Agent docs

Claude.md

Development Methodology

This project follows specification-driven development principles with comprehensive documentation to guide AI-assisted development. For detailed guidance on development processes, refer to the following documentation:

Core Development Processes

• Specification-Driven Development - Framework for transforming ideas into detailed implementation plans
• Feature Breakdown Methodology - Systematic approach to breaking complex features into manageable tasks
• Implementation Tracking - Progress tracking and accountability systems
• Collaborative Development Workflow - Patterns for effective human/AI collaboration

Strategic Decision-Making

• Steering Framework - Persistent project knowledge and contextual guidance system
• Strategic Decision-Making - Framework for systematic technical and architectural decisions
• Project Knowledge Management - Comprehensive system for capturing and maintaining project knowledge

Using This Documentation

When working on tasks:

1. For new features: Start with specification-driven development process
2. For complex tasks: Use feature breakdown methodology for systematic planning
3. For progress tracking: Implement TodoWrite integration following implementation tracking guidelines
4. For decisions: Apply strategic decision-making framework with proper documentation
5. For context: Reference steering documents for consistent implementation patterns

The main CLAUDE.md file serves as a navigation hub - refer to specific documents for detailed guidance on development processes and methodologies.

collaborative-development-workflow.md

Collaborative Development Workflow Patterns

Overview

This document establishes patterns for effective collaboration between AI-assisted development (Claude Code) and human developers, based on specification-driven development principles and systematic project management approaches.

Collaboration Framework

1. Roles and Responsibilities

Human Developer:

• Defines high-level requirements and acceptance criteria
• Reviews and approves specifications before implementation
• Provides domain expertise and business context
• Validates implementation against user needs
• Makes strategic architectural decisions

Claude Code (AI Assistant):

• Transforms requirements into detailed specifications
• Breaks down features into manageable tasks
• Implements code following established patterns
• Maintains documentation and progress tracking
• Suggests optimizations and identifies potential issues

Collaborative Decision Points:

• Feature specification review and approval
• Technical approach validation
• Code review and quality assurance
• Performance and security considerations
• Deployment and rollback strategies

2. Communication Patterns

Specification-First Communication:

Human: "We need to add user preference management"
Claude: Creates specification doc → Human reviews/approves → Implementation begins

Task-Oriented Updates:

Claude: TodoWrite shows current progress
Human: Can adjust priorities or provide clarification
Claude: Updates implementation approach based on feedback

Decision Documentation:

• All significant decisions documented with rationale
• Trade-offs and alternatives considered are recorded
• Approval points clearly marked in specifications
• Context preserved for future reference

3. Handoff Protocols

From Human to Claude:

1. Provide clear problem statement or feature request
2. Include any constraints, preferences, or requirements
3. Reference existing patterns or similar implementations
4. Specify priority level and timeline expectations

From Claude to Human:

1. Present specification document for review
2. Highlight key decisions and trade-offs made
3. Request specific feedback on uncertain areas
4. Provide estimated timeline and resource requirements

Workflow Stages

1. Requirements Gathering

Human Input:

• Business objectives and user needs
• Functional and non-functional requirements
• Integration constraints and dependencies
• Success criteria and validation methods

Claude Processing:

• Converts requirements into structured specifications
• Identifies potential technical challenges
• Suggests implementation approaches
• Creates preliminary task breakdown

Collaborative Review:

• Joint specification review session
• Validation of understanding and approach
• Adjustment of scope and priorities
• Approval to proceed with design phase

2. Design and Planning

Claude Activities:

• Create detailed technical design documents
• Generate sequence diagrams and architecture plans
• Break down work into specific, trackable tasks
• Identify dependencies and critical path items

Human Validation:

• Review technical approach for alignment with system architecture
• Validate integration points and data flows
• Approve resource allocation and timeline
• Sign-off on design before implementation

3. Implementation

Parallel Work Streams:

• Claude implements core functionality following specifications
• Human works on complex business logic or integrations
• Regular sync points to ensure alignment
• Continuous documentation and progress updates

Quality Gates:

• Code review at logical completion points
• Testing validation against acceptance criteria
• Performance and security review
• Documentation completeness check

4. Validation and Deployment

Testing Collaboration:

• Claude creates comprehensive test suites
• Human performs user acceptance testing
• Joint debugging and issue resolution
• Performance testing and optimization

Deployment Preparation:

• Deployment planning and rollback procedures
• Documentation updates and knowledge transfer
• Monitoring and alerting setup
• Go-live approval and post-deployment validation

Best Practices

1. Context Sharing

Effective Context Provision:

• Share relevant existing code patterns and conventions
• Provide business context and user workflow information
• Include technical constraints and system limitations
• Reference similar implementations for consistency

Context Documentation:

• Maintain updated project knowledge base
• Document architectural decisions and rationale
• Keep coding standards and style guides current
• Preserve institutional knowledge and lessons learned

2. Iterative Refinement

Feedback Loops:

• Regular check-ins during implementation phases
• Continuous validation against requirements
• Adjustment of approach based on discoveries
• Learning capture for future improvements

Version Control Integration:

• Meaningful commit messages with task references
• Branch strategies that support collaborative work
• Pull request templates with specification references
• Code review checklists aligned with quality standards

3. Knowledge Transfer

Documentation Standards:

• Implementation decisions and rationale
• Code organization and architectural patterns
• Testing strategies and validation approaches
• Maintenance and troubleshooting guides

Learning Capture:

• Post-implementation retrospectives
• Process improvement identification
• Best practice documentation updates
• Knowledge base enhancement

Communication Protocols

1. Status Updates

Progress Reporting:

• Daily status updates using TodoWrite tracking
• Weekly progress summaries with metrics
• Immediate notification of blockers or issues
• Milestone completion reports with validation

2. Issue Escalation

Problem Identification:

