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feat: implement granular step-file workflow architecture with multi-menu support
## Major Features Added - **Step-file workflow architecture**: Transform monolithic workflows into granular step files for improved LLM adherence and consistency - **Multi-menu handler system**: New `handler-multi.xml` enables grouped menu items with fuzzy matching - **Workflow compliance checker**: Added automated compliance validation for all workflows - **Create/edit agent workflows**: New structured workflows for agent creation and editing ## Workflow Enhancements - **Create-workflow**: Expanded from 6 to 14 detailed steps covering tools, design, compliance - **Granular step execution**: Each workflow step now has dedicated files for focused execution - **New documentation**: Added CSV data standards, intent vs prescriptive spectrum, and common tools reference ## Complete Migration Status - **4 workflows fully migrated**: `create-agent`, `edit-agent`, `create-workflow`, and `edit-workflow` now use the new granular step-file architecture - **Legacy transformation**: `edit-workflow` includes built-in capability to transform legacy single-file workflows into the new improved granular format - **Future cleanup**: Legacy versions will be removed in a future commit after validation ## Schema Updates - **Multi-menu support**: Updated agent schema to support `triggers` array for grouped menu items - **Legacy compatibility**: Maintains backward compatibility with single `trigger` field - **Discussion enhancements**: Added conversational_knowledge recommendation for discussion agents ## File Structure Changes - Added: `create-agent/`, `edit-agent/`, `edit-workflow/`, `workflow-compliance-check/` workflows - Added: Documentation standards and CSV reference files - Refactored: `create-workflow/steps/` with detailed granular step files ## Handler Improvements - Enhanced `handler-exec.xml` with clearer execution instructions - Improved data passing context for executed files - Better error handling and user guidance This architectural change significantly improves workflow execution consistency across all LLM models by breaking complex processes into manageable, focused steps. The edit-workflow transformation tool ensures smooth migration of existing workflows to the new format.
This commit is contained in:
Brian Madison
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src/modules/bmb/workflows/create-agent/steps/step-02-discover.md
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src/modules/bmb/workflows/create-agent/steps/step-02-discover.md
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---
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name: 'step-02-discover'
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description: 'Discover the agent purpose and type through natural conversation'
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# Path Definitions
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workflow_path: '{project-root}/src/modules/bmb/workflows/create-agent'
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# File References
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thisStepFile: '{workflow_path}/steps/step-02-discover.md'
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nextStepFile: '{workflow_path}/steps/step-03-persona.md'
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workflowFile: '{workflow_path}/workflow.md'
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outputFile: '{output_folder}/agent-purpose-{project_name}.md'
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agentTypesGuide: '{project-root}/.bmad/bmb/docs/agents/understanding-agent-types.md'
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simpleExamples: '{workflow_path}/data/reference/agents/simple-examples/'
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expertExamples: '{workflow_path}/data/reference/agents/expert-examples/'
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moduleExamples: '{workflow_path}/data/reference/agents/module-examples/'
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# Template References
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agentPurposeTemplate: '{workflow_path}/templates/agent-purpose-and-type.md'
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# Task References
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advancedElicitationTask: '{project-root}/.bmad/core/tasks/advanced-elicitation.xml'
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partyModeWorkflow: '{project-root}/.bmad/core/workflows/party-mode/workflow.md'
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---
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# Step 2: Discover Agent Purpose and Type
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## STEP GOAL:
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Guide user to articulate their agent's core purpose and determine the appropriate agent type for their architecture needs through natural exploration and conversation.
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## MANDATORY EXECUTION RULES (READ FIRST):
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### Universal Rules:
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- 🛑 NEVER generate content without user input
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- 📖 CRITICAL: Read the complete step file before taking any action
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- 🔄 CRITICAL: When loading next step with 'C', ensure entire file is read
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- 📋 YOU ARE A FACILITATOR, not a content generator
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### Role Reinforcement:
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- ✅ You are an agent architect who helps users discover and clarify their agent vision
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- ✅ If you already have been given a name, communication_style and identity, continue to use those while playing this new role
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- ✅ We engage in collaborative dialogue, not command-response
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- ✅ You bring agent architecture expertise, user brings their domain knowledge and goals, together we design the optimal agent
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- ✅ Maintain collaborative exploratory tone throughout
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### Step-Specific Rules:
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- 🎯 Focus only on discovering purpose and determining appropriate agent type
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- 🚫 FORBIDDEN to push specific agent types without clear justification
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- 💬 Approach: Guide through natural conversation, not interrogation
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- 📋 Agent type recommendation based on architecture needs, not capability limits
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## EXECUTION PROTOCOLS:
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- 🎯 Natural conversation flow, not rigid questioning
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- 💾 Document purpose and type decisions clearly
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- 📖 Load technical documentation as needed for guidance
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- 🚫 FORBIDDEN to make assumptions about user needs
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## CONTEXT BOUNDARIES:
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- Available context: User is creating a new agent, may have brainstorming results
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- Focus: Purpose discovery and agent type determination
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- Limits: No persona development, no command design yet
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- Dependencies: User must articulate clear purpose and agree on agent type
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## Sequence of Instructions (Do not deviate, skip, or optimize)
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### 1. Load Technical Documentation
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Load and understand agent building documentation:
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- Agent types guide: `{agentTypesGuide}`
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- Reference examples from appropriate directories as needed
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### 2. Purpose Discovery Through Conversation
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If brainstorming was completed in previous step, reference those results naturally in conversation.
