aidp-iceberg

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.

Standard single-step train/eval/export workflow for any TAO model. Use when training a TAO model on a dataset without iterative data augmentation, AutoML, or DEFT loops. Trigger phrases include "single train run", "train then evaluate then export", "plain TAO training", "normal training", "no AutoML", "skip the loop". Routes through the per-model SKILL.md for action specifics and through `tao-launch-workflow` for platform/credentials/dataset intake.

Look up public Instagram profile and post data for any Business or Creator account through Windsor.ai — follower, following, and media counts, bio, website, and recent post performance (likes, comments) for competitor or benchmark accounts. Use for public or competitor Instagram analysis. Do not use for the user's own connected Instagram account's private insights or publishing (use /instagram for that), Instagram Ads, or other platforms.

Prepare calibrated multi-camera data and train, evaluate, run inference, export, or quantize TAO Sparse4D 3D detection and tracking models. Includes 2D-to-3D geometric distillation with released LTT weights. Use for Sparse4D adaptation and temporal multi-camera perception workflows.

  Required reference for calling `noibu_*` tools — field and measure semantics,   query constraints (the `orderBy` requirement, row caps,   unique-measure rule), the call-metadata argument convention (`rationale`, `skill`), and domain   resolution. Also covers parsing of pasted `console.noibu.com` URLs.   Loads alongside topical Noibu skills   (e.g., `checkout-analysis`, `tech-diagnosis`), not instead of them — they   own routing for their respective domains.

An instant, at-a-glance read on your store's health from the last 24 hours — with the option to save it as a live Cowork dashboard artifact or schedule a recurring report. Use to check on how the overall store is doing, save a live dashboard, or schedule a recurring summary.

Analyze Google Analytics 4 (GA4) data through Windsor.ai. Use when the user asks about their GA4 website or app analytics — sessions, users, new users, engagement, conversions/key events, bounce/engagement rate, pageviews, or breakdowns by channel, source/medium, campaign, landing page, device, country, or date. Do not use for advertising cost/ROAS (those live in the ad-platform connectors like Google Ads), other analytics products, or changing GA4 settings.

Analyze and manage LinkedIn Ads through Windsor.ai. Use when the user asks about their LinkedIn Ads performance — spend, impressions, clicks, CTR, CPC, conversions, or leads at campaign, campaign-group, or creative level, with member demographic breakdowns — or wants to pause/enable a campaign, campaign group, or creative, or change a campaign or campaign-group budget. Do not use for LinkedIn organic company-page data (that's the LinkedIn Organic connector), other ad platforms, or GA4/web analytics.