639 个 · 按相关度排序
Ask data questions in everyday language, from checking a single metric to investigating trends or preparing a formal report. Receive findings supported by queries, tables, or charts, with data validation and clear limitations.
中文简介正在准备中。当前显示英语简介。
Design hybrid search on AlloyDB by combining meaning-based search, keywords, and SQL filters. Turn requirements into architecture diagrams, database setup scripts, deployment guidance, and validation plans for search applications on Google Cloud.
中文简介正在准备中。当前显示英语简介。
Audit server-side advertising measurement by comparing browser and server events, checking duplicate handling, consent, and data quality. Reconcile platform diagnostics with source logs and business conversions, then return prioritized findings, owners, and verification steps.
中文简介正在准备中。当前显示英语简介。
Scans code for security vulnerabilities with CodeQL by tracing data across functions. Builds and checks analysis databases, models project-specific APIs, and saves findings in SARIF, a structured results format, with broad or focused scan options.
中文简介正在准备中。当前显示英语简介。
Guides the design of data pipelines, continuous stream processing, and data quality checks. It compares tools such as Airflow, Dagster, and Flink, and provides implementation patterns and checklists for scheduling, retries, validation, and monitoring.
中文简介正在准备中。当前显示英语简介。
Search NCBI GEO for gene expression studies, retrieve experiment details and expression tables, and prepare public datasets for analysis. Includes workflows for data quality checks, comparisons between sample groups, and expression summaries across studies.
中文简介正在准备中。当前显示英语简介。
[omh] Data pipeline work -- an ETL or streaming job, a backfill or replay, duplicate events, a schema change downstream, a lineage question, a data-quality regression: make every rerun idempotent, bound every replay, and gate each load on observed checks. Use when the user says: data-pipelines, data pipeline, data pipelines, etl, elt, etl pipeline, etl job, etl backfill.
中文简介正在准备中。当前显示原文说明。
Runs SQL analytics on SageMaker Catalog asset metadata tables exported as Apache Iceberg in S3 Tables. Covers governance queries, asset growth tracking, ownership audits, time-travel over catalog state, and metadata quality analysis. Applies when querying catalog inventory, finding assets without descriptions, comparing catalog snapshots, or auditing data ownership. Trigger phrases: catalog inventory SQL, how many assets, assets without descriptions, asset growth over time, who owns this data, catalog governance, data quality audit, catalog analytics.
中文简介正在准备中。当前显示原文说明。
Builds and operates data pipelines — ingestion, transformation, orchestration, quality testing, and reliability of data delivery. Use this to design or debug a pipeline, decide batch versus streaming, add data quality checks, handle late or duplicate data, or work out why a dashboard's numbers changed without anyone changing the dashboard.
中文简介正在准备中。当前显示原文说明。
Extract structured specifications from electronic component datasheet PDFs — pinouts, electrical characteristics, peripherals, topology, and features. Cache extractions per project for consumption by schematic and PCB analyzers. Primary consumer infrastructure for `kicad`, `emc`, `spice`, and `thermal` analyzers. Use this skill whenever the user asks to extract, verify, or read specs from a component datasheet; when analyzers need verified IC knowledge (EN pin thresholds, PG presence, USB peripheral speed); or when a review mentions datasheet coverage, extraction quality, or per-MPN specifications. Also triggers on "extract this datasheet", "what are the specs for MPN X", "verify datasheet extraction", or "check pin functions for part Y".
中文简介正在准备中。当前显示原文说明。
Extract structured specifications from electronic component datasheet PDFs — pinouts, electrical characteristics, peripherals, topology, and features. Cache extractions per project for consumption by schematic and PCB analyzers. Primary consumer infrastructure for `kicad`, `emc`, `spice`, and `thermal` analyzers. Use this skill whenever the user asks to extract, verify, or read specs from a component datasheet; when analyzers need verified IC knowledge (EN pin thresholds, PG presence, USB peripheral speed); or when a review mentions datasheet coverage, extraction quality, or per-MPN specifications. Also triggers on "extract this datasheet", "what are the specs for MPN X", "verify datasheet extraction", or "check pin functions for part Y".
