Execute multi-agent YAML workflows with role-based task orchestration.
npx skills add https://github.com/dascard/nanobot --skill workflow-runner-dascard
Please help me install this Agent Skill. Skill: workflow-runner Source: https://github.com/dascard/nanobot/tree/main/.agents/skills/workflow-runner Command: npx skills add https://github.com/dascard/nanobot --skill workflow-runner-dascard
This Skill solves the complexity of managing multi-role AI collaborations by executing structured YAML workflows directly within the current chat session, eliminating the need for external API keys or complex CLI setups.
Use the workflow-runner skill to execute the multi-role task defined in the file workflows/story-creation.yaml.
High-intent search queries and answers about installing and using this skill.
You can execute multi-agent YAML workflows by parsing the YAML definitions and applying dynamic role-based prompt injection to maintain persona consistency across sequential tasks. This orchestration handles complex task dependencies using topological sorting to ensure correct logical order.
No, you do not need external API keys or complex CLI setups to run multi-role AI collaborations. The workflow runner executes structured YAML workflows directly within the current chat session, eliminating the need for external configurations.
DAG execution manages task dependencies in automated workflow orchestration by using topological sorting. This ensures that steps in a multi-role YAML workflow are executed in the correct logical order, automatically assigning tasks to specific agents.
The best way to structure a YAML file for multi-agent role-based task completion is to define specific agent roles and their sequential task dependencies. The runner parses these definitions to automatically manage role-switching and aggregate output for each step.
Yes, you can use this workflow orchestration for collaborative content creation and technical reviews. It applies to complex project management by orchestrating role-based task completion, such as having a narrative expert and a psychologist collaborate on a story.
Multi-agent workflows require topological sorting for sequential tasks to correctly handle complex task dependencies. This ensures that role-based prompt injection and task execution occur in the correct logical order within the session.
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