aidp-pipelines

First-time setup. Use when the user wants to install/upload the AIDP Spark connectors helper package into their AIDP workspace, or has just installed this plugin and asks "how do I set it up", "first-time setup", "install the helpers", "bootstrap aidp connectors". Drives the AIDP MCP tools to push the helper package to /Workspace/Shared/ and runs a sanity import.

Read or write Azure SQL Database from an AIDP notebook through the AIDP `aidataplatform` Spark format handler. Use when the user mentions Azure SQL, Azure SQL Database, AZURE_SQLSERVER, or a database.windows.net endpoint. Auth is SQL username/password.

Read and write Azure Data Lake Storage Gen2 (`abfss://`) from an AIDP notebook. Use when the user mentions ADLS, Azure Data Lake, abfss, or wants to ingest from a multi-cloud Azure source. Auth is OAuth client-credentials (Service Principal client_id + secret + tenant).

Read and write AWS S3 (`s3a://`) from an AIDP notebook. Use when the user mentions S3, AWS S3 bucket, s3a, or has AWS access keys. Auth is access key + secret key via the Hadoop S3A connector. boto3 is also available for non-Spark management operations (list, copy).

Connect from an AIDP notebook to Oracle AI Lakehouse (ALH), Autonomous Data Warehouse (ADW), or Autonomous Transaction Processing (ATP). Prefer the AIDP `aidataplatform` Spark format handler for wallet/password and catalog.id paths (`ORACLE_ALH` / `ORACLE_ATP`), but use raw Spark JDBC for IAM DB-token because the AIDP connector does not support DB-token auth yet.

Show the Snowflake-to-AIDP migration as a stage table before running it - which stages are supposed to run, which have run, and what each one found, with the writing stages marked. Use when the user asks what the plugin will do, what stage comes next, where a run got to, why something was skipped, or wants an overview before approving a migration - AND proactively after any stage finishes during a migration session, to report status and the next stage without waiting to be asked. Read-only; touches no environment.

Check connectivity and permissions on both ends before any migration work. Verifies Snowflake is reachable and readable (list databases, read INFORMATION_SCHEMA) and, when AIDP coordinates are supplied, that the target catalog is readable and optionally writable. Use before the first assessment, when credentials change, or whenever a stage fails with an authorization error and you need to know which end is at fault.

Provision the migration environment inside AIDP for the prod flow - the workspace (name auto-translated to the simplest safe charset), the migration-assets compute cluster, optional cluster libraries from requirements-aidp.txt, the backup-snowflake-migration/ workspace folder holding the data-migration scripts and the plan artifacts, and the migration jobs (discover, structure, reconcile, plus one copy job per schema of the approved plan, each passing its schema as a task parameter). Use when the user wants to set up AIDP for the migration, upload the migration scripts, create the migration workspace or cluster, or wire the migration jobs. Dry-run by default.

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".