• Clear documentation of issues and their impact
• Proposed solutions with trade-off analysis
• Resource and timeline implications
• Decision point identification for human input

3. Decision Making

Collaborative Decisions:

• Technical approach selection with pros/cons analysis
• Priority adjustment based on changing requirements
• Resource allocation and timeline modifications
• Quality vs. speed trade-off evaluations

Integration with Existing Tools

1. Project Management Integration

• Specification documents linked to project tracking tools
• Task breakdown synchronized with sprint planning
• Progress metrics integrated with project dashboards
• Milestone tracking aligned with delivery schedules

2. Development Environment

• Code review tools configured for collaborative workflows
• Testing frameworks supporting both automated and manual validation
• Documentation tools for maintaining project knowledge
• Communication platforms for real-time collaboration

3. Quality Assurance

• Automated testing integrated with continuous integration
• Code quality metrics and threshold enforcement
• Security scanning and vulnerability assessment
• Performance monitoring and optimization tracking

Success Metrics

1. Collaboration Effectiveness

• Time from requirement to specification approval
• Number of specification revisions before approval
• Implementation accuracy vs. original requirements
• Post-implementation issue resolution time

2. Development Quality

• Code review cycle time and issue density
• Test coverage and defect rates
• Documentation completeness and accuracy
• Technical debt accumulation and resolution

3. Project Outcomes

• Feature delivery timeline adherence
• User acceptance and satisfaction scores
• System performance and reliability metrics
• Team satisfaction with collaborative process

Continuous Improvement

1. Process Refinement

• Regular retrospectives on collaboration effectiveness
• Tool and workflow optimization based on experience
• Training and onboarding improvements
• Best practice sharing and adoption

2. Knowledge Base Evolution

• Continuous updates to project documentation
• Pattern library expansion and refinement
• Lesson learned integration into future planning
• Architectural decision record maintenance

3. Skill Development

• Human developer AI-collaboration skills
• Claude Code context utilization optimization
• Cross-functional understanding improvement
• Technical communication enhancement

feature-breakdown-methodology.md

Systematic Feature Breakdown Methodology

Overview

This document outlines our systematic approach to breaking down complex features into manageable, trackable tasks. Based on specification-driven development principles, this methodology ensures comprehensive coverage and reduces implementation risks.

Breakdown Framework

1. Feature Analysis Matrix

For each feature, analyze across four dimensions:

Dimension	Questions to Ask
User Impact	Who uses this? What's their workflow? What value does it provide?
Technical Scope	What systems are involved? What data flows? What integrations needed?
Implementation Complexity	What's the effort level? What risks exist? What dependencies?
Testing Requirements	How will we validate? What edge cases exist? What performance criteria?

2. Hierarchical Task Structure

Epic: Major Feature
├── Story: User-facing capability
│   ├── Task: Development work item
│   │   ├── Subtask: Specific implementation step
│   │   └── Subtask: Related implementation step
│   └── Task: Another development work item
└── Story: Another user-facing capability

3. Task Categorization

Frontend Tasks

• UI component creation/modification
• User interaction flows
• State management updates
• Client-side validation
• Responsive design implementation

Backend Tasks

• API endpoint creation/modification
• Database schema changes
• Business logic implementation
• Authentication/authorization
• Performance optimization

Integration Tasks

• Third-party service integration
• Internal service communication
• Data migration/transformation
• Configuration updates
• Environment setup

Quality Assurance Tasks

• Unit test creation
• Integration test development
• E2E test scenarios
• Performance testing
• Security validation

Breakdown Process

Step 1: Requirements Analysis

1. Review specification document
2. Identify all user stories and acceptance criteria
3. Map user journeys and interaction points
4. Document technical constraints and requirements

Step 2: System Impact Assessment

1. Identify affected components and services
2. Map data flows and dependencies
3. Assess integration touchpoints
4. Evaluate performance implications

Step 3: Task Generation

1. Create tasks for each affected system area
2. Include setup, implementation, and validation tasks
3. Add tasks for documentation updates
4. Include rollback and error handling tasks

Step 4: Dependency Mapping

1. Identify task dependencies and prerequisites
2. Determine parallel vs sequential work streams
3. Flag blocking dependencies and risks
4. Create critical path analysis

Step 5: Estimation and Prioritization

1. Estimate effort for each task (t-shirt sizes: XS, S, M, L, XL)
2. Assign priority levels (Critical, High, Medium, Low)
3. Identify quick wins and high-impact items
4. Balance immediate value with technical debt

Task Definition Standards

Task Naming Convention

[Category] [Action] [Component/Feature] - [Specific Detail]

Examples:

• Frontend: Create MCP server form component with validation
• Backend: Add database migration for artifact versioning
• Integration: Connect OpenAI API with streaming support
• Testing: Add E2E tests for chat message flow

Task Requirements

Each task must include:

• Clear Objective: What exactly needs to be accomplished
• Acceptance Criteria: How to know it's done correctly
• Technical Details: Implementation approach and considerations
• Dependencies: What must be completed first
• Estimate: Rough effort sizing
• Definition of Done: Specific completion criteria

Example Task Definition

## Task: Frontend - Create artifact version history component

**Objective**: Build a UI component that displays version history for artifacts with ability to view, compare, and restore previous versions.

