hai-ssot

Diagnoses SSOT violations across duplicated definitions, inconsistent data shapes, and drifting rules in codebases.

278|15|Updated Mar 25, 2026
One-click install
npx skills add https://github.com/hylarucoder/hai-stack --skill hai-ssot
Or copy as Structured Prompt for Agentβ–Ό
Please help me install this Agent Skill.
Skill: hai-ssot
Source: https://github.com/hylarucoder/hai-stack/tree/main/skills/hai-ssot
Command: npx skills add https://github.com/hylarucoder/hai-stack --skill hai-ssot

SYSTEM DOCUMENTATION & REQUIREMENTS

πŸ’‘ This Skill includes references (resource) components.

What problem does it solve?

This Skill identifies duplicated definitions, inconsistent shapes, vocabulary drift, and redundant transformations that cause systems to lose a reliable single source of truth.

Core Features & Use Cases

  • SSOT Violation Diagnosis: Detects ten categories of drift including duplicate literals, shape proliferation, scattered defaults, redundant conversions, and repeated derivations.
  • Evidence-Based Findings Reports: Produces numbered findings with file references, severity assessment, honest adjudication, and concrete treatment recommendations.
  • Use Case: Review a codebase where frontend schemas, backend types, database constraints, and documentation may have diverged, then generate an actionable SSOT audit report.

Quick Start

Ask the AI to audit this repository for single-source-of-truth violations and produce a findings report with evidence and recommended treatments.

Frequently Asked Questions about hai-ssot

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

FAQPage Schema
What is a single-source-of-truth violation in a codebase?β–Ό

A single-source-of-truth violation occurs when duplicated definitions, inconsistent data shapes, or scattered defaults cause systems to lose a reliable source of truth across APIs, databases, and schemas.

How do I audit my codebase for schema consistency and drift?β–Ό

You can audit schema consistency by diagnosing duplicated definitions, vocabulary drift, and redundant transformations, then generating an evidence-based findings report with severity assessment and treatment recommendations.

How do I detect inconsistent data shapes across frontend and backend boundaries?β–Ό

Detect inconsistent data shapes across boundaries by collecting evidence of shape proliferation and drifting rules, classifying their severity, and routing treatments to synchronize the divergent layers.

What is the best way to find duplicate definitions and scattered defaults in my code?β–Ό

The best way to find duplicate definitions is to scan for ten categories of drift including repeated derivations and redundant conversions, producing numbered findings with file references and concrete treatments.

Can I audit documentation and fixtures for vocabulary drift against my database schemas?β–Ό

Yes, you can audit documentation and fixtures for vocabulary drift by checking cross-layer boundaries where synchronization problems occur between database constraints, fixtures, and documentation.

When should I not use an SSOT violation diagnosis approach?β–Ό

You should avoid SSOT violation diagnosis when your codebase lacks cross-layer boundaries or when duplicated definitions are intentionally isolated, as evidence collection requires verifiable synchronization problems.