「query skill」の検索結果

This skill should be used whenever the user mentions "pbir", "pbir-cli", "Power BI reports", or "PBI reports", works with .pbir, .pbip, or .pbix files, or wants to refresh, screenshot, or visually verify a report that is open in Power BI Desktop. Covers creating, exploring, formatting, validating, and publishing Power BI reports through the pbir CLI and object model, plus driving Power BI Desktop (canvas reload, page screenshots) and querying connected or local semantic 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。

Automatically invoke this skill whenever the user asks why a Fabric capacity is busy, throttled, at 90 or 100 percent, or what is consuming CUs; asks for current capacity utilization, carry forward or per-item CU usage; wants to query or repair the Microsoft Fabric Capacity Metrics app or its semantic model; or wants to start, pause or monitor a capacity from a script or status bar.

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 analyze Google Search Console data, use the GSC API, or interpret search performance. Also use when the user mentions "GSC," "Search Console," "indexing report," "Core Web Vitals," "Enhancements," "Insights report," "search performance," "search queries," "search performance report," "URL inspection," "impressions," "CTR," "average position," "index coverage," "GSC data analysis," "Search Console API," or "searchanalytics.query." When the user wants to rewrite title tags (not only report on them), use title-tag. For meta description rewrites, use meta-description.

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。

Adds Dataverse tables to a Power Apps code app with generated TypeScript models and services. Can also create new Dataverse tables. Use when connecting to Dataverse, adding tables, creating schema, or querying Dataverse 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。

Adds Azure DevOps connector to a Power Apps code app. Use when querying work items, creating bugs, managing pipelines, or making ADO API calls.

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 an Unreal Engine 5.8 project misses its frame rate or stutters: "optimize to 60 fps", "we run at 38 fps", CPU or GPU bound, game or render thread too long, hitches, traversal or shader stutter (PSO), GC spikes, camera-cut spikes, Lumen, Nanite, VSM, ray tracing, foliage, crowds or Niagara too expensive, async compute, stat unit, ProfileGPU, Unreal Insights, TraceQuery, snapshothitches, scalability and device profiles, dynamic resolution and TSR, streaming budgets, or proving a fix with before and after captures.

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 journal indexing/inclusion status on CNKI - check which databases include a journal (北大核心, CSSCI, CSCD, SCI, EI, etc.), get impact factors and evaluation data. Use when user asks about a journal's level, indexing, or ranking.

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。

Running experiments out of the data warehouse instead of via dedicated experiment platforms. SQL-based assignment, exposure logging discipline, metric definitions in dbt models, statistical analysis in SQL or Python, variance reduction with CUPED, sequential testing, and the operational tradeoffs vs platforms like Statsig and Optimizely. Triggers on warehouse-native experimentation, run experiments in BigQuery, run experiments in Snowflake, dbt experiments, SQL t-test, CUPED variance reduction, exposure log, sample ratio mismatch, sequential testing, mSPRT, doubly robust estimation, build vs buy experimentation. Also triggers when the team is choosing between platform and warehouse, building warehouse-native experiment infrastructure, auditing one, or running an experiment with a custom metric the platform cannot handle.

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。

Design, review, and validate Google Cloud (GCP) architectures. Use when choosing GCP compute, storage, networking, or identity services, or applying the Google Cloud Architecture Framework (reliability, security, cost, performance).

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。

Performance profiling for Node.js, Python, and Go: CPU flamegraphs, memory leak detection, bundle analysis, query optimization, and k6 load testing. Use when diagnosing slow endpoints, memory growth, large bundles, or traffic spikes.

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。

Edit, query, and transform Word documents (.docx) with the SuperDoc CLI. Use when the user asks to read, search, modify, format, comment, track changes, or review changes in .docx files. Triggers on any task involving Word documents — text replacement, redlining, contract markup, template filling, bulk edits, content extraction, or document review.

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。

Edit, query, and transform Word documents with the SuperDoc CLI v1 operation surface. Use when the user asks to read, search, modify, comment, or review changes in .docx files.

