「tao」の検索結果

Search Taobao and Tmall product listings by keyword, returning paginated product cards with title, price, shop, image, sales, and tags. Use when user asks to search Taobao, find products on Taobao/Tmall, scrape Taobao search results, get product listings from Taobao, collect Taobao items by keyword, 搜索淘宝, 淘宝关键词搜索, 采集淘宝商品, 抓取淘宝搜索结果, 淘宝天猫商品列表. Also applies to bulk keyword searches, price monitoring across keywords, and competitive product research on Taobao.

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。

Fetch full product detail from a Taobao or Tmall product page by itemId, returning title, price, shop info, images, SKU variants, and product attributes. Use when user asks to get product details from Taobao, scrape a Taobao item page, extract product info by item ID, fetch Tmall product data, 抓取淘宝商品详情, 获取淘宝商品信息, 淘宝商品页面采集, 天猫商品详情, 按商品ID获取信息. Also applies to building product databases, price tracking by itemId, and product comparison research.

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。

Fetch customer reviews for a Taobao or Tmall product by itemId, returning reviewer name, date, purchased variant, review text, and photo URLs. Use when user asks to get product reviews from Taobao, scrape Taobao customer feedback, extract buyer reviews by item ID, collect Tmall ratings and comments, 采集淘宝商品评价, 抓取淘宝买家评论, 获取淘宝商品评论, 天猫商品评价抓取, 按商品ID获取评价. Also applies to sentiment analysis of product reviews, building review datasets, and monitoring product rating changes.

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。

Browse a Taobao or Tmall shop's product catalog by shopId, returning paginated product listings with itemId and title. Use when user asks to scrape a Taobao shop, get all products from a store, list items in a Taobao/Tmall shop, fetch shop catalog by userId or shopId, 采集淘宝店铺商品, 抓取淘宝店铺所有商品, 获取天猫店铺商品列表, 淘宝店铺目录, 按店铺ID采集商品. Also applies to shop inventory monitoring, competitor store analysis, and bulk itemId collection from a specific seller.

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。

Shop on the 淘宝 (Taobao) app. Use whenever the user wants to buy, order, or price something on 淘宝 / Taobao / 淘宝闪购, or names the Taobao app.

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

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。

Run TAO Data Services KPI analysis for object detection, comparing inference annotations against ground truth to compute per-class TP/FP/FN/TN, precision, recall, accuracy, and AP at a fixed IoU. Use when an object detection workflow needs per-class mAP reported after inference, or when the user asks to "run KPI analyze", "compute detection mAP", or "score my OD predictions against ground truth".

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。

Taobaoke all-in-one toolkit for Taobao, JD, and Pinduoduo affiliate marketing. Convert product links to affiliate links, cross-platform price comparison, one-click price protection, and commission tracking. Use for e-commerce affiliate 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。

Handle ZenTao (禅道) Bugs and Tasks end to end, including updating or writing back an item after code changes, managing Task status and hours, and reading linked Stories. Use for referenced ZenTao items, requirements, status changes, time entries, or post-implementation synchronization.

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。

Search Taobao/Tmall, inspect products, select SKUs, add to cart. Persistent browser session with stdin/stdout JSON protocol. AI decides, script executes.

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 operating PAIDF Curation and Retrieval or NVIDIA Cosmos Curator pipelines (split, filter, caption, embed, dedup, shard, image annotate) or PAIDF Data Mining nearest-neighbor matching on Curator embeddings. Activate for Make or CLI pipeline config, GPU run preflight, FFmpeg sidecar, SAM3 keys, or Curator-to-TAO handoff. Do not use for generic ETL, vector-database RAG, model training, orchestration, or embeddings outside Cosmos Curator and PAIDF Data Mining.

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。

Performs deep Root Cause Analysis (RCA) on NVIDIA TAO Visual ChangeNet classification experiments with image-evidence-driven investigation. Use when analyzing ChangeNet model failures, investigating poor recall / FAR / PASS-NO_PASS metrics, auditing visual inspection pipeline quality, or running an RCA report for an AOI defect-detection model. Trigger phrases include "RCA on my ChangeNet model", "why is my AOI model failing", "audit ChangeNet predictions", "investigate FAR regressions", "root cause analysis on visual-changenet".

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。

DataOps and DS/MLOps testing reference for data tiering, Bronze-to-Silver validation placement, pipeline invariants, pytest categories, and validation-versus-drift. Use when designing, reviewing, or generating data pipelines, transformation code, data validation, or data-science test suites.

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。

Bo tai lieu ban hang cong voi ky luat giu pipeline cho SME VN, gop mot skill vi cung mot nguoi lam ca hai viec — the persona nguoi mua (co ca nguoi can duong), one-pager, outline bo slide tu van, thu vien xu ly phan doi kieu VN viet o hai dang (bang tra nhanh khi dang goi va ban day du de dao tao), kich ban demo co moc thoi gian, bao gia, giai doan pipeline co tieu chi RA cong nguoi chiu trach nhiem cong SLA, quy tac toc do phan hoi inbox Zalo, va phuong an khong co CRM bang Google Sheet cong nhan Zalo. Kich hoat khi user nhac 'tai lieu ban hang', 'sales deck', 'xu ly phan doi', 'kich ban tu van', 'pipeline ban hang', 'quan ly khach tiem nang', 'bao gia cho khach', 'khach noi de suy nghi them'. Khong dung cho — tim va tao lead moi thi dung skill 33-b2b-lead-gen; thiet ke goi ban va muc gia thi dung skill 31-offer-design va skill 17-pricing-strategy.

