customer-success-manager

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Tool: SKILL.md [1/2] Description: --- name: customer-success-manager description: Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success license: MIT metadata: version: 1.0.0 author: Alireza Rezvani category: business-growth domain: customer-success updated: 2026-02-06 python-tools: health_score_calculator.py, churn_risk_analyzer.py, expansion_opportunity_scorer.py tech-stack: customer-success, saas-metrics, h

**Combine signals**: Use all three scripts together for a complete customer picture 4.

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Tool: assets/executive_business_review_template.md [1/2] Description: # Executive Business Review **Customer:** [Customer Name] **Date:** [Review Date] **Prepared for:** [Executive Name, Title] **Prepared by:** [CSM Name] | [VP Customer Success Name] **Classification:** [Strategic / Enterprise / Key Account] --- ## 1.

Description: [Decision Maker / Influencer / User] | [Positive / Neutral / Negative] | [Date] | | [Name] | [Title] | [Decision Maker / Influencer / User] | [Positive / Neutral / Negative] | [Date] | ### Competitive Landscape (If Applicable) - **Known competitors in evaluation:** [List] - **Our differentiators:** [Key strengths vs.

Malicious tool definition detected

Tool: assets/expected_output.json Description: { "report": "customer_health_scores", "summary": { "total_customers": 4, "average_score": 78.8, "green_count": 3, "yellow_count": 1, "red_count": 0 }, "customers": [ { "customer_id": "CUST-001", "name": "Acme Corp", "segment": "enterprise", "arr": 120000, "overall_score": 86.2, "classification": "green", "dimensions": { "usage": { "score": 91.6, "weight": "30%", "classification": "green" }, "engagement": { "score": 82.0, "weight": "25%", "classifica

Malicious tool definition detected

Tool: assets/onboarding_checklist_template.md [1/2]

Tool: assets/onboarding_checklist_template.md [2/2]

Malicious tool definition detected

Tool: assets/qbr_template.md Description: # Quarterly Business Review (QBR) **Customer:** [Customer Name] **Date:** [QBR Date] **Prepared by:** [CSM Name] **Attendees:** [List attendees and titles] --- ## 1.

Malicious tool definition detected

Tool: assets/sample_customer_data.json Description: { "customers": [ { "customer_id": "CUST-001", "name": "Acme Corp", "segment": "enterprise", "arr": 120000, "contract_end_date": "2026-12-31", "usage": { "login_frequency": 85, "feature_adoption": 72, "dau_mau_ratio": 0.45 }, "engagement": { "support_ticket_volume": 3, "meeting_attendance": 90, "nps_score": 8, "csat_score": 4.2 }, "support": { "open_tickets": 2, "escalation_rate": 0.05, "avg_resolution_hours": 18 }, "relationship": { "executive_

Malicious tool definition detected

Tool: assets/success_plan_template.md [1/2] Description: # Customer Success Plan **Customer:** [Customer Name] **CSM:** [CSM Name] **Account Executive:** [AE Name] **Plan Created:** [Date] **Last Updated:** [Date] **Review Cadence:** [Monthly / Quarterly] --- ## 1.

Tool: assets/success_plan_template.md [2/2]

Malicious tool definition detected

Tool: references/cs-metrics-benchmarks.md [1/2] Description: # Customer Success Metrics and Benchmarks Industry benchmarks for key customer success metrics, segmented by company size, customer segment, and industry vertical.

Tool: references/cs-metrics-benchmarks.md [2/2] Description: | 35 | < 20 | | Average CSAT | > 4.2/5 | 3.8/5 | < 3.0/5 | | Response time (days) | < 2 | 3 | > 5 | | QBR completion rate | > 90% | 75% | < 60% | ### Time to First Value (TTFV) | Segment | Target TTFV | Median TTFV | Warning Threshold | |---------|------------|------------|-------------------| | Enterprise | < 30 days | 45 days | > 60 days | | Mid-Market | < 21 days | 30 days | > 45 days | | SMB | < 14 days | 21 days | > 30 days | ---

Malicious tool definition detected

Tool: references/cs-playbooks.md [1/2] Description: # Customer Success Playbooks Comprehensive intervention, onboarding, renewal, expansion, and escalation playbooks for SaaS customer success management. --- ## Risk Tier Intervention Playbooks ### Critical Risk (Score 80-100) **Situation:** Customer is at imminent risk of churn.

Tool: references/cs-playbooks.md [2/2] Description: | Review documented | **Critical Milestone:** Time to First Value must be under 30 days.

Malicious tool definition detected

Tool: references/health-scoring-framework.md [1/2] Description: # Health Scoring Framework Complete methodology for multi-dimensional customer health scoring in SaaS customer success.

Tool: references/health-scoring-framework.md [2/2] Description: it drops further | | Yellow | Declining | CRITICAL -- trajectory leads to Red | | Yellow | Improving | MEDIUM -- reinforce positive momentum | | Red | Improving | HIGH -- support the recovery | | Red | Stable | CRITICAL -- needs new intervention approach | --- ## Calibration Guidelines ### When to Recalibrate 1.

Malicious tool definition detected

Tool: scripts/churn_risk_analyzer.py [1/2] Description: #!/usr/bin/env python3 """ Churn Risk Analyzer Identifies at-risk customer accounts by scoring behavioral signals across usage decline, engagement drop, support issues, relationship signals, and commercial factors.

Tool: scripts/churn_risk_analyzer.py [2/2] Description: Any]) -> Tuple[float, List[Dict[str, str]]]: """Score relationship risk signals (0-100, higher = more risk).""" warnings: List[Dict[str, str]] = [] risk_points = 0.0 champion_left = data.get("champion_left", False) sponsor_change = data.get("sponsor_change", False) competitor_mentions = data.get("competitor_mentions", 0) if champion_left: risk_points += 45.0 warnings.append({"severity": "critical", "signal": "Internal champion has left the

Malicious tool definition detected

Tool: scripts/expansion_opportunity_scorer.py [1/2] Description: #!/usr/bin/env python3 """ Expansion Opportunity Scorer Analyses customer product adoption depth, maps whitespace for unused features/products, estimates revenue opportunities, and prioritises expansion plays by effort vs impact.

Tool: scripts/expansion_opportunity_scorer.py [2/2] Description: List[str] = [] lines.append("=" * 72) lines.append("EXPANSION OPPORTUNITY REPORT") lines.append("=" * 72) lines.append("") total_rev = sum(r["total_estimated_revenue"] for r in results) total_opps = sum(r["opportunity_count"] for r in results) lines.append(f"Portfolio Summary: {len(results)} customers") lines.append(f" Total Expansion Revenue Potential: ${total_rev:,.0f}") lines.append(f" Total Opportunities Identified: {total_opps

Malicious tool definition detected

Tool: scripts/health_score_calculator.py [1/2] Description: #!/usr/bin/env python3 """ Customer Health Score Calculator Multi-dimensional weighted health scoring across usage, engagement, support, and relationship dimensions.

Tool: scripts/health_score_calculator.py [2/2] Description: overall, "classification": classification, "dimensions": { "usage": {"score": usage_score, "weight": "30%", "classification": classify(usage_score, segment)}, "engagement": {"score": engagement_score, "weight": "25%", "classification": classify(engagement_score, segment)}, "support": {"score": support_score, "weight": "20%", "classification": classify(support_score, segment)}, "relationship": {"score": relationship_score, "weight": "25%