Can I automate the field mapping without user approval?

No. To ensure compliance and ground truth, interactive validation via AskUserQuestion is mandatory for every field mapping where a dictionary match isn't found.

Why is snake_case mandatory in this skill?

The skill enforces snake_case to align with PEP 8 standards in Python/Django ecosystems, preventing maintenance debt and mixed-convention errors.

What happens if the mapping confidence is below 80%?

The quality gate will fail. The skill requires additional research or user input to reach an 80% confidence threshold before allowing the mapping to proceed to Gate 2.

What is the primary purpose of the Gate 1 mapping skill?

Gate 1 maps placeholders in regulatory templates to specific backend data sources (like Midaz) while enforcing naming standards and data lineage requirements.

How does the skill handle data lineage?

It requires every field to be prefixed with its domain source (e.g., midaz_onboarding), ensuring that auditors can trace every value back to its origin.

Regulatory Template Mapping Gate 1

Name: Regulatory Template Mapping Gate 1
Author: LerianStudio

byLerianStudio

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Security & Testing

Maps regulatory template placeholders to backend data sources while enforcing strict naming conventions and audit-ready data lineage.

About

This skill serves as the foundational quality gate for regulatory reporting workflows, ensuring that every template placeholder is accurately mapped to a verified backend data source. It enforces mandatory PEP 8 snake_case conversion and explicit data source prefixing to satisfy BACEN audit requirements for data lineage. By utilizing a hierarchical search through data dictionaries and interactive human-in-the-loop validation, it guarantees that only high-confidence mappings proceed to production, preventing costly compliance errors and technical debt in downstream reporting gates.

Key Features

Strict 80% confidence threshold enforcement for quality assurance
Mandatory snake_case conversion for PEP 8 and Django standards
Hierarchical mapping discovery using local dictionaries and MCP schemas
Explicit data source prefixing for complete audit traceability
Interactive validation requiring user approval for every field mapping
17 GitHub stars

Use Cases

Mapping financial data fields to Brazilian Central Bank (BACEN) regulatory templates
Standardizing naming conventions across large-scale regulatory reporting systems
Ensuring data lineage for Midaz onboarding and transactional records

About

Key Features

Strict 80% confidence threshold enforcement for quality assurance
Mandatory snake_case conversion for PEP 8 and Django standards
Hierarchical mapping discovery using local dictionaries and MCP schemas
Explicit data source prefixing for complete audit traceability
Interactive validation requiring user approval for every field mapping
17 GitHub stars

Use Cases

Mapping financial data fields to Brazilian Central Bank (BACEN) regulatory templates
Standardizing naming conventions across large-scale regulatory reporting systems
Ensuring data lineage for Midaz onboarding and transactional records