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Synapse Migration

OrganizationPopular
microsoft
synapse-migration

Converts Synapse workspaces to Fabric: Spark items, Lake Databases, Linked Services, and Dedicated SQL Pool schema/procedures for Lakehouse or Warehouse. During Pool-to-Lakehouse migration only, can migrate approved dependent procedure callers after notebook readiness. Excludes standalone pipelines and Warehouse administration. Triggers: Synapse workspace to Fabric, Dedicated SQL Pool to Lakehouse, Dedicated SQL Pool to Fabric Warehouse.

Overview

Publishermicrosoft
Repositoryskills-for-fabric
Skill namesynapse-migration
Stars
1.2K
Forks
354
Bundled files
37
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 37 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by microsoft on GitHub. Read the source before you install it.

Installation

Install the Synapse Migration AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/microsoft/skills-for-fabric/tree/main/plugins/fabric-skills/skills/synapse-migration
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/microsoft/skills-for-fabric.git /tmp/skills-for-fabric
mkdir -p .claude/skills
cp -r /tmp/skills-for-fabric/plugins/fabric-skills/skills/synapse-migration .claude/skills/synapse-migration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Synapse Migration in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Synapse Migration on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Synapse Migration is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Telemetry — MANDATORY. Every api.fabric.microsoft.com call must carry x-ms-fabric-skill: synapse-migration (az rest: --headers "x-ms-fabric-skill=synapse-migration"), including every LRO poll, fabric_lro and retry. Snippets omit it — add it anyway.

OFFLINE LAKEHOUSE ARTIFACT FAST PATH — DECIDE FIRST. For a no-live-call request with complete supplied inputs and contracts, load only the fast path; do not load migration-planning-reference.md, dedicated-pool-conversion.md, or implementation scripts. Use one generator for artifacts and final-byte hashes, then one verifier. Do not inspect generated files or run post-pass spot checks. For every optional non-null source default, set missingValueBehavior to UseSourceDefault and restore that default before validation. Do not use this path for a Warehouse target; incomplete and large-procedure requests use their own routes.

CRITICAL NOTES

  1. To find workspace details (including its ID) from a workspace name: list all workspaces, then use JMESPath filtering.
  2. To find item details (including its ID) from workspace ID, item type, and item name: list all items of that type in that workspace, then use JMESPath filtering.
  3. Treat a source Dedicated SQL Pool as read-only. Do not run source DDL, DML, stored procedures, or create sample objects.
  4. Dedicated Pool-to-Lakehouse data movement is out of scope. Migrate schema and executable code artifacts only; do not copy source rows or run row-level/business-result equivalence queries. The separate Warehouse route may move data only from a separately approved restored export copy under its own consent gate and restored-source contract.
  5. For Dedicated Pool-to-Lakehouse requests, complete gap assessment, explicit mapping approval, manifest creation, conversion, deployment, and validation in order. Read dedicated-pool-to-lakehouse.md once as the router, then load only the current phase reference.
  6. Stored-procedure 1:1, N:1, and N:N mappings never apply to views. Views remain schema objects; procedures become traceable Spark SQL notebooks with executable %%sql transformation cells.
  7. Dependent ADF/Synapse callers are conditional scope, not automatic scope. Discover them during assessment, require a separate complete-closure approval, and migrate them only after notebook readiness. Route standalone pipeline requests to pipeline-migration.
  8. Before executing a specialized Dedicated Pool workflow, read the matching contract in dedicated-pool-execution-contracts.md. Its validation, hashing, request-body, resume, and completion-language requirements are mandatory.
  9. Keep Lakehouse and Warehouse paths separate. If the target is unclear, ask before loading path-specific resources. Lakehouse conversion never moves source rows; Warehouse data migration requires its own explicit consent gate and restored-source contract.
  10. For a Fabric Warehouse target, explicitly state that the complete DISTRIBUTION and CLUSTERED COLUMNSTORE INDEX declarations are removed because Fabric manages distribution, storage, and indexing; a bare mention of either source clause is insufficient.
  11. Run setup DDL and CETAS only against the separately approved restored export copy after proving it is not the original production source.

