「snow」の検索結果

Vehicle/automotive image editing for cars, trucks, SUVs, motorcycles — car scenes, reflections, tires refinement with snow/mud/grass, segment windshield/wheels/body/windows/hubcaps, atmospheric effects (dust, fog, snow, light leaks, lens flare), and lighting harmonization (hot-day, cold-day, hot-night, cold-night presets). Powered by Bria.ai's dedicated automotive pipeline. Use this skill whenever the user mentions a car, vehicle, truck, SUV, motorcycle, auto photo, car reflections, tire enhancement, muddy tires, vehicle background, automotive marketing, car dealership visual, vehicle configurator, or car scene generation. ALWAYS prefer this skill over general image tools when the subject is a vehicle — faster and more accurate for automotive workflows.

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。

Snowflake best practices for cloud data warehouse design, query optimization, and cost management

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。

Star schema, snowflake schema, SCD types, and Kimball methodology for analytical data modeling. Activate on: dimensional model, star schema, snowflake schema, SCD, fact table, dimension table, Kimball, grain, surrogate key. NOT for: dbt implementation (use dbt-analytics-engineer), warehouse tuning (use data-warehouse-optimizer).

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 data warehousing (Snowflake, BigQuery, Redshift, star schema, slowly changing dimensions)

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。

Star schema, snowflake schema, SCD types, and Kimball methodology for analytical data modeling. Activate on: dimensional model, star schema, snowflake schema, SCD, fact table, dimension table, Kimball, grain, surrogate key. NOT for: dbt implementation (use dbt-analytics-engineer), warehouse tuning (use data-warehouse-optimizer).

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 correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.

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 optimized SQL for your dialect with best practices. Use when translating a natural-language data need into SQL, building a multi-CTE query with joins and aggregations, optimizing a query against a large partitioned table, or getting dialect-specific syntax for Snowflake, BigQuery, Postgres, etc.

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。

Wren CLI for AI agents — a semantic SQL layer over 22+ databases (Postgres, MySQL, BigQuery, Snowflake, Spark, …). The actual workflow guides live inside the `wren` CLI itself; this is just a discovery stub. Use whenever the user asks a data question (how many, show me, top N, compare, trend, breakdown, metric, revenue, customers, orders), wants to install / set up Wren Engine, connect a new database, connect SaaS data via dlt (HubSpot, Stripe, Salesforce, GitHub, Slack), generate or regenerate an MDL project from a database schema, enrich a project with business context (enum meanings, units, cubes like ARR / DAU / churn), or turn a project's context layer into a shareable GenBI web app / dashboard and deploy it to Vercel or Cloudflare. Triggers: 'install wren', 'set up wren engine', 'connect database to wren', 'connect SaaS to wren', 'load hubspot / stripe / salesforce data', 'generate mdl', 'scaffold wren project', 'enrich wren context', 'augment my project', 'add cubes', 'build a dashboard', 'make a shareable analytics app', 'deploy my context layer as a web app', 'genbi app', 'wren onboarding', 'wren usage', 'wren generate mdl', 'wren dlt connector', 'wren enrich context', 'wren genbi'.

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 falling leaves that read as leaves, with each one tumbling on its own axis so it presents a face, thins to an edge, and opens out again, and with its sideways slip driven by that same tumble. Covers the 2-D canvas build and the instanced-3-D variant, where leaves are recycled from, density-versus-count maths, depth layering, colour under a tone-mapped composite, reduced motion, and visibility pausing. Use for autumn maple, sakura petals, blossom, ash, snowfall shapes, or any drifting foliage where a generic particle field reads as confetti.

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。

Add a restrained particle atmosphere inside one section with configurable shapes, density, gravity, wind, sway, rotation, recycling or settling, pointer disturbance, visibility pausing, responsive limits, and reduced-motion fallbacks. Use for petals, leaves, snow, sparks, confetti, dots, paper, icons, or brand fragments that support a section's mood without obscuring content.

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。

Add coordinated spring, summer, fall, and winter states to a 3D scene, blending foliage, sunlight, sky, ground materials, snow, and particles without rebuilding the world. Use for seasonal controls, architectural walkthroughs, and outdoor scenes with four-season transitions.

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。

Add a blockchain explorer transaction to the TRES Finance ledger. Use when the user provides a blockchain explorer URL (Etherscan, Polygonscan, Arbiscan, Basescan, Snowtrace, BscScan, etc.) or a raw transaction hash and wants to record it in the platform ledger. Trigger phrases include "add this transaction", "import tx from etherscan", "record this on-chain tx", "add to ledger".