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。

Dung khi mot CA NHAN muon len hinh bang AI thay vi tu quay — pipeline avatar AI: 3 tier cong cu, 4 workflow gom avatar don, dich da ngon ngu, san xuat hang loat va hybrid nguoi that cong AI; nhan anh reference de tao prompt avatar; thay mat, style, logo; ghep voice clone; tranh bi nen tang danh dau; cong bo AI theo Nghi dinh 147/2024 va QA 100 diem. Cong cu HeyGen, Synthesia, ElevenLabs, Captions, Vbee. Kich hoat khi user nhac 'tao avatar AI', 'video HeyGen', 'talking head AI', 'lam video ma ngai lo mat', 'batch video AI', 'dich video sang tieng Anh', 'prompt avatar'. Khong dung cho — clone giong cho podcast va audio thi dung skill 25-voice-clone-podcast; viet loi thoai video thi dung skill 04-script-video; anh tinh thi dung skill 30-thiet-ke-master.

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。

Dung khi bat dau lam viec voi mot san pham hoac khach hang moi — skill nay tao FILE CONTEXT ma hon 60 skill khac se doc, gom san pham, khach hang, dinh vi, proof, objection, brand voice va KPI. Chay mot lan, dung lai mai ve sau. Kich hoat khi user nhac 'tao context', 'setup product marketing context', 'bat dau du an moi', 'AI chua hieu san pham cua toi', 'khai bao thong tin san pham'. Khong dung cho — context cho thuong hieu CA NHAN thi dung skill 22-personal-brand-context; nghien cuu insight sau thi dung skill 09-insight-khach-hang.

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。

Dung khi can TAO RA anh hoac prompt anh bang AI — anh thuong hieu ca nhan, logo, key visual campaign, anh dang hang ngay, editorial, infographic, mockup web, quote graphic. Tu doc brand identity trong context roi dung prompt, gen anh hoac xuat prompt cho MidJourney, DALL-E, Imagen, Leonardo. Co che do nhan anh reference va tra ve master prompt copy-paste duoc. Kich hoat khi user nhac 'thiet ke anh', 'lam logo', 've banner', 'prompt MidJourney', 'tao key visual', 'lam anh giong anh nay', 'gen anh AI', 'doi mau thuong hieu tren anh'. Khong dung cho — viet brief giao designer lam thi dung skill 42-brief-hinh-anh; quy trinh asset ca campaign thi dung skill 41-campaign-asset-list; video avatar AI thi dung skill 24-ai-avatar-production; UI wireframe tuong tac thi dung web-prototype; animation thi dung motion-frames.

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。

Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.

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 for NVIDIA-related requests where an NVIDIA skill might help, even if the user did not ask for a skill. Trigger on NVIDIA products, hardware, software, SDKs, GPUs, Jetson/JetPack/L4T/BSP/SDK Manager/driver/flashing/setup, CUDA, NIM, NeMo, Omniverse/OpenUSD/SimReady, RAPIDS/cuDF, cuPyNumeric, cuOpt, Dynamo, Holoscan, TensorRT, DeepStream, VSS, TAO, NGC/NVCF. Do not use for generic non-NVIDIA route, optimize, deploy, AI, video, data, or infrastructure tasks.

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 for NVIDIA-related requests where an NVIDIA skill might help, even if the user did not ask for a skill. Trigger on NVIDIA products, hardware, software, SDKs, GPUs, Jetson/JetPack/L4T/BSP/SDK Manager/driver/flashing/setup, CUDA, NIM, NeMo, Omniverse/OpenUSD/SimReady, Isaac, RAPIDS/cuDF, cuPyNumeric, cuOpt, Dynamo, Holoscan, TensorRT, DeepStream, VSS, TAO, NGC/NVCF. Do not use for generic non-NVIDIA route, optimize, deploy, AI, video, data, or infrastructure tasks.

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。

Adapt BOP datasets, run the FoundationPose perception pipeline with TAO depth, and evaluate or re-score pose results. Use for dataset runs and result comparisons; environment installation belongs to foundationpose-setup.

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 repair the FoundationPose perception pipeline and build its FoundationStereo TensorRT engines. Use for SAM3/TAO dependency conflicts, CUDA library failures, and depth-engine shape or precision decisions.

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 Super Agent Party (SAP) extensions. This skill should be used when users want to create, build, or scaffold a new extension for Super Agent Party - including static HTML extensions (pure frontend) and Node.js backend extensions. Triggers on requests like "create a new SAP extension", "build an extension for Super Agent Party", "scaffold a plugin", "make a chat UI extension", or when working with sap extension projects.

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, analyze, proofread, and modify Office documents (.docx, .xlsx, .pptx) using the officecli CLI tool. Use when the user wants to create, inspect, check formatting, find issues, add charts, or modify Office documents.

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。