Morning Standup CLI - User Guide

Status: ✅ PRODUCTION READY with ISSUE INTELLIGENCE INTEGRATION Created: August 24, 2025 Last Updated: August 24, 2025

🎯 Overview

The Morning Standup CLI provides automated daily standup generation using persistent context infrastructure, GitHub activity analysis, and optional Issue Intelligence integration. Built for <2 second performance with 15+ minutes of manual preparation time savings.

🚀 Quick Start

Basic Usage

# Generate standard morning standup
python cli/commands/standup.py

# Generate standup with integrated issue priorities
python cli/commands/standup.py --with-issues

# Generate Slack-formatted output
python cli/commands/standup.py --format slack

# Generate Slack output with issue integration
python cli/commands/standup.py --with-issues --format slack

Command Options

# Available formats
--format cli     # Terminal-optimized output (default)
--format slack   # Slack-ready markdown format

# Issue integration
--with-issues    # Include top 3 priority issues from Issue Intelligence
--without-issues # Standard standup only (default)

📊 Standup Components

Yesterday’s Accomplishments

GitHub Commits: Automatically pulls commits from last 24 hours
Session Context: Leverages persistent context from previous sessions
Issue Closures: Shows recently closed GitHub issues
Manual Context: User-defined accomplishments from preferences

Today’s Priorities

Active Repositories: Focus areas from user preferences
Contextual Tasks: Derived from yesterday’s unfinished work
Issue Priorities: Top issues from Issue Intelligence (when --with-issues used)
Default Priorities: Intelligent defaults when no context available

Blockers

System Issues: Detected configuration or connectivity problems
GitHub API Issues: Rate limiting or authentication problems
Context Issues: Missing or incomplete session data
Integration Issues: Issue Intelligence connectivity problems

🔄 Issue Intelligence Integration

Status: ✅ ACTIVE INTEGRATION - Available since August 24, 2025

Integration Features

When using --with-issues flag:

Top 3 Priority Issues: Automatically included in “Today’s Priorities”
Contextual Filtering: Issues filtered based on current project focus
Graceful Degradation: Standup continues if Issue Intelligence unavailable
Error Reporting: Clear warnings when integration fails

Sample Integrated Output

🎯 Today's Priorities
----------------------------------------
ℹ️   🎯 Continue work on piper-morgan
ℹ️   🎯 Issue #123: Fix authentication bug
ℹ️   🎯 Issue #124: Implement user preferences
ℹ️   🎯 Issue #125: Update documentation
ℹ️   📊 Review project status

Integration Architecture

Canonical Query Pattern: Uses shared CanonicalHandlers infrastructure
Async Integration: Non-blocking issue priority retrieval
Performance Optimized: Minimal overhead added to standup generation
Cross-Feature Learning: Issue patterns inform standup prioritization

⚙️ Configuration

User Preferences

The standup leverages user preferences for personalization:

# Preferences automatically managed by UserPreferenceManager
{
    "active_repos": ["piper-morgan", "other-projects"],
    "yesterday_context": {"database": "resolved", "testing": "in-progress"},
    "last_session_time": "2025-08-24T17:30:00"
}

Session Persistence

Automatically maintains context across sessions:

Yesterday’s Work: Tasks and accomplishments from previous sessions
Active Projects: Currently focused repositories and areas
Focus Areas: Technical areas requiring continued attention
Time Tracking: Last session timestamps for context calculation

📈 Performance Metrics

Performance Targets

Generation Time: <2 seconds (consistently achieved)
Time Savings: 15+ minutes of manual preparation time
Context Accuracy: 90%+ relevant information inclusion
Integration Overhead: <200ms for Issue Intelligence integration

Real-Time Reporting

Each standup includes performance metrics:

📊 Performance Summary
----------------------------------------
ℹ️   Context Source: persistent
ℹ️   GitHub Activity: 5 commits
ℹ️   Generation Time: 450ms
ℹ️   Performance Target: ✅ MET

🔧 Technical Architecture

Dependencies

UserPreferenceManager: User settings and context persistence
SessionPersistenceManager: Cross-session context continuity
GitHubAgent: GitHub activity retrieval and analysis
CanonicalHandlers: Integration foundation for cross-feature functionality
IssueIntelligenceCanonicalQueryEngine: Priority issue analysis (optional)

Error Handling

Comprehensive fallback mechanisms ensure standup generation succeeds:

GitHub API Failures: Graceful degradation with context-based content
Session Context Missing: Intelligent defaults and user guidance
Integration Failures: Clear warnings with continued functionality
Performance Issues: Timeout handling with partial results

Integration Points

CLI Interface: Clean command-line interface with help system
Slack Integration: Markdown-formatted output for team sharing
Issue Intelligence: Seamless priority issue inclusion
GitHub Integration: Real-time activity analysis and reporting

