Model, deploy, and monitor AI workflows as code via a public API.
npx skills add https://github.com/contrario/aetherlang --skill aetherlang
Please help me install this Agent Skill. Skill: aetherlang Source: https://github.com/contrario/aetherlang/tree/main Command: npx skills add https://github.com/contrario/aetherlang --skill aetherlang
AetherLang Ω provides a production-grade DSL to orchestrate multi-model AI workflows. It enables teams to define, validate, and execute complex AI pipelines as code, reducing integration friction and speeding up deployment across domains like recipes, market analysis, research, and education.
Install via pip and run a sample flow to validate end-to-end execution and visualization.
High-intent search queries and answers about installing and using this skill.
You can orchestrate multi-model AI workflows as code using a domain-specific language that defines, validates, and executes complex pipelines. The system supports 39 node types across culinary, business, and research domains, reducing integration friction for production deployment.
A DSL for AI workflow orchestration is a specialized language to model, deploy, and monitor multi-model pipelines. It enables teams to define flows as code, validate inputs, enforce safety guards, and execute tasks across domains like recipe optimization and market analytics.
To add safety guards and input validation to AI pipelines, define your flows using a security-first DSL that enforces prompt sanitization, rate limiting, and automated safety reviews. This ensures all pipeline inputs are validated before execution.
Yes, you can execute AI workflows via a public API endpoint. Once you define your multi-model pipeline as code, the system exposes an execution API, allowing you to trigger flows and integrate orchestration directly into your applications.
Yes, OpenClaw integration works for running AI pipelines on chat platforms. By connecting your code-defined AI workflows to OpenClaw, you can execute orchestration flows directly through chat interfaces and API endpoints.
A limitation of using a DSL for AI workflow orchestration is the need to learn its specific syntax and 39 node types. While it provides structured validation and safety guards, teams must map complex logic into its supported culinary, business, and research domains.
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