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Customer Success Manager

aaaaqwq/claude-code-skills

  • 12 installs
  • 82 repo stars
  • Updated August 2, 2026
  • aaaaqwq/claude-code-skills

customer-success-manager is a Claude Code skill that scores SaaS customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models.

About

This Claude Code skill scores SaaS customer health, predicts churn risk, and identifies account expansion opportunities. It runs three deterministic Python CLI tools that take a JSON file of customer data and output text or JSON. A customer-success or account team uses it to triage at-risk accounts and prioritize expansion by effort versus impact.

  • Weighted multi-dimensional customer health scoring (usage, engagement, support, relationship) with Red/Yellow/Green clas
  • Churn-risk analysis and expansion-opportunity scoring via three Python CLI tools using only the standard library
  • Ships QBR, success-plan and executive-business-review templates plus benchmark and playbook references

Customer Success Manager by the numbers

  • 12 all-time installs (skills.sh)
  • Ranked #677 of 853 Sales & Marketing skills by installs in the Skillselion catalog
  • Data as of Aug 3, 2026 (Skillselion catalog sync)
At a glance

customer-success-manager capabilities & compatibility

Free; no external dependencies or API calls

Capabilities
customer health scoring · churn risk analysis · expansion scoring · segment benchmarking · qbr reporting
Use cases
data analysis
Pricing
Free
From the docs

What customer-success-manager says it does

Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success
SKILL.md
Three Python CLI tools provide deterministic, repeatable analysis using standard library only -- no external dependencies, no API calls, no ML models.
SKILL.md
npx skills add https://github.com/aaaaqwq/claude-code-skills --skill customer-success-manager

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Installs12
repo stars★ 82
Last updatedAugust 2, 2026
Repositoryaaaaqwq/claude-code-skills ↗

What it does

Score a SaaS customer portfolio for health, churn risk, and expansion so a CS team can prioritize renewals and upsells.

Who is it for?

SaaS customer-success and account teams triaging portfolio health and expansion

Skip if: Teams needing ML-based prediction or live API integration; the tools are deterministic and offline

When should I use this skill?

You have customer usage, engagement, support, and relationship data and need health, churn, or expansion scoring

What you get

A ranked view of at-risk accounts and expansion opportunities plus QBR and success-plan documents

  • Health scores with Red/Yellow/Green classification
  • Churn-risk ranking with intervention playbooks
  • Expansion opportunity scores

By the numbers

  • 3 Python CLI tools
  • 4 health-scoring dimensions (usage, engagement, support, relationship)
  • Segment thresholds for Enterprise, Mid-Market, and SMB

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

---

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

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FAQ

Does it use machine learning or external APIs?

No. The three Python tools use the standard library only, with no external dependencies, API calls, or ML models.

What input does it need?

Each script accepts a JSON file of customer records covering usage, engagement, support, and relationship data.

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