Agent skill · marketing growth · borghei
customer-success-manager
Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success
Why this skill is useful
Adds three executable Python scripts for customer health scoring, churn risk analysis, and expansion opportunity scoring that provide unique capabilities not found in standard tools.
What it needs
Requires python installed locally. About 8k tokens when loaded. Last updated 2026-07-21. 462 stars on the source repository.
What this skill does
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 Input Requirements Output Formats How to Use Scripts Reference Guides Templates Best Practices 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/samplecustomerdata.json for complete examples. Health Score Calculator Churn Risk Analyzer Expansion Opportunity Scorer --- 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills borghei/customer-success-manager