Generates linked, realistic test data for Frappe DocTypes via CLI. Supports automated rollbacks.

Install

mkdir -p .claude/skills/faker-data && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/13414" && unzip -o skill.zip -d .claude/skills/faker-data && rm skill.zip

Installs to .claude/skills/faker-data

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Generate realistic, correctly-linked Frappe/ERPNext test records for any DocType using the `bench faker` CLI, then roll them back when done. Use when a test, demo, or local repro needs seed data and the site is missing it — e.g. "I need some Sales Orders", "seed data so I can test this API", "create test Employees in Chennai", or "populate the database to test this endpoint".
378 chars✓ has a “when” triggerlonger than Claude Code's old 250-char listing cap (fine on current versions)
Intermediate

Key capabilities

  • Inspect dependency trees for Frappe/ERPNext DocTypes
  • Generate realistic, linked test records for any DocType
  • Apply natural language constraints to record generation
  • Skip specific DocTypes during generation
  • Clean up all generated records using a batch ID

How it works

The skill uses the `bench faker` CLI to inspect dependency trees, generate linked test records for a specified DocType with optional instructions, and allows cleanup of records using a batch ID.

Inputs & outputs

You give it
DocType name, count, and optional instructions for record generation
You get back
Generated test records with a Faker Batch ID, or a dependency plan

When to use faker-data

  • Seed test records
  • Simulate business transactions
  • Test API endpoints with sample data
  • Cleanup generated test records

About this skill

Frappe Faker — Generate Test Data from the CLI

Use this skill when you need realistic, linked Frappe/ERPNext test records for a specific DocType. Faker resolves the full dependency graph, generates all linked records via LLM, and inserts them in FK-safe order. Every run produces a Faker Batch ID that you can use to delete all generated records when testing is done.

Prerequisites

  • The Frappe Faker app must be installed on the site
  • Faker Settings must have a configured AI provider (check /app/faker-settings)
  • The RQ background worker is not needed — CLI runs synchronously in the terminal

Workflow

1. Inspect dependencies first

bench --site <site> faker plan --doctype "<DocType>"

This shows the full dependency tree without generating anything. Look for:

  • [Has data — will skip] — existing records will be reused; no generation needed
  • [Cyclic] — cycle detected; generation will still proceed
  • [Target] — the DocType you requested

Example output:

Dependency plan for: Salary Slip

Salary Slip  [Target]
  Salary Structure Assignment
    Employee
      Department  [Has data — will skip]
    Company  [Has data — will skip]
  Salary Structure

2. Generate records

bench --site <site> faker generate \
  --doctype "<DocType>" \
  --count <N> \
  --instructions "<free-text requirements>"

Options:

  • --count / -n — number of target doctype records (dependencies get min(count, 5))
  • --skip — comma-separated list of DocTypes to skip (e.g. --skip "Employee,Company")
  • --instructions — natural language constraints passed to the LLM
  • --no-fast-insert — use standard doc.insert() path (runs Python hooks; slower)

Example:

bench --site demo.localhost faker generate \
  --doctype "Salary Slip" --count 3 \
  --instructions "Employees are software engineers in Bangalore, monthly salary 80k-120k INR, use existing company"

Output:

Generating 3 record(s) for Salary Slip...

Batch: abc123def456  |  Created: 12  |  Failed: 0
  Department: 0 created  (skipped — has data)
  Employee: 3 created
  Salary Structure: 3 created
  Salary Structure Assignment: 3 created
  Salary Slip: 3 created

3. Run your tests

Use the generated records for whatever testing or API calls you need.

4. Clean up

bench --site <site> faker cleanup --batch <batch_id>

This deletes all records from the batch in reverse dependency order (FK-safe). Use the batch ID printed by faker generate.

bench --site demo.localhost faker cleanup --batch abc123def456
# → Deleted 12 record(s). Status: Rolled Back

Tips

  • Use --instructions liberally — the LLM respects free-text constraints like dates, locations, salary ranges, roles, and relationships between records
  • Skip doctypes with existing data — if your site already has Employees, use --skip "Employee" to reuse them and avoid duplication
  • Check for errors — if Failed > 0, re-run with a lower count or simpler instructions; per-record errors are logged in the output
  • Batch rollback is partial-crash-safe — if generation crashes mid-way, the batch still tracks all successfully inserted records; cleanup will roll back only what was tracked

Rollback from the UI

Open the Frappe Faker web app at /faker, go to History, click a run row, and use the Rollback button in the detail dialog to delete that batch's records without touching the terminal.

When not to use it

  • When the Frappe Faker app is not installed on the site
  • When Faker Settings do not have a configured AI provider
  • When the RQ background worker is required for synchronous CLI runs

Prerequisites

The Frappe Faker app must be installed on the siteFaker Settings must have a configured AI provider

Limitations

  • Requires the Frappe Faker app to be installed.
  • Requires Faker Settings to have a configured AI provider.
  • The RQ background worker is not used; CLI runs synchronously.

How it compares

This skill automates the generation of dependency-aware, realistic test data for Frappe/ERPNext using an LLM and provides batch tracking for cleanup, unlike manual data entry or simple script-based generation.

Compared to similar skills

faker-data side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
faker-data (this skill)02moReviewIntermediate
extract-test-set16moNo flagsIntermediate
generating-test-data127dReviewIntermediate
senior-data-engineer217moReviewAdvanced

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