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Customer Success Manager

borghei/claude-skills

  • 147 installs
  • 451 repo stars
  • Updated July 21, 2026
  • borghei/claude-skills

Design customer onboarding, health checks, renewal playbooks, QBR templates, and escalation paths that reduce churn and expand accounts after customers go live.

About

Supports customer success planning with onboarding flows, health checks, renewal playbooks, churn prevention tactics, and account expansion frameworks tailored to subscription and service businesses.

  • Onboarding and activation playbooks
  • Customer health scoring frameworks
  • Renewal and expansion planning
  • Churn risk identification tactics
  • QBR and success metric templates

Customer Success Manager by the numbers

  • 147 all-time installs (skills.sh)
  • Ranked #1,192 of 3,282 Productivity & Planning skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/borghei/claude-skills --skill customer-success-manager

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Listed on Skillselion
Installs147
repo stars★ 451
Last updatedJuly 21, 2026
Repositoryborghei/claude-skills ↗

What it does

Design customer onboarding, health checks, renewal playbooks, QBR templates, and escalation paths that reduce churn and expand accounts after customers go live.

Files

SKILL.mdMarkdownGitHub ↗

Customer Success Manager

Production-grade customer success analytics with multi-dimensional health scoring, churn risk prediction, and expansion opportunity identification. Three Python CLI tools provide deterministic, repeatable analysis using standard library only -- no external dependencies, no API calls, no ML models.

---

Table of Contents

---

Capabilities

  • Customer Health Scoring: Multi-dimensional weighted scoring across usage, engagement, support, and relationship dimensions with Red/Yellow/Green classification
  • Churn Risk Analysis: Behavioral signal detection with tier-based intervention playbooks and time-to-renewal urgency multipliers
  • Expansion Opportunity Scoring: Adoption depth analysis, whitespace mapping, and revenue opportunity estimation with effort-vs-impact prioritization
  • Segment-Aware Benchmarking: Configurable thresholds for Enterprise, Mid-Market, and SMB customer segments
  • Trend Analysis: Period-over-period comparison to detect improving or declining trajectories
  • Executive Reporting: QBR templates, success plans, and executive business review templates

---

Input Requirements

All scripts accept a JSON file as positional input argument. See assets/sample_customer_data.json for complete examples.

Health Score Calculator

{
  "customers": [
    {
      "customer_id": "CUST-001",
      "name": "Acme Corp",
      "segment": "enterprise",
      "arr": 120000,
      "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_sponsor_engagement": 80,
        "multi_threading_depth": 4,
        "renewal_sentiment": "positive"
      },
      "previous_period": {
        "usage_score": 70,
        "engagement_score": 65,
        "support_score": 75,
        "relationship_score": 60
      }
    }
  ]
}

Churn Risk Analyzer

{
  "customers": [
    {
      "customer_id": "CUST-001",
      "name": "Acme Corp",
      "segment": "enterprise",
      "arr": 120000,
      "contract_end_date": "2026-06-30",
      "usage_decline": {
        "login_trend": -15,
        "feature_adoption_change": -10,
        "dau_mau_change": -0.08
      },
      "engagement_drop": {
        "meeting_cancellations": 2,
        "response_time_days": 5,
        "nps_change": -3
      },
      "support_issues": {
        "open_escalations": 1,
        "unresolved_critical": 0,
        "satisfaction_trend": "declining"
      },
      "relationship_signals": {
        "champion_left": false,
        "sponsor_change": false,
        "competitor_mentions": 1
      },
      "commercial_factors": {
        "contract_type": "annual",
        "pricing_complaints": false,
        "budget_cuts_mentioned": false
      }
    }
  ]
}

Expansion Opportunity Scorer

{
  "customers": [
    {
      "customer_id": "CUST-001",
      "name": "Acme Corp",
      "segment": "enterprise",
      "arr": 120000,
      "contract": {
        "licensed_seats": 100,
        "active_seats": 95,
        "plan_tier": "professional",
        "available_tiers": ["professional", "enterprise", "enterprise_plus"]
      },
      "product_usage": {
        "core_platform": {"adopted": true, "usage_pct": 85},
        "analytics_module": {"adopted": true, "usage_pct": 60},
        "integrations_module": {"adopted": false, "usage_pct": 0},
        "api_access": {"adopted": true, "usage_pct": 40},
        "advanced_reporting": {"adopted": false, "usage_pct": 0}
      },
      "departments": {
        "current": ["engineering", "product"],
        "potential": ["marketing", "sales", "support"]
      }
    }
  ]
}

---

Output Formats

All scripts support two output formats via the --format flag:

  • `text` (default): Human-readable formatted output for terminal viewing
  • `json`: Machine-readable JSON output for integrations and pipelines

