1,009 件 ・ 関連度順
概要と使いどころ
Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (>512 tokens), encountering GPU memory issues with attention, or need faster inference. Supports PyTorch native SDPA, flash-attn library, H100 FP8, and sliding window attention.
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Humanization Quality Verifier - Ensures transformation integrity and quality Validates that humanization preserves meaning, citations, and academic standards Use when: after G6 transformation, before final export, for quality assurance Triggers: verify humanization, check transformation, validate changes
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VS-Enhanced Academic Style Humanizer - Transforms writing patterns to achieve authentic scholarly voice Applies transformations from G5 analysis to create natural academic prose Use when: improving AI-assisted writing quality, preparing manuscripts, enhancing scholarly voice Triggers: humanize, transform, make natural, improve writing quality, improve style
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Apply named refactoring transformations to improve code structure without changing behavior. Use when the user mentions "refactor this", "code smells", "extract method", "replace conditional", "technical debt", "move method", "inline variable", "decompose conditional", or "clean up this messy code". Also trigger when cleaning up legacy code, preparing code for new features by restructuring, or identifying which transformation fits a specific code smell. Covers smell-driven refactoring, safe transformation sequences, and testing guards. For code-quality foundations, see clean-code. For managing complexity, see software-design-philosophy.
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Corrects fluorophore spillover (conventional compensation) or spectral overlap (spectral unmixing) and applies variance-stabilizing transforms (logicle/biexponential, arcsinh, log) for flow and mass cytometry. Covers spillover-matrix estimation from single-stain controls, AutoSpill, the spillover spreading matrix and why panel design (not compensation) bounds resolution, compensate-then-transform ordering, and arcsinh cofactor choice (5 for CyTOF, ~150 for fluorescence, per-channel via flowVS). Use when correcting spectral overlap, preparing data for gating/clustering, choosing logicle vs arcsinh, deciding a cofactor, or distinguishing compensation from spectral unmixing.
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 agile coaching: framework selection, maturity assessment, retrospective facilitation, transformation roadmaps. Use when selecting an agile framework, coaching teams, facilitating retrospectives, or designing a transformation.
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Variance-stabilizing transformations help with the analysis of heteroskedastic data (i.e., data where the variance is not constant, like count data). This package provide two types of variance stabilizing transformations: (1) methods based on the delta method (e.g., 'acosh', 'log(x+1)'), (2) model residual based (Pearson and randomized quantile residuals).
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Train or fine-tune SentenceTransformer bi-encoders, CrossEncoder rerankers, or SparseEncoder models, including losses, negatives, evaluation, distillation, LoRA, and Matryoshka. Use for sentence-transformers training tasks.
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Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.
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Plan and execute database migrations, data transformations, and system migrations safely with rollback strategies and data integrity validation. Use when migrating databases, transforming data schemas, moving between database systems, implementing versioned migrations, handling data transformations, ensuring data integrity, or planning zero-downtime migrations.
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Implements data lineage tracking for privacy compliance including origin tracking, transformation logging, access auditing, deletion verification, and cross-system lineage graphs. Covers source-to-sink mapping, GDPR Art. 30 RoPA integration, automated lineage discovery, and breach impact scoping. Keywords: data lineage, data provenance, data flow mapping, transformation logging, deletion verification.
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Apply dbt transformation patterns in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses dbt transformation patterns or its strengths fit the task.