Synapse Analytics to Microsoft Fabric Migration

Core Workflow

  1. Discover the Synapse workspace and classify every source artifact.
  2. Choose targets using workload-routing.md.
  3. Assess gaps before conversion, including runtime, library, connectivity, T-SQL, security, and capacity constraints.
  4. Obtain approval for behavior-changing decisions, target placement, and every stored-procedure-to-notebook mapping.
  5. Create or update migration-manifest.json and persist approval, per-object status, hashes, and checkpoints atomically.
  6. Convert and deploy in dependency order, loading only the current phase resource.
  7. Validate persisted definitions and artifacts without executing generated notebooks or pipelines unless a separately approved workflow explicitly requires execution.
  8. Report completed, blocked, skipped, and pending objects without exposing credentials or sensitive values.

For full-workspace orchestration, read migration-orchestrator.md. For a Dedicated Pool-to-Lakehouse migration, use dedicated-pool-to-lakehouse.md as the phase router. For a Warehouse target, load only the matching dw-* resource.

Scope Routing

RequestActionLoad
Full Synapse workspace migrationRun phased discovery and migrationmigration-orchestrator.md
Dedicated SQL Pool schema/code to LakehouseRun the strict assessment-to-validation workflow. For guidance-only notebook publication, explicitly state: "The stored procedures were converted from T-SQL to Spark SQL before publication."dedicated-pool-to-lakehouse.md
Guidance-only Dedicated Pool incremental deployment hardeningAnswer directly from the focused checklist; do not search scripts or load phase resources. State: "For columns with special characters, enable Delta columnMapping selectively and only with approval."dedicated-pool-execution-contracts.md
Large or complex stored procedureFor a complete offline contract, read only the audit resource once, use one run-local generator and one verifier, do not inspect implementation scripts or generated outputs, and stop after successful verificationdedicated-pool-large-procedure-audit.md
Spark poolConvert configuration and libraries to a Fabric Environmentspark-pool-migration.md
Lake Database or HMSCreate or bind the target Lakehouselake-database-migration.md or external-hms-migration.md
Notebook or Spark Job DefinitionPort code, bindings, parameters, and runtime settingsspark-item-migration.md
Linked ServiceChoose a Fabric Data Connection for external services or a OneLake Shortcut for ADLS Gen2/Blobconnectivity-migration.md
Approved dependent procedure callersRun the internal closure-preserving sub-flow after notebook readbackdedicated-pool-dependent-pipelines.md
Standalone pipeline migrationDelegatepipeline-migration
Dedicated SQL Pool to Fabric WarehouseRun the source extraction, DDL compatibility, approval, optional data, security, and validation routedw-source-and-extraction.md, then the matching dw-* phase resource
Standalone Fabric Warehouse administrationDelegatesqldw-cli
Synapse LinkUse Fabric Mirroring; not covered here—

Always report the Lake Database/HMS phase. If none is discovered, mark it NotApplicable (none discovered) while retaining the required target Lakehouse binding.

Load References On Demand

Do not preload all files. Load only the references needed for the current phase:

NeedReference
Routing, phase order, API audiences, quick examples, troubleshooting, or post-migration handoffworkload-routing.md
Dedicated Pool execution variants and response contractsdedicated-pool-execution-contracts.md
Dedicated Pool discoverydedicated-pool-discovery.md
Gap assessment and mapping approvaldedicated-pool-gap-assessment.md
Procedure conversion and notebook shapededicated-pool-conversion.md
Schema/notebook deploymentdedicated-pool-deployment.md
Dedicated Pool validationdedicated-pool-validation.md
Runtime/library compatibilityfeature-parity.md and library-compatibility.md
mssparkutils and connector refactoringutility-api-mapping.md, connector-refactoring.md, and code-patterns.md
Capacity planningcapacity-sizing.md
Dedicated Pool-to-Warehouse source discovery and extractiondw-source-and-extraction.md
Warehouse DDL compatibility and deploymentdw-ddl-compatibility.md
Separately approved Warehouse data migrationdw-data-migration.md
Warehouse security and validationdw-security-validation.md
Validation, reporting, and production readinessvalidation-testing.md, migration-report.md, and security-governance.md

Use COMMON-CLI.md only for shared authentication and az rest mechanics not already covered by the phase resource. Use COMMON-CORE.md only for shared Fabric REST patterns.

