「table」の検索結果

Reads one spreadsheet, CSV export or pasted table together with the user's question and drafts an insight pack: a data profile, observations cited to named columns and values and framed as leads, caveats, and suggested charts and follow-up analyses. Every figure carries the label "as read, verify in source"; correlation is never stated as causation. Use when the user asks to "profile this spreadsheet", "what does this data say", "find anomalies in this extract", "give me a first read on this CSV" or "summarise this table". Do not use for a purchase order log or spend extract, use purchase-order-anomaly-review instead; for actuals versus budget, use budget-variance-explainer instead; for a report that arrives by email on a schedule, use report-attachment-analyzer instead. Drafts for human review; never approves, authorises or signs 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。

Turns an actuals-versus-budget extract (spreadsheet, export or pasted table) into a DRAFT variance table with absolute and percentage variances, favourable or adverse marking and a ranking by size, then drafts plain-language driver hypotheses for each material variance as questions to verify with a named owner, never as asserted causes. Use when the user asks to "explain these variances", "why are we over budget on this line", "write the variance commentary for the management pack", "actuals versus budget analysis" or "which lines drive the variance". Do not use for a dataset with no budget or comparison column, use dataset-insight-pack instead; for weekly operational figures against last week, use kpi-weekly-report-writer. Drafts for human review; never approves, authorises or signs 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。

Builds a DRAFT data dictionary from a schema export, table definition, sample rows or a partial existing dictionary: one row per field with the name as stated, type as stated or observed, meaning quoted from the documentation supplied, owner, allowed values, and UNKNOWN wherever no source defines the field, plus a gap list for the data owner. Never infers a definition from a column name. Use when the user asks to "build a data dictionary for this table", "document these columns", "what does each field mean", "turn this schema export into a dictionary" or "fill in the field definitions from these documents". Do not use for profiling values or finding patterns in the data, use dataset-insight-pack instead; for metric definitions, use kpi-definition-sheet; for logging data defects, use data-quality-issue-log. Drafts for human review; never approves, authorises or signs 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。

Creates data visualizations (charts, graphs, tables) optimized for AI engine parsing and citation. Produces inline SVG/HTML with text summaries, data tables, and JSON-LD so AI engines can quote the 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。

Make a WooCommerce plugin HPOS-compatible (High-Performance Order Storage, default-on in WC 10.x) — declare via FeaturesUtil::declare_compatibility on before_woocommerce_init, replace $wpdb->postmeta / WP_Query order code with wc_get_orders + WC_Order::get_meta / update_meta_data, gate runtime branches on OrderUtil::custom_orders_table_usage_is_enabled, build admin hook names dynamically with OrderUtil::get_order_admin_screen (HPOS = woocommerce_page_wc-orders, legacy = shop_order). Use when scaffolding an order-touching plugin, auditing existing code for HPOS readiness, or fixing post-WC-10 silent breakage. Triggers on shop_order, get_post_meta with order context, FeaturesUtil, OrderUtil, custom_order_tables, before_woocommerce_init.

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。

Riff visuals, then build the graphics beat table. Use at the beats stage after gate 2, or when the user says "plan the graphics", "beat table", or "what visuals go here".

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。

Install or synchronize BMS差分 from a beatoraja-compatible difficulty table, optionally download missing parent songs, and place charts into matching music folders without overwriting existing files. Use for 難易度表 URL, header.json, url_diff, or beatoraja差分 installation requests; do not use for ordinary BMS library organization unrelated to a difficulty table.

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 an Estuary derivation that joins two or more collections on a shared key, merging their fields into a single enriched collection. Use for data enrichment, denormalisation, building wide tables for BI, and real-time CDC joins. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this (a destination-side SQL join or BI tool view is often simpler). Use when user says "join-collections derivation", "derivation to join two collections", "transformation to enrich with customer data", "derive a wide table", "lookup-join derivation", or "build a join derivation".

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。

Generates immutable Dart data classes, sealed union types, and deep copyWith using Freezed (v3.2.x) with optional json_serializable integration. Use when creating DTOs, API response models, app state classes, or any model requiring immutability, pattern matching, or JSON serialization. Activates on: @freezed annotation, freezed_annotation import, immutable data class request, copyWith deep nesting, union/sealed types for loading/data/error states, generic response wrappers, or build_runner code generation for models.

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。

Master of defensive Bash scripting for production automation, CI/CD pipelines, and system utilities. Expert in safe, portable, and testable shell scripts.

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 Tables SDK for Python (Storage and Cosmos DB). Use for NoSQL key-value storage, entity CRUD, and batch operations.

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 working with Coda — coda document management covering docs, pages, tables, formulas, and workspace analytics. Use when auditing Coda workspace usage, managing documents and tables, querying structured data, or analyzing collaboration patterns across a Coda organization.

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。

Airtable REST API via curl. Records CRUD, filters, upserts.

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。

RSI stable: zero proposals after full 6-file rotation

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。

Expert en direction artistique IA (Midjourney, DALL-E, Stable Diffusion, prompt engineering, cohérence de style)

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。

AI vocal agents — Vapi, Airtable, phone answering, booking, 24/7, DZ market

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。

Consolidate the five tdmcp surveyor reports into one prioritized FEATURE_BACKLOG.md — dedupe cross-surface overlaps, reconcile every candidate against the roadmap (NEW vs planned vs extension), rank by a selectable impact×effort weighting profile into P0/P1/P2 (high-confidence first), and emit per-surface tables + a recommended-next shortlist that hands straight to the build pipeline. Use when a td-synthesizer agent is consolidating surveys during the feature-discovery harness.

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。

Remove AI writing patterns and enforce rigorous technical-prose conventions in Japanese. Derived from hardikpandya/stop-slop + k16shikano japanese-tech-writing. NEVER 33 + 論証と構成の規範 + 用語と register の自然さ + クイックチェック 28 + 5 軸スコアリング + 翻訳調 AI 語の検出を含む。Use when drafting, editing, or reviewing Japanese text to eliminate predictable AI tells and tighten argumentation.

Bridges UIKit and SwiftUI by wrapping UIKit views and view controllers in SwiftUI with UIViewRepresentable and UIViewControllerRepresentable, embedding SwiftUI in UIKit with UIHostingController, and coordinating delegate callbacks. Use when integrating camera previews, map views, mail compose, document scanners, PDF renderers, text views with attributed text, or other UIKit-only or third-party UIKit SDK surfaces into a SwiftUI app, or when migrating a UIKit app to SwiftUI incrementally.

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。

Patterns for scaling read-heavy Convex apps to millions of users. Use when optimizing bandwidth, reducing query costs, fixing slow queries, creating digest tables, replacing reactive subscriptions with one-shot fetches, adding compound indexes, debouncing writes, rate-controlling backfills, or running npx convex insights. Trigger when users mention "scale", "bandwidth", "performance", "optimize", "slow queries", "expensive queries", "digest table", "denormalize", or "thundering herd" in the context of Convex.

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。

Refactor SwiftUI view files into stable, testable structure. Use when splitting large views, tightening data flow, or cleaning Observation ownership.

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。

Master of defensive Bash scripting for production automation, CI/CD pipelines, and system utilities. Expert in safe, portable, and testable shell scripts.

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 Tables SDK for Python (Storage and Cosmos DB). Use for NoSQL key-value storage, entity CRUD, and batch operations.

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。

Generates high-quality image prompts for AI image generators (Midjourney, DALL-E, Stable Diffusion). Use when user mentions "イラスト作成", "画像プロンプト", "プロンプト生成", or "画像生成".