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。
Web 线上展览开发 (虚拟展厅) (Web 线上展览 / 虚拟展厅开发 (从业者/工程视角) — 用 web 技术开发线上展览、虚拟展厅、云展会、数字展馆，覆盖 3D 空间、全景漫游、WebXR、文博数字化、营销云展厅、3D 商品展示等形态。覆盖: (a) 技术栈 — 3D web(Three.js / React Three Fiber / Babylon.js / Google model-viewer / WebGL / WebGPU)、全景(Pannellum / Marzipano / Photo Sphere Viewer / krpano / 720云)、WebXR(WebXR Device API / A-Frame)、资产管线(glTF·GLB / Draco / Meshopt / KTX2 纹理压缩 / gltf-transform / LOD); (b) 自研 vs SaaS 平台选型(国内: 众趣 / 酷雷曼 / 比目鱼 / 会鸽 / 3DVista; 海外: Matterport / Kuula / Artsteps / Spatial); (c) 性能优化(大场景加载 / 模型轻量化 / draw call / 移动端 / 首屏 / 显存)——web 3D 第一性难题; (d) 策展与导览(动线设计 / 热点 hotspot / 语音讲解 / 交互); (e) 文博数字化(IIIF / 数字孪生 / 高清文物) 与 会展·电商商业化(营销云展厅 / 数据埋点 / 线索转化); (f) 部署(CDN / 流式加载 / 跨端兼容 / WebGL 降级)。学派分歧: 自研 Three.js vs SaaS 平台、真 3D 重场景 vs 720 全景轻量、WebGL vs WebGPU、文博考据派 vs 营销转化派、沉浸优先 vs 加载优先。不含: 线下展陈 / 展台搭建、原生 VR App(非 web)、通用 3D 游戏开发、纯 3D 建模教程。) Master OS — automated mastery of Web 线上展览 / 虚拟展厅开发 (从业者/工程视角) — 用 web 技术开发线上展览、虚拟展厅、云展会、数字展馆，覆盖 3D 空间、全景漫游、WebXR、文博数字化、营销云展厅、3D 商品展示等形态。覆盖: (a) 技术栈 — 3D web(Three.js / React Three Fiber / Babylon.js / Google model-viewer / WebGL / WebGPU)、全景(Pannellum / Marzipano / Photo Sphere Viewer / krpano / 720云)、WebXR(WebXR Device API / A-Frame)、资产管线(glTF·GLB / Draco / Meshopt / KTX2 纹理压缩 / gltf-transform / LOD); (b) 自研 vs SaaS 平台选型(国内: 众趣 / 酷雷曼 / 比目鱼 / 会鸽 / 3DVista; 海外: Matterport / Kuula / Artsteps / Spatial); (c) 性能优化(大场景加载 / 模型轻量化 / draw call / 移动端 / 首屏 / 显存)——web 3D 第一性难题; (d) 策展与导览(动线设计 / 热点 hotspot / 语音讲解 / 交互); (e) 文博数字化(IIIF / 数字孪生 / 高清文物) 与 会展·电商商业化(营销云展厅 / 数据埋点 / 线索转化); (f) 部署(CDN / 流式加载 / 跨端兼容 / WebGL 降级)。学派分歧: 自研 Three.js vs SaaS 平台、真 3D 重场景 vs 720 全景轻量、WebGL vs WebGPU、文博考据派 vs 营销转化派、沉浸优先 vs 加载优先。不含: 线下展陈 / 展台搭建、原生 VR App(非 web)、通用 3D 游戏开发、纯 3D 建模教程。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on Web 线上展览 / 虚拟展厅开发 (从业者/工程视角) — 用 web 技术开发线上展览、虚拟展厅、云展会、数字展馆，覆盖 3D 空间、全景漫游、WebXR、文博数字化、营销云展厅、3D 商品展示等形态。覆盖: (a) 技术栈 — 3D web(Three.js / React Three Fiber / Babylon.js / Google model-viewer / WebGL / WebGPU)、全景(Pannellum / Marzipano / Photo Sphere Viewer / krpano / 720云)、WebXR(WebXR Device API / A-Frame)、资产管线(glTF·GLB / Draco / Meshopt / KTX2 纹理压缩 / gltf-transform / LOD); (b) 自研 vs SaaS 平台选型(国内: 众趣 / 酷雷曼 / 比目鱼 / 会鸽 / 3DVista; 海外: Matterport / Kuula / Artsteps / Spatial); (c) 性能优化(大场景加载 / 模型轻量化 / draw call / 移动端 / 首屏 / 显存)——web 3D 第一性难题; (d) 策展与导览(动线设计 / 热点 hotspot / 语音讲解 / 交互); (e) 文博数字化(IIIF / 数字孪生 / 高清文物) 与 会展·电商商业化(营销云展厅 / 数据埋点 / 线索转化); (f) 部署(CDN / 流式加载 / 跨端兼容 / WebGL 降级)。学派分歧: 自研 Three.js vs SaaS 平台、真 3D 重场景 vs 720 全景轻量、WebGL vs WebGPU、文博考据派 vs 营销转化派、沉浸优先 vs 加载优先。不含: 线下展陈 / 展台搭建、原生 VR App(非 web)、通用 3D 游戏开发、纯 3D 建模教程。 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「线上展览」「虚拟展厅」「web 线上展览」「云展厅」「数字展馆」
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。
数据工程 — 数据平台从业者的认知操作系统, 覆盖把数据从源系统搬运成可靠 / 可查询 / 可信赖形态供分析 / ML / 数据产品消费的全生命周期 (生成 → 摄取 → 存储 → 转换 → 服务 + 安全/数据管理/DataOps/数据架构/编排/软件工程 六条暗流, Reis & Housley 框架): 摄取与集成 (批 + CDC 变更数据捕获 Debezium + EL 工具 Fivetran/Airbyte/Meltano/dlt + Kafka Connect + schema drift) / 存储与文件表格式 (对象存储数据湖 + 列存 Parquet/ORC/Arrow/Avro + 开放表格式 Apache Iceberg/Delta Lake/Apache Hudi + lakehouse + 分区/compaction) / 转换与建模 (ELT dbt/SQLMesh + Spark + 维度建模 Kimball + Inmon + Data Vault + 大宽表 OBT + 渐变维 SCD + 增量模型 + 语义/指标层) / 编排与工作流 (Apache Airflow/Dagster/Prefect/Mage/Kestra/Apache DolphinScheduler + DAG + 幂等 + 回填 backfill + 数据资产调度) / 批流与实时 (Apache Kafka/Apache Flink/Spark Structured Streaming/Kinesis/Pulsar/Redpanda + Lambda vs Kappa + watermark/窗口/exactly-once + 流式 SQL Materialize/RisingWave + 实时 OLAP ClickHouse/Apache Druid/Apache Pinot/StarRocks/Apache Doris) / 数仓与查询引擎 (Snowflake/BigQuery/Redshift/Databricks SQL/Trino/Presto/DuckDB/Polars + 存算分离 + MPP) / 数据质量测试与可观测性 (dbt tests/Great Expectations/Soda + 数据契约 + Monte Carlo data downtime + 新鲜度/量/schema 异常检测) / 数据治理编目与血缘 (DataHub/Amundsen/OpenMetadata/Unity Catalog + 列级血缘 + PII 分类 + 访问控制 + GDPR) / DataOps 与可靠性 (数据 CI/CD + 转换版本控制 + 环境隔离 + 幂等重处理 + 数据 SLA/SLO + 计算存储 FinOps) / 数据架构范式 (现代数据栈 + lakehouse + data mesh + data fabric + 去中心化 vs 中心化所有权) / 分析工程角色 (dbt 时代连接数据工程与分析的桥) — 不含 数据科学/ML 建模本身 (是下游消费者) / BI 仪表盘制作 (serving 下游) / 数据分析报表为终点 / 'data engineer = 跑 Hadoop 的' 过时窄化 / 通用后端应用开发 (平行学科) (Data Engineering — the cognitive operating system of practitioners who design, build, and operate the data platform: moving data from source systems into reliable, queryable, trustworthy form for analytics / ML / products, covering (a) the data engineering lifecycle (generation → ingestion → storage → transformation → serving, with the undercurrents security / data management / DataOps / data architecture / orchestration / software engineering — Reis & Housley framing), (b) ingestion & integration (batch + CDC change-data-capture with Debezium, EL tools Fivetran / Airbyte / Meltano / dlt, Kafka Connect, API + file + database sources, schema drift handling), (c) storage & file/table formats (object storage data lakes, columnar formats Parquet / ORC / Arrow / Avro, open table formats Apache Iceberg / Delta Lake / Apache Hudi, lakehouse architecture, partitioning / compaction / Z-ordering), (d) transformation & modeling (ELT with dbt / SQLMesh, Spark, dimensional modeling Kimball, Inmon CIF, Data Vault, One Big Table / wide tables, normalization vs denormalization, slowly changing dimensions, incremental models, the semantic / metrics layer), (e) orchestration & workflow (Apache Airflow, Dagster, Prefect, Mage, Kestra, Apache DolphinScheduler, DAGs, idempotency, backfills, data-aware / asset-based scheduling), (f) batch vs streaming & real-time (Apache Kafka, Apache Flink, Spark Structured Streaming, Kinesis / Pulsar / Redpanda, the Lambda vs Kappa debate, watermarks / windowing / exactly-once, streaming SQL Materialize / RisingWave, real-time OLAP ClickHouse / Apache Druid / Apache Pinot / StarRocks / Apache Doris), (g) warehouses & query engines (Snowflake, BigQuery, Redshift, Databricks SQL, Trino / Presto, DuckDB, Polars, decoupled storage & compute, MPP), (h) data quality, testing & observability (dbt tests, Great Expectations, Soda, data contracts, Monte Carlo / data downtime, freshness / volume / schema anomaly detection, unit / integration testing of pipelines), (i) data governance, catalog & lineage (DataHub, Amundsen, OpenMetadata, Unity Catalog, column-level lineage, PII / data classification, access control, GDPR / data privacy), (j) DataOps & reliability (CI/CD for data, version control of transformations, environments, idempotent reprocessing, SLAs / SLOs for data, cost / FinOps for compute & storage), (k) data architecture paradigms (modern data stack, data lakehouse, data mesh, data fabric, decentralized vs centralized ownership), (l) the analytics engineering role (the dbt-era bridge between data engineering and analysis); N
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Design a data cleaning and transformation pipeline — missing values, outliers, and deduplication. Use when asked to "clean this dataset", "handle missing values", or "deduplicate this data".