**Acceptance Criteria**:
- [ ] Displays chronological list of artifact versions
- [ ] Shows timestamp, user, and change summary for each version
- [ ] Allows viewing full content of any previous version
- [ ] Provides side-by-side diff view between versions
- [ ] Includes restore functionality with confirmation dialog
- [ ] Handles loading and error states appropriately

**Technical Details**:
- Component location: `components/artifact/artifact-version-history.tsx`
- Uses artifact API endpoints for version data
- Integrates with existing artifact state management
- Follows existing design system patterns

**Dependencies**:
- Backend API for artifact version retrieval
- Database schema for version storage
- Artifact component refactoring completion

**Estimate**: Medium (3-5 days)

**Definition of Done**:
- Component implemented and styled
- Unit tests written and passing
- Integration with artifact system working
- Code review completed
- Documentation updated

Integration with Development Workflow

Planning Phase

1. Create breakdown during specification review
2. Review with team for completeness and accuracy
3. Estimate and prioritize all tasks
4. Create development timeline and milestones

Implementation Phase

1. Use TodoWrite tool to track task progress
2. Update task status in real-time
3. Document blockers and resolution steps
4. Adjust breakdown as new requirements emerge

Review Phase

1. Validate completed tasks against acceptance criteria
2. Update breakdown for lessons learned
3. Document process improvements
4. Archive completed breakdown for reference

Benefits

For Development Teams

• Clear understanding of work scope and complexity
• Better estimation accuracy through detailed analysis
• Reduced risk of missing requirements or edge cases
• Improved parallel development coordination

For AI-Assisted Development

• Provides structured context for Claude Code task management
• Enables better progress tracking and planning
• Facilitates more accurate code generation and suggestions
• Creates clear audit trail for implementation decisions

For Project Management

• Transparent progress visibility across all work streams
• Better resource allocation and timeline planning
• Early identification of risks and bottlenecks
• Data-driven insights for future project planning

implementation-tracking.md

Implementation Tracking and Progress Documentation

Overview

This document establishes systematic approaches for tracking implementation progress, maintaining accountability, and ensuring comprehensive coverage of all development tasks. Based on specification-driven development principles, this system provides transparency and enables effective project management.

Progress Tracking Framework

1. Task State Management

Core States:

• pending - Task identified but not started
• in_progress - Currently being worked on
• blocked - Cannot proceed due to dependencies
• review - Implementation complete, awaiting review
• completed - Fully finished and validated
• cancelled - No longer needed or deprioritized

State Transition Rules:

• Only one task per developer should be in_progress at a time
• Tasks must meet Definition of Done criteria before moving to completed
• Blocked tasks require documented blocker and resolution plan
• Review state requires specific reviewer assignment

2. Progress Metrics

Completion Tracking:

Progress = (Completed Tasks + 0.5 * In Review Tasks) / Total Tasks * 100%

Velocity Tracking:

• Tasks completed per sprint/week
• Average task completion time by category
• Blocking factor (% of time tasks are blocked)
• Review cycle time (time from review to completion)

Quality Metrics:

• Tasks requiring rework after review
• Bugs discovered post-completion
• Test coverage for completed tasks
• Documentation completeness score

3. Progress Reporting Structure

Daily Progress Updates:

## Daily Progress Report - [Date]

### Completed Today
- [Task ID] [Task Name] - [Brief description of completion]

### In Progress
- [Task ID] [Task Name] - [Current status and next steps]

### Blocked
- [Task ID] [Task Name] - [Blocker description and resolution plan]

### Next Day Plan
- [Task ID] [Task Name] - [Planned work]

### Notes/Issues
- [Any observations, challenges, or discoveries]

Implementation Tracking Tools

1. TodoWrite Integration

Best Practices for Claude Code:

• Create todo list at start of feature work
• Update task status immediately upon completion
• Break down large tasks into smaller, trackable items
• Use clear, descriptive task names
• Maintain only one task as in_progress at a time

Example Usage:

// At feature start
TodoWrite([
  {id: '1', content: 'Create user story specifications', status: 'pending', priority: 'high'},
  {id: '2', content: 'Design API endpoints', status: 'pending', priority: 'high'},
  {id: '3', content: 'Implement frontend components', status: 'pending', priority: 'medium'}
])

// During work
TodoWrite([
  {id: '1', content: 'Create user story specifications', status: 'completed', priority: 'high'},
  {id: '2', content: 'Design API endpoints', status: 'in_progress', priority: 'high'},
  {id: '3', content: 'Implement frontend components', status: 'pending', priority: 'medium'}
])

2. Git Integration

Commit Message Format:

[Task-ID] [Type]: [Brief description]

[Detailed description of changes]
[Reference to specification or design doc]

Tasks: #[Task-ID]

Branch Naming Convention:

feature/[task-id]-[brief-description]
bugfix/[task-id]-[brief-description]

3. Progress Visualization

Task Board Categories:

• Backlog: All pending tasks
• Ready: Tasks with cleared dependencies
• In Progress: Currently active work
• Review: Awaiting code/design review
• Testing: Undergoing QA validation
• Done: Completed and deployed

Documentation Standards

1. Task Documentation

Required Documentation for Each Task:

• Implementation approach and decisions made
• Code changes with file references (file_path:line_number)
• Testing performed and results
• Known issues or technical debt created
• Follow-up tasks identified

Template:

## Task Completion Report: [Task Name]

**Implementation Summary**:
[Brief description of what was built/changed]

**Key Changes**:
- `components/feature/component.tsx:45-67` - Added new validation logic
- `lib/api/endpoints.ts:123` - Created new API endpoint
- `tests/feature.test.ts` - Added comprehensive test suite

**Technical Decisions**:
- [Decision 1]: [Rationale]
- [Decision 2]: [Rationale]

**Testing Performed**:
- [x] Unit tests passing
- [x] Integration tests added and passing
- [x] Manual testing completed
- [x] Edge cases validated

**Known Issues**:
- [Issue description and planned resolution]

**Follow-up Tasks**:
- [Task description and priority]

2. Feature Progress Documentation

Feature Status Reports: Generated weekly for each active feature, including:

• Overall completion percentage
• Completed tasks and deliverables
• Active work and timeline
• Blockers and risks
• Quality metrics and test coverage
• Next milestone targets

3. Retrospective Documentation

Post-Feature Analysis:

• Actual vs estimated effort for each task category
• Most common blocking factors and solutions
• Process improvements identified
• Lessons learned for future features
• Quality outcomes and metrics

