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.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Trigger when the user mentions notebook-connector, secure config store, Secrets, configure notebook-connector, set up credentials for TE, set up credentials for TXAIE, first-time notebook-connector setup, or similar setup tasks.
This skill establishes the Python-managed secure configuration that later notebook-connector skills depend on.
After configuration is complete:
Choose the narrowest path that matches the user request:
Python-based configuration
Secrets, AILabConfig, StorageBackend, save config in python, notebook cell, scriptreferences/secrets-python.mdscripts/Validation / smoke tests
check config, verify connection, validate notebook-connector, smoke testreferences/validation.mdscripts/Downstream notebook-connector work
bring_itde_up, open_pyexasol_connection, open_sqlalchemy_connection, open_ibis_connection, open_bucketfs_bucket, initialize_te_extension, initialize_text_ai_extension, deploy_licenseMultiple routes can apply. Load all matching references before responding.
scripts/validate_config.py to confirm the Secrets store is populated for the expected backend.AILabConfig as the source of the common key names stored in Secrets.After writing or updating config, verify it before handing off to downstream skills:
scripts/validate_config.pySuccess signals:
onprem or saas)Expected failure mode:
storage_backend, db_schema, backend-specific connection values, or real credentials are missing, or the store still contains template values such as my-db-host or placeholder SaaS IDs/PATs, setup validation should fail until the user replaces them with real values还没有评价。欢迎分享你的使用感受。
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还没有评论。来写第一条吧。
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.
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Exasol BucketFS file system management via the `exapump bucketfs` CLI. Covers listing, uploading, downloading, and deleting files and directories in BucketFS, the `bfsdefault` service and other bucket services, bucket structure, `bfs_*` profile settings, the `/buckets/<service>/<bucket>/<path>` UDF path, and staging JARs, models, and Script Language Containers for UDFs.
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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.
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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.
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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).
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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.
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