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    "name": "Customer Success Manager",
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    "briefUrl": "https://openagent3.xyz/skills/customer-success-manager/agent.md"
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  "agentAssist": {
    "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
    "steps": [
      "Download the package from Yavira.",
      "Extract it into a folder your agent can access.",
      "Paste one of the prompts below and point your agent at the extracted folder."
    ],
    "prompts": [
      {
        "label": "New install",
        "body": "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."
      },
      {
        "label": "Upgrade existing",
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  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Customer Success Manager",
        "body": "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."
      },
      {
        "title": "Table of Contents",
        "body": "Input Requirements\nOutput Formats\nHow to Use\nScripts\nReference Guides\nTemplates\nBest Practices\nLimitations"
      },
      {
        "title": "Input Requirements",
        "body": "All scripts accept a JSON file as positional input argument. See assets/sample_customer_data.json for complete schema examples and sample data."
      },
      {
        "title": "Health Score Calculator",
        "body": "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."
      },
      {
        "title": "Churn Risk Analyzer",
        "body": "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."
      },
      {
        "title": "Expansion Opportunity Scorer",
        "body": "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)."
      },
      {
        "title": "Output Formats",
        "body": "All scripts support two output formats via the --format flag:\n\ntext (default): Human-readable formatted output for terminal viewing\njson: Machine-readable JSON output for integrations and pipelines"
      },
      {
        "title": "Quick Start",
        "body": "# Health scoring\npython scripts/health_score_calculator.py assets/sample_customer_data.json\npython scripts/health_score_calculator.py assets/sample_customer_data.json --format json\n\n# Churn risk analysis\npython scripts/churn_risk_analyzer.py assets/sample_customer_data.json\npython scripts/churn_risk_analyzer.py assets/sample_customer_data.json --format json\n\n# Expansion opportunity scoring\npython scripts/expansion_opportunity_scorer.py assets/sample_customer_data.json\npython scripts/expansion_opportunity_scorer.py assets/sample_customer_data.json --format json"
      },
      {
        "title": "Workflow Integration",
        "body": "# 1. Score customer health across portfolio\npython scripts/health_score_calculator.py customer_portfolio.json --format json > health_results.json\n# Verify: confirm health_results.json contains the expected number of customer records before continuing\n\n# 2. Identify at-risk accounts\npython scripts/churn_risk_analyzer.py customer_portfolio.json --format json > risk_results.json\n# Verify: confirm risk_results.json is non-empty and risk tiers are present for each customer\n\n# 3. Find expansion opportunities in healthy accounts\npython scripts/expansion_opportunity_scorer.py customer_portfolio.json --format json > expansion_results.json\n# Verify: confirm expansion_results.json lists opportunities ranked by priority\n\n# 4. Prepare QBR using templates\n# Reference: assets/qbr_template.md\n\nError handling: If a script exits with an error, check that:\n\nThe input JSON matches the required schema for that script (see Input Requirements above)\nAll required fields are present and correctly typed\nPython 3.7+ is being used (python --version)\nOutput files from prior steps are non-empty before piping into subsequent steps"
      },
      {
        "title": "1. health_score_calculator.py",
        "body": "Purpose: Multi-dimensional customer health scoring with trend analysis and segment-aware benchmarking.\n\nDimensions and Weights:\n\nDimensionWeightMetricsUsage30%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\n\nClassification:\n\nGreen (75-100): Healthy -- customer achieving value\nYellow (50-74): Needs attention -- monitor closely\nRed (0-49): At risk -- immediate intervention required\n\nUsage:\n\npython scripts/health_score_calculator.py customer_data.json\npython scripts/health_score_calculator.py customer_data.json --format json"
