Frappe Data Migration Generator

Generate robust data migration scripts with validation, error handling, and progress tracking for importing data into Frappe.

Global Rules

These Frappe conventions apply to everything this skill generates, and override any conflicting example below.

• Bench commands: use bare bench (never ./env/bin/bench or a full path). Always pass --site <site> explicitly — never run a bare bench migrate / bench run-tests. Run bench start in the background and only if it isn't already running. Don't run discovery commands (which bench, bench --version).
• DocType files live at apps/<app>/<app>/<module>/doctype/<name>/<name>.json — the app name appears twice (directory + Python package) — with an empty init.py alongside. Never mkdir the folder; write the JSON and run bench --site <site> migrate to create the structure. Don't add creation, modified, owner, modified_by, or docstatus as fields — Frappe manages them.
• Database & ORM: prefer frappe.qb.getquery() over raw frappe.db.sql(). Use frappe.db.getall() for server logic (ignores permissions) and frappe.db.getlist() for user-facing APIs (enforces them). Never use frappe.db.setvalue() on a field with validation or lifecycle logic — load the doc and doc.save() so controller hooks run. Batch-fetch related records; never query inside a loop (N+1).
• Never call frappe.db.commit() in controllers, request handlers, background jobs, or patches — Frappe auto-commits on success and rolls back on uncaught errors. Flush manually only to make a write visible to a subsequent frappe.enqueue() (or pass enqueueaftercommit=True).
• Permissions & APIs: put permission checks inside controller methods (enforced on every call path), not in API wrappers. Type-hint every @frappe.whitelist() parameter so Frappe validates and casts it, and pass methods=[...] to pin the HTTP verb.

When to Use This Skill

Claude should invoke this skill when:

• User wants to migrate data from legacy systems
• User needs to import large CSV/Excel files
• User mentions data migration, ETL, or data import
• User wants to transform data structures
• User needs bulk data operations

Capabilities

1. CSV Import Script

Migrate through doc.save() so controller validation and lifecycle hooks run. Make the importer idempotent — guard each row with frappe.db.exists(...) (or a flag) so re-running doesn't create duplicates. Do not call frappe.db.commit() inside the loop: when this runs as a patch, a POST request, or a background job, Frappe auto-commits on success and auto-rolls-back on an uncaught error. Reserve frappe.db.set_value() for derived/cached/counter fields with no validation.

Production-Ready CSV Importer:

import csv
import frappe
from frappe.utils import flt, cint, getdate

def import_customers_from_csv(file_path):
    """Import customers — validated, idempotent, no manual commit"""
    success = []
    errors = []

    with open(file_path, 'r', encoding='utf-8-sig') as f:
        reader = csv.DictReader(f)

        for idx, row in enumerate(reader, start=2):
            try:
                # Validate required fields
                if not row.get('Customer Name'):
                    raise ValueError('Customer name required')

                # Transform data
                customer = {
                    'doctype': 'Customer',
                    'customer_name': row['Customer Name'].strip(),
                    'customer_group': row.get('Customer Group', 'Commercial'),
                    'territory': row.get('Territory', 'All Territories'),
                    'email_id': row.get('Email', '').strip(),
                    'mobile_no': row.get('Phone', '').strip(),
                    'credit_limit': flt(row.get('Credit Limit', 0))
                }

                # Idempotency guard — skip/update existing instead of duplicating
                existing = frappe.db.exists('Customer',
                    {'customer_name': customer['customer_name']})

                if existing:
                    doc = frappe.get_doc('Customer', existing)
                    doc.update(customer)
                    doc.save()  # runs validate() + lifecycle hooks
                else:
                    doc = frappe.get_doc(customer)
                    doc.insert()  # runs validate() + lifecycle hooks

                success.append(row['Customer Name'])

            except Exception as e:
                # Row failure is recorded; the surrounding transaction
                # auto-rolls-back only on an uncaught error.
                errors.append({'row': idx, 'data': row, 'error': str(e)})

    # No frappe.db.commit() — Frappe auto-commits on successful completion.
    return {'success': success, 'errors': errors}

2. Patch Migrations

Patches run inside a managed transaction: Frappe auto-commits after a successful execute() and auto-rolls-back on error. Don't sprinkle frappe.db.commit(). Prefer the ORM / query builder over raw SQL, and batch-fetch lookups to avoid N+1.

import frappe

def execute():
    """Backfill customer_group — idempotent, ORM-based, no manual commit."""
    rows = frappe.db.get_all(
        "Customer",
        filters={"customer_group": ["in", ["", None]]},
        fields=["name", "territory"],
    )
    if not rows:
        return  # already migrated — safe to re-run

    # Batch-fetch the territory -> default group map (avoid querying in the loop)
    territory_ids = {r.territory for r in rows if r.territory}
    group_map = {
        t.name: t.default_customer_group
        for t in frappe.db.get_all(
            "Territory",
            filters={"name": ["in", list(territory_ids)]},
            fields=["name", "default_customer_group"],
        )
    }

    for row in rows:
        group = group_map.get(row.territory) or "Commercial"
        # Has validation/lifecycle → go through the doc so hooks run
        doc = frappe.get_doc("Customer", row.name)
        doc.customer_group = group
        doc.save()
    # auto-commit on successful execute(); no frappe.db.commit() here

For a derived/cached field with no validation, a single bulk UPDATE via the query builder is preferable to looping:

# Bulk UPDATE via frappe.qb (preferred over raw frappe.db.sql("UPDATE ..."))
Customer = frappe.qb.DocType("Customer")
(
    frappe.qb.update(Customer)
    .set(Customer.customer_group, "Commercial")
    .where(Customer.customer_group.isin(["", None]))
).run()

If you keep ANY manual commit, restrict it to a deliberate checkpoint in a long standalone backfill (run outside a request, where a mid-way crash would otherwise lose hours of work) and say why — the default is no manual commit.

3. Very Large Datasets

Stream rows instead of loading them all into memory: combine frappe.db.unbufferedcursor() with query.run(asiterator=True).

def backfill_large_table():
    query = frappe.qb.get_query(
        "Sales Invoice",
        fields=["name", "customer"],
        filters={"customer_group": ["in", ["", None]]},
    )
    with frappe.db.unbuffered_cursor():
        for row in query.run(as_iterator=True, as_dict=True):
            process(row)

References

Frappe Data Import:

• Data Import: https://github.com/frappe/frappe/blob/develop/frappe/core/doctype/dataimport/dataimport.py
• CSV Utils: https://github.com/frappe/frappe/blob/develop/frappe/utils/csvutils.py