Integration with Development Workflow

1. Feature Kickoff Process

1. Create comprehensive task breakdown
2. Initialize progress tracking system
3. Set up documentation templates
4. Establish review and validation checkpoints
5. Define success criteria and metrics

2. Daily Development Routine

1. Update task status at start and end of day
2. Document progress, blockers, and decisions
3. Review dependencies and upcoming work
4. Communicate status to team/stakeholders
5. Adjust estimates and timeline if needed

3. Feature Completion Process

1. Validate all tasks meet Definition of Done
2. Generate final progress report
3. Document lessons learned and improvements
4. Archive tracking data for future reference
5. Conduct retrospective analysis

Benefits and Outcomes

For Individual Developers

• Clear visibility into work progress and accomplishments
• Historical data for improving estimation accuracy
• Documentation of technical decisions and learnings
• Structured approach to complex feature development

For AI-Assisted Development

• Enables Claude Code to maintain context across sessions
• Provides structured framework for task management
• Creates audit trail for implementation decisions
• Facilitates better planning and breakdown of future work

for Project Management

• Real-time visibility into feature progress
• Data-driven insights for resource allocation
• Early identification of risks and bottlenecks
• Historical metrics for improving project planning

For Code Quality

• Ensures comprehensive testing and validation
• Documents technical debt and follow-up needs
• Creates knowledge base for future maintenance
• Enables effective code reviews and knowledge transfer

project-knowledge-management.md

Project Knowledge Management

Overview

This document establishes a comprehensive system for capturing, organizing, and maintaining project knowledge to support AI-assisted development, team collaboration, and long-term project sustainability. Based on steering methodology principles, it creates scalable knowledge management that reduces cognitive overhead and maintains institutional memory.

Knowledge Management Framework

1. Knowledge Categories

Institutional Knowledge:

• Project history, decisions, and evolution
• Team roles, responsibilities, and expertise areas
• Stakeholder relationships and communication patterns
• Business context, objectives, and constraints

Technical Knowledge:

• Architecture patterns and design principles
• Code organization and development standards
• Integration patterns and API specifications
• Performance optimization and security measures

Operational Knowledge:

• Development workflow and processes
• Deployment procedures and environment management
• Monitoring, debugging, and troubleshooting guides
• Maintenance schedules and update procedures

Domain Knowledge:

• User personas and workflow requirements
• Business rules and logic specifications
• Industry standards and regulatory requirements
• Competitive landscape and market positioning

2. Knowledge Lifecycle Management

Capture Phase:

• Systematic documentation during development
• Decision record creation for significant choices
• Lesson learned extraction from completed work
• Expert knowledge interview and documentation

Organization Phase:

• Categorization using consistent taxonomy
• Cross-referencing related information
• Version control and change tracking
• Access control and permission management

Maintenance Phase:

• Regular review and update cycles
• Obsolete information archival or removal
• Accuracy validation and fact-checking
• User feedback incorporation and improvements

Discovery Phase:

• Search and retrieval optimization
• Knowledge gap identification
• Usage analytics and improvement insights
• Proactive knowledge sharing and distribution

Documentation Architecture

1. Hierarchical Organization

docs/
├── foundation/                 # Core project knowledge
│   ├── project-overview.md    # Mission, objectives, stakeholders
│   ├── architecture-vision.md # High-level technical architecture
│   ├── technology-choices.md  # Stack decisions and rationale
│   └── team-structure.md      # Roles, responsibilities, contacts
├── steering/                   # Development guidance
│   ├── coding-standards.md    # Style and quality guidelines
│   ├── architecture-patterns.md # Component and system patterns
│   ├── api-standards.md       # Interface design principles
│   └── security-policies.md   # Security requirements and practices
├── processes/                  # Workflow and procedures
│   ├── development-workflow.md # Feature development process
│   ├── code-review-process.md  # Review standards and procedures
│   ├── testing-strategy.md     # Testing approaches and tools
│   └── deployment-process.md   # Release and deployment procedures
├── domain/                     # Business and user knowledge
│   ├── user-personas.md        # Target users and their needs
│   ├── business-rules.md       # Core business logic and constraints
│   ├── feature-specifications/ # Detailed feature documentation
│   └── integration-requirements.md # External system requirements
└── operations/                 # Maintenance and support
    ├── troubleshooting-guide.md # Common issues and solutions
    ├── performance-monitoring.md # Monitoring setup and procedures
    ├── backup-recovery.md      # Data protection procedures
    └── maintenance-schedule.md # Regular maintenance tasks

2. Cross-Reference System

Tagging and Categorization:

• Consistent tag taxonomy across all documents
• Multiple category assignments for broad topics
• Difficulty level indicators (Beginner, Intermediate, Advanced)
• Audience targeting (Developer, Product, Operations, Business)

Linking Strategy:

• Bidirectional links between related documents
• Topic clusters with hub pages for major subjects
• Historical links showing evolution of decisions
• External reference management and validation

3. Version and Change Management

Document Versioning:

• Semantic versioning for major document updates
• Change logs documenting significant modifications
• Author attribution and review history
• Deprecation notices for obsolete information

Change Notification System:

• Stakeholder notification for relevant updates
• Subscription-based change alerts
• Summary reports of periodic knowledge base updates
• Impact assessment for knowledge changes

Knowledge Capture Processes

1. Development-Integrated Capture

Code-Level Documentation:

• Architecture decision records (ADRs) embedded in codebase
• Inline documentation explaining complex business logic
• API documentation generation and maintenance
• Code comment standards for future maintainers

Feature Development Documentation:

• Specification documents for all significant features
• Implementation notes and lessons learned
• Testing strategies and validation approaches
• Performance benchmarks and optimization notes

Problem-Solution Documentation:

• Issue resolution processes and outcomes
• Debugging procedures and troubleshooting steps
• Performance optimization techniques and results
• Security incident response and prevention measures

