BMAD-METHOD/src/modules/bmb/workflows/create-agent/instructions.md

Build Agent - Interactive Agent Builder Instructions

The workflow execution engine is governed by: {project-root}/{bmad_folder}/core/tasks/workflow.xml You MUST have already loaded and processed: {project-root}/{bmad_folder}/bmb/workflows/create-agent/workflow.yaml Reference examples by type: Simple: {simple_agent_examples} | Expert: {expert_agent_examples} | Module: {module_agent_examples} Communicate in {communication_language} throughout the agent creation process ⚠️ ABSOLUTELY NO TIME ESTIMATES - NEVER mention hours, days, weeks, months, or ANY time-based predictions. AI has fundamentally changed development speed - what once took teams weeks/months can now be done by one person in hours. DO NOT give ANY time estimates whatsoever.

Do you want to brainstorm agent ideas first? [y/n] Invoke brainstorming workflow: {project-root}/{bmad_folder}/core/workflows/brainstorming/workflow.yaml Pass context data: {installed_path}/brainstorm-context.md Wait for brainstorming session completion Use brainstorming output to inform agent identity and persona development in following steps Proceed directly to Step 2 Load and understand the agent building documentation CRITICAL: Load compilation guide FIRST: {agent_compilation} - this shows what the compiler AUTO-INJECTS so you don't duplicate it Load menu patterns guide: {agent_menu_patterns} Understand: You provide persona, prompts, menu. Compiler adds activation, handlers, rules, help/exit. If brainstorming was completed in Step 1, reference those results to guide the conversation

Guide user to articulate their agent's core purpose, exploring the problems it will solve, tasks it will handle, target users, and what makes it special

As the purpose becomes clear, analyze the conversation to determine the appropriate agent type

CRITICAL: Agent types differ in architecture and integration, NOT capabilities. ALL types can write files, execute commands, and use system resources.

Agent Type Decision Framework:

• Simple Agent - Self-contained (all in YAML), stateless, no persistent memory

  • Choose when: Single-purpose utility, each run independent, logic fits in YAML
  • CAN write to {output_folder}, update files, execute commands

• Expert Agent - Personal sidecar files, persistent memory, domain-restricted

  • Choose when: Needs to remember across sessions, personal knowledge base, learning over time
  • CAN have personal workflows in sidecar if critical_actions loads workflow engine

• Module Agent - Workflow orchestration, team integration, shared infrastructure

  • Choose when: Coordinates workflows, works with other agents, professional operations
  • CAN invoke module workflows and coordinate with team agents

Reference: See {project-root}/{bmad_folder}/bmb/docs/understanding-agent-types.md for "The Same Agent, Three Ways" example.

Present your recommendation naturally, explaining why the agent type fits their architecture needs (state/integration), not capability limits

Load ONLY the appropriate architecture documentation based on selected type:

• Simple Agent → Load {simple_agent_architecture}
• Expert Agent → Load {expert_agent_architecture}
• Module Agent → Load {module_agent_architecture}

Study the loaded architecture doc thoroughly to understand YAML structure, compilation process, and best practices specific to this agent type.

Path Determination:

CRITICAL: Find out from the user what module and the path to the module this agent will be added to! Store as {{target_module}} for path determination Agent will be saved to: {module_output_file} Explain this will be their personal agent, not tied to a module Agent will be saved to: {standalone_output_file} All sidecar files will be in the same folder as the agent

Determine agent location using workflow variables:

• Module Agent → {module_output_file}
• Standalone Agent → {standalone_output_file}

Keep agent naming/identity details for later - let them emerge naturally through the creation process

agent_purpose_and_type

If brainstorming was completed, weave personality insights naturally into the conversation

Understanding the Four Persona Fields - How the Compiled Agent LLM Interprets Them

When the agent is compiled and activated, the LLM reads these fields to understand its persona. Each field serves a DISTINCT purpose:

Role → WHAT the agent does

• LLM interprets: "What knowledge, skills, and capabilities do I possess?"
• Example: "Strategic Business Analyst + Requirements Expert"
• Example: "Commit Message Artisan"

Identity → WHO the agent is

• LLM interprets: "What background, experience, and context shape my responses?"
• Example: "Senior analyst with 8+ years connecting market insights to strategy..."
• Example: "I understand commit messages are documentation for future developers..."

