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tessl/npm-bmad-method

Universal AI agent framework for agentic agile-driven development with progressive disclosure documentation optimized for AI agents

Overview
Eval results
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real-world-scenarios.mddocs/examples/

Real-World Usage Scenarios

Comprehensive examples of BMad Method in action across different project types and team configurations.

Scenario 1: Startup MVP Development

Context: Building a SaaS MVP from scratch with tight timeline (5-day sprint)

Timeline: Day 1 planning, Days 2-5 development

Phase 1: Planning (Web Platform - Day 1)

# Morning: Project Brief and PRD
# Load team-fullstack bundle in ChatGPT/Claude

*agent analyst
*create-project-brief
# 30 minutes: Complete project brief
# Output: docs/project-brief.md

*agent pm
*create-prd
# 2 hours: Complete PRD with 3 epics, 12 stories
# Interactive elicitation creates comprehensive requirements
# Output: docs/prd.md

# Afternoon: Architecture
*agent architect
*create-architecture
# 1 hour: Full-stack architecture document
# Defines tech stack, database schema, API structure
# Output: docs/fullstack-architecture.md

# Evening: Validation and Sharding
*agent po
*shard-doc
# 15 minutes: Documents sharded into manageable chunks
# Output: docs/prd/ and docs/architecture/ directories

Phase 2: Development (IDE - Days 2-5)

# Load team-ide-minimal in Cursor/Claude Code
# Each day: 2-3 stories

*agent sm
*draft
# Creates story with full context:
# - Requirements from sharded PRD
# - Architecture sections (tech-stack, coding-standards, etc.)
# - Previous story learnings
# Output: docs/stories/1.1.user-authentication.md

*agent dev
*develop-story
# Implements story sequentially:
# - Reads story file (contains ALL context)
# - Implements tasks in order
# - Updates Dev Agent Record section
# - Never loads PRD/architecture (story has everything)
# Output: Implemented code + updated story file

*agent qa
*review
# Reviews implementation:
# - Checks against acceptance criteria
# - Validates code quality
# - Makes gate decision (PASS/CONCERNS/FAIL/WAIVED)
# Output: docs/qa/1.1-user-authentication-qa-gate.md

# If QA feedback exists:
*agent dev
*review-qa
# Dev addresses issues and updates story
# QA reviews again until PASS

Result: MVP ready in 5 days with complete documentation, all requirements met, and quality gates passed.

Scenario 2: Legacy System Enhancement

Context: Adding new features to existing 10-year-old codebase with complex dependencies

Challenge: Minimize risk while extending functionality

Step 1: Document Existing System

# Flatten entire codebase to XML for context
npx bmad-method flatten -i . -o legacy-system.xml
# Creates 50MB XML file with complete codebase structure
# Includes all files, dependencies, and relationships

Step 2: Brownfield Planning (Web Platform)

*agent pm
*create-prd
# Uses brownfield-prd-tmpl.yaml
# References legacy-system.xml for context
# Creates enhancement PRD with:
# - Integration points identified
# - Risk assessment
# - Rollback strategies
# Output: docs/brownfield-prd.md

*agent architect
*create-architecture
# Documents integration points:
# - Existing system architecture
# - New feature architecture
# - Integration patterns
# - Technical debt identified
# Output: docs/brownfield-architecture.md

Step 3: Development (IDE)

*agent sm
*draft
# Uses create-brownfield-story task
# Gathers context from:
#   - Brownfield PRD
#   - Existing codebase patterns (from flattened XML)
#   - Integration requirements
#   - Safety checks and rollback plans
# Output: docs/stories/brownfield-feature.md

*agent dev
*develop-story
# Implements with safety checks:
#   - Existing functionality protection
#   - Rollback plan documented
#   - Risk mitigation strategies
#   - Integration testing approach
# Output: Safe implementation with minimal risk

Result: Safe enhancement with minimal risk, complete documentation of changes, and rollback plan ready.

Scenario 3: Team Collaboration

Context: Multiple developers working on different stories in parallel

Challenge: Maintain context without conflicts

Developer A: Working on Epic 1

*agent sm
*draft
# Creates 1.1.user-authentication.md
# Story includes complete context from:
# - Epic 1 requirements
# - Architecture sections needed
# - No dependencies on other stories

*agent dev
*develop-story
# Implements 1.1 independently
# Story file contains all needed context
# No need to read PRD or architecture

Developer B: Working on Epic 2

*agent sm
*draft
# Creates 2.1.product-listing.md
# SM agent:
#   - Detects 1.1 in progress (not done)
#   - Creates 2.1 (different epic, can work in parallel)
#   - Includes learnings from 1.1 if available
#   - Loads appropriate architecture sections

*agent dev
*develop-story
# Implements 2.1 independently
# Story contains all needed context
# No conflicts with Developer A's work

Result: Both developers work independently, stories contain all needed context, no conflicts or context loss.

