prompt-db-audit

Audit Jinja2 prompt templates, operation mappings, and variable aliases against database schemas and Python references.

1|2|Updated Apr 6, 2026

Search Tags:#templates #validation #audit #prompts #references #schemas

Ready to run

npx skills add https://github.com/parisgroup-ai/imersao-ia-setup --skill prompt-db-audit

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: prompt-db-audit
Source: https://github.com/parisgroup-ai/imersao-ia-setup/tree/main/skills/prompt-db-audit
Command: npx skills add https://github.com/parisgroup-ai/imersao-ia-setup --skill prompt-db-audit

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Audits and validates the alignment between prompt templates, operation mappings, and database schemas to prevent mismatches, dead templates, and runtime errors in AI-assisted workflows.

Core Features & Use Cases

Verifies that every code-defined operation type has a corresponding prompt template, and flags orphan templates and unmapped system prompts.
Validates template context variables against database schemas using prefix and alias mappings, ensuring variables map to real columns and correct types.
Scans and reports on code references to ensure all template references exist and are properly integrated, enabling safer maintenance of large, multi-domain prompt libraries.
Use cases include onboarding new domain models, enforcing consistency during schema migrations, and CI checks for prompt integrity across projects.

Quick Start

Run the /prompt-db-audit command to generate a comprehensive report that highlights errors, warnings, and recommended fixes across templates, schemas, and references.

Frequently Asked Questions about prompt-db-audit

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I validate Jinja2 prompt templates against database schemas?▼

You validate Jinja2 templates by auditing context variables against database schemas using prefix and alias mappings, ensuring variables map to real columns and correct types to prevent runtime errors.

What causes orphan templates and unmapped operation types in AI workflows?▼

Orphan templates and unmapped operation types occur when code-defined operation calls lack corresponding prompt templates. Auditing flags these by verifying every set_llm_operation_type call maps to a template and checking references exist in scope.

Can I use prompt auditing for CI checks across multi-domain prompt libraries?▼

Yes, prompt auditing supports CI checks across multi-domain prompt libraries by scanning code references and reporting errors, warnings, and recommended fixes to enforce consistency during schema migrations and onboarding.

How do I detect alias inconsistencies between Python code and prompt templates?▼

You detect alias inconsistencies by auditing variable aliases across Python code and prompt templates, checking that schema prefixes match existing tables and verifying template references are properly integrated across domains.

What is the best way to prevent runtime errors in AI-assisted workflows using schema definitions?▼

The best way to prevent runtime errors is harmonizing prompt templates with schema definitions through comprehensive auditing that validates operation mappings, flags dead templates, and ensures variable types align with real database columns.

When do I need to audit prompt templates for schema migration consistency?▼

You need to audit prompt templates during schema migrations when onboarding new domain models or enforcing changes across large prompt libraries, ensuring template references remain valid and schema prefixes match updated tables.