aidp-ingest-file-to-table

Use this Oracle Analytics domain skill when creating or editing Oracle Analytics Cloud workbooks, visualizations, calculations, filters, layouts, governed analyses, or when routing an analytics request to a more specific nested skill.

Parse and summarize the JOB_REPORT.md produced by a prior migration run. Surfaces cell pass/fail/fix counts per task, lists the failing cells, and recommends a next-step skill (fixup-cell, resume, or all-clear). Use after any aidp-migrate-job run, or when the user asks "how did the migration go", "what failed", "did it pass".

Review a migrated .ipynb for correctness — NOT just "did it run". Catches latent issues the cell-execute loop missed — wrong write-mode, lost rows, dropped columns, hardcoded paths, dead Databricks-isms, builtins shadowing, format downgrades. Outputs a structured Blockers / Important / Style / Good review report. Use after aidp-migrate-job completes, especially on the first few notebooks of a new workload before scaling out.

Guided full-job migration flow. Walks the user from "I want to migrate this Databricks job" through DAG → data check → migrate → status, asking before each phase. Composes aidp-build-dag, aidp-check-data, aidp-migrate-job, and migration-status. Use when the user wants the end-to-end guided experience instead of running individual phases manually.

Guided catalog migration flow. Extracts Databricks Unity Catalog / HMS metadata, previews the 18-rule DDL rewrite, then batched replay on AIDP. Asks before the destructive replay step. Use when the user wants to migrate the catalog layer (schemas + tables) with checkpoints, rather than running aidp-migrate-catalog directly.

Read a single Databricks notebook (.ipynb / .py / .scala source format) and produce a migration-readiness report — what it does, what it depends on, what risks the migrator will hit, which gotchas (from references/gotchas.md) will apply. Run before manually authoring an entry in a migration manifest, OR as a one-off "should we migrate this?" assessment. Read-only — does not modify the notebook and does not invoke the migrator.

Pre-migration data-availability scan. Reads a migration manifest, probes every spark.read.* / saveAsTable target on the AIDP cluster, reports OK / MISSING / EMPTY with remediation tips per category. Use before any aidp-migrate-job run, or whenever the user asks "is the data ready?", "pre-migration check", "what's missing on AIDP".

Consume an OCI Streaming stream from an AIDP notebook via Spark structured streaming (Kafka-compat). Use when the user mentions OCI Streaming, Kafka on OCI, stream pool, structured streaming, or wants to read Kafka messages into Spark. Auth is SASL/PLAIN with an OCI auth token. Pattern matches the official Oracle AIDP sample.

About 450 ready-made HyperFrames blocks and components installed as editable source - transitions, overlays, titles, kinetic type, captions, charts, map overlays, 3D, camera moves. Use it before hand-building any named visual (CRT scanlines, glitch, film grain, a chart); also for hyperframes add and catalog.

Diagnose and fix common FiftyOne issues automatically. Use when a dataset disappeared, the App won't open, changes aren't saving, MongoDB errors occur, video codecs fail, notebook connectivity breaks, operators are missing, or any recurring FiftyOne pain point needs solving.

Generate a Data Lens connector from an external database schema. Use when users want to connect an external data source (PostgreSQL, BigQuery, Databricks, MySQL, etc.) to FiftyOne Data Lens, or when they have a database schema and want to browse/import that data through the FiftyOne App.

Visualizes datasets in 2D using embeddings with UMAP or t-SNE dimensionality reduction. Use when exploring dataset structure, finding clusters, identifying outliers, or understanding data distribution.

Imports datasets into FiftyOne with automatic format detection. Supports all media types (images, videos, point clouds, MCAP multimodal recordings), label formats (COCO, YOLO, VOC, KITTI), multimodal grouped datasets, LeRobot v3 robot-learning episode datasets, and Hugging Face Hub datasets. Use when importing datasets from local files or Hugging Face, loading autonomous driving data, importing robotics/AV sensor logs (MCAP, ROS bag), importing LeRobot robot-learning episodes (teleop recordings, observation.state/action data), or creating grouped datasets.

Exports FiftyOne datasets to standard formats (COCO, YOLO, VOC, CVAT, CSV, etc.) and Hugging Face Hub. Use when converting datasets, exporting for training, creating archives, sharing data in specific formats, or publishing datasets to Hugging Face.

Creates Jupyter notebooks for FiftyOne workflows including getting-started guides, tutorials, recipes, and full ML pipelines. Use when creating notebooks, writing tutorials, building demos, or generating FiftyOne walkthroughs covering data loading, exploration, inference, evaluation, and export.

Answer finance and market-data questions with the You.com `you-finance` MCP tool: stock prices, tickers, earnings, company financials, and market trends, with payment-aware fallbacks for keyless hosts.