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。

Complete sanctions and export control analysis tool — individual designations, sectoral sanctions, dual-use goods, payment systems (SWIFT/USD), and extraterritorial regimes (EAR/ITAR/ FDPR US, ECL China). Triggers: "is X sanctioned?", "can I do business with X?", "can I sell Y to Z?", "pétrole Russie", "biens à double usage", "gel des avoirs", "sanctions internationales", "liste OFAC", "liste UE", "double usage", "embargo", "licence d'exportation", "SWIFT", "paiement en dollars", "US goods", "FDPR", "EAR", "ITAR", "BIS", "ECL chine", "no re-export clause", "sanctions sectorielles", "puis-je travailler avec X?", or any transaction involving a foreign person, product, sector, destination country, or payment in USD/EUR. Always adapt to user profile. Always run for any sanctions or export control query — even vague or exploratory ones.

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 blueprint for physics queries using RayCast, ShapeCast, and DirectSpaceState. Covers hit detection, volume overlap, mouse picking, and high-performance server-side intersection queries. Use when implementing projectiles, LOS, terrain grounding, or AI sensors. Keywords raycast, shapecast, direct_space_state, intersect_ray, intersect_shape, PhysicsRayQueryParameters, collision mask, mouse picking.

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 blueprint for First-Person Shooters: fps_camera_look, fps_movement_logic, hitscan_weapon_query, weapon_bobbing_system, procedural recoil, and FPS NEVER rules. Shared TPS/cover theory links to godot-genre-shooter. Keywords: FPS, movement physics, weapon bobbing, camera sway, hitscan, viewmodel, air control.

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 blueprint for physics queries using RayCast, ShapeCast, and DirectSpaceState. Covers hit detection, volume overlap, mouse picking, and high-performance server-side intersection queries. Use when implementing projectiles, LOS, terrain grounding, or AI sensors. Keywords raycast, shapecast, direct_space_state, intersect_ray, intersect_shape, PhysicsRayQueryParameters, collision mask, mouse picking.

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 blueprint for First-Person Shooters: fps_camera_look, fps_movement_logic, hitscan_weapon_query, weapon_bobbing_system, procedural recoil, and FPS NEVER rules. Shared TPS/cover theory links to godot-genre-shooter. Keywords: FPS, movement physics, weapon bobbing, camera sway, hitscan, viewmodel, air control.

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。

React 18 patterns for LlamaFarm Designer. Covers components, hooks, TanStack Query, and testing.

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。

Designer subsystem patterns for LlamaFarm. Covers React 18, TanStack Query, TailwindCSS, and Radix UI.

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。

SwiftData class inheritance patterns for hierarchical models with type-based querying, polymorphic relationships, and when to choose inheritance vs enums. Use when designing SwiftData model hierarchies.

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。

Deep dive into SwiftData design patterns and best practices. Covers schema design, query patterns, repository pattern, and performance optimization. Use when designing data models or improving SwiftData usage.

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 photos, albums, locations, tags, and people from a TrailSnap instance through its command-line client. Use when TrailSnap MCP tools are unavailable and the user asks about their own photo library; Agent Tokens are read-only, so do not use this skill to mutate library 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。

Expert knowledge for Azure Digital Twins development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when modeling DTDL graphs, integrating IoT Hub/ADx, configuring endpoints/routes, securing access, or tuning query units, and other Azure Digital Twins related development tasks. Not for Azure IoT Hub (use azure-iot-hub), Azure IoT Central (use azure-iot-central), Azure IoT Operations (use azure-iot-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。

Checks whether a company's own website mentions a specific keyword, using one simple Google site: query per keyword through a SERP API, and turns the hit into a copy-ready line. Triggers on "does their site mention X", "site: search", "find companies whose website says X", "filter this list by a keyword on their site", "check if they talk about X", "google search enrichment". Outputs site_keyword_hit plus a one-clause personalization line with an evidence URL.

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。