| name | project-status-manager |
| name_pretty | Project Status Manager |
| description | Generates a comprehensive project health report by aggregating task status and QA test results. Provides unified monitoring of process health (tasks) and product health (tests) with risk indicators and trend analysis. |
| command | scripts/project_status_manager.py |
| version | 1.0.0 |
Project Status Manager - Comprehensive Health Monitoring
Purpose
This skill is the primary reporting and monitoring tool for the lead-developer. It provides a comprehensive, system-wide health check by aggregating data from the two most critical sources of truth: the task "database" (process health) and the QA test reports (product health). The unified report enables data-driven decision making and proactive risk management.
When to Use
- Daily Standups: Generate current project status for team alignment
- Stakeholder Reporting: Provide comprehensive progress reports to stakeholders
- Health Monitoring: Identify project risks and quality issues early
- Trend Analysis: Track project health metrics over time
- Decision Support: Inform strategic decisions with objective data
- Quality Gates: Validate project readiness for milestones
R&D Workflow
This skill follows the "Reduce and Delegate" philosophy:
- Agent Role: Primary monitoring tool for the lead-developer (Orchestrator)
- Data Sources: Two high-truth-value sources (task database + QA reports)
- Delegation: Agent MUST delegate all complex aggregation to
ProjectWorkflowManager.generate_status_report()
- Deterministic Process: Script performs reliable data synthesis and analysis
Input Requirements
The skill accepts optional configuration parameters:
{
"include_trends": true,
"trend_days": 14,
"risk_threshold": "medium",
"include_predictions": true,
"forecast_days": 7,
"quality_gates": {
"min_test_pass_rate": 80,
"max_failed_tasks": 5,
"min_completion_rate": 60
}
}
Parameters Explained
- include_trends: Include historical trend analysis in the report
- trend_days: Number of days to analyze for trends (default: 14)
- risk_threshold: Minimum risk level to include in report (low/medium/high/critical)
- include_predictions: Include predictive analytics and forecasts
- forecast_days: Number of days to forecast project metrics
- quality_gates: Thresholds for quality gate validation
Output Format
Returns comprehensive project health report:
{
"generated_at": "2025-01-01T12:00:00Z",
"report_period": {
"start_date": "2024-12-18",
"end_date": "2025-01-01",
"trend_analysis_days": 14
},
"tasks": {
"total": 77,
"by_status": {
"completed": 23,
"in_progress": 15,
"in_sprint": 12,
"failed": 2,
"pending": 25
},
"by_assignee": {
"gdscript-engineer": 18,
"asset-pipeline-engineer": 15,
"ui-engineer": 12,
"unassigned": 32
},
"by_sprint": {
"Sprint-01": 15,
"Sprint-02": 12,
"none": 50
},
"unestimated": 8,
"overdue": [],
"completion_rate": 29.9,
"sprint_progress": {
"current_sprint": "Sprint-02",
"sprint_completion": 45.8,
"sprint_capacity_utilization": 83.3
}
},
"qa": {
"tests_run": 245,
"failures": 8,
"errors": 3,
"pass_rate": 95.5,
"coverage": {
"line_coverage": 82.3,
"branch_coverage": 76.8,
"function_coverage": 89.1
},
"last_run": "2025-01-01T11:30:00Z",
"test_trend": "improving"
},
"health_indicators": {
"overall_health": "good",
"task_completion_rate": 29.9,
"test_pass_rate": 95.5,
"burndown_health": "good",
"quality_trend": "improving",
"velocity_trend": "stable"
},
"risk_indicators": [
{
"type": "task_distribution",
"level": "medium",
"description": "32 tasks remain unassigned",
"impact": "potential resource bottlenecks",
"recommendation": "Assign tasks to team members or adjust capacity planning"
},
{
"type": "quality",
"level": "low",
"description": "8 test failures detected",
"impact": "code quality issues requiring attention",
"recommendation": "Address failing tests before next release"
}
],
"trend_analysis": {
"completion_rate_trend": "increasing",
"velocity_trend": "stable",
"quality_trend": "improving",
"risk_trend": "stable"
},
"predictions": {
"estimated_completion": "2025-03-15",
"confidence_level": 75,
"risk_factors": [
"unassigned_tasks",
"new requirements"
]
},
"quality_gates": {
"status": "pass",
"gates_checked": [
{
"name": "min_test_pass_rate",
"threshold": 80,
"actual": 95.5,
"status": "pass"
},
{
"name": "max_failed_tasks",
"threshold": 5,
"actual": 2,
"status": "pass"
},
{
"name": "min_completion_rate",
"threshold": 60,
"actual": 29.9,
"status": "fail"
}
]
},
"recommendations": [
"Assign unassigned tasks to balance team workload",
"Focus on completing in-progress tasks before starting new ones",
"Address test failures to maintain code quality standards",
"Consider breaking down large tasks for better progress tracking"
]
}
Deterministic Data Synthesis Process
The ProjectWorkflowManager backend implements this analysis process:
Phase 1: Task Database Analysis (Process Health)
- Status Aggregation: Count tasks by status (pending, in_progress, in_sprint, failed, completed)