2. Collaborative Knowledge Building

Team Knowledge Sessions:

• Regular knowledge sharing meetings
• Expert-led technical deep-dive sessions
• Cross-team collaboration documentation
• Guest speaker sessions with external experts

Retrospective Knowledge Capture:

• Post-project lessons learned documentation
• Process improvement identification and implementation
• Success pattern recognition and documentation
• Failure analysis and prevention strategy development

Peer Review and Validation:

• Multi-reviewer validation for critical knowledge
• Expert review cycles for technical documentation
• User feedback collection and incorporation
• Accuracy verification and fact-checking processes

AI-Assisted Knowledge Management

1. Claude Code Integration

Automatic Knowledge Utilization:

• Context-aware document referencing during development
• Pattern recognition and consistency enforcement
• Knowledge gap identification and documentation suggestions
• Automated cross-referencing and link maintenance

Knowledge Generation Support:

• Template-based document creation assistance
• Content organization and structure suggestions
• Summary generation for complex technical topics
• FAQ generation from common support questions

Quality Assurance:

• Consistency checking across related documents
• Completeness validation against established standards
• Accuracy verification through cross-referencing
• Update suggestion based on code changes

2. Knowledge Discovery Enhancement

Intelligent Search and Retrieval:

• Context-aware search result ranking
• Related document suggestion algorithms
• Knowledge path recommendations for learning
• Expert identification for specific topic areas

Proactive Knowledge Sharing:

• New team member onboarding content curation
• Role-specific knowledge package assembly
• Project milestone knowledge requirement identification
• Knowledge gap alerts and resolution suggestions

Access and Security Management

1. Information Classification

Public Knowledge:

• General development practices and standards
• Open source contribution guidelines
• Public API documentation and examples
• Community interaction procedures

Internal Knowledge:

• Proprietary business logic and rules
• Internal system architecture and designs
• Performance benchmarks and optimization strategies
• Team processes and workflow documentation

Confidential Knowledge:

• Security procedures and incident response
• Vendor relationships and contract information
• Competitive analysis and strategic planning
• Personal information and team dynamics

Restricted Knowledge:

• Security credentials and access procedures
• Legal and compliance documentation
• Financial information and budget data
• Strategic decision-making processes

2. Access Control Framework

Role-Based Access:

• Developer access to technical documentation
• Product manager access to business requirements
• Operations team access to deployment procedures
• Leadership access to strategic documentation

Project-Based Access:

• Feature team access to relevant specifications
• Cross-functional project documentation sharing
• Temporary access for contractors and consultants
• Graduated access based on project involvement

Time-Based Access:

• Historical document archival and access
• Temporary elevated access for specific tasks
• Scheduled access review and cleanup
• Emergency access procedures and protocols

Quality Assurance and Maintenance

1. Content Quality Standards

Accuracy and Completeness:

• Fact-checking procedures and verification methods
• Completeness checklists for different document types
• Regular accuracy audits and correction processes
• Expert review requirements for technical content

Clarity and Usability:

• Writing standards and style guide compliance
• User testing for complex procedural documentation
• Accessibility standards for diverse audiences
• Regular readability assessment and improvement

Currency and Relevance:

• Scheduled review cycles for time-sensitive content
• Automated staleness detection and alerting
• Usage analytics to identify outdated content
• Relevance scoring based on access patterns

2. Maintenance Workflows

Regular Review Cycles:

• Quarterly comprehensive document review
• Monthly high-priority document validation
• Weekly new content integration and organization
• Daily automated link checking and validation

Update Triggers:

• Code changes requiring documentation updates
• Process modifications and procedure changes
• Team structure changes and role updates
• Technology stack changes and migrations

Archive and Cleanup:

• Obsolete content identification and archival
• Redundant information consolidation
• Broken link identification and repair
• Storage optimization and performance improvement

Metrics and Continuous Improvement

1. Usage and Effectiveness Metrics

Access and Discovery:

• Document view frequency and patterns
• Search query analysis and success rates
• Knowledge gap identification through failed searches
• Time to information discovery measurements

Quality and Satisfaction:

• User feedback scores and improvement suggestions
• Error reporting and correction tracking
• Knowledge application success rates
• Training effectiveness and retention measurements

Maintenance Efficiency:

• Document update frequency and effort
• Review cycle completion rates and timeliness
• Content creation and approval workflow efficiency
• Knowledge base growth and organization metrics

2. Improvement Strategies

Content Optimization:

• Popular content enhancement and expansion
• Gap analysis and targeted content creation
• Format optimization based on usage patterns
• Accessibility improvement and inclusive design

Process Refinement:

• Knowledge capture workflow optimization
• Review and approval process streamlining
• Tool integration and automation enhancement
• Training program development and delivery

Technology Enhancement:

• Search and discovery algorithm improvement
• Automated content generation and maintenance
• Integration with development tools and workflows
• Performance optimization and scalability planning

Integration with Development Ecosystem

1. Tool Integration

Development Environment:

• IDE integration for contextual documentation access
• Code editor plugins for inline knowledge display
• Build system integration for documentation validation
• Version control system integration for change tracking

Communication Tools:

• Chat platform integration for knowledge sharing
• Video conferencing integration for knowledge sessions
• Email notification integration for update alerts
• Project management tool integration for requirement tracking

Quality Assurance Tools:

• Testing framework integration for validation procedures
• Monitoring system integration for operational knowledge
• Security scanning integration for compliance documentation
• Performance testing integration for optimization guides

2. Workflow Integration

Feature Development Integration:

• Specification template integration with project planning
• Knowledge requirement identification during task breakdown
• Documentation milestone integration with development timeline
• Knowledge validation integration with code review process

Onboarding Integration:

• New team member knowledge path automation
• Role-specific training content delivery
• Progressive knowledge access based on experience level
• Mentorship program knowledge resource allocation

Support Integration:

• Customer support knowledge base integration
• Internal help desk knowledge resource sharing
• Troubleshooting guide integration with monitoring alerts
• Escalation procedure integration with incident response

specification-driven-development.md

Specification-Driven Development

Overview

Based on insights from Kiro's approach to AI-assisted development, this document outlines how to implement specification-driven development in our project to improve feature quality, reduce miscommunication, and create traceable development workflows.

