What is the purpose of the dual-file format in doc-ctr?

It separates concerns: the Markdown file contains human-centric information like business context and traceability, while the YAML file contains the formal technical schema for validation and code generation.

How does doc-ctr handle versioning?

It enforces Semantic Versioning (Major.Minor.Patch) within the document control section to track breaking changes, new features, and bug fixes.

What does a 'SPEC-Ready' score represent?

It is a quality metric (target ≥90%) that evaluates schema completeness, error handling coverage, and traceability to ensure the contract is mature enough for the technical specification phase.

Does this skill support both REST APIs and data models?

Yes, it supports OpenAPI 3.0 for API definitions and JSON Schema for standalone data models and configurations.

Data Contract Architect (doc-ctr)

Name: Data Contract Architect (doc-ctr)
Author: vladm3105

byvladm3105

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Desarrollo de API

Standardizes API and data contract creation using a dual-file Markdown and YAML approach for robust system integration.

Acerca de

The doc-ctr skill is a specialized tool within the aidoc-flow-framework designed to manage Layer 9 Data Contracts (CTR). It enforces a rigorous dual-file methodology where Markdown files provide business context, usage examples, and traceability, while companion YAML files define technical specifications using OpenAPI 3.0 or JSON Schema. This skill is critical for teams building microservices or AI agent architectures, ensuring that all interfaces are versioned, validated, and 'SPEC-Ready' before moving into implementation.

Características Principales

Automated SPEC-Ready scoring to measure contract maturity and quality gates
Support for OpenAPI 3.0 and JSON Schema standards
1 GitHub stars
Standardized traceability mapping from upstream requirements to downstream code
Dual-file format synchronizing human-readable documentation with machine-readable schemas
Semantic versioning enforcement for breaking change management

Casos de Uso

Defining formal API contracts between microservices or distributed systems
Validating technical interface readiness before generating implementation tasks
Creating shared data schemas for AI agent-to-agent communication

Acerca de

Características Principales

Automated SPEC-Ready scoring to measure contract maturity and quality gates
Support for OpenAPI 3.0 and JSON Schema standards
1 GitHub stars
Standardized traceability mapping from upstream requirements to downstream code
Dual-file format synchronizing human-readable documentation with machine-readable schemas
Semantic versioning enforcement for breaking change management

Casos de Uso

Defining formal API contracts between microservices or distributed systems
Validating technical interface readiness before generating implementation tasks
Creating shared data schemas for AI agent-to-agent communication