CLI Reference

Both packages ship an identical indian-fakedata CLI binary. Run with no arguments to display the full help screen.

# Node.js
npx @abhay557/indian-fakedata [options]

# Python (or globally installed Node package)
indian-fakedata [options]

Core options

Flag	Alias	Description	Default
`--count <n>`	`-c`	Number of profiles to generate	`100`
`--output <path>`	`-o`	File path to save output	stdout
`--format <fmt>`	`-f`	Output format: `json`, `jsonl`, `csv`	`json`
`--seed <value>`	`-s`	Reproducibility seed (number or string, e.g. `011`)	random
`--no-metrics`		Exclude probability metrics from output	included
`--family`		Full household (head + spouse + parents + children + siblings) from one seed; json/jsonl only	off
`--fields <a,b,c>`	Output only these fields (dot paths allowed, repeatable)	all fields
`--stats`	Distribution summary (religion/state/gender/area/education/occupation) on stderr	off
`--strip-pii`	Empty identifiers in output (profile fields only)	off
`--mask-names`	With `--strip-pii`, reduce names to initials	off
`--validate`	Validate every full profile; exit 1 on the first invalid record	off
`--help`	`-h`	Show help screen

Demographic constraints

Flag	Values
`--religion <string>`	`Hindu`, `Muslim`, `Christian`, `Sikh`, `Buddhist`, `Jain`
`--state <string>`	e.g. `Maharashtra`, `Tamil Nadu`, `Punjab`
`--gender <gender>`	`male`, `female`, `other`
`--caste <string>`	e.g. `Brahmin`, `Maratha`, `Jat`
`--socialCategory <cat>`	`SC`, `ST`, `OBC`, `General`
`--areaType <type>`	`urban`, `rural`
`--minAge <n>`	Minimum age (0–100)
`--maxAge <n>`	Maximum age (0–100)
`--education <level>`	`illiterate`, `primary`, `secondary`, `graduate`, etc.
`--occupation <sector>`	`cultivator`, `other_worker`, `non_worker`, etc.
`--maritalStatus <status>`	`never_married`, `married`, `widowed`, etc.

Enrichment layers

Flag	Description
`--enrich`	Enable ALL enrichment layers (outcomes + narrative:all + persona)
`--outcomes`	[Layer 2] Add credit score, health risk, employment outcome, education attainment
`--bias <0-1>`	Bias dial for outcome simulation. `0.0` = pure meritocracy, `1.0` = max historical discrimination. Default `0.3` (calibrated to CMIE/CIBIL observed gaps)
`--narrative <type>`	[Layer 3] `loan_application`, `medical_consultation`, `school_enrollment`, `ration_card_application`, `hinglish_conversation`, `resume`, `customer_support_chat`, `all`. Repeat for multiple types
`--persona`	[Layer 4] LLM-ready agent persona (system prompt + full roleplay prompt, beliefs, memory seeds, behavior rules)
`--persona-lang <lang>`	[Layer 4] Persona prompt language: `english`, `hindi`, `hinglish`

Quick examples

# 1000 profiles as CSV
indian-fakedata -c 1000 -f csv -o profiles.csv

# 50K Tamil Nadu Hindus as JSONL
indian-fakedata -c 50000 -f jsonl -o tn_data.jsonl --state "Tamil Nadu" --religion Hindu

# All enrichment layers with moderate bias
indian-fakedata -c 100 --enrich --bias 0.3 -f jsonl -o enriched.jsonl

# SC community fairness audit
indian-fakedata -c 5000 --outcomes --bias 0.5 --socialCategory SC -f jsonl -o sc_bias.jsonl

# LLM training corpus (Hinglish + loan apps)
indian-fakedata -c 10000 --narrative hinglish_conversation --narrative loan_application -f jsonl -o corpus.jsonl

# Agent personas for multi-agent simulation
indian-fakedata -c 500 --persona -f jsonl -o agents.jsonl

# Single detailed profile, pretty-printed
indian-fakedata -c 1 --enrich --bias 0.0 --seed 42

# Full family from one string seed
indian-fakedata --family --seed 011

# Slim output plus distribution summary
indian-fakedata -c 100 --fields firstName,state,appearance.skinTone --stats -f jsonl

# Sanitised Hindi personas, validated
indian-fakedata -c 50 --persona --persona-lang hindi --strip-pii --validate -f jsonl -o agents.jsonl

Notes

--family rejects CSV output (nested structure does not flatten cleanly).
String seeds are hashed (FNV-1a); numeric seeds are used directly.
Output is UTF-8 everywhere; on Windows, stdout is wrapped to avoid cp1252 errors.