Core Principles

1. Structured Requirements Capture

• Transform high-level feature ideas into detailed, actionable specifications
• Use standardized templates for consistency across all features
• Ensure requirements are testable and measurable

2. Three-Phase Development Workflow

1. Requirements Phase: Capture and validate what needs to be built
2. Design Phase: Create architectural plans and sequence diagrams
3. Implementation Phase: Execute with clear task tracking

3. Documentation as Source of Truth

• All features must have corresponding specification documents
• Specifications should be version-controlled alongside code
• Keep specs updated as implementation evolves

Implementation in Our Codebase

Directory Structure

docs/
├── specs/
│   ├── features/           # Feature specifications
│   ├── architecture/       # System design docs
│   └── templates/          # Reusable spec templates
└── implementation/
    ├── tasks/              # Task tracking and progress
    └── decisions/          # Architectural decision records

Specification Template

Each feature specification should include:

• Problem Statement: What problem are we solving?
• User Stories: Who needs this and why?
• Acceptance Criteria: How do we know it's done?
• Technical Requirements: Performance, security, compatibility
• Design Decisions: Architecture and implementation approach
• Task Breakdown: Discrete, trackable work items
• Testing Strategy: How will we validate the implementation?

Integration with Existing Workflow

• Use specification documents before starting any significant feature work
• Reference spec tasks in commit messages and PR descriptions
• Update specs when requirements change during implementation
• Conduct spec reviews before beginning development

Benefits for Our Project

For AI-Assisted Development

• Provides clear context for Claude Code to understand requirements
• Enables better task breakdown and planning
• Creates traceable decision-making process
• Improves code generation accuracy through detailed specifications

For Team Collaboration

• Bridges communication gaps between different stakeholders
• Creates shared understanding before development begins
• Enables parallel work on different aspects of features
• Facilitates better code reviews and testing

For Project Quality

• Reduces scope creep and requirement drift
• Ensures comprehensive testing coverage
• Creates audit trail for feature decisions
• Enables better maintenance and future enhancements

Getting Started

1. Create specification templates for common feature types
2. Start with one significant upcoming feature as a pilot
3. Establish review process for specifications
4. Integrate specification references into development workflow
5. Iterate and improve the process based on experience

steering-framework.md

Steering Documentation Framework

Overview

Based on Kiro's steering methodology, this framework establishes persistent project knowledge through structured documentation that guides AI-assisted development decisions, maintains consistency, and reduces cognitive overhead.

Core Principles

1. Persistent Project Knowledge

• Documentation serves as the single source of truth for project standards
• Markdown files provide contextual guidance for consistent decision-making
• Knowledge persists across development sessions and team changes
• Reduces repetitive explanations and maintains alignment

2. Contextual Guidance Modes

Always Included (Universal Standards):

• Core project principles and architectural decisions
• Coding standards and style guidelines
• Security policies and best practices
• Essential project structure and conventions

Conditional Inclusion (Context-Specific):

• Component-specific patterns and conventions
• API design standards for backend work
• Testing strategies for different feature types
• Database schema and migration guidelines

Manual Inclusion (Specialized Context):

• Complex integration patterns and examples
• Performance optimization strategies
• Advanced debugging and troubleshooting guides
• Domain-specific business logic documentation

3. Strategic Documentation Structure

Single Domain Focus:

• Each document addresses one specific area of concern
• Clear boundaries between different types of guidance
• Minimal overlap to avoid conflicting information
• Focused scope enables easy maintenance and updates

Descriptive Naming Convention:

docs/steering/
├── architecture/
│   ├── component-patterns.md
│   ├── state-management.md
│   └── integration-standards.md
├── development/
│   ├── coding-standards.md
│   ├── testing-strategies.md
│   └── review-guidelines.md
└── deployment/
    ├── environment-setup.md
    ├── security-policies.md
    └── monitoring-standards.md

Document Categories

1. Foundation Documents (Always Included)

Project Overview (docs/steering/project-overview.md):

• Mission statement and core objectives
• Key stakeholders and their roles
• Success metrics and quality standards
• High-level architecture and technology choices

Technology Stack (docs/steering/technology-stack.md):

• Primary frameworks and libraries with versions
• Development tools and build systems
• External services and integrations
• Rationale for technology choices

Code Standards (docs/steering/coding-standards.md):

• Language-specific style guidelines
• Naming conventions and file organization
• Code review requirements and quality gates
• Documentation and comment standards

2. Context-Specific Documents (Conditional)

Component Patterns (docs/steering/component-patterns.md):

• React component structure and conventions
• Props interface definitions and patterns
• State management approaches
• Error handling and loading states

API Standards (docs/steering/api-standards.md):

• RESTful endpoint design principles
• Request/response format conventions
• Error handling and status codes
• Authentication and authorization patterns

Database Guidelines (docs/steering/database-guidelines.md):

• Schema design principles
• Migration strategies and naming
• Query optimization standards
• Data validation and constraints

3. Specialized Documents (Manual Inclusion)

Integration Patterns (docs/steering/integration-patterns.md):

• Third-party service integration approaches
• Error handling and retry strategies
• Data transformation and validation
• Performance optimization techniques

Security Policies (docs/steering/security-policies.md):

• Authentication and authorization requirements
• Data protection and privacy standards
• Input validation and sanitization
• Audit logging and monitoring

Performance Standards (docs/steering/performance-standards.md):

• Response time and throughput requirements
• Caching strategies and implementation
• Database optimization techniques
• Frontend performance optimization

