faker-data
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.zipInstalls 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".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
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 getmin(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 standarddoc.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
--instructionsliberally — 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;
cleanupwill 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
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.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| faker-data (this skill) | 0 | 2mo | Review | Intermediate |
| extract-test-set | 1 | 6mo | No flags | Intermediate |
| generating-test-data | 1 | 27d | Review | Intermediate |
| senior-data-engineer | 21 | 7mo | Review | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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