Provides strategic project management analysis for enterprise initiatives, including quantitative risk assessment (EMV, Monte Carlo), resource capacity optimization, and executive-level health reporting using frameworks like WSJF and RAG status.
Say it in your own words; the assistant finds this recipe by itself. Examples:
Use this skill when the user needs to assess the health of a complex project portfolio, quantify financial and schedule risks using statistical methods, or optimize resource allocation across multiple workstreams. It is ideal for generating board-ready status reports, creating strategic roadmaps for enterprise-scale digital transformations, or resolving high-level stakeholder conflicts in large budgets.
Strategic project management for enterprise software, SaaS, and digital transformation initiatives. Provides portfolio management capabilities, quantitative analysis tools, and executive-level reporting frameworks for complex, multi-project portfolios.
Portfolio Management & Strategic Alignment - Multi-project portfolio optimization using advanced prioritization models (WSJF, RICE, ICE, MoSCoW) - Strategic roadmap development aligned with business objectives and market conditions - Resource capacity planning and allocation optimization across portfolio - Portfolio health monitoring with multi-dimensional scoring frameworks
Quantitative Risk Management - Expected Monetary Value (EMV) analysis for financial risk quantification - Monte Carlo simulation for schedule risk modeling and confidence intervals - Risk appetite framework implementation with enterprise-level thresholds - Portfolio risk correlation analysis and diversification strategies
Executive Communication & Governance - Board-ready executive reports with RAG status and strategic recommendations - Stakeholder alignment through sophisticated RACI matrices and escalation paths - Financial performance tracking with risk-adjusted ROI and NPV calculations - Change management strategies for large-scale digital transformations
Tier 1: Portfolio Health Assessment
Uses project_health_dashboard.py to provide comprehensive multi-dimensional scoring:
python3 scripts/project_health_dashboard.py assets/sample_project_data.json
Health Dimensions (Weighted Scoring): - Timeline Performance (25% weight): Schedule adherence, milestone achievement, critical path analysis - Budget Management (25% weight): Spend variance, forecast accuracy, cost efficiency metrics - Scope Delivery (20% weight): Feature completion rates, requirement satisfaction, change control - Quality Metrics (20% weight): Code coverage, defect density, technical debt, security posture - Risk Exposure (10% weight): Risk score, mitigation effectiveness, exposure trends
RAG Status Calculation: - 🟢 Green: Composite score >80, all dimensions >60 - 🟡 Amber: Composite score 60-80, or any dimension 40-60 - 🔴 Red: Composite score <60, or any dimension <40
Tier 2: Risk Matrix & Mitigation Strategy
Leverages risk_matrix_analyzer.py for quantitative risk assessment:
python3 scripts/risk_matrix_analyzer.py assets/sample_project_data.json
Risk Quantification Process: 1. Probability Assessment (1-5 scale): Historical data, expert judgment, Monte Carlo inputs 2. Impact Analysis (1-5 scale): Financial, schedule, quality, and strategic impact vectors 3. Category Weighting: Technical (1.2x), Resource (1.1x), Financial (1.4x), Schedule (1.0x) 4. EMV Calculation:
# EMV and risk-adjusted budget calculation
def calculate_emv(risks):
category_weights = {"Technical": 1.2, "Resource": 1.1, "Financial": 1.4, "Schedule": 1.0}
total_emv = 0
for risk in risks:
score = risk["probability"] * risk["impact"] * category_weights[risk["category"]]
emv = risk["probability"] * risk["financial_impact"]
total_emv += emv
risk["score"] = score
return total_emv
def risk_adjusted_budget(base_budget, portfolio_risk_score, risk_tolerance_factor):
risk_premium = portfolio_risk_score * risk_tolerance_factor
return base_budget * (1 + risk_premium)
Risk Response Strategies (by score threshold): - Avoid (>18): Eliminate through scope/approach changes - Mitigate (12-18): Reduce probability or impact through active intervention - Transfer (8-12): Insurance, contracts, partnerships - Accept (<8): Monitor with contingency planning
Tier 3: Resource Capacity Optimization
Employs resource_capacity_planner.py for portfolio resource analysis:
python3 scripts/resource_capacity_planner.py assets/sample_project_data.json
Capacity Analysis Framework: - Utilization Optimization: Target 70-85% for sustainable productivity - Skill Matching: Algorithm-based resource allocation to maximize efficiency - Bottleneck Identification: Critical path resource constraints across portfolio - Scenario Planning: What-if analysis for resource reallocation strategies
Apply each model in the specific context where it provides the most signal:
Weighted Shortest Job First (WSJF) — Resource-constrained agile portfolios with quantifiable cost-of-delay
def wsjf(user_value, time_criticality, risk_reduction, job_size):
return (user_value + time_criticality + risk_reduction) / job_size
RICE — Customer-facing initiatives where reach metrics are quantifiable
def rice(reach, impact, confidence_pct, effort_person_months):
return (reach * impact * (confidence_pct / 100)) / effort_person_months
ICE — Rapid prioritization during brainstorming or when analysis time is limited
def ice(impact, confidence, ease):
return (impact + confidence + ease) / 3
Model Selection — Use this decision logic:
if resource_constrained and agile_methodology and cost_of_delay_quantifiable:
→ WSJF
elif customer_facing and reach_metrics_available:
→ RICE
elif quick_prioritization_needed or ideation_phase:
→ ICE
elif multiple_stakeholder_groups_with_differing_priorities:
→ MoSCoW
elif complex_tradeoffs_across_incommensurable_criteria:
→ Multi-Criteria Decision Analysis (MCDA)
Reference: references/portfolio-prioritization-models.md
Reference: references/risk-management-framework.md