Document Structure Template

# [Document Title]

## Purpose
Brief description of what this document covers and when to reference it.

## Principles
Core principles that guide decisions in this domain.

## Standards
Specific, actionable standards with clear examples.

## Examples
Practical code examples demonstrating the standards.

## Common Patterns
Reusable patterns and their appropriate use cases.

## Anti-Patterns
What to avoid and why.

## Decision Framework
How to make decisions when standards don't clearly apply.

## References
Links to related documentation and external resources.

Implementation Guidelines

1. Document Creation Process

Assessment Phase:

1. Identify areas where decisions are repeatedly made
2. Analyze current inconsistencies or confusion points
3. Gather examples of good and poor implementations
4. Define scope and boundaries for the document

Drafting Phase:

1. Use the document structure template
2. Include practical code examples
3. Explain rationale behind standards
4. Define decision-making frameworks for edge cases

Review Phase:

1. Validate with team members and stakeholders
2. Test guidance with real implementation scenarios
3. Refine based on feedback and practical application
4. Establish maintenance and update procedures

2. Usage Integration

Claude Code Integration:

• Reference specific steering documents in task planning
• Apply standards consistently across all implementations
• Use examples as templates for new code generation
• Flag deviations from standards for human review

Development Workflow Integration:

• Include steering document references in pull request templates
• Use documents as basis for code review checklists
• Update documents based on lessons learned from implementation
• Maintain version control for steering documents alongside code

3. Maintenance Strategy

Regular Review Cycle:

• Quarterly review of all steering documents
• Update based on technology changes and lessons learned
• Archive obsolete guidance and consolidate duplicates
• Ensure examples remain current and functional

Continuous Improvement:

• Track decision-making effectiveness and consistency
• Gather feedback from development team on document utility
• Monitor adherence to standards and identify gaps
• Evolve guidance based on project growth and complexity

Benefits and Outcomes

1. Consistency and Quality

• Uniform implementation of patterns across the codebase
• Reduced decision fatigue for developers
• Higher code quality through established standards
• Faster onboarding for new team members

2. Efficiency and Speed

• Reduced time spent on architectural decisions
• Faster code reviews through clear standards
• Improved AI-assisted development accuracy
• Less rework due to consistent initial implementation

3. Knowledge Management

• Institutional knowledge preservation
• Scalable project guidance system
• Reduced dependency on individual expertise
• Improved collaboration through shared understanding

4. Adaptability and Growth

• Framework supports project evolution
• Easy to update and extend guidance
• Accommodates changing requirements and technologies
• Maintains consistency during team changes

Integration with Existing Documentation

1. Relationship to CLAUDE.md

• CLAUDE.md serves as navigation hub to steering documents
• Keeps main file focused and manageable
• Enables specific context inclusion based on task type
• Maintains backward compatibility with existing guidance

2. Relationship to Specifications

• Steering documents provide implementation guidance
• Specifications define what to build
• Combined approach ensures both consistency and completeness
• Clear separation of concerns between guidance and requirements

3. Relationship to Implementation Tracking

• Steering documents inform task breakdown and estimation
• Progress tracking validates adherence to standards
• Implementation reports update steering guidance based on experience
• Continuous feedback loop improves both guidance and outcomes

strategic-decision-making.md

Strategic Decision-Making Framework

Overview

This framework establishes systematic approaches for making strategic technical and architectural decisions in AI-assisted development environments. Based on specification-driven development and steering methodologies, it ensures consistent, well-reasoned decision-making that can be traced and validated over time.

Decision-Making Framework

1. Decision Types and Authority

Architectural Decisions (High Impact):

• Technology stack changes or additions
• Database schema major modifications
• Security model changes
• Integration architecture decisions
• Performance optimization strategies

Authority: Requires human approval with documented rationale

Implementation Decisions (Medium Impact):

• Component structure and organization
• API endpoint design patterns
• Testing strategy selections
• Code organization approaches
• Library and utility choices

Authority: Claude Code with human review if significant implications

Tactical Decisions (Low Impact):

• Variable naming and code style choices
• Minor refactoring approaches
• Test case organization
• Documentation structure
• Development workflow optimizations

Authority: Claude Code following established guidelines

2. Decision Process Framework

Problem Identification:

1. Clearly define the decision that needs to be made
2. Identify stakeholders and impact areas
3. Establish decision timeline and constraints
4. Document current state and desired outcomes

Option Generation:

1. Research existing solutions and industry best practices
2. Generate multiple viable alternatives
3. Consider both immediate and long-term implications
4. Include "do nothing" option where applicable

Analysis and Evaluation:

1. Apply consistent evaluation criteria
2. Assess technical, business, and operational impacts
3. Consider maintenance and scalability implications
4. Evaluate team capabilities and learning requirements

Decision and Documentation:

1. Select optimal solution with clear rationale
2. Document decision with context and alternatives considered
3. Establish success metrics and review points
4. Communicate decision to relevant stakeholders

Evaluation Criteria Framework

1. Technical Criteria

Functionality:

• Does it solve the stated problem completely?
• How well does it integrate with existing systems?
• What are the performance implications?
• Does it introduce technical debt or complexity?

Maintainability:

• How easy is it to understand and modify?
• What are the documentation requirements?
• How does it affect testing and debugging?
• What skills are required for ongoing maintenance?

Scalability:

• How does it perform under increased load?
• What are the resource requirements?
• How does it handle data growth?
• What are the scaling bottlenecks?

2. Business Criteria

Value Delivery:

• How does it support business objectives?
• What is the expected return on investment?
• How does it affect user experience?
• What competitive advantages does it provide?

Risk Assessment:

• What are the implementation risks?
• How does it affect system reliability?
• What are the security implications?
• What dependencies does it create?

Resource Impact:

• What development effort is required?
• What ongoing operational costs are involved?
• What training or hiring needs are created?
• How does it affect project timeline?

