bmad-workflow-builder

To build an AI workflow from scratch, you articulate your desired outcomes and any existing skill references. The builder then guides you through constructing the skill structure while ensuring outcomes-driven design principles are applied.

To convert existing AI skills into BMad-compliant equivalents, the builder analyzes the original skill and generates an outcome-driven version. It also produces before and after HTML comparison reports to visualize the conversion changes.

Yes, you can perform comprehensive quality analysis on existing AI skills. The analysis identifies over-specification, structural issues, and enhancement opportunities to ensure your skills meet outcomes-driven design standards.

Automating AI skill building and analysis requires an LLM and supporting tools for discovery and processing. This builder streamlines the process by combining construction, conversion, and quality analysis into a single automated workflow.

Yes, an LLM is required to use the bmad-workflow-builder. It relies on LLM capabilities and other tools to perform skill discovery, conversion, and quality analysis tasks during the workflow automation process.

Over-specification issues occur when an AI skill contains excessive constraints that hinder outcomes-driven design. Quality analysis detects these structural issues and identifies enhancement opportunities to streamline the skill.