「agent-skills」の検索結果

Exasol database interaction via the exapump CLI and Exasol SQL. Covers SQL queries and DML/DDL such as `SELECT`, `INSERT`, `UPDATE`, `DELETE`, `MERGE`, `CREATE TABLE`, `ALTER TABLE`, and `DROP TABLE`, `CREATE CONNECTION` connection objects without import or export intent, schema inspection, table design, `exapump sql` and `exapump profile`, query profiling, analytics, and Exasol-specific SQL behavior outside exasol-import and exasol-export.

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 Exasol Cloud Storage Extension workflows for moving structured object-storage files through extension UDFs. Covers setup prerequisites, the fixed `CLOUD_STORAGE_EXTENSION.IMPORT_PATH` and `CLOUD_STORAGE_EXTENSION.EXPORT_PATH` entrypoints used with `FROM SCRIPT` and `INTO SCRIPT`, Parquet, Avro, ORC, and Delta files on S3, Google Cloud Storage, Azure Blob Storage, Azure Data Lake, HDFS, and Alluxio, connection-object credential patterns, and routing away from native IMPORT or EXPORT when the extension path is the right fit.

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 Exasol `IMPORT` and `IMPORT INTO` SQL plus `exapump upload` local file workflows for moving data into Exasol. Covers CSV, FBV, and Parquet, `CREATE CONNECTION` connection objects for import, cloud credential patterns for S3, Azure Blob Storage, and Google Cloud Storage (GCS), reject handling, and staging-based import workflows. Native IMPORT does not read Avro, ORC, or Delta — those object-storage formats need the Cloud Storage Extension instead.

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。

Set up notebook-connector configuration for Exasol AI workflows through the Secrets Python API. Covers secure config store (`scs`) creation, the `AILabConfig` and `StorageBackend` key sources, backend-specific config values such as `db_host_name`, `db_schema`, `storage_backend`, and `huggingface_token`, Python validation, and handoff to downstream notebook-connector skills.

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。

Catalog and selection guide for Exasol tools, extensions, connectors, integrations, and architecture patterns. Use when the primary intent is comparison, discovery, support-status research, or architecture selection rather than executing a workflow; hand operational work to the selected dedicated skill. Covers deploy, load, explore, enrich, surface, and scale capability families, including named products with no dedicated skill of their own such as Lakehouse Turbo, the Exasol MCP Server, Governed SQL / Text-to-SQL MCP Server, Agent Control Plane, Extension Manager, Terraform and Ansible tooling, and BI, ETL, and warehouse integrations such as Databricks, SAP, Kafka, Power BI, and Tableau.

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 Exasol JDBC-based virtual schemas for federated read-only access to external databases such as PostgreSQL, Oracle, MySQL, SQL Server, and DB2. Covers database-source adapter selection, `CREATE VIRTUAL SCHEMA`, `ALTER VIRTUAL SCHEMA`, `EXPLAIN VIRTUAL`, metadata refresh, and query-side troubleshooting.

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 notebook-connector's Python connection helpers for Exasol, BucketFS, SQLAlchemy, and Ibis. Covers open_pyexasol_connection, open_sqlalchemy_connection, open_ibis_connection, open_bucketfs_bucket, open_bucketfs_location, get_backend, and related helper functions.

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。

Guided setup of Exasol Personal — a free Exasol database running locally on a Mac or deployed to your own AWS, Azure, Exoscale, or STACKIT account. Covers picking the right deployment flavor, the `exasol` launcher CLI (`exasol install <preset>`, `exasol connect`, `exasol info`, `exasol deployments list`, `exasol destroy`), the `local`, `aws`, `azure`, `exoscale`, and `stackit` presets, and following the official exasol/exasol-personal instructions.

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 Exasol document-file virtual schemas for federated read-only access to object and file storage — S3, Google Cloud Storage, Azure Blob Storage, and Azure Data Lake Storage Gen2 document files. Covers document-file adapter family selection, the EDML `MAPPING` definition, `CREATE VIRTUAL SCHEMA` usage, refresh workflows, and query-side troubleshooting.

