bmad-agent-builder

Build, edit, and analyze agent skills through guided conversational discovery.

Updated May 5, 2026

Search Tags:#architecture #capabilities #agent-builder #prompt-design #sanctum #memory-agent

Ready to run

npx skills add https://github.com/b566776/whitebox --skill bmad-agent-builder-b566776

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bmad-agent-builder
Source: https://github.com/b566776/whitebox/tree/main/.gemini/skills/bmad-agent-builder
Command: npx skills add https://github.com/b566776/whitebox --skill bmad-agent-builder-b566776

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyyaml, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Builds, edits or analyzes Agent Skills through conversational discovery to help users craft effective, outcome-driven agents.

Core Features & Use Cases

Walks users through identifying an agent's identity, capabilities, memory needs, and activation flow.
Produces a lean bootloader + sanctum templates, with progressive disclosure to finalize a complete agent skill.
Supports converting existing agents, editing behaviors, and rebuilding from intent.

Quick Start

Describe the agent you want to build and I will guide you through the discovery and construction of its sanctum and capabilities.

Frequently Asked Questions about bmad-agent-builder

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

FAQPage Schema

How do I design an AI agent through guided discovery for domain-specific workflows?▼

Guided discovery for AI agent design walks teams through conversational prompts to identify agent identity, capabilities, memory needs, and activation flow. This process outputs a complete agent skill structure with identity seeds, sanctum templates, and capability prompts for domain-specific workflows.

Can I convert existing agents or edit behaviors using conversational discovery?▼

Conversational discovery supports converting existing agents, editing behaviors, and rebuilding from intent. It produces a lean bootloader with sanctum templates, using progressive disclosure to finalize the complete agent skill structure for memory-enabled assistants.

What's the best way to build memory-enabled assistants and coaching copilots?▼

Building memory-enabled assistants and coaching copilots requires identifying memory needs and activation flow through guided discovery. The process outputs configuration for both headless and memory modes, creating sanctum templates and capability prompts tailored for education, software, and business workflows.

Does this agent builder require Python dependencies like pyyaml?▼

The agent builder requires the pyyaml Python dependency to function. It uses scripts, references, and assets to guide teams through creating domain-specific agents, memory-enabled assistants, and coaching copilots across various workflow contexts.

How does progressive disclosure work when refining AI agent capabilities?▼

Progressive disclosure refines AI agent capabilities by gradually revealing complexity during conversational discovery. It starts with a lean bootloader and sanctum templates, then progressively finalizes the complete agent skill structure with identity seeds and capability prompts.

Why do I need sanctum templates and identity seeds for AI agent architecture?▼

Sanctum templates and identity seeds provide the foundational architecture for AI agents by establishing core identity and memory structure. They enable progressive disclosure to finalize capability prompts and configuration for headless and memory modes across domain-specific workflows.