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Generate multi-table synthetic data matching exact revenue curves and fraud rates.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "io.github.rasinmuhammed/misata" MCP server from askskill: Run: claude mcp add 'io-github-rasinmuhammed-misata' -- npx -y misata
Generate a multi-table synthetic dataset for an ecommerce platform with users, orders, payments, and chargebacks tables covering the last 12 months. Make monthly revenue follow a specified growth curve with clear peaks during promotion months. Set the overall fraud rate to exactly 2.3%, with new users having a higher fraud rate than existing users. Return schema notes, sample fields, and a summary of the generated data.
A consistent multi-table synthetic dataset plan and summary with revenue trends and fraud rates matching the targets exactly.
Generate multi-table synthetic data for payment risk testing with merchants, transactions, refunds, and alerts tables. Weekend transaction volume should be higher, and Black Friday week should be the annual revenue peak. Set the chargeback-related fraud rate to exactly 1.1%. Also ensure valid primary and foreign key relationships for join-query testing.
A test-ready multi-table sample data plan with valid relationships and exact revenue peak and fraud-rate constraints.
Generate multi-table synthetic data for a SaaS BI dashboard demo, including accounts, subscriptions, invoices, and failed_payments. The quarterly revenue curve should start steady and then accelerate, with visible growth during renewal season. Set the risk-event rate caused by failed payments to exactly 0.8%. The output should support cohort, MRR, and risk metric demos.
A multi-table synthetic dataset summary suitable for BI demos of revenue and risk visualizations.
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