Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Customer Success Manager
Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success
skill openclawclawhub Free
Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success
β¬ 0 downloads β
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, assets/executive_business_review_template.md, assets/expected_output.json, assets/onboarding_checklist_template.md, assets/qbr_template.md, assets/sample_customer_data.json
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
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New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
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I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 2.1.1
Provenance
- Publisher
- alirezarezvani
- Source page
- View original listing
- Canonical URL
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Documentation
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
Input Requirements Output Formats How to Use Scripts Reference Guides Templates Best Practices Limitations
Input Requirements
All scripts accept a JSON file as positional input argument. See assets/sample_customer_data.json for complete schema examples and sample data.
Health Score Calculator
Required fields per customer object: customer_id, name, segment, arr, and nested objects usage (login_frequency, feature_adoption, dau_mau_ratio), engagement (support_ticket_volume, meeting_attendance, nps_score, csat_score), support (open_tickets, escalation_rate, avg_resolution_hours), relationship (executive_sponsor_engagement, multi_threading_depth, renewal_sentiment), and previous_period scores for trend analysis.
Churn Risk Analyzer
Required fields per customer object: customer_id, name, segment, arr, contract_end_date, and nested objects usage_decline, engagement_drop, support_issues, relationship_signals, and commercial_factors.
Expansion Opportunity Scorer
Required fields per customer object: customer_id, name, segment, arr, and nested objects contract (licensed_seats, active_seats, plan_tier, available_tiers), product_usage (per-module adoption flags and usage percentages), and departments (current and potential).
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
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 # Verify: confirm health_results.json contains the expected number of customer records before continuing # 2. Identify at-risk accounts python scripts/churn_risk_analyzer.py customer_portfolio.json --format json > risk_results.json # Verify: confirm risk_results.json is non-empty and risk tiers are present for each customer # 3. Find expansion opportunities in healthy accounts python scripts/expansion_opportunity_scorer.py customer_portfolio.json --format json > expansion_results.json # Verify: confirm expansion_results.json lists opportunities ranked by priority # 4. Prepare QBR using templates # Reference: assets/qbr_template.md Error handling: If a script exits with an error, check that: The input JSON matches the required schema for that script (see Input Requirements above) All required fields are present and correctly typed Python 3.7+ is being used (python --version) Output files from prior steps are non-empty before piping into subsequent steps
1. health_score_calculator.py
Purpose: Multi-dimensional customer health scoring with trend analysis and segment-aware benchmarking. Dimensions and Weights: DimensionWeightMetricsUsage30%Login frequency, feature adoption, DAU/MAU ratioEngagement25%Support ticket volume, meeting attendance, NPS/CSATSupport20%Open tickets, escalation rate, avg resolution timeRelationship25%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 CategoryWeightIndicatorsUsage Decline30%Login trend, feature adoption change, DAU/MAU changeEngagement Drop25%Meeting cancellations, response time, NPS changeSupport Issues20%Open escalations, unresolved critical, satisfaction trendRelationship Signals15%Champion left, sponsor change, competitor mentionsCommercial 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
ReferenceDescriptionreferences/health-scoring-framework.mdComplete health scoring methodology, dimension definitions, weighting rationale, threshold calibrationreferences/cs-playbooks.mdIntervention playbooks for each risk tier, onboarding, renewal, expansion, and escalation proceduresreferences/cs-metrics-benchmarks.mdIndustry benchmarks for NRR, GRR, churn rates, health scores, expansion rates by segment and industry
Templates
TemplatePurposeassets/qbr_template.mdQuarterly Business Review presentation structureassets/success_plan_template.mdCustomer success plan with goals, milestones, and metricsassets/onboarding_checklist_template.md90-day onboarding checklist with phase gatesassets/executive_business_review_template.mdExecutive stakeholder review for strategic accounts
Best Practices
Combine signals: Use all three scripts together for a complete customer picture Act on trends, not snapshots: A declining Green is more urgent than a stable Yellow Calibrate thresholds: Adjust segment benchmarks based on your product and industry per references/health-scoring-framework.md Prepare with data: Run scripts before every QBR and executive meeting; reference references/cs-playbooks.md for intervention guidance
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
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
4 Docs2 Config
- SKILL.md
- assets/executive_business_review_template.md
- assets/onboarding_checklist_template.md
- assets/qbr_template.md
- assets/expected_output.json
- assets/sample_customer_data.json