---

How to Use

Quick Start

# Health scoring
python scripts/health_score_calculator.py assets/sample_customer_data.json
python scripts/health_score_calculator.py assets/sample_customer_data.json --format json

# Churn risk analysis
python scripts/churn_risk_analyzer.py assets/sample_customer_data.json
python scripts/churn_risk_analyzer.py assets/sample_customer_data.json --format json

# Expansion opportunity scoring
python scripts/expansion_opportunity_scorer.py assets/sample_customer_data.json
python scripts/expansion_opportunity_scorer.py assets/sample_customer_data.json --format json

Workflow Integration

# 1. Score customer health across portfolio
python scripts/health_score_calculator.py customer_portfolio.json --format json > health_results.json

# 2. Identify at-risk accounts
python scripts/churn_risk_analyzer.py customer_portfolio.json --format json > risk_results.json

# 3. Find expansion opportunities in healthy accounts
python scripts/expansion_opportunity_scorer.py customer_portfolio.json --format json > expansion_results.json

# 4. Prepare QBR using templates
# Reference: assets/qbr_template.md

---

Scripts

1. health_score_calculator.py

Purpose: Multi-dimensional customer health scoring with trend analysis and segment-aware benchmarking.

Dimensions and Weights:

DimensionWeightMetrics
Usage30%Login frequency, feature adoption, DAU/MAU ratio
Engagement25%Support ticket volume, meeting attendance, NPS/CSAT
Support20%Open tickets, escalation rate, avg resolution time
Relationship25%Executive sponsor engagement, multi-threading depth, renewal sentiment

Classification:

  • Green (75-100): Healthy -- customer achieving value
  • Yellow (50-74): Needs attention -- monitor closely
  • Red (0-49): At risk -- immediate intervention required

Usage:

python scripts/health_score_calculator.py customer_data.json
python scripts/health_score_calculator.py customer_data.json --format json

2. churn_risk_analyzer.py

Purpose: Identify at-risk accounts with behavioral signal detection and tier-based intervention recommendations.

Risk Signal Weights:

Signal CategoryWeightIndicators
Usage Decline30%Login trend, feature adoption change, DAU/MAU change
Engagement Drop25%Meeting cancellations, response time, NPS change
Support Issues20%Open escalations, unresolved critical, satisfaction trend
Relationship Signals15%Champion left, sponsor change, competitor mentions
Commercial Factors10%Contract type, pricing complaints, budget cuts

Risk Tiers:

  • Critical (80-100): Immediate executive escalation
  • High (60-79): Urgent CSM intervention
  • Medium (40-59): Proactive outreach
  • Low (0-39): Standard monitoring

Usage:

python scripts/churn_risk_analyzer.py customer_data.json
python scripts/churn_risk_analyzer.py customer_data.json --format json

3. expansion_opportunity_scorer.py

Purpose: Identify upsell, cross-sell, and expansion opportunities with revenue estimation and priority ranking.

Expansion Types:

  • Upsell: Upgrade to higher tier or more of existing product
  • Cross-sell: Add new product modules
  • Expansion: Additional seats or departments

Usage:

python scripts/expansion_opportunity_scorer.py customer_data.json
python scripts/expansion_opportunity_scorer.py customer_data.json --format json

---

Reference Guides

ReferenceDescription
references/health-scoring-framework.mdComplete health scoring methodology, dimension definitions, weighting rationale, threshold calibration
references/cs-playbooks.mdIntervention playbooks for each risk tier, onboarding, renewal, expansion, and escalation procedures
references/cs-metrics-benchmarks.mdIndustry benchmarks for NRR, GRR, churn rates, health scores, expansion rates by segment and industry

---

Templates

TemplatePurpose
assets/qbr_template.mdQuarterly Business Review presentation structure
assets/success_plan_template.mdCustomer success plan with goals, milestones, and metrics
assets/onboarding_checklist_template.md90-day onboarding checklist with phase gates
assets/executive_business_review_template.mdExecutive stakeholder review for strategic accounts

---

Best Practices

1. Score regularly: Run health scoring weekly for Enterprise, bi-weekly for Mid-Market, monthly for SMB 2. Act on trends, not snapshots: A declining Green is more urgent than a stable Yellow 3. Combine signals: Use all three scripts together for a complete customer picture 4. Calibrate thresholds: Adjust segment benchmarks based on your product and industry 5. Document interventions: Track what actions you took and outcomes for playbook refinement 6. Prepare with data: Run scripts before every QBR and executive meeting

---

Limitations

  • No real-time data: Scripts analyze point-in-time snapshots from JSON input files
  • No CRM integration: Data must be exported manually from your CRM/CS platform
  • Deterministic only: No predictive ML -- scoring is algorithmic based on weighted signals
  • Threshold tuning: Default thresholds are industry-standard but may need calibration for your business
  • Revenue estimates: Expansion revenue estimates are approximations based on usage patterns