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Data cleaning, transformation, reshaping, joins, missing data handling, and tidy data principles. Covers the full pipeline from raw ingestion to analysis-ready datasets -- type coercion, deduplication, outlier detection, normalization, melting/pivoting, regex extraction, and reproducible transformation chains. Use when preparing, cleaning, or transforming data for analysis.
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Processes real-time event streams using KafkaJS consumer groups and transforms messages with configurable schemas. Handles partition rebalancing, offset commits, and dead-letter queue routing for failed transformations.
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Design practical electromagnetic devices including electromagnets, DC and brushless motors, generators, and transformers by bridging theory to application. Use when sizing a solenoid or toroidal electromagnet for a target field or force, selecting motor topology and computing torque and efficiency, designing a transformer for a given voltage ratio and power rating, or analyzing losses from copper resistance, core hysteresis, and eddy currents.
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Evaluate a system's current structural form, identify transformation pressure, and classify transformation readiness. Covers structural inventory, pressure mapping, rigidity assessment, change capacity estimation, and readiness classification for architectural metamorphosis. Use before any significant architectural change to understand the starting point, when a system feels stuck without clear reasons, when external pressure from growth or tech debt is mounting, or as periodic health checks for long-lived systems.
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Author Prisma Next migrations — choose db update vs migration plan, edit the framework-rendered migration.ts (replace placeholder sentinels with dataTransform closures), recover from MIGRATION.HASH_MISMATCH or PN-MIG-2001 unfilled placeholder. Use for prisma migrate dev, prisma migrate deploy, prisma db push, db update, db update --dry-run, migration plan, migrate, migration new, migration show, db verify, db sign, data migration, this.dataTransform, dataTransform, placeholder, generated migration.ts, edit migration.ts, MIGRATION.HASH_MISMATCH, schema drift.
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。
Corrects fluorophore spillover (conventional compensation) or spectral overlap (spectral unmixing) and applies variance-stabilizing transforms (logicle/biexponential, arcsinh, log) for flow and mass cytometry. Covers spillover-matrix estimation from single-stain controls, AutoSpill, the spillover spreading matrix and why panel design (not compensation) bounds resolution, compensate-then-transform ordering, and arcsinh cofactor choice (5 for CyTOF, ~150 for fluorescence, per-channel via flowVS). Use when correcting spectral overlap, preparing data for gating/clustering, choosing logicle vs arcsinh, deciding a cofactor, or distinguishing compensation from spectral unmixing.
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。
dbt Core/Cloud data transformations, testing, documentation, and CI/CD. Activate on: dbt, data transformation, analytics engineering, ref, source, staging model, mart, dbt test. NOT for: orchestration/scheduling (use airflow-dag-orchestrator), data 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。
Otimiza atenção em transformers com Flash Attention para ganho de 2-4x em velocidade e redução de 10-20x em memória. Use ao treinar/executar transformers com sequências longas (>512 tokens), ao encontrar problemas de memória GPU com atenção, ou quando precisa de inferência mais rápida. Suporta SDPA nativo do PyTorch, biblioteca flash-attn, H100 FP8 e sliding window attention.
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Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.
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。