Scenario 4: QA Gate Failure Recovery

Context: QA finds critical issues, dev must fix before proceeding

Challenge: Systematic issue resolution with quality assurance

QA Review

*agent qa
*review
*gate
# Gate Decision: FAIL
# Issues identified:
#   1. Security: Password stored in plain text
#   2. Missing: Input validation
#   3. Test coverage: 45% (target: 80%)
# Output: docs/qa/1.1-user-authentication-qa-gate.md

Dev Addresses Issues

*agent dev
*review-qa
# Dev agent:
#   1. Reads QA feedback in story file
#   2. Prioritizes: Security first (critical)
#   3. Fixes password storage (implements bcrypt)
#   4. Adds input validation (all endpoints)
#   5. Increases test coverage to 85%
#   6. Updates Dev Agent Record with changes
# Output: Updated implementation + story file

QA Re-Review

*agent qa
*review
*gate
# Gate Decision: PASS
# All issues resolved:
#   - Password encryption implemented
#   - Input validation added
#   - Test coverage: 85% (exceeds target)
# Story complete and ready for production

Result: All critical issues resolved, quality gates passed, story ready for production.

Scenario 5: Single-Session Brownfield Enhancement

Context: Very small change (< 4 hours) to existing system

Approach: Streamlined workflow for quick enhancements

# For very small changes (< 4 hours)
*agent pm
*create-brownfield-story
# Creates focused story for single-session completion:
# - Minimal PRD documentation
# - Integration context included
# - Safety checks embedded
# - Rollback plan documented
# Output: docs/stories/brownfield-quick-fix.md

*agent dev
*develop-story
# Implements with:
# - Focused scope
# - Integration safety checks
# - Quick validation
# Output: Quick enhancement completed safely

Result: Small enhancement completed in single session with proper documentation and safety checks.

Scenario 6: Workflow with Orchestrator

Context: New team member learning BMad Method

Approach: Use Orchestrator for guided workflow execution

# Load Orchestrator
*help
# Shows available agents and workflows

*workflow-guidance
# Get help selecting appropriate workflow
# Orchestrator asks questions:
#   - New or existing project?
#   - Full-stack, service, or UI only?
#   - Team size and experience?
# Recommendation: greenfield-service

*workflow greenfield-service
# Orchestrator guides through workflow:
#   - Loads appropriate agents in sequence
#   - Provides context at each step
#   - Explains what each agent does

*plan
# Create detailed plan before starting
# Orchestrator outlines:
#   - All steps in workflow
#   - Expected outputs
#   - Time estimates
#   - Dependencies

*plan-status
# Check progress through workflow
# Shows:
#   - Completed steps
#   - Current step
#   - Remaining steps
#   - Blockers (if any)

Result: New team member successfully completes workflow with guided assistance.

Scenario 7: Multi-Agent Collaboration

Context: Complex project requiring multiple agents in sequence

Approach: Orchestrator manages agent handoffs

# Orchestrator manages agent handoffs
*agent analyst
*create-project-brief
# Analyst creates project brief
# Output: docs/project-brief.md

*agent pm
# Orchestrator transforms into PM
*create-prd
# PM uses project brief to create PRD
# Output: docs/prd.md

*agent architect
# Orchestrator transforms into Architect
*create-architecture
# Architect uses PRD to create architecture
# Output: docs/architecture.md

*agent po
# Orchestrator transforms into PO
*shard-doc
# PO shards PRD and architecture
# Output: docs/prd/ and docs/architecture/

Result: Seamless handoff between agents, each building on previous work, no context loss.

Scenario 8: Expansion Pack Integration

Context: Game development project using Godot expansion pack

Approach: Use domain-specific expansion pack

# Install expansion pack
npx bmad-method install --expansion-packs bmad-godot-game-dev

# Expansion pack provides:
# - Game-specific agents (Game Designer, Level Designer)
# - Game development tasks
# - Godot-specific templates
# - GDScript and C# patterns

# Use game-specific workflow
*workflow greenfield-game
# Workflow includes:
#   - Game design document creation
#   - Level design specifications
#   - Godot project structure
#   - GDScript coding standards

*agent game-designer
*create-game-design-doc
# Creates game design document with:
#   - Core mechanics
#   - Level progression
#   - Asset requirements
# Output: docs/game-design.md

*agent dev
*develop-story
# Implements with Godot-specific context:
#   - GDScript patterns
#   - Scene structure
#   - Node architecture
#   - Game-specific best practices

Result: Domain-specific development with specialized agents and patterns for game development.

Install with Tessl CLI

npx tessl i tessl/npm-bmad-method

docs

examples

edge-cases.md

real-world-scenarios.md

index.md

tile.json