415 skills · sorted by relevance
图像NSFW内容检测过滤器。过滤器保留图像具有低nsfw分数的样本。 本SKILL使用依赖data_juicer，请在调用前安装好python环境并安装data_juicer，你可用以下指令进行安装： pip install py-data-juicer 当用户提到图片安全检测、NSFW过滤、不良内容过滤、图片审核、图像内容过滤等需求时使用此skill。
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图像对相似度过滤器。过滤器将图像之间具有相似性的图像对保持在特定范围内。 本SKILL使用依赖data_juicer，请在调用前安装好python环境并安装data_juicer，你可用以下指令进行安装： pip install py-data-juicer 当用户提到图像相似度检测、图像对过滤、双图相似度、图片相似度筛选、图像pair过滤等需求时使用此skill。
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语言识别分数过滤器。过滤器以保留置信度得分大于特定最小值的特定语言的样本。 本SKILL使用依赖data_juicer，请在调用前安装好python环境并安装data_juicer，你可用以下指令进行安装： pip install py-data-juicer 当用户提到语言识别过滤、语言检测、文本语言筛选、按语言过滤等需求时使用此skill。
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图像文本匹配过滤器。过滤器将图像和文本之间的匹配分数保持在特定范围内。 本SKILL使用依赖data_juicer，请在调用前安装好python环境并安装data_juicer，你可用以下指令进行安装： pip install py-data-juicer 当用户提到图像文本匹配过滤、图片文字匹配度、文图匹配检测、图像文本相关性过滤等需求时使用此skill。
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图像人脸面积比例过滤器。过滤以保持面面积比在特定范围内的样本。 本SKILL使用依赖data_juicer，请在调用前安装好python环境并安装data_juicer，你可用以下指令进行安装： pip install py-data-juicer 当用户提到过滤图片、人脸占比、面部面积比例、图片人脸区域筛选、按人脸占比过滤等需求时使用此skill。
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CSV格式化器。用于加载和格式化csv类型的文件。 本SKILL使用依赖data_juicer，请在调用前安装好python环境并安装data_juicer，你可用以下指令进行安装： pip install py-data-juicer 当用户提到CSV格式化、CSV数据加载、读取CSV文件、CSV转JSONL等需求时使用此skill。
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Dataspace 文件输出终止算子。用于接收上游算子的单个文件输出，按指定相对目录上传到 Dataspace 空间目录中，实际文件名自动沿用输入文件名。该算子必须作为 DAG 的终止节点使用。
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Dataspace 文件输入源算子。用于根据 Dataspace 数据源实例 ID 和空间内文件路径下载单个文件，并将该文件作为工作流输入提供给下游算子。该算子没有上游输入，必须作为 DAG 的起始节点使用。
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Access USPTO patent data via PatentsView REST API and Google Patents Public Data (BigQuery). Search by inventor, assignee, CPC, or keywords; download metadata and claims; analyze portfolios; track tech trends. For IP landscape analysis, competitor monitoring, prior art search, and tech forecasting in life sciences and biotech.
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BigID integration. Manage data, records, and automate workflows. Use when the user wants to interact with BigID data.
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BigMailer integration. Manage data, records, and automate workflows. Use when the user wants to interact with BigMailer data.
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BigBox integration. Manage data, records, and automate workflows. Use when the user wants to interact with BigBox data.
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Big Cartel integration. Manage data, records, and automate workflows. Use when the user wants to interact with Big Cartel data.
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BigML integration. Manage data, records, and automate workflows. Use when the user wants to interact with BigML data.
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Create and read bigWig browser tracks for visualizing continuous genomic data. Convert bedGraph to bigWig, extract signal values, and generate coverage tracks using UCSC tools and pyBigWig. Use when preparing coverage tracks for genome browsers or extracting signal at specific regions.
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Expert guide for Data Pipelines, ETL/ELT, and Analytics Engineering. Covers dbt, Apache Airflow, Dagster, BigQuery, ClickHouse, and DuckDB / Panduan ahli untuk Data Pipelines, ETL/ELT. Mencakup dbt, Airflow, Dagster, BigQuery, ClickHouse, dan DuckDB.
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This skill should be used when the user asks to "make a Spotify Wrapped style video", "build a year in review / year-in-review video", "create a personalized data video", "generate a recap video", "build a wrapped video generator", "turn a data table into shareable videos", or "make per-user stat videos". Covers the data row → one shareable video pattern, the Wrapped scene grammar (big-number reveals, top-X lists, superlatives), animated data counters, vertical 9:16 framing, and batch-rendering many personalized videos from one template.
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Specific optimization strategies for Python scripts working with larger-than-memory datasets via Dask.
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Provides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA, Cloud Monitoring, and the REST API. Use when the telemetry to fetch is already known, selecting telemetry tools, writing performant INFORMATION_SCHEMA queries, retrieving telemetry for diagnosing single-job performance bottlenecks, investigating slot contention, job concurrency and queue latency, analyzing reservation capacity, utilization and autoscaling saturation, or auditing capacity-based and on-demand compute and storage resource billable usage. Don't use for root-cause diagnosis or symptom troubleshooting when the cause is unknown (use bigquery-troubleshooting first), or for writing or optimizing business logic SQL (use bigquery-optimization).
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Snowflake, BigQuery, clustering, partitioning, and materialized views for warehouse performance. Activate on: Snowflake, BigQuery, Redshift, query optimization, clustering, partitioning, materialized view, warehouse cost, query profile. NOT for: dbt model structure (use dbt-analytics-engineer), data modeling (use dimensional-modeler).
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Run read-only SQL against BigQuery public datasets with local result capture, cost safeguards, and reproducibility outputs.
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Expert en data warehousing (Snowflake, BigQuery, Redshift, star schema, slowly changing dimensions)
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Guide the user through connecting a new data warehouse source — Postgres, MySQL, Stripe, Hubspot, MongoDB, Salesforce, BigQuery, Snowflake, and so on. Use when the user wants to "connect Stripe", "import data from Postgres", "add a new data source", "sync my warehouse tables", or wants to pick sync methods for each table. Walks through source-type discovery, credential validation, table discovery, per-table sync_type selection, and the final create call. Also covers picking a good prefix and what to do right after creation.
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Snowflake, BigQuery, clustering, partitioning, and materialized views for warehouse performance. Activate on: Snowflake, BigQuery, Redshift, query optimization, clustering, partitioning, materialized view, warehouse cost, query profile. NOT for: dbt model structure (use dbt-analytics-engineer), data modeling (use dimensional-modeler).
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