results for “text”

Use when a task involves game textures or materials through the Scenario MCP: seamless or tileable textures, themed texture packs (brick, wood, stone, floors, hand-painted), PBR materials (albedo, metallic, roughness, normal) on 3D assets, retexturing a mesh, material iteration from reference images, texture upscaling that must preserve tiling, or sizing textures for game engines. Keywords: seamless texture, tileable, PBR, material, retexture, texture upscale, surface.

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Use Neo4j GenAI Plugin ai.text.* functions and procedures for in-Cypher embedding generation, text completion, structured output, chat, tokenization, and batch ingestion. Covers ai.text.embed(), ai.text.embedBatch(), ai.text.completion(), ai.text.structuredCompletion(), ai.text.aggregateCompletion(), ai.text.chat(), ai.text.tokenCount(), ai.text.chunkByTokenLimit(), and provider configuration for OpenAI, Azure OpenAI, VertexAI, and Amazon Bedrock. Requires CYPHER 25. Replaces deprecated genai.vector.encode(). Use when writing pure-Cypher GraphRAG, embedding nodes in-graph, generating structured maps from prompts, or calling LLMs inside Cypher queries. Does NOT handle neo4j-graphrag Python library pipelines — use neo4j-graphrag-skill. Does NOT handle vector index creation/search — use neo4j-vector-index-skill.

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/context-builder と入力されたときだけ起動する、コンテキストドキュメント作成インタビュースキル。人・プロジェクト・プロダクトなどの対象について、1問ずつ掘り下げる対話を通じて情報を集め、今後 Claude との対話の冒頭に貼り付けるための context.md（自分についてなら aboutme.md）を作り上げる。/context-builder me（自分＝aboutme）/ project / product のように対象を指定できる。「aboutme を作りたい」「プロジェクトのコンテキストをまとめたい」「プロダクトの前提情報を用意したい」ときに使う。自動起動はせず、ユーザーが /context-builder と明示的に入力した場合のみ使用すること。

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Build text translation into Python applications with Azure AI Translator. Covers translation into multiple languages, language detection, transliteration to another writing system, dictionary entries, usage examples, and asynchronous requests for handling larger workloads.

• Add multilingual text translation to a Python app
• Detect the language of incoming text
• Convert text between writing systems

Find previously indexed project files, documentation, and session memory in context-mode's persistent full-text search database. Search related questions together and narrow results to a named project or source without rereading raw material into the conversation.

• Find information in previously indexed project files
• Look up saved documentation or session memory
• Search several related questions together

Diagnose why an AI agent becomes less reliable as conversations and documents accumulate. Distinguish missed information, persistent false claims, irrelevant material, mixed tasks, and conflicting sources, then choose ways to clean, reorganize, or separate the context.

• Investigating declining quality in long conversations
• Recovering from false claims that persist after correction
• Resolving conflicts between retrieved documents

Build text tokenizers, which split text into units for AI models, directly from raw multilingual text. Learn custom vocabularies with BPE or Unigram, convert text to pieces or numeric IDs, and decode the results.

• Preparing text for multilingual AI models
• Training a vocabulary from Japanese text
• Reusing a tokenizer for reproducible text splitting

Provides fallback methods for extracting PDF text when the usual file reader fails or returns incomplete content. Uses command-line tools or Python libraries to retrieve text, preserve spacing, inspect metadata, and extract tables.

• Recovering PDF text after file reading fails
• Extracting text while preserving layout spacing
• Extracting tables from PDFs

Add CSS animations to web interfaces using ready-made patterns for menus, modals, loading states, text changes, and more. It selects a suitable pattern, connects markup and any required JavaScript, and preserves reduced-motion support.

• Adding opening and closing animations to menus and modals
• Animating loading placeholders and loaded content
• Adding transitions for changing text and numbers

Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rotUse when "context window, token limit, context management, context engineering, long context, context overflow, llm, context, tokens, memory, summarization, optimization" mentioned.

