How does this data differ from standard mock data generated by libraries like Faker?

Unlike generic libraries, this skill uses statistical distributions from 500,000+ real records to replicate authentic user behaviors, such as "round-number" deposits and realistic goal abandonment rates, which are crucial for valid fintech testing.

Which AI agents or development environments can I use this skill with?

The skill is compatible with any AI agent or environment that supports the Model Context Protocol (MCP) or REST APIs, including popular tools like Claude Desktop, Cursor, and custom Node.js/Python scripts.

Can I generate the same dataset multiple times for consistent testing?

Yes, by using the seed-based generation feature, you can produce the exact same dataset across different environments, ensuring consistent test fixtures for your CI/CD pipelines.

Is the generated data safe to use in a public-facing staging environment?

While the data is statistically modeled after real-world trends, it contains no PII (Personally Identifiable Information) and is 100% synthetic, making it safe for use in staging environments and compliant with privacy regulations.

What specific financial modules are included in the generated data?

The skill focuses specifically on the savings lifecycle, including user profiles, goal-setting, and transaction histories (deposits/withdrawals); it does not currently support lending, payroll, or credit card authorization data.

LatAm Synth — Synthetic Fintech Test Data

Example session with this skill installed

Generate a test fixture for 50 users in Mexico and Colombia for a savings app demo. I need reproducible JSON data for 2024.

Read your context and instructions
Compiled the latam synth

I have generated a synthetic dataset for 50 users across Mexico and Colombia using the latam-synth actor. The generation used seed 42 to ensure these results can be reproduced in your test suite. The data includes linked user profiles, savings goals (e.g., "Emergency Fund", "Travel"), and a full transaction history for the 2024 calendar year.

Synthetic Dataset Summary

Table	Records	Format	Primary Key
`users`	50	JSON	`user_id`
`goals`	84	JSON	`goal_id`
`transactions`	612	JSON	`transaction_id`

// Sample of generated users.json
[
  {
    "user_id": "u_88291",
    "country": "Mexico",
    "gamification_score": 740,
    "savings_discipline_score": 0.82,
    "goals_completed_ratio": 0.66
  },
  {
    "user_id": "u_10293",
    "country": "Colombia",
    "gamification_score": 410,
    "savings_discipline_score": 0.45,
    "goals_completed_ratio": 0.25
  }
]

// Sample of generated goals.json
[
  {
    "goal_id": "g_551",
    "user_id": "u_88291",
    "category": "Education",
    "name": "Master's Degree",
    "required_amount": 5000.00,
    "created_at": "2024-01-15",
    "deadline": "2024-12-30",
    "goal_status": "in_progress",
    "is_shared": false
  }
]

Next steps

Save the full OUTPUT_DATA to your tests/fixtures/ directory.
Use seed 42 in your CI configuration to maintain consistent test state.
Verify that your dashboard frontend correctly calculates the goals_completed_ratio from the synthetic user records.

Connects securely to your tools. The creator never sees your data.

About this skill

Give your agent realistic financial test data for Latin America instead of Faker output.

This skill teaches your agent when and how to call the LatAm Synth generator on Apify. It produces three linked tables (users, savings goals, deposit/withdrawal transactions) that are 100% synthetic, contain zero personal data, and are statistically calibrated on 506,311 real records from Coinch, a savings app I built and ran across Latin America from 2015 to 2024.

What makes the data realistic

Multimodal deposit amounts, including the round-number deposits real users make
Real monthly seasonality (January peak, December dip)
8 goal categories with realistic amounts, horizons and abandonment rates
Coherent per-goal histories: achieved goals actually reach their target
Reproducible by seed, so your test fixtures stay identical across runs

Use it for Test fixtures and seed data, integration and QA testing, realistic demo dashboards, and bootstrap data for churn or segmentation models.

Requirements An Apify account. Your agent runs the generator through the Apify MCP server or the Apify API with your own token. Generation is billed by Apify at about $0.25 per 1,000 synthetic users; the agent estimates and confirms the cost before large runs.

Not for Card authorizations, merchant acquiring, fraud labels or lending data. The generator models savings behavior only.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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LatAm Synth — Synthetic Fintech Test Data