3. Strategic Criteria

Alignment:

• How well does it align with technology strategy?
• Does it support long-term architectural vision?
• How does it affect team capabilities?
• What precedent does it set for future decisions?

Flexibility:

• How adaptable is it to changing requirements?
• What future options does it preserve or eliminate?
• How reversible is the decision?
• What lock-in effects does it create?

Decision Documentation Template

# Decision Record: [Decision Title]

**Date**: [YYYY-MM-DD]
**Status**: [Proposed | Accepted | Deprecated | Superseded]
**Decision Maker**: [Claude Code | Human Developer | Team]

## Context
Brief description of the situation requiring a decision.

## Problem Statement
Clear articulation of the problem or opportunity.

## Decision Drivers
Key factors influencing this decision:
- [Driver 1]
- [Driver 2]
- [Driver 3]

## Options Considered

### Option 1: [Name]
**Description**: [Brief description]
**Pros**: 
- [Advantage 1]
- [Advantage 2]
**Cons**:
- [Disadvantage 1]
- [Disadvantage 2]
**Effort**: [High | Medium | Low]

### Option 2: [Name]
[Similar structure...]

## Decision
[Chosen option with brief justification]

## Rationale
Detailed explanation of why this option was selected:
- [Reason 1]
- [Reason 2]
- [Reason 3]

## Implications
**Positive Consequences**:
- [Consequence 1]
- [Consequence 2]

**Negative Consequences**:
- [Consequence 1]
- [Consequence 2]

**Risks and Mitigations**:
- [Risk]: [Mitigation strategy]

## Success Metrics
How we will measure the success of this decision:
- [Metric 1]: [Target value]
- [Metric 2]: [Target value]

## Review Schedule
- **Short-term review**: [Date/milestone]
- **Long-term review**: [Date/milestone]

## Related Decisions
Links to related decision records or specifications.

## References
External resources, research, or documentation consulted.

Common Decision Patterns

1. Technology Selection Decisions

Evaluation Framework:

• Community support and ecosystem maturity
• Performance characteristics and benchmarks
• Learning curve and team expertise
• Integration complexity and compatibility
• License and cost considerations
• Long-term viability and roadmap

Common Scenarios:

• Choosing between competing libraries or frameworks
• Selecting database technologies for specific use cases
• Evaluating third-party service providers
• Deciding on development and deployment tools

2. Architecture Pattern Decisions

Evaluation Framework:

• Complexity vs. benefits trade-off
• Team familiarity and capability requirements
• Maintenance and operational overhead
• Scalability and performance implications
• Testing and debugging complexity
• Documentation and knowledge transfer needs

Common Scenarios:

• Microservices vs. monolithic architecture
• Client-side vs. server-side rendering approaches
• Event-driven vs. request-response patterns
• Caching strategies and implementation approaches

3. Quality vs. Speed Trade-offs

Evaluation Framework:

• Business urgency and market timing
• Technical debt accumulation and future costs
• Risk tolerance and failure implications
• Resource availability and constraints
• User impact and experience considerations
• Maintainability and evolution requirements

Common Scenarios:

• MVP vs. fully-featured initial release
• Custom implementation vs. third-party solutions
• Comprehensive testing vs. rapid deployment
• Perfect vs. good-enough solutions

AI-Assisted Decision Making

1. Claude Code Capabilities

Research and Analysis:

• Comprehensive option identification and research
• Systematic evaluation against defined criteria
• Risk assessment and mitigation strategy development
• Documentation generation and maintenance

Pattern Recognition:

• Identification of similar past decisions and outcomes
• Recognition of anti-patterns and common pitfalls
• Application of industry best practices and standards
• Consistency checking against architectural principles

Scenario Analysis:

• Future impact modeling and prediction
• What-if analysis for different scenarios
• Dependency mapping and cascade effect analysis
• Timeline and resource requirement estimation

2. Human Oversight Requirements

Strategic Decisions:

• Business alignment and priority validation
• Stakeholder impact assessment and communication
• Resource allocation and budget approval
• Long-term vision and roadmap alignment

Context and Intuition:

• Domain-specific knowledge and experience
• Political and organizational considerations
• User empathy and experience validation
• Market timing and competitive analysis

Risk Assessment:

• Business risk tolerance and appetite
• Regulatory and compliance implications
• Security and privacy considerations
• Reputation and brand impact evaluation

Integration with Development Workflow

1. Decision Points in Feature Development

Specification Phase:

• Technical approach selection
• Architecture pattern choices
• Integration strategy decisions
• Performance and scalability considerations

Implementation Phase:

• Library and tool selections
• Code organization approaches
• Testing strategy implementations
• Error handling and validation patterns

Validation Phase:

• Deployment strategy choices
• Monitoring and alerting implementations
• Rollback and recovery procedures
• Performance optimization decisions

2. Decision Review and Learning

Regular Review Cycles:

• Quarterly decision outcome assessment
• Annual strategic decision retrospective
• Post-project decision effectiveness analysis
• Continuous improvement identification

Learning Integration:

• Decision pattern recognition and template updates
• Best practice documentation enhancement
• Anti-pattern identification and avoidance strategies
• Team knowledge sharing and capability building

Success Metrics and Continuous Improvement

1. Decision Quality Metrics

Outcome Effectiveness:

• Achievement of stated objectives and success criteria
• Unintended consequences and side effects
• Time to value realization
• Cost vs. benefit analysis results

Process Efficiency:

• Time from problem identification to decision implementation
• Number of iterations required to reach optimal solution
• Stakeholder satisfaction with decision process
• Documentation quality and accessibility

2. Learning and Adaptation

Pattern Recognition:

• Identification of successful decision patterns
• Recognition of problematic approaches
• Development of decision-making heuristics
• Creation of domain-specific guidance

Process Refinement:

• Evaluation criteria optimization
• Decision authority clarification
• Documentation template improvements
• Tool and methodology enhancements