autogluon-database-audit

Audit the database-facing AutoGluon contract and artifact boundaries across governance loops.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/zincdigitalofmiami/zinc-fusion-v16 --skill autogluon-database-audit
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: autogluon-database-audit
Source: https://github.com/zincdigitalofmiami/zinc-fusion-v16/tree/main/.kilo/skills/autogluon-database-audit
Command: npx skills add https://github.com/zincdigitalofmiami/zinc-fusion-v16 --skill autogluon-database-audit

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Audit and design the database contract around AutoGluon outputs. This skill sits between the local AutoGluon pipeline and the cloud schemas that serve validated ML artifacts.

Core Features & Use Cases

  • Audit and align database contracts with the pre-flight, contract loop, artifact-boundary loop, promotion gate loop, and approval gate requirements.
  • Ensure clear separation between local artifacts and cloud persistence, including horizon and promotion checks.
  • Use case: during audits you verify that AutoGluon outputs map correctly to training, forecasts, and analytics while enforcing governance gates.

Quick Start

Run a pre-flight audit against the AutoGluon DB contract and verify loop transitions.

Frequently Asked Questions about autogluon-database-audit

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

FAQPage Schema
How do I audit AutoGluon database contracts for ML pipeline governance?

Auditing AutoGluon database contracts involves running pre-flight checks and validating artifact boundary loops. This enforces governance across contract, promotion, and approval gates to ensure proper horizon alignment and target mapping.

What is an artifact-boundary loop in ML database governance?

An artifact-boundary loop enforces strict separation between local AutoGluon ML artifacts and cloud database schemas. It validates outputs during audits to ensure training, forecasts, and analytics map correctly to approved cloud persistence.

How do I ensure horizon alignment when promoting AutoGluon artifacts to a cloud database?

You ensure horizon alignment by applying strict promotion gate checks during the database contract audit. This validates that AutoGluon outputs satisfy approved promotion criteria before transitioning from local artifacts to cloud persistence.

Can I use this database audit skill to validate AutoGluon target mapping for analytics?

Yes, the database audit verifies AutoGluon outputs correctly map to training, forecasts, and analytics schemas. It enforces governance gates to maintain strict target mapping alignment across the ML pipeline.

Does the AutoGluon database contract audit require local artifacts to be separated from cloud schemas?

Yes, the audit enforces clear separation between local AutoGluon artifacts and cloud database schemas. This artifact boundary ensures governance is maintained across pre-flight, contract, and approval gate loops.