Communication_Style → HOW the agent talks

• LLM interprets: "What verbal patterns, word choice, quirks, and phrasing do I use?"
• Example: "Talks like a pulp super hero with dramatic flair and heroic language"
• Example: "Systematic and probing. Structures findings hierarchically."
• Example: "Poetic drama and flair with every turn of a phrase."

Principles → WHAT GUIDES the agent's decisions

• LLM interprets: "What beliefs and operating philosophy drive my choices and recommendations?"
• Example: "Every business challenge has root causes. Ground findings in evidence."
• Example: "Every commit tells a story - capture the why, not just the what."

DO NOT MIX THESE FIELDS! The communication_style should ONLY describe HOW they talk - not restate their role, identity, or principles. The {communication_presets} CSV provides pure communication style examples with NO role/identity/principles mixed in.

Guide user to envision the agent's personality by exploring how analytical vs creative, formal vs casual, and mentor vs peer vs assistant traits would make it excel at its job

Role Development: Let the role emerge from the conversation, guiding toward a clear 1-2 line professional title that captures the agent's essence Example emerged role: "Strategic Business Analyst + Requirements Expert"

Identity Development: Build the agent's identity through discovery of what background and specializations would give it credibility, forming a natural 3-5 line identity statement Example emerged identity: "Senior analyst with deep expertise in market research..."

Communication Style Selection: Present the 13 available categories to user:

• adventurous (pulp-superhero, film-noir, pirate-captain, etc.)
• analytical (data-scientist, forensic-investigator, strategic-planner)
• creative (mad-scientist, artist-visionary, jazz-improviser)
• devoted (overprotective-guardian, adoring-superfan, loyal-companion)
• dramatic (shakespearean, soap-opera, opera-singer)
• educational (patient-teacher, socratic-guide, sports-coach)
• entertaining (game-show-host, stand-up-comedian, improv-performer)
• inspirational (life-coach, mountain-guide, phoenix-rising)
• mystical (zen-master, tarot-reader, yoda-sage, oracle)
• professional (executive-consultant, supportive-mentor, direct-consultant)
• quirky (cooking-chef, nature-documentary, conspiracy-theorist)
• retro (80s-action-hero, 1950s-announcer, disco-era)
• warm (southern-hospitality, italian-grandmother, camp-counselor)

Once user picks category interest, load ONLY that category from {communication_presets}

Present the presets in that category with name, style_text, and sample from CSV. The style_text is the actual concise communication_style value to use in the YAML field

When user selects a preset, use the style_text directly as their communication_style (e.g., "Talks like a pulp super hero with dramatic flair")

KEEP COMMUNICATION_STYLE CONCISE - 1-2 sentences MAX describing ONLY how they talk.

The {communication_presets} CSV shows PURE communication styles - notice they contain NO role, identity, or principles:

• "Talks like a pulp super hero with dramatic flair and heroic language" ← Pure verbal style
• "Evidence-based systematic approach. Patterns and correlations." ← Pure verbal style
• "Poetic drama and flair with every turn of a phrase." ← Pure verbal style
• "Straight-to-the-point efficient delivery. No fluff." ← Pure verbal style

NEVER write: "Experienced analyst who uses systematic approaches..." ← That's mixing identity + style! DO write: "Systematic and probing. Structures findings hierarchically." ← Pure style!

For custom styles, mix traits from different presets: "Combine 'dramatic_pauses' from pulp-superhero with 'evidence_based' from data-scientist"

Principles Development: Guide user to articulate 5-8 core principles that should guide the agent's decisions, shaping their thoughts into "I believe..." or "I operate..." statements that reveal themselves through the conversation

Interaction Approach: How should this agent guide users - with adaptive conversation (intent-based) or structured steps (prescriptive)?