Must

  • Preserve every stored-procedure input contract, source default, source decision, and approved target mapping.
  • Block conversion until gap dispositions, notebook names, dependency grouping, and target workspace placement are explicitly approved.
  • Use SqlPackage/DMVs for source discovery, Fabric REST for item management, and Fabric Livy statements for Lakehouse schema and Delta execution.
  • Run the maintained validators required by the selected execution contract; never replace them with ad hoc JSON or cell-shape checks.
  • Choose the Lakehouse or Warehouse target before loading implementation resources; never apply Warehouse data-movement rules to Lakehouse artifact conversion.
  • Before persisting any dependent-pipeline source definition, recursively inspect it for secret-bearing or uncertain properties; block the complete selected closure without writing the artifact or mutating a target when any are found.
  • In every dependent-pipeline response, including guidance-only and pre-approval explanations, state: "Use a TridentNotebook activity with exact target identities typeProperties.notebookId and typeProperties.workspaceId, and pass inputs through typeProperties.notebookParameters." Then state: "After deployment, read back each pipeline with POST /v1/workspaces/{workspaceId}/items/{dataPipelineId}/getDefinition and validate its canonical hash and structural bindings."
  • For every Linked Service response, first state both replacement classes: Fabric Data Connections for external databases/services and OneLake Shortcuts for ADLS Gen2/Blob storage; then identify which class applies.
  • Replace mssparkutils with notebookutils, Linked Services with the correct Fabric connection class, and spark.read.synapsesql() with a supported OneLake or JDBC pattern.
  • Externalize workspace and item IDs through parameters or Variable Libraries.
  • Keep phase status concise, emit bounded heartbeats for long operations, and persist per-object evidence in the manifest.

Prefer

  • OneLake Shortcuts over copying data.
  • Fabric Environments for reproducible libraries and Spark settings.
  • Incremental, dependency-ordered migration over big-bang cutover.
  • Parameterized notebooks and Variable Libraries for environment promotion.
  • Lakehouse SQL Endpoint for compatible Serverless SQL Pool read workloads.

Avoid

  • Do not execute generated notebooks or pipelines as part of artifact migration.
  • Do not broaden a selected dependent-pipeline closure or route that internal sub-flow to pipeline-migration.
  • Do not use sqlcmd against the target Fabric Warehouse; use the registered SQL endpoint tooling required by the Warehouse resource.
  • Do not preserve Synapse distribution/storage hints, PolyBase DDL, Linked Service connection strings, or pool-level library installation patterns verbatim.
  • Do not use target notebooks to orchestrate the migration itself.
  • Do not hardcode workspace IDs, item IDs, credentials, connection strings, or environment-specific paths.

Examples

Prompt: "Migrate this Dedicated SQL Pool DACPAC to a Fabric Lakehouse and convert the procedures."

Response behavior: Assess gaps first, present and obtain approval for the complete procedure-to-notebook mapping and workspace placement, create the manifest, convert approved schema/code artifacts without moving source rows, validate persisted definitions, and report the resolved Lakehouse and artifact evidence.

Prompt: "Migrate this ADF pipeline to Fabric."

Response behavior: Route to pipeline-migration. Keep it here only when the pipeline is an explicitly approved dependent caller discovered inside an active Dedicated Pool-to-Lakehouse migration.

Prompt: "Migrate this Dedicated SQL Pool to Fabric Warehouse."

Response behavior: Keep this inside synapse-migration, assess DDL compatibility, remove unsupported distribution and columnstore declarations, obtain provisioning consent, and ask separately before any restored-source data migration.

For code transformations and edge cases, load workload-routing.md and the phase-specific resource rather than expanding the main instructions.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Synapse Migration AI skill do?

Converts Synapse workspaces to Fabric: Spark items, Lake Databases, Linked Services, and Dedicated SQL Pool schema/procedures for Lakehouse or Warehouse. During Pool-to-Lakehouse migration only, can migrate approved dependent procedure callers after notebook readiness. Excludes standalone pipelines and Warehouse administration. Triggers: Synapse workspace to Fabric, Dedicated SQL Pool to Lakehouse, Dedicated SQL Pool to Fabric Warehouse.

Why use Synapse Migration on TypingMind?

Because you install it once and use it with any model. Synapse Migration is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Synapse Migration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/microsoft/skills-for-fabric/tree/main/plugins/fabric-skills/skills/synapse-migration. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Synapse Migration?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Synapse Migration?

As many as you like. As long as a model supports skills, you can use Synapse Migration with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Synapse Migration AI skill free?

Yes. It is published on GitHub by microsoft under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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