- Assignee Distribution: Analyze workload distribution across team members
- Sprint Analysis: Evaluate current and historical sprint performance
- Dependency Analysis: Identify blocking dependencies and critical paths
- Estimation Analysis: Track unestimated tasks and estimation accuracy
Phase 2: QA Report Analysis (Product Health)
- Test Execution: Parse JUnit XML reports for test results
- Coverage Analysis: Extract code coverage metrics from test reports
- Quality Trends: Track quality metrics over time
- Failure Analysis: Categorize and analyze test failures
- Performance Metrics: Monitor test execution performance
Phase 3: Health Indicator Calculation
- Completion Rate: Calculate percentage of completed tasks
- Quality Metrics: Synthesize test pass rates and coverage
- Velocity Tracking: Monitor team velocity and burndown progress
- Risk Assessment: Identify project-level risks and indicators
- Trend Analysis: Calculate historical trends and changes
Phase 4: Predictive Analytics
- Completion Forecasting: Estimate project completion dates
- Risk Prediction: Identify potential future risks based on current trends
- Resource Planning: Forecast future resource needs
- Quality Prediction: Predict quality trends based on current metrics
Health Indicator Framework
Overall Health Score
The system calculates an overall health score based on multiple factors:
- Task Health (40%): Completion rate, sprint progress, task distribution
- Quality Health (35%): Test pass rate, coverage, failure trends
- Velocity Health (15%): Team velocity consistency, burndown progress
- Risk Health (10%): Risk indicators, dependency health
Health Categories
- Excellent: All indicators green, no significant risks
- Good: Most indicators green, minor issues identified
- Warning: Some indicators yellow, moderate risks present
- Critical: Multiple indicators red, significant risks requiring immediate attention
Risk Assessment Framework
Risk Categories
- Task Distribution Risks: Unbalanced workloads, unassigned tasks
- Quality Risks: Falling test pass rates, coverage gaps
- Schedule Risks: Delayed tasks, dependency blocks
- Resource Risks: Team capacity issues, skill gaps
- Technical Risks: Integration issues, architectural problems
Risk Scoring
- Critical: Immediate impact on project success, requires urgent action
- High: Significant impact if not addressed within 1-2 weeks
- Medium: Moderate impact, should be addressed in current sprint
- Low: Minor impact, can be addressed during routine maintenance
Trend Analysis Features
Historical Trends
- Completion Rate: Track project completion over time
- Team Velocity: Monitor velocity consistency and changes
- Quality Metrics: Track test pass rates and coverage trends
- Risk Evolution: Monitor how risk indicators change over time
Predictive Analytics
- Completion Forecasting: Estimate project completion dates with confidence intervals
- Velocity Prediction: Forecast future team velocity based on historical data
- Quality Forecasting: Predict quality trends based on current metrics
- Risk Prediction: Identify potential future risks based on trend analysis
Quality Gates Integration
Gate Definitions
The skill integrates with configurable quality gates:
- Minimum Test Pass Rate: Ensure code quality standards
- Maximum Failed Tasks: Limit work-in-progress and failures
- Minimum Completion Rate: Ensure adequate project progress
- Coverage Thresholds: Maintain adequate test coverage
Gate Validation
- Real-time Validation: Check gates against current metrics
- Historical Validation: Validate gates against historical trends
- Trend Validation: Ensure gates are being met consistently over time
Integration Points
With sprint-planner
- Capacity Planning: Use status data for future sprint planning
- Velocity Tracking: Monitor team velocity for capacity estimation
- Risk Assessment: Identify risks that impact sprint planning
With qa-engineer
- Quality Metrics: Aggregate test results for health reporting
- Failure Analysis: Analyze test failures for risk identification
- Coverage Tracking: Monitor code coverage trends
With requirements-engineer
- Requirements Coverage: Track requirements implementation progress
- Traceability Health: Monitor requirements traceability status
- Gap Analysis: Identify requirements not yet implemented
Report Generation Features
Executive Summary
- High-level project health overview
- Key metrics and trends
- Critical risks and recommendations
- Quality gate status
Detailed Analysis
- Comprehensive task breakdown by status, assignee, and sprint
- Detailed QA metrics and coverage analysis
- Risk indicators with mitigation strategies
- Trend analysis with visualizations
Actionable Insights
- Specific recommendations for improvement
- Risk mitigation strategies
- Resource allocation suggestions
- Process improvement opportunities
This skill provides the lead-developer with a comprehensive, data-driven view of project health that enables proactive management and informed decision making throughout the migration process.
