Customer Success Manager

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

.codex/skills/a11y-audit

.codex/skills/ab-test-setup

.codex/skills/account-executive

.codex/skills/ad-creative

.codex/skills/aeo

Audit 0.4.1 · source sha 6e3fcbdbb5cf · 19 Oct 2026Report an issue or request a re-scan

• Publisherborghei GitHub userOwner of the repository this skill is read from
• Source folderborghei/Claude-Skills/business-growth/customer-success-managerThe exact folder we audited
• LicenceNOASSERTIONAs the repository states it
• Commit pinned for this verdict6e3fcbdbb5cfA new audit runs when the source changes
• Repository statusNot archivedLast push 12 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success

---
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 + Commons Clause
metadata:
  version: 1.0.0
  author: borghei
  category: business-growth
  domain: customer-success
  updated: 2026-02-06
  tags: [customer-success, churn, health-score, expansion, saas]
  python-tools: health_score_calculator.py, churn_risk_analyzer.py, expansion_opportunity_scorer.py
  tech-stack: customer-success, saas-metrics, health-scoring
---
# 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](#capabilities)
- [Input Requirements](#input-requirements)
- [Output Formats](#output-formats)
- [How to Use](#how-to-use)
- [Scripts](#scripts)
- [Reference Guides](#reference-guides)
- [Templates](#templates)
- [Best Practices](#best-practices)
- [Limitations](#limitations)

---

## 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

```json
{
  "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

```json
{
  "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

```json
{
  "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

---

## Clarify First

Before running the analysis, confirm these inputs. If any is unknown or vague, ASK — do not assume:

- [ ] **Which analysis** — health score, churn risk, or expansion opportunity (selects which of the three scripts and its input schema)
- [ ] **Customer segment** — Enterprise / Mid-Market / SMB (segment-aware thresholds change every Red/Yellow/Green and risk-tier cutoff)
- [ ] **Previous-period data availability** — without it, trend analysis (declining vs improving) cannot run
- [ ] **Renewal date / contract end** — drives the time-to-renewal urgency multiplier in churn scoring

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the output.

## How to Use

### Quick Start

```bash
# Health scoring
python scripts/health_score_calculator.py assets/sample_customer_data.json
python

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/borghei/Claude-Skills
git -C Claude-Skills checkout 6e3fcbdbb5cf

Works in

1. 2026-10-19
   !CAUTION B 89 / 100

   first audit · source 6e3fcbdbb5cf

Questions about Customer Success Manager

• github.com/borghei/Claude-Skills/tree/main/business-growth/custo — the audited source at commit 6e3fcbdbb5cf and every finding aboveObserved · 19 Oct 2026

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