中文简介正在准备中。当前显示原文说明。
Query Fabric lakehouse and warehouse data using DuckDB, either locally or inside a Fabric notebook. Automatically invoke when the user mentions "DuckDB", "query Delta tables locally", or asks to "attach DuckDB to a lakehouse", "query OneLake data", "explore lakehouse data", "data freshness check", "validate data quality", "use DuckDB in Fabric".
中文简介正在准备中。当前显示原文说明。
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
中文简介正在准备中。当前显示原文说明。
Use for writing, reviewing, debugging, or testing Pandera schemas and runtime validation for Polars DataFrame or LazyFrame pipelines installed with pandera[polars]. Trigger on pandera.polars DataFrameSchema, DataFrameModel, Column, Field, Check, PolarsData, decorators, coercion, strictness, lazy error collection, and validation-depth decisions. Do not use for pandas-backed Pandera, Pydantic object models, Polars transformations without Pandera, static dataframe typing alone, or generic data-quality platforms.
中文简介正在准备中。当前显示原文说明。
Data quality and pipeline health check — freshness, schema drift, null rates, orphaned records, pipeline status. Use when asked about "data quality check", "pipeline health", "is our data fresh", or "schema drift".
中文简介正在准备中。当前显示原文说明。
Use when auditing CRM data quality, finding duplicate records, identifying stale leads, cleaning pipeline data, or improving data hygiene. Triggers: 'clean my CRM', 'audit CRM data', 'find duplicates', 'CRM hygiene', 'data quality check', 'stale leads', 'pipeline cleanup'.
中文简介正在准备中。当前显示原文说明。
Set up systematic data labeling workflows using Label Studio or similar tools. Implement quality controls, measure inter-annotator agreement, manage labeler teams, and integrate labeled data into ML training pipelines. Use when starting a supervised ML project that requires labeled training data, when model performance is limited by insufficient labeled examples, when labeling text, images, audio, or video, or when implementing active learning to prioritize the most valuable examples.
中文简介正在准备中。当前显示原文说明。
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
中文简介正在准备中。当前显示原文说明。
Annotate speech/TTS datasets with per-clip quality metrics (MOS, SNR, STOI, pitch, bandwidth, speaking rate, WER/CER, gender/emotion), multi-ASR consensus transcription and speaker labeling using the audiogear pipeline; then filter clips by quality and build listenable HTML QA reports. Use when asked to compute audio dataset metrics, build extended_metadata.csv, transcribe unlabeled audio, select clean TTS training data, or QA a speech dataset.
中文简介正在准备中。当前显示原文说明。
Audit a dataset or access rule before it joins an identity-graph build (access rules behave like datasets in NQL here). Enumerates failure modes (hub identifiers, high-degree nodes, suspicious values, over-connected identifiers), tests hypotheses in parallel, quantifies damage by rows / edges / entities, and proposes minimal filters ranked by severity. When issues are found, returns a validated `CREATE MATERIALIZED VIEW` NQL the caller can run to produce a graph-ready clean source; if the data passes, says so and recommends it unchanged. Plans and authors the clean-view NQL; does not execute it. Use when: "audit this dataset before the graph build", "find bad edges in <source>", "check identity data quality", "recommend filters for the graph build", "quantify damage from <identifier_type>", "pre-graph DQ". (narrative-identity)
中文简介正在准备中。当前显示原文说明。
Data Quality Specialist IA — Expert en qualité des données (profiling, cleaning, déduplication, validation de schéma, Great Expectations)
中文简介正在准备中。当前显示原文说明。
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
中文简介正在准备中。当前显示原文说明。
Converts documents into clean, chunked datasets suitable for embeddings and vector search. Produces chunked JSONL files with metadata, deduplication logic, and quality checks. Use when preparing "training data", "vector datasets", "document processing", or "embedding data".
中文简介正在准备中。当前显示原文说明。
Plan large sets of search-focused pages using templates and data. The skill helps assess keyword demand, design useful page variations, organize internal links, choose a publishing platform, and check quality before launch.
中文简介正在准备中。当前显示英语简介。