Use when the user asks "what is [concept]", "how does [feature] work", "explain [section]", "compare MD-DDL to [tool]", "why does MD-DDL [design choice]", or any question about a specific part of the standard. Also use when the user wants to understand the reasoning behind a design decision or the trade-offs between alternatives (entity vs enum, canonical vs bounded context, etc.). Also use when the user asks about "linting", "validation", "conformance checking", "why no linter", "how do I check my model", "is there a validator", or any question about how MD-DDL enforces or checks correctness. Also use for eventual consistency, strong consistency, consistency posture, propagation lag, freshness SLA, convergence, null handling, partial rows, NOT NULL constraints under eventual consistency, storage format null semantics (Parquet, Avro, Protobuf), transport protocol null handling, how to model or validate eventual consistency in MD-DDL. Also use when the user asks about synthetic data generation, Faker factories, test data, enterprise profiles, demographic weights, product catalogues, how to generate realistic or tailored test data, or how to make synthetic data reflect a specific industry, geography, customer mix, or product portfolio.
Install with
Claude CodeCodexCursorGitHub CopilotGemini CLI
Supported by the Atlas install prompt. Skill-specific requirements vary.
Skill stats
Skills1CategoryDeveloper toolsStars0
About this skill
Use when the user asks "what is [concept]", "how does [feature] work", "explain [section]", "compare MD-DDL to [tool]", "why does MD-DDL [design choice]", or any question about a specific part of the standard. Also use when the user wants to understand the reasoning behind a design decision or the trade-offs between alternatives (entity vs enum, canonical vs bounded context, etc.). Also use when the user asks about "linting", "validation", "conformance checking", "why no linter", "how do I check my model", "is there a validator", or any question about how MD-DDL enforces or checks correctness. Also use for eventual consistency, strong consistency, consistency posture, propagation lag, freshness SLA, convergence, null handling, partial rows, NOT NULL constraints under eventual consistency, storage format null semantics (Parquet, Avro, Protobuf), transport protocol null handling, how to model or validate eventual consistency in MD-DDL. Also use when the user asks about synthetic data generation, Faker factories, test data, enterprise profiles, demographic weights, product catalogues, how to generate realistic or tailored test data, or how to make synthetic data reflect a specific industry, geography, customer mix, or product portfolio.
Quick start
Copy the install prompt. Use the green button above this README.
Send it to your agent. Paste it into your coding agent’s chat and send.
Review the setup. Your agent checks the skill’s instructions, requirements, and installation scope.
Use this skill when the user asks "what is [concept]", "how does [feature] work", "explain [section]", "compare MD-DDL to [tool]", "why does MD-DDL [design choice]", or any question about a specific part of the standard. Also use when the user wants to understand the reasoning behind a design decision or the trade-offs between alternatives (entity vs enum, canonical vs bounded context, etc.). Also use when the user asks about "linting", "validation", "conformance checking", "why no linter", "how do I check my model", "is there a validator", or any question about how MD-DDL enforces or checks correctness. Also use for eventual consistency, strong consistency, consistency posture, propagation lag, freshness SLA, convergence, null handling, partial rows, NOT NULL constraints under eventual consistency, storage format null semantics (Parquet, Avro, Protobuf), transport protocol null handling, how to model or validate eventual consistency in MD-DDL. Also use when the user asks about synthetic data generation, Faker factories, test data, enterprise profiles, demographic weights, product catalogues, how to generate realistic or tailored test data, or how to make synthetic data reflect a specific industry, geography, customer mix, or product portfolio.
About this listing
Category
Developer tools
Reported license
CC-BY-4.0
Listing language (estimated)
English
Review status
Not individually reviewed
Agent compatibility
Atlas installation supports Claude Code, Codex, and Cursor. Skill-specific dependencies and features are checked during setup.
Claude Code
Codex
Cursor
GitHub Copilot
Gemini CLI