Builds Orchestra pipeline YAML using the native Data Reconciliation tasks (DATARECONCILIATIONMANUALQUERY, DATARECONCILIATIONCURSORFIELD) to prove two data stores match — the "did the migration land correctly" check between a source and destination integration (SNOWFLAKE, SQLSERVER, DATABRICKS, any pairing). Use whenever the user wants to compare, validate, or reconcile data across two systems for a migration, replatform, cutover, or CDC/replication setup — phrases like "make sure the migration matches", "reconcile Snowflake and Databricks", "validate the cutover", "did we lose any rows moving to the new warehouse", "set up drift monitoring", or any mention of Data Reconciliation / DataRec. Produces a one-off full-match validation pipeline plus, optionally, an ongoing cursor-field drift monitor. Don't use create-orchestra-pipeline for this — Data Reconciliation tasks have sharp edges (single-scalar results, thresholds that silently no-op if omitted) covered in this skill's references.
Install with
Claude CodeCodexCursorGitHub CopilotGemini CLI
Supported by the Atlas install prompt. Skill-specific requirements vary.
Skill stats
Skills1CategoryGeneralStars0
About this skill
Builds Orchestra pipeline YAML using the native Data Reconciliation tasks (DATARECONCILIATIONMANUALQUERY, DATARECONCILIATIONCURSORFIELD) to prove two data stores match — the "did the migration land correctly" check between a source and destination integration (SNOWFLAKE, SQLSERVER, DATABRICKS, any pairing). Use whenever the user wants to compare, validate, or reconcile data across two systems for a migration, replatform, cutover, or CDC/replication setup — phrases like "make sure the migration matches", "reconcile Snowflake and Databricks", "validate the cutover", "did we lose any rows moving to the new warehouse", "set up drift monitoring", or any mention of Data Reconciliation / DataRec. Produces a one-off full-match validation pipeline plus, optionally, an ongoing cursor-field drift monitor. Don't use create-orchestra-pipeline for this — Data Reconciliation tasks have sharp edges (single-scalar results, thresholds that silently no-op if omitted) covered in this skill's references.
Quick start
Copy the install prompt. Use the green button above this README.
Send it to your agent. Paste it into your coding agent’s chat and send.
Review the setup. Your agent checks the skill’s instructions, requirements, and installation scope.
Builds Orchestra pipeline YAML using the native Data Reconciliation tasks (DATA_RECONCILIATION_MANUAL_QUERY, DATA_RECONCILIATION_CURSOR_FIELD) to prove two data stores match — the "did the migration land correctly" check between a source and destination integration (SNOWFLAKE, SQL_SERVER, DATABRICKS, any pairing). Use whenever the user wants to compare, validate, or reconcile data across two systems for a migration, replatform, cutover, or CDC/replication setup — phrases like "make sure the migration matches", "reconcile Snowflake and Databricks", "validate the cutover", "did we lose any rows moving to the new warehouse", "set up drift monitoring", or any mention of Data Reconciliation / DataRec. Produces a one-off full-match validation pipeline plus, optionally, an ongoing cursor-field drift monitor. Don't use create-orchestra-pipeline for this — Data Reconciliation tasks have sharp edges (single-scalar results, thresholds that silently no-op if omitted) covered in this skill's references.
About this listing
Category
General
Reported license
MIT
Listing language (estimated)
English
Review status
Not individually reviewed
Agent compatibility
Atlas installation supports Claude Code, Codex, and Cursor. Skill-specific dependencies and features are checked during setup.
Claude Code
Codex
Cursor
GitHub Copilot
Gemini CLI