Generate synthetic financial data for banking and card-processing systems.
npx skills add https://github.com/sandalsoft/agent-skills --skill datagen-financial
Please help me install this Agent Skill. Skill: datagen-financial Source: https://github.com/sandalsoft/agent-skills/tree/main/datagen-financial Command: npx skills add https://github.com/sandalsoft/agent-skills --skill datagen-financial
Generates realistic, referentially coherent synthetic financial data for banking and card-processing systems to enable safe testing, demos, and analytics without exposing real customer data.
This data spans customers, accounts, loans, cards, transactions, investments, wealth management, and related financial activities, all with guaranteed referential integrity and realistic timing.
Generate the banking data with the generator script and load it into PostgreSQL with the loader.
High-intent search queries and answers about installing and using this skill.
You can generate synthetic banking data with referential integrity by running a dedicated banking data generator script that produces coherent datasets across 11 banking tables plus card networks. It enforces email-name coherence, merchant-country alignment, and temporal constraints, outputting CSV files for PostgreSQL loading.
Referential integrity in synthetic financial data generation ensures relationships between customers, accounts, loans, cards, and transactions remain logically consistent. This generator enforces foreign-key relationships, email-name coherence, and merchant-country alignment to create realistic banking scenarios.
Yes, you can use this synthetic banking data generator with PostgreSQL. It includes PostgreSQL loading utilities that ingest the generated CSV files, requiring the psycopg2-binary dependency to connect and populate your database for development and testing.
The generated synthetic datasets include retail and corporate customers, accounts, loans, cards, transactions, wealth management, investments, wire transfers, and bill payments. These records span 11 banking tables plus card networks with configurable distributions.
You populate a development sandbox with realistic card and transaction data by running the generator script to create CSV files, then using the PostgreSQL loader utilities to import them. It enforces business logic and temporal constraints for accurate testing scenarios.
Yes, the generated synthetic data maintains temporal and spatial realism for fraud detection pipelines. It enforces temporal and spatial constraints, merchant-country coherence, and business logic, making it suitable for validating fraud-detection pipelines and performance testing.
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