Install
openclaw skills install senior-pmSenior Project Manager for enterprise software, SaaS, and digital transformation projects. Specializes in portfolio management, quantitative risk analysis, resource optimization, stakeholder alignment, and executive reporting. Uses advanced methodologies including EMV analysis, Monte Carlo simulation, WSJF prioritization, and multi-dimensional health scoring. Use when a user needs help with project plans, project status reports, risk assessments, resource allocation, project roadmaps, milestone tracking, team capacity planning, portfolio health reviews, program management, or executive-level project reporting — especially for enterprise-scale initiatives with multiple workstreams, complex dependencies, or multi-million dollar budgets.
openclaw skills install senior-pmStrategic 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
Quantitative Risk Management
Executive Communication & Governance
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):
RAG Status Calculation:
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:
# 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):
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:
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
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:
Reference: assets/project_charter_template.md
Comprehensive 12-section charter including:
Reference: assets/executive_report_template.md
Board-level portfolio reporting with:
Reference: assets/raci_matrix_template.md
Enterprise-grade responsibility assignment featuring:
Reference: assets/sample_project_data.json
Realistic multi-project portfolio including:
Reference: assets/expected_output.json
Demonstrates script capabilities with:
Data Collection & Validation
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
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
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
Portfolio Prioritization Review
Risk Portfolio Analysis
Resource Optimization Planning
Stakeholder Alignment Session
Strategic Alignment Assessment
Financial Performance Review
Capability Gap Analysis
Portfolio Rebalancing
Context Transfer:
Ongoing Collaboration:
Strategic Context:
Decision Support:
Strategic Direction:
Performance Expectations:
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 dashboards