Jrv Mock Data

Generate realistic fake/mock data for testing and development. Supports names, emails, addresses, phone numbers, UUIDs, dates, lorem ipsum, credit cards, com...

Security Scan

The skill appears to do what it says — local mock data generation with no network access, no credentials, and a small, self-contained Python script — aside from a minor doc/code mismatch about credit-card generation.

Purpose & Capability

Name/description (mock data generator) matches the shipped code: a single Python script with many field generators and output formatters. One small inconsistency: the top-level description and brief header mention credit cards, but I do not see any credit card generator in the code or the documented data-type table. This looks like a documentation mismatch rather than malicious behavior.

Instruction Scope

SKILL.md instructs running the included script (python3 scripts/mock_data.py) with flags and saving output; the script only reads its arguments and writes output (stdout/file). There are no instructions to read unrelated files, environment variables, or to send data to external endpoints.

Install Mechanism

No install spec — instruction-only skill with a bundled script. No downloads, package installs, or archive extraction. Running the script runs local Python only.

Credentials

No required environment variables, no credentials, and the script does not access environment-specific config. The requested capabilities are proportional to a local mock-data generator.

Persistence & Privilege

always is false and model invocation is normal. The skill does not request persistent system changes or modify other skills/configs.

Assessment

This skill is self-contained and appears safe to run locally: it generates fake names, emails, addresses, UUIDs, etc., and does not call the network or request secrets. Before installing/using: (1) note the documentation mentions credit-card generation but the code does not include it — ask the author if you need that feature; (2) avoid seeding production systems with this synthetic data where it could be mistaken for real user data, and do not mix generated records with real PII; (3) inspect any future versions for added network calls or requests for credentials before trusting them; and (4) run the script in a restricted/dev environment if you want extra caution.

Generate realistic test data instantly — no API key, no network. Supports dozens of data types, bulk generation, and multiple output formats including JSON, CSV, and SQL.

Quick Start

# Generate 10 fake users as JSON
python3 scripts/mock_data.py user --count 10

# Generate fake email addresses
python3 scripts/mock_data.py email --count 5

# Generate addresses
python3 scripts/mock_data.py address --count 3

# Generate a custom record with multiple fields
python3 scripts/mock_data.py record --fields "name,email,phone,company" --count 5

# Output as CSV
python3 scripts/mock_data.py user --count 20 --format csv

# Output as SQL INSERT
python3 scripts/mock_data.py user --count 10 --format sql --table users

# Single values (no count)
python3 scripts/mock_data.py uuid
python3 scripts/mock_data.py name
python3 scripts/mock_data.py lorem --words 50

# Save to file
python3 scripts/mock_data.py user --count 100 --format csv --output test_users.csv

Commands & Data Types

Type	Description	Example Output
user	Full user record (name, email, phone, address)	{"name": "Jane Smith", "email": "[e-mail]", ...}
name	Full name	"Marcus Rivera"
email	Email address	"[e-mail]"
phone	US phone number	"[phone]"
address	Street address	"1234 Oak Ave, Austin TX 78701"
company	Company name	"Nexigen Solutions LLC"
uuid	UUID v4	"f47ac10b-58cc-..."
date	Random date	"2024-07-15"
datetime	Random datetime	"2024-07-15T14:23:00"
lorem	Lorem ipsum text	"Lorem ipsum dolor sit amet..."
number	Random integer	42
float	Random float	3.14159
bool	True/false	true
color	Hex color	"#3a7bd5"
url	Fake URL	"https://fakecorp.io/api/v1"
ip	IPv4 address	"192.168.1.104"
record	Custom fields combo	Use --fields name,email,phone

Formats

Format	Flag	Notes
JSON	--format json (default)	Pretty-printed array
CSV	--format csv	With header row
SQL	--format sql --table <name>	INSERT statements
Lines	--format lines	One value per line

Options

Flag	Description
--count N	Number of records (default: 1)
--format <fmt>	Output format: json, csv, sql, lines
--table <name>	Table name for SQL output
--fields <list>	Comma-separated fields for record type
--seed N	Random seed for reproducible output
--output <file>	Write to file instead of stdout

Use Cases

• API testing: Seed databases with realistic-looking test records
• UI prototyping: Fill mockups with plausible names and emails
• QA automation: Generate test fixtures in CSV or JSON
• SQL seeding: Ready-to-paste INSERT statements for dev databases
• Load testing: Generate thousands of unique records instantly