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Manages context passing between DAG nodes and spawned agents. Handles context summarization, selective forwarding, and token budget optimization. Activate on 'bridge context', 'pass context', 'summarize context', 'context management', 'agent context'. NOT for execution (use dag-parallel-executor) or aggregation (use dag-result-aggregator).

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Context and scope-tagging UI. Use when adding a context picker to a surface, tagging an entity to scopes, setting or filtering by the active (working) context, showing per-entity context status, prompting for context on upload, or editing context-assignment/**, active-context/**, or appContextSlice.

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Look up current library and framework documentation through Context7 MCP, a connection to live documentation tools. Supports setup questions, API references, and code examples, using version-specific sources when available to ground answers in retrieved material.

• Looking up framework setup and configuration
• Checking library API methods and usage
• Finding documentation examples for Prisma queries

Create a standard folder layout for a DDD bounded context, a module organized around one business area. Generate TypeScript export files, keep infrastructure outside the public exports, and record the context in a domain model graph.

• Starting a module for a new business area
• Splitting a monolith into bounded contexts
• Listing existing contexts

Review rendered HTML for links, buttons, or inputs inside role="text" containers. Identify controls hidden from screen readers, suggest markup changes, and explain how to check keyboard access and screen reader output after fixing them.

• Reviewing links and buttons inside text containers
• Investigating controls missing from screen reader output
• Checking accessibility in design system components

Configure images and videos as surface textures in Three.js, and use environment images for backgrounds and reflections. Adjust placement, color settings, repetition, and filtering, then manage texture memory through compression, reuse, and disposal.

• Adjust image placement and tiling on 3D surfaces
• Use HDR images for backgrounds and reflections
• Apply video or Canvas content to a 3D surface

Retrieve current documentation and code examples from Context7 for libraries, frameworks, and developer tools. Use the results to answer questions about API syntax, configuration, installation, version migration, and library-specific problems.

• Checking current API syntax
• Finding setup and configuration examples
• Investigating version migration issues

Find current documentation and code examples through the Context7 MCP server. Look up library setup, API usage, migration steps, and library-specific problems, using documentation for the requested version when available.

• Looking up documentation for a specific version
• Finding library setup and configuration steps
• Finding examples for code that uses external libraries

Read text aloud or save it as a speech audio file using voices already installed on the computer. Works through local platform tools without an account or API key, with long text split into shorter passages.

• Listening to a passage read aloud
• Saving text as a speech audio file
• Splitting long text into spoken paragraphs

Build text tokenizers that split multilingual text into small units for AI models. Learn how to train SentencePiece on raw text, configure BPE or Unigram splitting methods, and convert text to token IDs and back.

• Preparing Japanese, Chinese, or Korean text for AI models
• Training a custom vocabulary from raw text
• Choosing between BPE and Unigram

Extend the amount of text a language model can process at once. Compares RoPE, YaRN, ALiBi, and position interpolation, with implementation examples, additional training guidance, and memory optimization approaches for long document processing.

• Extend an existing model to handle longer inputs
• Compare methods for representing token positions
• Plan additional training with long documents

Refactor state held in Redux or React Context by separating API data, URL settings, shared interface state, and local component state. Move each part to React Query, nuqs, Zustand, or React’s local state hooks.

• Migrating away from Redux or React Context
• Removing duplicate storage of API responses
• Sharing filters and pagination through URLs

Route data analysis, large-file processing, and web retrieval through context-mode tools, returning only relevant results to the conversation. This helps preserve the context window, the limited amount of information an AI can work with at once.

• Counting or filtering large datasets
• Retrieving relevant information from web pages
• Collecting and searching results from multiple commands

Delete stored context-mode data for one session or an entire project after confirming the scope. Remove an unwanted session while preserving others, or clear project knowledge, session records, and statistics for a fresh start.

• Remove data from one unwanted session
• Clear a project database containing outdated information
• Reset all stored project data for a fresh start