Shopify Admin Inventory Adjustment

Purpose

Applies inventory quantity corrections to specific variants at specific locations — the programmatic equivalent of manually editing inventory in the Shopify admin. Use after a cycle count reveals discrepancies, after a 3PL return batch posts late, or after the multi-location-inventory-audit skill identifies Available/Committed drift. Replaces manual row-by-row inventory editing in the Shopify admin.

Prerequisites

• shopify auth login --store <domain>
• API scopes: read_products, write_inventory

Parameters

Safety

⚠️ Step 2 executes inventoryAdjustQuantities which immediately changes live inventory quantities. Incorrect adjustments can cause overselling (if you reduce too far) or inflated stock counts (if you add incorrectly). Run with dry_run: true to see the before/after quantities per SKU before committing. The reason field is logged permanently in Shopify's inventory activity history.

Workflow Steps

1. OPERATION: productVariants — query Inputs: For each {sku} in adjustments: look up the variant by SKU to get its inventoryItem.id; also fetch current inventoryQuantity for before/after comparison Expected output: Map of {sku → inventoryItemId, currentQuantity} for all adjustment targets; abort if any SKU is not found

2. OPERATION: inventoryAdjustQuantities — mutation Inputs: changes array of {inventoryItemId, locationId, delta, ledgerDocumentUri} using the reason and reference_document_uri parameters Expected output: inventoryAdjustmentGroup.changes with quantityAfterChange per item; userErrors

GraphQL Operations

# productVariants:query — validated against api_version 2025-01
query VariantBySku($first: Int!, $query: String) {
  productVariants(first: $first, query: $query) {
    edges {
      node {
        id
        sku
        inventoryQuantity
        inventoryItem {
          id
          tracked
        }
        product {
          title
        }
      }
    }
  }
}

# inventoryAdjustQuantities:mutation — validated against api_version 2025-01
mutation InventoryAdjustQuantities($input: InventoryAdjustQuantitiesInput!) {
  inventoryAdjustQuantities(input: $input) {
    inventoryAdjustmentGroup {
      id
      reason
      changes {
        name
        delta
        quantityAfterChange
        item {
          id
          sku
        }
        location {
          id
          name
        }
      }
    }
    userErrors {
      field
      message
    }
  }
}

Session Tracking

Claude MUST emit the following output at each stage. This is mandatory.

On start, emit:

╔══════════════════════════════════════════════╗
║  SKILL: inventory-adjustment                 ║
║  Store: <store domain>                       ║
║  Started: <YYYY-MM-DD HH:MM UTC>             ║
╚══════════════════════════════════════════════╝

After each step, emit:

[N/TOTAL] <QUERY|MUTATION>  <OperationName>
          → Params: <brief summary of key inputs>
          → Result: <count or outcome, e.g., "143 records returned">

If dry_run: true, prefix every mutation step with [DRY RUN] and do not execute it.

On completion, emit:

For format: human (default):

══════════════════════════════════════════════
OUTCOME SUMMARY
  SKUs adjusted:        <value>
  Total units added:    <value>
  Total units removed:  <value>
  Errors:               <count, 0 if none>
  Output:               <filename or "none">
══════════════════════════════════════════════

For format: json, emit a JSON object with this schema:

{
  "skill": "inventory-adjustment",
  "store": "<domain>",
  "started_at": "<ISO8601>",
  "completed_at": "<ISO8601>",
  "dry_run": false,
  "steps": [
    {
      "step": 1,
      "operation": "<OperationName>",
      "type": "query|mutation",
      "params_summary": "<string>",
      "result_summary": "<string>",
      "skipped": false
    }
  ],
  "outcome": {
    "skus_adjusted": 0,
    "total_units_added": 0,
    "total_units_removed": 0,
    "errors": 0,
    "output_file": "<filename|null>"
  }
}

Output Format

CSV file inventory-adjustments-<YYYY-MM-DD>.csv with columns: sku, product_title, location_name, quantity_before, delta, quantity_after, reason.

Error Handling

Best Practices

1. Always run dry_run: true first — the before/after CSV preview lets you confirm every change before it hits live inventory.
2. Use reason: cycle_count with a reference_document_uri pointing to your count sheet — this creates a permanent audit trail in Shopify's inventory activity log.
3. Batch all corrections from a single count session into one command rather than applying one-at-a-time — the audit trail groups them under a single inventoryAdjustmentGroup.
4. After adjusting, run multi-location-inventory-audit again to confirm all discrepancies are resolved.
5. For 3PL return batches, use reason: received and link the return batch document URI — this makes reconciliation with your 3PL invoice straightforward.