Step 1: Risk Classification by Category - Technical: Architecture, integration, performance - Resource: Availability, skills, retention - Schedule: Dependencies, critical path, external factors - Financial: Budget overruns, currency, economic factors - Business: Market changes, competitive pressure, strategic shifts
Step 2: Three-Point Estimation for Monte Carlo Inputs
def three_point_estimate(optimistic, most_likely, pessimistic):
expected = (optimistic + 4 * most_likely + pessimistic) / 6
std_dev = (pessimistic - optimistic) / 6
return expected, std_dev
Step 3: Portfolio Risk Correlation
import math
def portfolio_risk(individual_risks, correlations):
# individual_risks: list of risk EMV values
# correlations: list of (i, j, corr_coefficient) tuples
sum_sq = sum(r**2 for r in individual_risks)
sum_corr = sum(2 * c * individual_risks[i] * individual_risks[j]
for i, j, c in correlations)
return math.sqrt(sum_sq + sum_corr)
Risk Appetite Framework: - Conservative: Risk scores 0-8, 25-30% contingency reserves - Moderate: Risk scores 8-15, 15-20% contingency reserves - Aggressive: Risk scores 15+, 10-15% contingency reserves
Reference: assets/project_charter_template.md
Comprehensive 12-section charter including: - Executive summary with strategic alignment - Success criteria with KPIs and quality gates - RACI matrix with decision authority levels - Risk assessment with mitigation strategies - Budget breakdown with contingency analysis - Timeline with critical path dependencies
Reference: assets/executive_report_template.md
Board-level portfolio reporting with: - RAG status dashboard with trend analysis - Financial performance vs. strategic objectives - Risk heat map with mitigation status - Resource utilization and capacity analysis - Forward-looking recommendations with ROI projections
Reference: assets/raci_matrix_template.md
Enterprise-grade responsibility assignment featuring: - Detailed stakeholder roster with decision authority - Phase-based RACI assignments (initiation through deployment) - Escalation paths with timeline and authority levels - Communication protocols and meeting frameworks - Conflict resolution processes with governance integration
Reference: assets/sample_project_data.json
Realistic multi-project portfolio including: - 4 projects across different phases and priorities - Complete financial data (budgets, actuals, forecasts) - Resource allocation with utilization metrics - Risk register with probability/impact scoring - Quality metrics and stakeholder satisfaction data - Dependencies and milestone tracking
Reference: assets/expected_output.json
Demonstrates script capabilities with: - Portfolio health scores and RAG status - Risk matrix visualization and mitigation priorities - Resource capacity analysis with optimization recommendations - Integration examples showing how outputs complement each other
Data Collection & Validation
bash
python3 scripts/project_health_dashboard.py current_portfolio.json
⚠️ If any project composite score <60 or a critical data field is missing, STOP and resolve data integrity issues before proceeding.
Risk Assessment Update
bash
python3 scripts/risk_matrix_analyzer.py current_portfolio.json
⚠️ If any risk score >18 (Avoid threshold), STOP and initiate escalation to project sponsor before proceeding.
Capacity Analysis
bash
python3 scripts/resource_capacity_planner.py current_portfolio.json
⚠️ If any team utilization >90% or <60%, flag for immediate reallocation discussion before step 4.
Executive Summary Generation
Identify optimization opportunities
Risk Portfolio Analysis
Adjust risk mitigation investments
Resource Optimization Planning
Identify skill gaps and training needs
Stakeholder Alignment Session
Update strategic priorities and success criteria
Financial Performance Review
Optimize investment allocation for maximum value
Capability Gap Analysis
Assess make vs. buy vs. partner decisions
Portfolio Rebalancing
Context Transfer: - Strategic priorities and success criteria - Resource allocation and team composition - Risk factors requiring sprint-level attention - Quality standards and acceptance criteria
Ongoing Collaboration: - Weekly velocity and health metrics review - Sprint retrospective insights for portfolio learning - Impediment escalation and resolution support - Team capacity and utilization feedback
Strategic Context: - Market prioritization and competitive analysis - User value frameworks and measurement criteria - Feature prioritization aligned with portfolio objectives - Resource and timeline constraints
Decision Support: - ROI analysis for feature investments - Risk assessment for product decisions - Market intelligence and customer feedback integration - Strategic roadmap alignment and dependencies
Strategic Direction: - Business objective updates and priority changes - Budget allocation and resource approval decisions - Risk appetite and tolerance level adjustments - Market strategy and competitive response decisions
Performance Expectations: - Portfolio health and value delivery targets - Timeline and milestone commitment expectations - Quality standards and compliance requirements - Stakeholder satisfaction and communication standards
Reference: references/portfolio-kpis.md for full definitions and measurement guidance.
product-team/product-strategist/) — Product OKRs align with portfolio objectivesproject-management/scrum-master/) — Sprint velocity data feeds project health dashboardsLICENSESKILL.mdassets/executive_report_template.mdassets/expected_output.jsonassets/project_charter_template.mdassets/raci_matrix_template.mdassets/sample_project_data.jsonreferences/portfolio-kpis.mdreferences/portfolio-prioritization-models.mdreferences/risk-management-framework.mdscripts/project_health_dashboard.pyscripts/resource_capacity_planner.pyscripts/risk_matrix_analyzer.pyVendored from alirezarezvani/claude-skills, commit 27cfcd0fb0a4, path project-management/skills/senior-pm, licence MIT. The upstream licence file ships inside the skill.