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  "item": {
    "slug": "afrexai-data-migration",
    "name": "Data Migration Planner",
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          "label": "Upgrade existing",
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  "agentAssist": {
    "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
    "steps": [
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      "Extract it into a folder your agent can access.",
      "Paste one of the prompts below and point your agent at the extracted folder."
    ],
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      {
        "label": "New install",
        "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete."
      },
      {
        "label": "Upgrade existing",
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    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Data Migration Planner",
        "body": "Plan, execute, and validate data migrations between systems. Covers schema mapping, ETL pipeline design, rollback strategies, and post-migration validation."
      },
      {
        "title": "What It Does",
        "body": "Given source and target system details, this skill:\n\nMaps source → target schemas with field-level transformation rules\nGenerates an ETL pipeline plan with staging, transform, and load phases\nCreates validation queries (row counts, checksum, referential integrity)\nBuilds a rollback plan with point-of-no-return criteria\nProduces a migration runbook with go/no-go gates"
      },
      {
        "title": "Usage",
        "body": "Tell your agent:\n\n\"Plan a migration from Salesforce to HubSpot CRM\"\n\"Create a data migration runbook for moving from MySQL to PostgreSQL\"\n\"Map our legacy ERP data to the new system schema\""
      },
      {
        "title": "Phase 1: Discovery",
        "body": "Inventory all source tables/objects and record counts\nDocument data types, constraints, and relationships\nIdentify data quality issues (nulls, duplicates, orphans)\nMap business rules that affect data interpretation"
      },
      {
        "title": "Phase 2: Schema Mapping",
        "body": "For each source entity, document:\n\nSource FieldTypeTarget FieldTypeTransformNotes(field)(type)(field)(type)(rule)(edge cases)"
      },
      {
        "title": "Phase 3: ETL Pipeline",
        "body": "Extract → Stage (raw) → Clean → Transform → Validate → Load → Verify\n\nExtract: Full vs incremental, API vs direct DB, rate limits\nStage: Raw landing zone, no transforms, audit trail\nClean: Dedup, null handling, encoding fixes\nTransform: Type conversions, lookups, calculated fields\nValidate: Pre-load checks (counts, checksums, business rules)\nLoad: Batch size, parallelism, error handling\nVerify: Post-load reconciliation"
      },
      {
        "title": "Phase 4: Validation",
        "body": "Row count match (source vs target, per table)\nChecksum validation on key columns\nReferential integrity checks\nBusiness rule validation (e.g., all active accounts migrated)\nUser acceptance sampling (random 5% manual review)"
      },
      {
        "title": "Phase 5: Cutover",
        "body": "Go/no-go criteria checklist\nPoint-of-no-return definition\nRollback procedure and time estimate\nCommunication plan (users, stakeholders)\nParallel run period (if applicable)"
      },
      {
        "title": "Risk Factors",
        "body": "Data volume: >10M rows = batch strategy required\nDowntime window: Zero-downtime needs CDC/dual-write\nData quality: Garbage in = garbage out. Clean BEFORE migrating\nDependencies: Other systems reading from source during migration\nCompliance: GDPR/HIPAA data handling during transit"
      },
      {
        "title": "Output Format",
        "body": "Deliver a migration runbook as structured markdown with:\n\nExecutive summary (what, why, when, risk level)\nSchema mapping tables\nETL pipeline specification\nValidation test suite\nCutover runbook with rollback\nTimeline with milestones"
      },
      {
        "title": "Cost Estimation",
        "body": "Typical migration costs by complexity:\n\nSimple (1-5 tables, <1M rows): $5K-$15K or 1-2 weeks internal\nMedium (10-50 tables, 1-10M rows): $25K-$75K or 1-2 months\nComplex (50+ tables, 10M+ rows, multiple systems): $100K-$500K or 3-6 months\n\nBuilt by AfrexAI — AI Context Packs for business automation.\n\nCalculate your AI automation ROI: Revenue Calculator"
      }
    ],
    "body": "Data Migration Planner\n\nPlan, execute, and validate data migrations between systems. Covers schema mapping, ETL pipeline design, rollback strategies, and post-migration validation.\n\nWhat It Does\n\nGiven source and target system details, this skill:\n\nMaps source → target schemas with field-level transformation rules\nGenerates an ETL pipeline plan with staging, transform, and load phases\nCreates validation queries (row counts, checksum, referential integrity)\nBuilds a rollback plan with point-of-no-return criteria\nProduces a migration runbook with go/no-go gates\nUsage\n\nTell your agent:\n\n\"Plan a migration from Salesforce to HubSpot CRM\"\n\"Create a data migration runbook for moving from MySQL to PostgreSQL\"\n\"Map our legacy ERP data to the new system schema\"\nMigration Framework\nPhase 1: Discovery\nInventory all source tables/objects and record counts\nDocument data types, constraints, and relationships\nIdentify data quality issues (nulls, duplicates, orphans)\nMap business rules that affect data interpretation\nPhase 2: Schema Mapping\n\nFor each source entity, document:\n\nSource Field\tType\tTarget Field\tType\tTransform\tNotes\n(field)\t(type)\t(field)\t(type)\t(rule)\t(edge cases)\nPhase 3: ETL Pipeline\nExtract → Stage (raw) → Clean → Transform → Validate → Load → Verify\n\nExtract: Full vs incremental, API vs direct DB, rate limits\nStage: Raw landing zone, no transforms, audit trail\nClean: Dedup, null handling, encoding fixes\nTransform: Type conversions, lookups, calculated fields\nValidate: Pre-load checks (counts, checksums, business rules)\nLoad: Batch size, parallelism, error handling\nVerify: Post-load reconciliation\nPhase 4: Validation\nRow count match (source vs target, per table)\nChecksum validation on key columns\nReferential integrity checks\nBusiness rule validation (e.g., all active accounts migrated)\nUser acceptance sampling (random 5% manual review)\nPhase 5: Cutover\nGo/no-go criteria checklist\nPoint-of-no-return definition\nRollback procedure and time estimate\nCommunication plan (users, stakeholders)\nParallel run period (if applicable)\nRisk Factors\nData volume: >10M rows = batch strategy required\nDowntime window: Zero-downtime needs CDC/dual-write\nData quality: Garbage in = garbage out. Clean BEFORE migrating\nDependencies: Other systems reading from source during migration\nCompliance: GDPR/HIPAA data handling during transit\nOutput Format\n\nDeliver a migration runbook as structured markdown with:\n\nExecutive summary (what, why, when, risk level)\nSchema mapping tables\nETL pipeline specification\nValidation test suite\nCutover runbook with rollback\nTimeline with milestones\nCost Estimation\n\nTypical migration costs by complexity:\n\nSimple (1-5 tables, <1M rows): $5K-$15K or 1-2 weeks internal\nMedium (10-50 tables, 1-10M rows): $25K-$75K or 1-2 months\nComplex (50+ tables, 10M+ rows, multiple systems): $100K-$500K or 3-6 months\n\nBuilt by AfrexAI — AI Context Packs for business automation.\n\nCalculate your AI automation ROI: Revenue Calculator"
  },
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    "sourceLabel": "tencent",
    "provenanceUrl": "https://clawhub.ai/1kalin/afrexai-data-migration",
    "publisherUrl": "https://clawhub.ai/1kalin/afrexai-data-migration",
    "owner": "1kalin",
    "version": "1.0.0",
    "license": null,
    "verificationStatus": "Indexed source record"
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