'spark' 검색 결과

Create or update the snapshots testing for a spark-ios-component-XXX.

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Create or update an environment file in the Environment folder in Sources of a spark-ios-component-XXX and add or update unit tests.

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Create or update a constants file in the Constants folder in Sources of a spark-ios-component-XXX and add or update unit tests.

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Spark, pandas, polars, DuckDB optimization for batch data processing. Activate on: batch processing, Spark optimization, polars, DuckDB, pandas performance, data frame, shuffle, partition, memory optimization. NOT for: streaming pipelines (use streaming-pipeline-architect), warehouse queries (use data-warehouse-optimizer).

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Apache Spark 4.0.2 best practices for PySpark and Scala distributed data processing

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Spark, pandas, polars, DuckDB optimization for batch data processing. Activate on: batch processing, Spark optimization, polars, DuckDB, pandas performance, data frame, shuffle, partition, memory optimization. NOT for: streaming pipelines (use streaming-pipeline-architect), warehouse queries (use data-warehouse-optimizer).

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Use when adding or repairing Spark regression tests for asynchronous ownership, cancellation, persistence, restart recovery, or cross-surface projections. Not a coverage campaign, performance benchmark, source-text policy check, or full delivery review.

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Use when Spark engineering knowledge must be placed, deduplicated, migrated, or validated across AGENTS, Notes, Roles, Skills, and Workflows.

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Use when a running Spark or DSH web service needs real-browser verification — page load, client-plugin boot errors, console errors, failed requests, or onboarding/dialog rendering — with the repository's own Playwright chromium.

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Use when designing a Spark feature or writing or materially revising a design proposal that needs repository-first research, user input on unresolved key choices, and an owner-aligned implementation plan before code changes.

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Use when a Spark change needs its authoritative owner, affected boundaries, risks, acceptance criteria, and smallest verifiable slice established before implementation or review.

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Use when adding or updating Spark AI provider models or their compact and preflight metrics, especially baidu-oneapi, Grok, Claude, GPT, DeepSeek, OneAPI, context windows, output budgets, prices, thinking maps, or transports.

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Use when a Spark branch or pull request needs final diff hygiene, validation selection, commit scope, remote topology, and readiness checked before publication.

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Use when a working Spark implementation should be simplified without changing its observed behavior, ownership, compatibility, or public contract.

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Use when a Spark implementation needs an evidence-based review for correctness, ownership, compatibility, failure handling, maintainability, and unnecessary complexity.

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Guide a macOS app release through versioning, signing, Apple notarization, update feed checks, and Homebrew updates. Includes CodexBar release scripts, published asset verification, and preparation of the next unreleased version.

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• Releasing a signed and notarized macOS app
• Verifying update feeds and published release assets
• Updating Homebrew app and CLI packages

Review TikTok Ads measurement, mobile video creative, audiences, commerce campaigns, bidding, and budgets. Receive prioritized recommendations with supporting evidence, missing information, and uncertainty clearly identified, while keeping the review read-only.

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• Checking TikTok Pixel and conversion measurement
• Reviewing mobile video creative and audiences
• Auditing Smart+ and TikTok Shop Ads

Plan personalized video outreach for prospective customers, from useful sample deliverables and short scripts to tool selection and follow-up sequences. Use viewing data to guide next steps and design product demos prospects can watch independently.

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• Add personalized videos to cold outreach
• Choose video tools for sales volume and budget
• Plan recorded demos prospects can watch independently

Build particle trails that keep consistent spacing as the pointer speeds up or slows down. Covers distance-based emission, drifting motion, placement in 3D scenes, performance checks, and alternatives for touch, keyboard use, and reduced motion.

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• Creating sparks or wisps that follow the cursor
• Fixing gaps in trails during fast pointer movement
• Keeping trails aligned with the pointer in 3D scenes

Create coordinated combat visual effects in Three.js, combining branching lightning, a storm orb, black trails, sparks, and impact frames. A shared clock keeps charge-ups, dashes, and hits synchronized, including brief pauses that emphasize contact.

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• Create a lightning fist or energy orb
• Add black trails to a dash
• Coordinate ground strikes and wet reflections

Build fire effects in Three.js, including fireballs, moving flame fronts, whirls, and smouldering ground. A reusable module combines rising flames, temperature-based color, smoke, sparks, heat distortion, and shared firelight, with a five-stage demonstration.

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• Creating held flames and thrown fireballs
• Building expanding walls and rings of fire
• Staging fire whirls with smoke columns

Design or review Apache Hudi tables through questions about data volume, updates, and usage. Get architecture recommendations, a decision record explaining tradeoffs, and configuration artifacts for supported processing paths.

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• Designing a new Hudi table
• Reviewing an existing table design
• Planning a small prototype

星火燎原：从零起步、资源极少时，先评估客观条件，选定一个能站稳、能复用、能扩展的"根据地"（最小切入点），扎根后再逐步扩张，拒绝四处出击的流寇主义。当启动新项目、做 MVP / pilot、面对宏大目标不知从何下手、或做了很多零散事却没有积累时触发；项目已有稳固基础、只是修改现有系统时不触发。 English: Spark a prairie fire. When starting from almost nothing, assess the conditions, choose one base area (a minimal foothold that can hold, be reused, and expand), dig in, then grow step by step instead of scattering effort. Trigger for new projects, MVPs, pilots, daunting goals with no obvious first step, or busy work that accumulates nothing; skip when a solid base already exists or the task modifies an existing system.

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Use when picking the next Spark expression to implement natively in Comet, or when asked whether a specific codegen-dispatched expression is worth a native Rust implementation. Scores the candidate on how likely a native path can be 100% Spark-compatible and how much throughput or allocation it would save versus the JVM codegen dispatcher, records the verdict in the per-expression audit log so disqualified candidates are not re-litigated, and files a GitHub issue for the recommendation.

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