---

---

Tool Reference

1. health_score_calculator.py

Purpose: Multi-dimensional customer health scoring with trend analysis and segment-aware benchmarking.

python scripts/health_score_calculator.py customer_data.json
python scripts/health_score_calculator.py customer_data.json --format json
FlagRequiredDescription
customer_data.jsonYesJSON file with customer health data (usage, engagement, support, relationship metrics)
--formatNoOutput format: text (default) or json

Dimensions and Weights: Usage (30%), Engagement (25%), Support (20%), Relationship (25%)

Classification: Green (75-100), Yellow (50-74), Red (0-49) -- thresholds adjust by segment (Enterprise, Mid-Market, SMB)

2. churn_risk_analyzer.py

Purpose: Identify at-risk accounts with behavioral signal detection and tier-based intervention recommendations.

python scripts/churn_risk_analyzer.py customer_data.json
python scripts/churn_risk_analyzer.py customer_data.json --format json
FlagRequiredDescription
customer_data.jsonYesJSON file with churn risk signals (usage decline, engagement drop, support issues, relationship signals, commercial factors)
--formatNoOutput format: text (default) or json

Risk Tiers: Critical (80-100), High (60-79), Medium (40-59), Low (0-39)

Signal Weights: Usage Decline (30%), Engagement Drop (25%), Support Issues (20%), Relationship Signals (15%), Commercial Factors (10%)

3. expansion_opportunity_scorer.py

Purpose: Identify upsell, cross-sell, and expansion opportunities with revenue estimation and priority ranking.

python scripts/expansion_opportunity_scorer.py customer_data.json
python scripts/expansion_opportunity_scorer.py customer_data.json --format json
FlagRequiredDescription
customer_data.jsonYesJSON file with customer contract, product usage, and department data
--formatNoOutput format: text (default) or json

Expansion Types: Upsell (tier upgrade), Cross-sell (new modules), Expansion (seats/departments)

---

Troubleshooting

ProblemLikely CauseSolution
Health scores do not correlate with actual churnDefault thresholds do not match your productCalibrate segment thresholds using historical churn data; compare 90-day retained vs churned cohorts
All accounts show as YellowThresholds too strict or data quality issuesReview input data completeness; adjust benchmarks in health_score_calculator.py constants for your industry
Churn risk scores are uniformly lowMissing key signals (champion left, competitor mentions)Ensure all signal categories have data; missing data defaults to low risk, which understates actual risk
Expansion scores do not reflect realityProduct usage data is incomplete or staleVerify product_usage fields cover all modules; run with fresh data exports from your product analytics
Scripts error on input dataJSON format does not match expected schemaReference the Input Requirements section for exact JSON structure; validate JSON before running
Trend analysis shows no changePrevious period data not providedInclude the previous_period block in health score input for meaningful trend comparison
Intervention recommendations feel genericSegment is not specifiedAlways include the segment field (enterprise, mid-market, smb) for segment-appropriate playbooks

---

Success Criteria

  • Health scores run weekly for Enterprise, bi-weekly for Mid-Market, monthly for SMB accounts
  • Portfolio health distribution: 60%+ Green, less than 15% Red
  • Churn risk critical accounts have executive escalation within 48 hours
  • Expansion pipeline generated covers 20%+ of net retention target
  • Health score trends (improving/declining) drive proactive outreach before renewal window
  • QBR preparation includes health score, risk assessment, and expansion opportunities for every strategic account
  • Intervention playbooks followed for all High and Critical risk accounts

---

Scope & Limitations

  • In scope: Customer health scoring, churn risk analysis, expansion opportunity identification, segment benchmarking, trend analysis, QBR preparation
  • Out of scope: CRM integration, real-time monitoring, predictive ML modeling, automated outreach
  • Data dependency: Scripts analyze point-in-time JSON snapshots; data must be exported manually from your CRM/CS platform
  • Deterministic scoring: All analysis is algorithmic based on weighted signals -- no machine learning predictions
  • Threshold tuning: Default thresholds are industry-standard benchmarks; calibrate for your specific product and customer base
  • Revenue estimates: Expansion revenue estimates are approximations based on usage patterns, not binding forecasts

---

Integration Points

  • churn-prevention -- High-risk accounts from churn_risk_analyzer.py should trigger cancel flow optimization and save offer review
  • revenue-operations -- Expansion opportunities feed into pipeline forecasting; health scores inform forecast confidence
  • onboarding-cro -- When health scores show low usage in early lifecycle, the root cause is often poor activation
  • pricing-strategy -- When expansion analysis reveals pricing as a barrier to upsell, feed into pricing-strategy for packaging review
  • competitive-teardown -- When churn risk signals include competitor mentions, use teardown data to build counter-positioning

---

Last Updated: March 2026 Tools: 3 Python CLI tools Dependencies: Python 3.7+ standard library only

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