aidp-migrate-job

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.

Read from Oracle Siebel CRM into a Spark DataFrame in an AIDP notebook via the AIDP `aidataplatform` Spark format handler. Use when the user mentions Siebel, Siebel CRM, S_CONTACT, S_ORG_EXT, or has a Siebel host/port. Auth is host/port + database name + user/password. Read-only.

Manage the sales CRM in ActiveCampaign — deals, pipelines, stages, deal notes, owner assignment, and custom objects. Use when the user wants to create or update deals, build or reorganize a pipeline, move deals between stages, reassign deal owners, add notes, or model custom data with custom objects.

Prepare for an upcoming meeting with a company. Identify the account by ZoomInfo account/company ID (preferred) or by company name, domain, or ticker (which triggers a lookup step) — or name no account at all and let the skill pick the meeting from your upcoming calendar. Provide attendee names or emails and rich context on the meeting purpose, stakes, and known dynamics to get a tight, decision-ready brief — headline, relationship posture per attendee, prior-conversation context (open threads and unresolved questions from past calls and emails), ranked talking points, discovery questions, suggested agenda, and what NOT to do. Use when prepping for a sales or customer call, QBR, renewal, or demo. Optimized for a 30-min slot.

Analyze meeting notes to find action items and create Jira tasks for assigned work. When an agent needs to: (1) Create Jira tasks or tickets from meeting notes, (2) Extract or find action items from notes or Confluence pages, (3) Parse meeting notes for assigned tasks, or (4) Analyze notes and generate tasks for team members. Identifies assignees, looks up account IDs, and creates tasks with proper context.

Generate project status reports from Jira issues and publish to Confluence. When an agent needs to: (1) Create a status report for a project, (2) Summarize project progress or updates, (3) Generate weekly/daily reports from Jira, (4) Publish status summaries to Confluence, or (5) Analyze project blockers and completion. Queries Jira issues, categorizes by status/priority, and creates formatted reports for delivery managers and executives.

Automatically convert Confluence specification documents into structured Jira backlogs with Epics and implementation tickets. When an agent needs to: (1) Create Jira tickets from a Confluence page, (2) Generate a backlog from a specification, (3) Break down a spec into implementation tasks, or (4) Convert requirements into Jira issues. Handles reading Confluence pages, analyzing specifications, creating Epics with proper structure, and generating detailed implementation tickets linked to the Epic.

Intelligently triage bug reports and error messages by searching for duplicates in Jira and offering to create new issues or add comments to existing ones. When an agent needs to: (1) Triage a bug report or error message, (2) Check if an issue is a duplicate, (3) Find similar past issues, (4) Create a new bug ticket with proper context, or (5) Add information to an existing ticket. Searches Jira for similar issues, identifies duplicates, checks fix history, and helps create well-structured bug reports.

Validate a user AnomalyGenNext dataset and recipe, freeze its validation set, and prepare a canonical texture fine-tuning recipe. Use when AnomalyGenNext task weights do not yet exist. Do not use for ordinary defect generation.

OneFormer for universal image segmentation. Unifies panoptic, instance, and semantic segmentation with a single architecture using task-conditioned queries. Use when training, evaluating, exporting, quantizing, or running inference for a TAO OneFormer model. Trigger phrases include "train OneFormer", "universal segmentation", "task-conditioned segmentation", "panoptic / instance / semantic in one model".