🚀 Advanced Usage

Development Workflow Integration

# Daily workflow integration
python cli/commands/standup.py --with-issues --format slack > standup.md
cat standup.md  # Review before sharing
# Share standup.md with team via Slack/email

Automation

# Add to daily routine (crontab example)
0 9 * * 1-5 cd /path/to/piper-morgan && python cli/commands/standup.py --with-issues

Debugging

# Check specific components
python -c "from services.features.morning_standup import MorningStandupWorkflow; print('Available')"

# Test integration separately
python -c "from services.features.issue_intelligence import IssueIntelligenceCanonicalQueryEngine; print('Available')"

🛠️ Troubleshooting

Common Issues

Standup Generation Slow:

Check GitHub API rate limits
Verify database connectivity
Review session context size

Integration Failures:

Verify Issue Intelligence service status
Check CanonicalHandlers initialization
Review error messages in standup output

Missing Context:

Run standup daily to build context
Check UserPreferenceManager settings
Verify GitHub token configuration

Support

Architecture Documentation: Canonical Queries Architecture
Development Guide: Morning Standup MVP Guide
Integration Testing: Integration Guide

📝 Changelog

August 24, 2025 - Issue Intelligence Integration

✅ Added --with-issues flag for Issue Intelligence integration
✅ Implemented CanonicalHandlers architecture alignment
✅ Added graceful degradation for integration failures
✅ Enhanced CLI with comprehensive help system
✅ Added Slack formatting with issue integration support

Previous Versions

See Morning Standup MVP Guide for complete development history.

Status: ✅ PRODUCTION READY with ACTIVE ISSUE INTELLIGENCE INTEGRATION CLI Command: python cli/commands/standup.py --with-issues Performance: <2 second generation, 15+ minutes time savings Integration: Issue Intelligence via canonical query pattern Next Enhancement: Issue Intelligence initialization optimization (PM-124)

UI Guide Section

Morning Standup UI Guide

User Experience Features

Dark Mode Interface

High contrast design addressing readability concerns
Professional appearance suitable for daily use
Optimized typography and spacing

Mobile Responsiveness

Tablet optimization (768px breakpoint)
Phone optimization (480px breakpoint)
Touch-friendly button sizing

Performance Metrics Display

Badge-style performance indicators
Generation time, time saved, context source
Centered highlight section for visibility

Error Handling

Professional error state design
Helpful error messaging with user guidance
Connection error differentiation

Multi-Project Support

Clear project context display
Repository information and user identification
Detailed GitHub commit information (limited to 5 for readability)

Daily Usage Workflow

Navigate to http://localhost:8001/assets/standup.html
Click “Run Morning Standup” button
View results with prominent performance metrics
Review accomplishments, priorities, and GitHub activity

Browser Compatibility

Modern browsers with JavaScript enabled
Responsive design across device sizes
Dark mode with high contrast for accessibility

Technical Implementation

File Structure

Location: web/assets/standup.html
Size: ~14KB comprehensive UI component
Dependencies: Fetches from /api/standup endpoint

UI Components

Header with project branding
Action button for standup execution
Loading state with user feedback
Results display with structured sections
Performance metrics badge
Error handling with recovery guidance

Styling Architecture

CSS-in-HTML for single-file deployment
Dark theme with high contrast ratios
Responsive breakpoints for mobile/tablet
Professional typography and spacing

Integration Points

API: http://localhost:8001/api/standup
Static serving: FastAPI StaticFiles mount
Error handling: Graceful degradation on API failures

Performance Characteristics

UI Load: <100ms static file serving
API Integration: 4.6-5.1s generation time
Total Experience: ~5s end-to-end

Web Interface Section

Morning Standup Web Interface

Implementation

FastAPI backend with /api/standup endpoint
Dark mode UI at /assets/standup.html
Integrates with CLI orchestrator for consistency
Performance: 4.6-5.1s generation time

Configuration

Port: 8001 (documented to avoid Docker conflicts)
Server: uvicorn with 127.0.0.1 binding
Static assets: served via FastAPI StaticFiles

Usage

Daily 6 AM standup with prominent performance metrics, project context, and GitHub activity display.

Quick Start

# Start FastAPI server
PYTHONPATH=. python web/app.py
# or
PYTHONPATH=. python -m uvicorn web.app:app --host 127.0.0.1 --port 8001

Access Points

Web UI: http://localhost:8001/assets/standup.html
API Endpoint: http://localhost:8001/api/standup
API Documentation: http://localhost:8001/docs

Performance

Generation Time: 4.6-5.1 seconds
Response: JSON with comprehensive standup data
Features: Dark mode, mobile responsive, error handling