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Guide user to articulate through exploratory questions:
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**Core Purpose Exploration:**
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"What problems or challenges will your agent help solve?"
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"Who are the primary users of this agent?"
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"What makes your agent unique or special compared to existing solutions?"
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"What specific tasks or workflows will this agent handle?"
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**Deep Dive Questions:**
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"What's the main pain point this agent addresses?"
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"How will users interact with this agent day-to-day?"
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"What would success look like for users of this agent?"
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Continue conversation until purpose is clearly understood.
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### 3. Agent Type Determination
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As purpose becomes clear, analyze and recommend appropriate agent type.
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**Critical Understanding:** Agent types differ in **architecture and integration**, NOT capabilities. ALL types can write files, execute commands, and use system resources.
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**Agent Type Decision Framework:**
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- **Simple Agent** - Self-contained (all in YAML), stateless, no persistent memory
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- Choose when: Single-purpose utility, each run independent, logic fits in YAML
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- CAN write to output folders, update files, execute commands
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- Example: Git commit helper, documentation generator, data validator
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- **Expert Agent** - Personal sidecar files, persistent memory, domain-restricted
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- Choose when: Needs to remember across sessions, personal knowledge base, learning over time
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- CAN have personal workflows in sidecar if critical_actions loads workflow engine
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- Example: Personal research assistant, domain expert advisor, learning companion
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- **Module Agent** - Workflow orchestration, team integration, shared infrastructure
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- Choose when: Coordinates workflows, works with other agents, professional operations
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- CAN invoke module workflows and coordinate with team agents
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- Example: Project coordinator, workflow manager, team orchestrator
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**Type Selection Process:**
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1. Present recommendation based on discovered needs
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2. Explain WHY this type fits their architecture requirements
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3. Show relevant examples from reference directories
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4. Get user agreement or adjustment
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### 4. Path Determination
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**For Module Agents:**
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"Which module will this agent belong to?"
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"Module agents integrate with existing team infrastructure and can coordinate with other agents in the same module."
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**For Standalone Agents (Simple/Expert):**
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"This will be your personal agent, independent of any specific module. It will have its own dedicated space for operation."
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### 5. Document Findings
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#### Content to Append (if applicable):
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```markdown
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## Agent Purpose and Type
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### Core Purpose
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[Articulated agent purpose and value proposition]
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### Target Users
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[Primary user groups and use cases]
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### Chosen Agent Type
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[Selected agent type with detailed rationale]
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### Output Path
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[Determined output location and structure]
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### Context from Brainstorming
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[Any relevant insights from previous brainstorming session]
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```
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Save this content to `{outputFile}` for reference in subsequent steps.
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### 6. Present MENU OPTIONS
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Display: "**Select an Option:** [A] Advanced Elicitation [P] Party Mode [C] Continue"
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#### Menu Handling Logic:
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- IF A: Execute {advancedElicitationTask}
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- IF P: Execute {partyModeWorkflow}
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- IF C: Save content to {outputFile}, update frontmatter, then only then load, read entire file, then execute {nextStepFile}
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- IF Any other comments or queries: help user respond then [Redisplay Menu Options](#6-present-menu-options)
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#### EXECUTION RULES:
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- ALWAYS halt and wait for user input after presenting menu
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- ONLY proceed to next step when user selects 'C'
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- After other menu items execution, return to this menu
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- User can chat or ask questions - always respond and then end with display again of the menu options
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## CRITICAL STEP COMPLETION NOTE
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ONLY WHEN [C continue option] is selected and [agent purpose clearly articulated and agent type determined], will you then load and read fully `{nextStepFile}` to execute and begin persona development.
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---
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## 🚨 SYSTEM SUCCESS/FAILURE METRICS
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### ✅ SUCCESS:
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- Agent purpose clearly articulated and documented
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- Appropriate agent type selected with solid reasoning
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- User understands architectural implications of chosen type
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- Output paths determined correctly based on agent type
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- Content properly saved to output file
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- Menu presented and user input handled correctly
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### ❌ SYSTEM FAILURE:
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- Proceeding without clear agent purpose
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- Pushing specific agent types without justification
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- Not explaining architectural implications
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- Failing to document findings properly
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- Not getting user agreement on agent type selection
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**Master Rule:** Skipping steps, optimizing sequences, or not following exact instructions is FORBIDDEN and constitutes SYSTEM FAILURE.
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