「data write」の検索結果

Build and edit tile-based 2D levels in Godot 4.7 with TileMapLayer and TileSet: paint layers, set up collision/navigation/custom-data on tiles, autotile with terrain sets, and read/write cells from code (set_cell, get_cell_tile_data, local_to_map). Use when working with TileMapLayer nodes, .tres TileSets, autotiling, or migrating a deprecated TileMap node to TileMapLayer.

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。

Write a compounding worksheet and record that reproduces the preparation exactly and survives inspection — formula, calculations shown, components with lot numbers, in-process checks, and the beyond-use date with its basis. Use when asked to document a compounded preparation, write a master formulation record, create a compounding worksheet, or prepare compounding documentation for inspection. Produces the master formula, the batch record, the component and lot table, in-process checks, labelling, and the beyond-use-date rationale. Documentation only; standards and stability data must come from current official references.

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。

Write a PRD for an AI-powered feature, covering the things normal PRDs miss. Use when asked to spec an AI/LLM feature, write a PRD for a feature that uses a model, or plan an AI capability (assistant, summarizer, generator, classifier). Produces an AI feature PRD — problem & UX of uncertainty, model approach, eval criteria, guardrails, fallback behaviour, the data flywheel, and cost/latency budget.

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。

Analyse a finished A/B test and write the readout — the result, whether it's statistically and practically significant, what it means, and the ship/no-ship call. Use when asked to analyse experiment results, write an A/B test readout, interpret test data, or decide whether to ship a variant. Produces a clear verdict with the lift and confidence, segment cuts, the risks (peeking, novelty, sample), and a recommendation. Distinct from planning a test — this reads results.

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。

Write an AI usage policy people can actually follow — approved tools, data rules, disclosure duties, and review obligations, in one page instead of legal fog. Use when asked for a company AI policy, acceptable-use rules for ChatGPT/Claude/Copilot at work, guidance on what data may go into AI tools, or to fix a policy nobody reads. Produces a one-page usable policy plus the decision log behind it. Not a substitute for legal advice; pairs with compliance-checklist for regulatory mapping and ai-ethics-review for system-level assessments.

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 this skill when producing a polished, Commonly-branded deliverable (.docx brief / memo, .xlsx data matrix, .pptx deck) and you do not have specific brand guidance from the user. Trigger on: 'write me a brief', 'one-pager', 'memo', 'data sheet', 'status matrix', 'short deck', 'summary deck', 'closing slide', 'final deliverable'. Routes to `officecli merge` with one of three pre-built starter templates that already carry the Commonly palette, fonts, and structure — so you populate content with one merge call instead of 30 individual `officecli set` commands. DO NOT use for fundraising decks (use `officecli-pitch-deck`), academic papers (use `officecli-academic-paper`), or financial models (use `officecli-financial-model`).

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。

Read and write Langfuse: browse traces and observations, list prompts and datasets, score traces, add dataset items. Trigger phrases: langfuse, llm observability.

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。

Writes product-led content pages optimized for both search engines and AI engine citations. Produces markdown files with frontmatter, following page-type frameworks (comparison, guide, use case, learn, trust) with verified sources and no fabricated data.

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 reviewing a DataFusion Comet pull request that touches native or JVM columnar shuffle, the shuffle writers and readers, partitioning, the Arrow IPC block format, shuffle compression, or the Celeborn integration. Load alongside review-comet-pr.

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 reviewing a DataFusion Comet pull request. Covers the workflow that applies to every PR and routes to the area-specific review skills for expressions, FFI, memory management, shuffle, and Iceberg writes. Provides guidance to a human reviewer rather than posting comments.

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。

Review generated or changed WordPress code — plugins, themes, and blocks — before it ships. Best used reactively after an agent writes, edits, or reviews code touching WordPress APIs: add_action/add_filter, shortcodes, meta boxes, AJAX handlers, REST routes, WP_Query or $wpdb, widgets, or WP-CLI commands. Use on 'review this plugin', 'is this safe to ship', 'make this translatable', 'speed up this query', or after tasks like 'write a plugin' or 'add an endpoint/shortcode/meta box'. Enforces escaping and sanitization, nonces plus capability checks, prepared database queries, core-API-first development, translation-ready strings, and query/caching discipline. DO NOT USE for WooCommerce-specific order, product, or checkout logic (use woo-guard), non-WordPress PHP, generic code quality review (use clean-code-guard), test code review (use test-guard), server or hosting configuration, or conceptual WordPress questions.