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。

Distributed machine learning, data mining, and iterative HPC with Exasol. Covers end-to-end ML pipelines (DISTRIBUTE BY + SET scripts + BucketFS), per-entity federated training with partial_fit and ctx.reset(), batch inference, map-reduce ensemble training, distributed ensemble and SON algorithm for frequent itemset mining (Apriori, FP-Growth, association rules, market-basket analysis), Lua execute script orchestration for iterative algorithms (k-means, SGD, gradient descent), scikit-learn model training, parallel hyperparameter search, per-entity forecasting, anomaly detection, model lifecycle in BucketFS (pickle/joblib/ONNX versioning), GPU acceleration via CUDA SLCs (PyTorch/TensorFlow/RAPIDS), and ML-specific performance tuning (skew, OOM, multi-pass chunking).

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。

Top-level router for Exasol work. Use for any Exasol database, exapump, SQL, BucketFS, extension, integration, UDF, Script Language Container, or Exasol Personal setup task, then route to the narrowest specialized Exasol skill.

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。

Deploy and use the Exasol Transformers Extension (TE) for NLP inference inside Exasol with notebook-connector. Covers `initialize_te_extension`, `deploy_scripts`, `PYTHON3_TE` language-container activation SQL, Hugging Face model-installation workflows, and the current TE SQL UDF surface.

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 notebook-connector's itde_manager to run a local Docker-based Exasol database. Covers bring_itde_up, get_itde_status, restart_itde, take_itde_down, and how ITDE populates notebook-connector configuration automatically.

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。

Deploy and use the Exasol Text AI Extension (TXAIE) with notebook-connector. Covers `deploy_license`, `initialize_text_ai_extension`, the `PYTHON3_TXAIE` language container, the Extraction API classes `StandardExtractor`, `NamedEntityExtractor`, `TopicClassifierExtractor`, `PipelineExtractor`, and `BranchExtractor`, named-entity, zero-shot classification, and feature extraction runs, default-model installation, and result-table querying such as `TXAIE_AUDIT_LOG`.

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。

Exasol User Defined Functions (UDFs) and Script Language Containers (SLCs). Covers `CREATE SCRIPT`, SCALAR and SET functions, variadic scripts with `EMITS(...)` and `default_output_columns`, the `ExaIterator` and `ExaMetadata` APIs, Python, Java, Lua, and R scripts, Lua execute scripts and `pquery`, BucketFS file access, GPU-accelerated UDFs, `ALTER SESSION SET SCRIPT_LANGUAGES` activation, and building custom Script Language Containers with `exaslct`, `exaslpm`, and `packages.yml`.

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 Exasol `EXPORT` and `EXPORT INTO` SQL plus `exapump export` local file workflows for moving data out of Exasol. Covers native CSV/FBV export, local CSV/Parquet exports with exapump, `CREATE CONNECTION` connection objects for export, FTP, SFTP, HTTP, HTTPS, and cloud targets on S3, Azure Blob Storage, and Google Cloud Storage (GCS), reject limits, and export credential patterns.

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 evaluating a completed or in-progress project across scope, correctness, quality, testing, and learning outcomes using structured Praise-Critique-Grow feedback.

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 a learner asks to learn from a repository and the agent must map curriculum structure and prerequisites before teaching.

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 a learner's technical level is unknown or uncertain, requiring diagnostic calibration before teaching begins.

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 simulating a technical interview to build confidence, expose reasoning gaps, and train structured communication under pressure.

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 verifying whether the learner truly understands a concept through reasoning and application, not recall trivia.

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 structuring a multi-session learning roadmap with milestones, gates, and pacing calibrated to learner level and goal.

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 creating, testing, or updating active-recall flashcards grounded in session concepts to reinforce retention.

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 a learner needs a structured review session prioritizing high-value topics by recency, error history, and upcoming deadlines.

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。