comanda

Generate YAML-based AI workflows from natural language prompts.

1|Updated Jan 30, 2026
One-click install
npx skills add https://github.com/jieni777/opencode-config-backup --skill comanda
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: comanda
Source: https://github.com/jieni777/opencode-config-backup/tree/main/skills/comanda
Command: npx skills add https://github.com/jieni777/opencode-config-backup --skill comanda

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Comanda enables teams to design and run AI workflows via natural language while maintaining reproducible YAML pipelines that can be versioned and shared.

Core Features & Use Cases

  • Generate: convert natural language prompts into YAML-based workflows that can be inspected and edited.
  • Visualize: display ASCII charts of workflow structure, steps, and model usage for quick validation.
  • Execute: run multi-model pipelines across providers such as OpenAI, Anthropic, Google Gemini, and Ollama from the CLI.
  • Use Case: a software team prototypes model experiments by exporting a deterministic workflow file that can be executed locally or in CI.

Quick Start

Describe your desired AI workflow in natural language and export it to a YAML file for execution.

Frequently Asked Questions about comanda

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

FAQPage Schema
How do I generate an AI workflow from natural language prompts?

To generate an AI workflow from natural language prompts, you can use a CLI to convert text descriptions into inspectable, editable YAML-based pipelines that can be versioned and shared.

Can I orchestrate multi-model workflows across different providers using YAML pipelines?

Yes, you can orchestrate multi-model workflows using YAML pipelines to execute models across providers like OpenAI, Anthropic, Google Gemini, and Ollama directly from the CLI.

What is the best way to visualize AI workflow structure and model usage?

The best way to visualize AI workflow structure is by using a CLI command that displays ASCII charts of your workflow steps and model usage for quick validation.

Does this tool support running reproducible AI pipelines in CI environments?

Yes, this tool supports running reproducible AI pipelines in CI environments by allowing teams to export deterministic YAML workflow files that can be executed locally or in CI.

Are there limitations to using declarative YAML workflows for multi-model orchestration?

While declarative YAML workflows provide reproducibility and multi-model orchestration, users must manually inspect and edit the generated YAML files to ensure the workflow logic matches their specific requirements.

Do I need to manually edit YAML files after generating an AI workflow?

You do not need to write YAML files from scratch, but you can inspect and edit the generated YAML pipelines to adjust multi-model orchestration steps before execution.