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。

评论区量化分析：对一批评论做情感分析（正/中/负占比 + 代表评论）、高频词与短语提取、 以及需求/吐槽/提问的诉求挖掘，为内容复盘和选题反哺提供数据。当用户说"评论情感分析" "评论区分析""用户在说什么""评论正负面比例""评论高频词""评论关键词""口碑分析""评论词云" "用户诉求""评论区吐槽"时使用。基于 jieba（分词）+ SnowNLP（情感）+ 社媒情感词典。

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。

Guides an end-to-end data-warehouse migration to Amazon Redshift — discovery, schema/SQL/stored-procedure/macro/script conversion, data migration, validation, performance comparison, and reporting. Source-routed via `references/<source>/`; Teradata (Vantage) is the supported source; additional sources are added as their own `references/<source>/` sets. Text-only knowledge (no executable code) — the AI generates all execution at runtime. Applies when a user wants to migrate Teradata to Amazon Redshift, convert Teradata DDL/SQL/stored procedures/macros/BTEQ to Redshift/RSQL, or assess Teradata-to-Redshift migration complexity. Applies only to migrations targeting Amazon Redshift; migrations to other platforms (Snowflake, BigQuery, Databricks, etc.) are out of scope regardless of source. Does not cover general Redshift administration, performance tuning, or troubleshooting of existing Redshift clusters (no migration involved), or sources not listed under 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。

Use when a Maya task involves simulation or procedural effects: nCloth or Nucleus (a cape, skirt, flag or garment on an animated character, cloth through the body, jittery or stretchy cloth), nParticles, Bifrost graphs (Aero smoke, dust puff, mist, fire, liquids, MPM cloth, snow, sand, "Bifrost smoke"), MASH scattering or motion graphics, sim caches (nCache, Alembic, OpenVDB, file_cache, USD) or FX delivered to lighting or a game engine (flipbook sprites).

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。

A distinctive GPU shader background rendered in dependency-free WebGL — a marketing-grade hero backdrop. One per view; scrim behind text. Renders a static frame under prefers-reduced-motion.

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。

META skill — build the largest possible qualified lead list for any request, end to end. Orchestrates discovery (Prospeo + GetLeads + Blitz + Google Maps + lookalike snowball from /list-expander), AI qualification with live-website verify, uncapped contact pull (GetLeads → Blitz → Prospeo), and provider emails (GetLeads/Blitz/Prospeo) validated with MillionVerifier before sending. Use for any "build me a list", "get me leads for X", "find everyone who...", client list request, or campaign list build. Starts with a 7-line intake brief; outputs to Postgres, Google Sheet, and/or CSV (asks which).

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 cost-safe geospatial SQL for BigQuery, DuckDB, Snowflake, Wherobots, and Postgres and/or render results on an interactive Dekart map.

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。

Collect auditable Reddit search results and full comment trees at scale, preserve the source JSON, and normalize posts and comments into analysis-ready JSONL. Use for VOC, market research, community discovery, keyword snowballing, or any task that needs Reddit post and comment text without inventing unavailable fields. Reddit data layer of the bggg VOC suite (shared project folder; orchestrated by industry-orchestrator, reported by bggg-voc-report).

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。

Learn from public breach disclosures — extract the audit question each one implies and check your own stack. Capital One IMDS abuse, LastPass vault exfiltration, Okta Lapsus$, Snowflake credential reuse, MOVEit, SolarWinds, Equifax, Target POS, Codecov, Uber, Twilio — what would you check now if your boss said 'could that happen to us?' Use when the user mentions 'breach analysis,' 'lessons learned,' 'security postmortem,' 'breach patterns,' 'breach lessons,' 'has this happened to us,' 'apply breach lessons,' 'preempt breaches,' 'security retrospective,' 'real-world security incidents,' or wants to harden against known attacker playbooks.

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。

This skill should be used when the user asks to "create a plot arc", "story structure", "add a plot point", "story timeline", "track foreshadowing", "pacing", "sagging middle", "act structure", "story arc", "plot outline", "snowflake method", or wants to plan and manage the narrative structure of a story. It owns book-level pacing; NOT for the craft of scene outcomes or chapter hooks, which this skill only plans in the outline (use scene-craft), or a pacing check as a revision pass (use revision-continuity).

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 efficient Blender repetition, Array-driven stairs/modules/openings, instancing, scattering, Bezier/NURBS curve-profile geometry, Geometry Nodes fields, repeat zones, simulation zones, particle-like systems, and collective motion. Use for stairs, railings, trim, pipes, cables, repeated windows/panels, vegetation, debris, crowds, rain, snow, modular structures, or any request involving dependent elements or reusable parameters.

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 correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.

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。