749건 · 관련도순
Extract structured product records from Amazon URLs across regional sites, including price, stock, specifications, rating breakdowns, sellers, and visible variants. Use the records for competitor research, catalog audits, or tracking product prices and availability.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Gather structured product data from Amazon search results using keywords, a brand filter, and a product limit. Collect prices, ratings, review counts, availability, and delivery details for market research or product catalog preparation.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Find language-specific guidance files for a code-testing pipeline. This reference catalog points agents to build and test commands, mocking patterns, common errors, and selected workflow examples across languages including .NET, Python, TypeScript, Go, and Java.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
[omh] Explaining OMH itself: current-source-first documentation for OMH itself: product identity, public capability catalog, model routing, local state, and long-term memory. Use when the user says: product-docs, OMH documentation, oh-my-hermes documentation, what is OMH, what is oh-my-hermes, how does OMH work, OMH capability catalog, OMH skill catalog.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build and troubleshoot Cloudflare Basin analytics workflows with Basin Pipelines, Basin Catalog, and Basin SQL. Use for streaming data into R2 Iceberg tables, managing catalogs, or querying those tables; also use for requests using the former Data Platform, Pipelines, R2 Data Catalog, or R2 SQL names.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables (migration). Default target is S3 Tables; standard Iceberg on a general purpose bucket is supported where S3 Tables is not adopted. Handles one-time loads, recurring pipelines, migrations. Triggers on: import data, load data, ingest, sync database, migrate table, move data to AWS, set up pipeline, ETL, pull from Snowflake, query BigQuery into S3, export DynamoDB, CTAS, convert to Iceberg. Do NOT use for setting up or troubleshooting Glue connections (use connecting-to-data-source), creating empty tables (use creating-data-lake-table), running queries (use querying-data-lake), finding tables by fuzzy name (use finding-data-lake-assets), catalog audit (use exploring-data-catalog), or SaaS platforms like Salesforce, ServiceNow, SAP, MongoDB, Kafka.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Discover public GitHub repositories that import defineTask from @a5c-ai/babysitter-sdk and maintain a deduplicated catalog of those repositories in docs/repo-with-babysitter-processes.md. Excludes any repo named "babysitter" (to filter out forks of this monorepo). Invoke when asked to find, discover, catalog, or refresh "repos using babysitter", "babysitter in the wild", or "who else is using babysitter".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Governs Microsoft Fabric OneLake catalog health, protection, and trust through Fabric Admin, Core, and Power BI REST APIs. Use for tenant or owner-scoped audits and guarded remediation of domains, workspace assignment, capacity, labels, tags, descriptions, refresh, and item identity. Catalog item discovery belongs to search-consumption-cli.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build and troubleshoot Cloudflare Basin analytics workflows with Basin Pipelines, Basin Catalog, and Basin SQL. Use for streaming data into R2 Iceberg tables, managing catalogs, or querying those tables; also use for requests using the former Data Platform, Pipelines, R2 Data Catalog, or R2 SQL names.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Unity Catalog governance, access control, and observability. Use to grant or revoke access (GRANT/REVOKE), reason about the privilege model and ownership, set up row-level security and column masks, create external locations and storage credentials, define catalogs/schemas/tables/volumes, answer "who can read this table", and query system tables (audit, lineage, billing) or work with volume files in /Volumes/.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Apache Iceberg tables on Databricks — Managed Iceberg tables, External Iceberg Reads (fka Uniform), Compatibility Mode, Iceberg REST Catalog (IRC), Iceberg v3, Snowflake interop, PyIceberg, OSS Spark, external engine access and credential vending. Use when creating Iceberg tables, enabling External Iceberg Reads (uniform) on Delta tables (including Streaming Tables and Materialized Views via compatibility mode), configuring external engines to read Databricks tables via Unity Catalog IRC, integrating with Snowflake catalog to read Foreign Iceberg tables