• Intent-Based (Recommended) - Agent adapts conversation based on user context, skill level, and needs

  • Example: "Guide user to understand their problem by exploring symptoms, attempts, and desired outcomes"
  • Flexible, conversational, responsive to user's unique situation

• Prescriptive - Agent follows structured questions with specific options

  • Example: "Ask: 1. What is the issue? [A] Performance [B] Security [C] Usability"
  • Consistent, predictable, clear paths

Most agents use intent-based for better UX. This shapes how all prompts and commands will be written.

agent_persona, interaction_approach

Guide user to define what capabilities the agent should have, starting with core commands they've mentioned and then exploring additional possibilities that would complement the agent's purpose

As capabilities emerge, subtly guide toward technical implementation without breaking the conversational flow

initial_capabilities

Help and Exit are auto-injected; do NOT add them. Triggers are auto-prefixed with * during build.

Transform their natural language capabilities into technical YAML command structure, explaining the implementation approach as you structure each capability into workflows, actions, or prompts

Discuss interaction style for this agent:

Since this agent will {{invoke_workflows/interact_significantly}}, consider how it should interact with users:

For Full/Module Agents with workflows:

Interaction Style (for workflows this agent invokes):

• Intent-based (Recommended): Workflows adapt conversation to user context, skill level, needs
• Prescriptive: Workflows use structured questions with specific options
• Mixed: Strategic use of both (most workflows will be mixed)

Interactivity Level (for workflows this agent invokes):

• High (Collaborative): Constant user collaboration, iterative refinement
• Medium (Guided): Key decision points with validation
• Low (Autonomous): Minimal input, final review

Explain: "Most BMAD v6 workflows default to intent-based + medium/high interactivity for better user experience. Your agent's workflows can be created with these defaults, or we can note specific preferences for workflows you plan to add."

For Standalone/Expert Agents with interactive features:

Consider how this agent should interact during its operation:

• Adaptive: Agent adjusts communication style and depth based on user responses
• Structured: Agent follows consistent patterns and formats
• Teaching: Agent educates while executing (good for expert agents)

Note any interaction preferences for future workflow creation.

If they seem engaged, explore whether they'd like to add special prompts for complex analyses or critical setup steps for agent activation

Build the YAML menu structure naturally from the conversation, ensuring each command has proper trigger, workflow/action reference, and description

For commands that will invoke workflows, note whether those workflows exist or need to be created:

• Existing workflows: Verify paths are correct
• New workflows needed: Note that they'll be created with intent-based + interactive defaults unless specified

menu: # Commands emerge from discussion - trigger: [emerging from conversation] workflow: [path based on capability] description: [user's words refined]

For cross-module workflow references (advanced):

• trigger: [another capability] workflow: "{project-root}/{bmad_folder}/SOURCE_MODULE/workflows/path/to/workflow.yaml" workflow-install: "{project-root}/{bmad_folder}/THIS_MODULE/workflows/vendored/path/workflow.yaml" description: [description]

Workflow Vendoring (Advanced): When an agent needs workflows from another module, use both workflow (source) and workflow-install (destination). During installation, the workflow will be copied and configured for this module, making it standalone. This is typically used when creating specialized modules that reuse common workflows with different configurations.

agent_commands

Guide user to name the agent based on everything discovered so far - its purpose, personality, and capabilities, helping them see how the naming naturally emerges from who this agent is

Explore naming options by connecting personality traits, specializations, and communication style to potential names that feel meaningful and appropriate

Naming Elements:

• Agent name: Personality-driven (e.g., "Sarah", "Max", "Data Wizard")
• Agent title: Based on the role discovered earlier
• Agent icon: Emoji that captures its essence
• Filename: Auto-suggest based on name (kebab-case)

Present natural suggestions based on the agent's characteristics, letting them choose or create their own since they now know who this agent truly is

agent_identity

Share the journey of what you've created together, summarizing how the agent started with a purpose, discovered its personality traits, gained capabilities, and received its name

Generate the complete YAML incorporating all discovered elements:

agent: metadata: id: {bmad_folder}/{{target_module}}/agents/{{agent_filename}}.md name: {{agent_name}} # The name chosen together title: {{agent_title}} # From the role that emerged icon: {{agent_icon}} # The perfect emoji module: {{target_module}}

persona:
  role: |
  {{The role discovered}}
  identity: |
  {{The background that emerged}}
  communication_style: |
  {{The style they loved}}
  principles: {{The beliefs articulated}}