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。

Research LinkedIn profiles and write personalized messages for any LinkedIn message type — connection requests, InMails, DMs, message requests, post comments, and comment replies. Takes LinkedIn URLs as input, researches each person (profile data + recent posts via Apify), and generates messages tailored to each lead's background, interests, and recent activity. Exports tool-ready CSVs for Dripify, Expandi, Botdog, PhantomBuster, or generic format. No LinkedIn cookies or login required.

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。

Database access patterns for performance. Separate read/write models, avoid N+1 queries, use AsNoTracking, apply row limits, and never do application-side joins. Works with EF Core and Dapper.

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 read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.

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。

Set up and run wireless network simulations using the Wireless Network Toolbox. Use this skill when creating system-level simulations with wirelessNetworkSimulator, adding traffic sources (networkTrafficOnOff, networkTrafficFTP, networkTrafficVoIP, networkTrafficVideoConference), configuring node mobility, logging events or IQ samples, visualizing traffic, scheduling actions during simulation, running parametric sweeps, writing PCAP files with pcapWriter or technology-specific PCAP writers, registering event callbacks on wireless nodes, accessing event data fields, or combining multiple capture/visualization tools in a simulation.

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 industrial-grade SCADA/HMI dashboards in MATLAB following industrial-HMI conventions (ISA-101-aligned): gray-field philosophy, alarms at source, write safeguards, fixed-range trends, drill-down layout. Produces a real App Designer app (.mlapp, or plain-text .m+.xml on R2026b+) by handing serialization to the matlab-build-app skill when available, and falls back to a programmatic .m app otherwise. Use when wrapping OPC UA / Modbus / MQTT / OSI PI / PI AF monitoring scripts into a live operator app, building plant overviews, designing operator dashboards, or any time a user asks for a "SCADA dashboard", "HMI", "plant dashboard", "operator screen", or "industrial monitoring app" in MATLAB. Trigger on: SCADA, HMI, industrial dashboard, plant overview, operator screen, uigauge, uilamp, alarm banner, gray-field, ISA-101, OPC UA dashboard, setpoint, write safeguards, alarm visualization, OSIsoft PI, AVEVA PI, PI Server, PI Data Archive, PI AF, PI Asset Framework, piclient, afclient.

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。

Read or write data files in MATLAB. Use when the task involves tables, spreadsheets, delimited text, or structured files in CSV, Excel, Parquet, JSON, or XML format — including but not limited to importing, exporting, loading, parsing, converting, validating, configuring import options, reading from URLs, handling locales or encodings, diagnosing file errors, and modernizing legacy file I/O code. MATLAB provides built-in functions for these workflows with no additional products required.

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 engaging, localized App Store release notes (What's New) from git log, bullet points, or free text using canonical metadata under `./metadata`. Optionally pairs with promotional text updates.

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 two sessions side-by-side using Agent Monitor data — per-model token usage (input/output/cache_read/cache_write + compaction baselines), pricing engine cost breakdowns, workflow intelligence (complexity scores, tool flow transitions, subagent effectiveness), session metadata (thinking_blocks, turn_count, turn_duration_ms, usage_extras), and full event timelines with all 10+ event types.

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 and manage Shopify pages, blog posts, navigation menus, redirects, and SEO metadata via the Admin API or browser automation. Use whenever the user wants to add a page to a Shopify store, write a Shopify blog post, update the storefront navigation, manage redirects, or tune SEO metadata on a Shopify site.

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 and executing C# scripts and macros against Power BI semantic models using Tabular Editor 2/3. Automatically invoke when the user mentions "C# script", "Tabular Editor script", "TOM scripting", "MacroActions.json", "XMLA", or asks to "automate model changes", "bulk update measures", "create calculation groups", "write a macro", "format DAX expressions", "manage model metadata".

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。

Author, validate, and test Power Query M expressions in semantic model partitions. Automatically invoke when the user mentions "Power Query", "M code", "M expression", "partition expression", "query folding", or asks to "write Power Query", "fix Power Query", "test a partition", "preview partition data", "debug Power Query step", "optimize Power Query".

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。

When the user wants to write, generate, or create article body content—blog post body, long-form content, how-to guide, listicle. Also use when the user mentions "write article," "article content," "blog post content," "article body," "long-form content creation," "generate article," "article draft," "how-to guide content," "listicle content," "information gain," or "content density." For single post page structure, schema, and SEO metadata, use article-page-generator. For blog index/listing page, use blog-page-generator. For short ad, landing, or email copy, use copywriting.

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 a self-contained JavaScript server runtime and registration metadata for an MCP codeful tool. Use when the user asks to create a codeful MCP tool, generate server logic for an MCP tool, write a runTool function, build a Dataverse-backed MCP tool, or pair MCP server logic with an MCP App widget.

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。