Migrate Informatica ETL onto Oracle AI Data Platform from Codex: mappings become PySpark notebooks, workflows become AIDP jobs carrying the session dependency graph, Quartz schedules and a declarable timezone, with worklets expanded. Reads IDMC/IICS cloud mapping JSON and PowerCenter 10.x repository XML; 8.x and 9.x are refused by a version gate rather than mis-migrated. The transformation compiler is deterministic and needs no model -- an OpenAI key only widens what the rules cannot reach, behind a validator and a hallucination check. Reads and writes are catalog-addressed, so no JDBC URLs or credentials appear in a generated notebook. Anything that cannot be converted faithfully becomes an explicit review item with a named reason and a rebuild path, never a silent approximation.
Plugin oracle-ai-data-platform-workbench-informatica-migrator in oracle-samples/oracle-aidp-samples. Commands read from the repo's manifest — today.
This plugin runs code on your machine — its hooks fire automatically on events like tool use. The exact commands are listed below; read them before installing.
infa-analyzeInventory, complexity, and compatibility report over an Informatica export — no cluster, no LLM call, no cost. Use before migrating anything, to know what's in an export, how complex it is, and which pieces this tool cannot convert. Accepts a directory or file of PowerCenter XML and/or IDMC/IICS JSO
infa-deployUpload generated notebooks and create AIDP jobs via `infa2aidp cli deploy` (OCI-signed REST calls — upload files, create job objects). Use once notebooks from infa-migrate-mapping have been reviewed and are ready to land in an AIDP workspace. The upload and job-creation calls are verified against a
infa-discoverExtract mappings and workflows from a live Informatica PowerCenter repository via `infa2aidp cli discover` (SOAP or pmrep). Use when the user wants to pull assets directly from a running PowerCenter installation rather than starting from an export file they already have. Does not talk to IDMC/IICS —
infa-lineageField-level data lineage report per mapping via `infa2aidp cli lineage`. Use when the user asks "where does this field come from" or wants a lineage artifact for documentation/audit purposes. Operates on the Informatica export directly (source mappings), not on the generated PySpark notebooks.
infa-migrate-mappingConvert Informatica mappings (PowerCenter XML or IDMC/IICS JSON) into PySpark notebooks for AIDP via `infa2aidp cli migrate`. Covers --use-llm/--agentic, --comparison fidelity reports, --workers batch mode, and --target-catalog-type delta|adw. This is the main run — use once infa-analyze has been re
infa-migrator-bootstrapOne-shot environment readiness check for the Informatica→AIDP migrator engine. Verifies Python + engine dependencies, which `infa2aidp` actually resolves, the LLM provider key (OPENAI_API_KEY on this Codex build), and (only if the user needs them) OCI/AIDP reachability for deploy and reconcile. Use
infa-migrator-overviewRouter skill. Read this first whenever the user mentions migrating Informatica ETL (IDMC/IICS cloud mappings, PowerCenter 10.x repository XML) onto Oracle AI Data Platform (AIDP). Lays out the toolkit, the CLI command sequence, which infa-* skill handles each phase, and the tool's honest limitations
infa-optimizeSpark performance suggestions over already-generated notebooks via `infa2aidp cli optimize`, with an optional --auto-apply for the subset of suggestions safe to apply mechanically. Use after infa-migrate-mapping to check for Spark anti-patterns in the generated code — this is a code-quality pass, no
infa-ragManage the learned conversion-pattern store via `infa2aidp cli rag` (stats/list/export/import/clear). Use when the user asks what the tool has learned, wants to back up or move approved patterns between environments, or wants to reset the store. This is local pattern-reuse bookkeeping, not a knowled
infa-reconcileCompare source-DB rows against a migrated AIDP target via `infa2aidp cli reconcile`, driven by a YAML config (row_count / schema / aggregate / data checks, with ignore_columns for audit/surrogate columns that never match). Use after a migrated notebook has actually run and populated its AIDP target
infa-reviewHuman approval gate over LOW/MEDIUM/MANUAL-confidence conversions via `infa2aidp cli review generate|import`. Use after infa-migrate-mapping, before trusting any generated notebook, to surface every low-confidence conversion and REVIEW REQUIRED marker for a human decision (approve/edit/reject) rathe
python "${PLUGIN_ROOT}/hooks/session_start.py"
Run the full Oracle AI Data Platform (AIDP) data-engineering surface from Codex in natural language - 37 skills: discover catalog, ask in English, AI-in-SQL, optimize/time-travel, pipelines, clusters, governance, agent flows, MLOps, migration. Self-contained (OCI CLI + api_key DEFAULT profile); the AIDP MCP is an optional accelerator.
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Oracle AI Data Platform Workbench Spark connectors for Codex. Includes 27 connector skills for Oracle, OCI, SaaS, JDBC, object storage, streaming, REST, Excel, and multi-cloud data sources, including DB2 guidance. Includes Google BigQuery read-only ingestion and external-catalog guidance.
29 skills
Migrate a Snowflake database to Oracle AI Data Platform with a fixed, auditable runbook. Provisions a migration workspace and cluster, registers the Snowflake database as an EXTERNAL catalog and creates the INTERNAL target catalog, discovers the estate with an in-AIDP workflow, translates types and names into a reviewed plan, creates the target schemas and empty Delta tables on AIDP compute, and registers one copy job per schema (INSERT-SELECT, verified by row counts) for the customer to run. Inventories what is not a table or a view, security posture and table-maintenance state, and presents the data-movement options without choosing for you. Read-only against Snowflake, dry-run by default against AIDP, every create read back; teardown releases the compute or removes everything the migration created.
11 skills · 9 commands
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16 skills · 1 MCP server
Drive the Oracle AI Data Platform (AIDP) Databricks Migration Toolkit in natural language. Self-contained for Codex: the OpenAI-based Python migration engine ships bundled under engine/ and is staged by the SessionStart hook to ~/.aidp-migrator/engine. Teaches Codex how to plan and execute an automated end-to-end migration of Databricks (Unity Catalog / HMS) notebooks, jobs, schedules, and catalog DDL onto AIDP - Pass-1 dependency resolution plus Pass-2 cell-by-cell execute/verify/fix on a live AIDP cluster using an OpenAI model with tool use. Covers job_migrate.py orchestration, catalog migration, workflow DAG manifest building, fixup_cell rewind, pre-migration data-availability checks, write-redirect sandbox schema, and the acceptance-contract pattern for batch/streaming convergence.
16 skills · hooks
Migrate a Microsoft Fabric workspace (notebooks, Warehouse T-SQL, OneLake shortcuts) to Oracle AI Data Platform: inventory, plan, translate deterministically, and verify.
1 skill · 5 commands · 1 MCP server
Matt Pocock's agent skills for real engineering (grilling, spec/ticket flows, TDD, code review, domain modelling and more).
39 skills
Persistent memory for Claude Code: the next session starts where the last one left off.
29 skills · 1 MCP server · hooks
Cross-session memory and token savings for coding agents.
6 skills · 1 agent · 1 MCP server · hooks
Up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your coding agent's context.
1 MCP server
Automates browser interactions for web testing, form filling, screenshots, and data extraction
12 skills
Orchestrate multi-agent teams for parallel code review, hypothesis-driven debugging, and coordinated feature development using Claude Code's Agent Teams
6 skills · 7 commands · 4 agents
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