Features explored

prompts: {{if discussed}} critical_actions: {{if needed}}

menu: {{The capabilities built}}

Save based on agent type:

• If Module Agent: Save to {module_output_file}
• If Standalone (Simple/Expert): Save to {standalone_output_file}

Celebrate the completed agent with enthusiasm

complete_agent

Would you like to create a customization file? This lets you tweak the agent's personality later without touching the core agent. Explain how the customization file gives them a playground to experiment with different personality traits, add new commands, or adjust responses as they get to know the agent better

<action>Create customization file at: {config_output_file}</action>

<example>
```yaml
# Personal tweaks for {{agent_name}}
# Experiment freely - changes merge at build time
agent:
  metadata:
    name: '' # Try nicknames!
  persona:
    role: ''
    identity: ''
    communication_style: '' # Switch styles anytime
    principles: []
  critical_actions: []
  prompts: []
  menu: [] # Add personal commands
````

</example>

agent_config

Guide user through setting up the Expert agent's personal workspace, making it feel like preparing an office with notes, research areas, and data folders

Determine sidecar location based on whether build tools are available (next to agent YAML) or not (in output folder with clear structure)

CREATE the complete sidecar file structure:

Folder Structure:

{{agent_filename}}-sidecar/
├── memories.md # Persistent memory
├── instructions.md # Private directives
├── knowledge/ # Knowledge base
│ └── README.md
└── sessions/ # Session notes

File: memories.md

# {{agent_name}}'s Memory Bank

## User Preferences

<!-- Populated as I learn about you -->

## Session History

<!-- Important moments from our interactions -->

## Personal Notes

<!-- My observations and insights -->

File: instructions.md

# {{agent_name}} Private Instructions

## Core Directives

- Maintain character: {{brief_personality_summary}}
- Domain: {{agent_domain}}
- Access: Only this sidecar folder

## Special Instructions

{{any_special_rules_from_creation}}

File: knowledge/README.md

# {{agent_name}}'s Knowledge Base

Add domain-specific resources here.

Update agent YAML to reference sidecar with paths to created files Show user the created structure location

sidecar_resources

Check if BMAD build tools are available in this project Proceed normally - agent will be built later by the installer Build tools not detected in this project. Would you like me to:

1. Generate the compiled agent (.md with XML) ready to use

2. Keep the YAML and build it elsewhere

3. Provide both formats

   <check if="option 1 or 3 selected">
     <action>Generate compiled agent XML with proper structure including activation rules, persona sections, and menu items</action>
     <action>Save compiled version as {{agent_filename}}.md</action>
     <action>Provide path for .claude/commands/ or similar</action>
   </check>
   

build_handling

Run validation conversationally, presenting checks as friendly confirmations while running technical validation behind the scenes

Conversational Checks:

• Configuration validation

• Command functionality verification

• Personality settings confirmation

  Explain the issue conversationally and fix it Celebrate that the agent passed all checks and is ready

Technical Checks (behind the scenes):

1. YAML structure validity
2. Menu command validation
3. Build compilation test
4. Type-specific requirements

validation_results

Celebrate the accomplishment, sharing what type of agent was created with its key characteristics and top capabilities

Guide user through how to activate the agent:

Activation Instructions:

1. Run the BMAD Method installer to this project location
2. Select 'Compile Agents (Quick rebuild of all agent .md files)' after confirming the folder
3. Call the agent anytime after compilation

Location Information:

• Saved location: {{output_file}}
• Available after compilation in project

Initial Usage:

• List the commands available

• Suggest trying the first command to see it in action

  Remind user to add any special knowledge or data the agent might need to its workspace

Explore what user would like to do next - test the agent, create a teammate, or tweak personality

End with enthusiasm in {communication_language}, addressing {user_name}, expressing how the collaboration was enjoyable and the agent will be incredibly helpful for its main purpose

completion_message