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Browse hookify rule catalog. Use when installing pre-built rules or browsing categories. Do not use when writing custom rules; use hookify:writing-rules.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Suede AI MCP release QA, scoped to this pack's own server (mcp/suede-skills-mcp.mjs) and its catalog, install, and docs surface. Runs the full JSON-RPC lifecycle against a live server (initialize, notifications/initialized, ping, tools/list, tools/call, resources/list, resources/read, prompts/list, prompts/get), plus protocol negotiation, closed input and output schemas, tool annotations, structuredContent with text fallbacks, malformed-input probes, clean stdio, catalog-to-folder agreement, and install-path language. Use when the MCP server, mcp/catalog.json, a tool, resource, or prompt definition, or the MCP install docs change, or before publishing an MCP release. A check that did not run against the live server is a FAIL, not a skip. NOT FOR: a generic third-party MCP server, which this skill's hardcoded surface does not describe; fixing or testing the public install path itself (use suede-launch-packaging).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when the user says 'fix my catalog for ai', 'improve my ai readiness score', 'get my catalog to 90', or 'my products keep getting rejected by google'. Runs an ordered fixlist that writes the missing alt text, descriptions, brands, GTINs, and category mappings, snapshotting before each change.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Discovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine). Use when designing data science and analytics workflows across structured and unstructured distributed data (including in S3, Azure Blob, AlloyDB, and Iceberg), establishing metadata governance with Knowledge Catalog aspect types, or grounding agentic IDEs (VS Code, Antigravity) by using the Google Cloud Data Agent Kit. Don't use for provisioning borderless data lakehouse infrastructure (use google-cloud-solution-agentic-ai-borderless-data-lakehouse instead).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guide for integrating NHGRI-EBI GWAS Catalog associations with ENCODE regulatory data. Use when users need to find GWAS variants in ENCODE peaks, connect regulatory elements to disease associations, or prioritize functional variants using ENCODE annotations. Trigger on: GWAS, genome-wide association, SNP association, trait association, GWAS Catalog, disease association, risk variant, lead SNP, LD proxy.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate and maintain a multi-channel product feed across Google Merchant Center, Meta Catalog (Instagram + Facebook Shops), TikTok Shop, Pinterest Catalogs, Bing Shopping, and Snapchat Catalogs from a single product source of truth (Shopify, BigCommerce, WooCommerce, custom DB)..
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Configure Communications Cloud for the first time: org setup sequence, permission sets, Enterprise Product Catalog (EPC) service catalog, TM Forum-aligned order decomposition setup, Account record-type segmentation, contract lifecycle activation. NOT for designing the commercial-to-technical order process itself - use admin/industries-process-design. NOT for calling TM Forum Open APIs (TMF622, TMF679) - use omnistudio/industries-api-extensions.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Author and own ONE content type's typed schema (the CONTENT-TYPE.md) at .unikit/gamedesign/content-types/CT-<slug>.md — the content / catalog layer of the GDD (cards, items, weapons, enemies, levels, quests, loot). Use whenever the user wants to ADD a new kind of content or UPDATE its schema, e.g. "add a new content type", "design the loot content", "define the card schema", "add an enemies content type", "add a rarity field to items", "switch this catalog to curated". Create the skeleton, fill its typed field schema (CT.fields), scale, relationships, and validation, AND revise the schema under the delta discipline (a schema change bumps the version + changelog). Selects the scale (bulk | curated) and self-registers content_types/content. This owns the content SCHEMA, not the catalog of values — adding individual entries/units is data handled in the editor, not a schema edit. To add or detail a system use /unikit-gd-system; to add a flow use /unikit-gd-flow; to apply a multi-zone edit use /unikit-gd-apply.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Central catalog of available skills with metadata, capabilities, and performance history. Provides skill discovery and lookup services. Activate on 'skill registry', 'list skills', 'skill catalog', 'available skills', 'skill metadata'. NOT for matching skills to tasks (use dag-semantic-matcher) or ranking (use dag-capability-ranker).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Matches natural language task descriptions to appropriate skills using semantic similarity, ranks candidates by fit and performance history, and maintains the skill catalog. Use when assigning skills to DAG nodes, searching for the right skill for a task, ranking competing skills, or browsing the skill catalog. Activate on "find skill", "match skill", "which skill", "skill for this task", "skill catalog", "rank skills", "best skill". NOT for executing DAGs (use dag-runtime), creating skills (use skill-architect), or grading skills (use skill-grader).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Add or improve canonical data catalog collections, fields, aliases, relations, evidence tags, docs, and tests.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Discover and execute specialized skills from a curated marketplace catalog on GitHub, fetched on-demand and run ephemerally (no permanent installation). The catalog covers a broad and growing range — document generation (PDF/Word/Excel/PowerPoint), visual design, presentations, web app testing, animated GIFs, dev tooling (MCP scaffolding, frontend components), creative outputs (algorithmic art), technical documentation lookup (library/API/framework references), and more. Consult this skill whenever the user asks for a capability that could plausibly be covered by a community skill — the catalog is the source of truth, not this description.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.