      },
      {
        "title": "2. churn_risk_analyzer.py",
        "body": "Purpose: Identify at-risk accounts with behavioral signal detection and tier-based intervention recommendations.\n\nRisk Signal Weights:\n\nSignal 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\n\nRisk Tiers:\n\nCritical (80-100): Immediate executive escalation\nHigh (60-79): Urgent CSM intervention\nMedium (40-59): Proactive outreach\nLow (0-39): Standard monitoring\n\nUsage:\n\npython scripts/churn_risk_analyzer.py customer_data.json\npython scripts/churn_risk_analyzer.py customer_data.json --format json"
      },
      {
        "title": "3. expansion_opportunity_scorer.py",
        "body": "Purpose: Identify upsell, cross-sell, and expansion opportunities with revenue estimation and priority ranking.\n\nExpansion Types:\n\nUpsell: Upgrade to higher tier or more of existing product\nCross-sell: Add new product modules\nExpansion: Additional seats or departments\n\nUsage:\n\npython scripts/expansion_opportunity_scorer.py customer_data.json\npython scripts/expansion_opportunity_scorer.py customer_data.json --format json"
      },
      {
        "title": "Reference Guides",
        "body": "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"
      },
      {
        "title": "Templates",
        "body": "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"
      },
      {
        "title": "Best Practices",
        "body": "Combine signals: Use all three scripts together for a complete customer picture\nAct on trends, not snapshots: A declining Green is more urgent than a stable Yellow\nCalibrate thresholds: Adjust segment benchmarks based on your product and industry per references/health-scoring-framework.md\nPrepare with data: Run scripts before every QBR and executive meeting; reference references/cs-playbooks.md for intervention guidance"
      },
      {
        "title": "Limitations",
        "body": "No real-time data: Scripts analyze point-in-time snapshots from JSON input files\nNo CRM integration: Data must be exported manually from your CRM/CS platform\nDeterministic only: No predictive ML -- scoring is algorithmic based on weighted signals\nThreshold tuning: Default thresholds are industry-standard but may need calibration for your business\nRevenue estimates: Expansion revenue estimates are approximations based on usage patterns\n\nLast Updated: February 2026\nTools: 3 Python CLI tools\nDependencies: Python 3.7+ standard library only"
      }
    ],
    "body": "Customer Success Manager\n\nProduction-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.\n\nTable of Contents\nInput Requirements\nOutput Formats\nHow to Use\nScripts\nReference Guides\nTemplates\nBest Practices\nLimitations\nInput Requirements\n\nAll scripts accept a JSON file as positional input argument. See assets/sample_customer_data.json for complete schema examples and sample data.\n\nHealth Score Calculator\n\nRequired 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.\n\nChurn Risk Analyzer\n\nRequired 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.\n\nExpansion Opportunity Scorer\n\nRequired 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).\n\nOutput Formats\n\nAll scripts support two output formats via the --format flag:\n\ntext (default): Human-readable formatted output for terminal viewing\njson: Machine-readable JSON output for integrations and pipelines\nHow to Use\nQuick Start\n# Health scoring\npython scripts/health_score_calculator.py assets/sample_customer_data.json\npython scripts/health_score_calculator.py assets/sample_customer_data.json --format json\n\n# Churn risk analysis\npython scripts/churn_risk_analyzer.py assets/sample_customer_data.json\npython scripts/churn_risk_analyzer.py assets/sample_customer_data.json --format json\n\n# Expansion opportunity scoring\npython scripts/expansion_opportunity_scorer.py assets/sample_customer_data.json\npython scripts/expansion_opportunity_scorer.py assets/sample_customer_data.json --format json\n\nWorkflow Integration\n# 1. Score customer health across portfolio\npython scripts/health_score_calculator.py customer_portfolio.json --format json > health_results.json\n# Verify: confirm health_results.json contains the expected number of customer records before continuing\n\n# 2. Identify at-risk accounts\npython scripts/churn_risk_analyzer.py customer_portfolio.json --format json > risk_results.json\n# Verify: confirm risk_results.json is non-empty and risk tiers are present for each customer\n\n# 3. Find expansion opportunities in healthy accounts\npython scripts/expansion_opportunity_scorer.py customer_portfolio.json --format json > expansion_results.json\n# Verify: confirm expansion_results.json lists opportunities ranked by priority\n\n# 4. Prepare QBR using templates\n# Reference: assets/qbr_template.md\n\n\nError handling: If a script exits with an error, check that:\n\nThe input JSON matches the required schema for that script (see Input Requirements above)\nAll required fields are present and correctly typed\nPython 3.7+ is being used (python --version)\nOutput files from prior steps are non-empty before piping into subsequent steps\nScripts\n1. health_score_calculator.py\n\nPurpose: Multi-dimensional customer health scoring with trend analysis and segment-aware benchmarking.\n\nDimensions and Weights:\n\nDimension\tWeight\tMetrics\nUsage\t30%\tLogin frequency, feature adoption, DAU/MAU ratio\nEngagement\t25%\tSupport ticket volume, meeting attendance, NPS/CSAT\nSupport\t20%\tOpen tickets, escalation rate, avg resolution time\nRelationship\t25%\tExecutive sponsor engagement, multi-threading depth, renewal sentiment\n\nClassification:\n\nGreen (75-100): Healthy -- customer achieving value\nYellow (50-74): Needs attention -- monitor closely\nRed (0-49): At risk -- immediate intervention required\n\nUsage:\n\npython scripts/health_score_calculator.py customer_data.json\npython scripts/health_score_calculator.py customer_data.json --format json\n\n2. churn_risk_analyzer.py\n\nPurpose: Identify at-risk accounts with behavioral signal detection and tier-based intervention recommendations.\n\nRisk Signal Weights:\n\nSignal Category\tWeight\tIndicators\nUsage Decline\t30%\tLogin trend, feature adoption change, DAU/MAU change\nEngagement Drop\t25%\tMeeting cancellations, response time, NPS change\nSupport Issues\t20%\tOpen escalations, unresolved critical, satisfaction trend\nRelationship Signals\t15%\tChampion left, sponsor change, competitor mentions\nCommercial Factors\t10%\tContract type, pricing complaints, budget cuts\n\nRisk Tiers:\n\nCritical (80-100): Immediate executive escalation\nHigh (60-79): Urgent CSM intervention\nMedium (40-59): Proactive outreach\nLow (0-39): Standard monitoring\n\nUsage:\n\npython scripts/churn_risk_analyzer.py customer_data.json\npython scripts/churn_risk_analyzer.py customer_data.json --format json\n\n3. expansion_opportunity_scorer.py\n\nPurpose: Identify upsell, cross-sell, and expansion opportunities with revenue estimation and priority ranking.\n\nExpansion Types:\n\nUpsell: Upgrade to higher tier or more of existing product\nCross-sell: Add new product modules\nExpansion: Additional seats or departments\n\nUsage:\n\npython scripts/expansion_opportunity_scorer.py customer_data.json\npython scripts/expansion_opportunity_scorer.py customer_data.json --format json\n\nReference Guides\nReference\tDescription\nreferences/health-scoring-framework.md\tComplete health scoring methodology, dimension definitions, weighting rationale, threshold calibration\nreferences/cs-playbooks.md\tIntervention playbooks for each risk tier, onboarding, renewal, expansion, and escalation procedures\nreferences/cs-metrics-benchmarks.md\tIndustry benchmarks for NRR, GRR, churn rates, health scores, expansion rates by segment and industry\nTemplates\nTemplate\tPurpose\nassets/qbr_template.md\tQuarterly Business Review presentation structure\nassets/success_plan_template.md\tCustomer success plan with goals, milestones, and metrics\nassets/onboarding_checklist_template.md\t90-day onboarding checklist with phase gates\nassets/executive_business_review_template.md\tExecutive stakeholder review for strategic accounts\nBest Practices\nCombine signals: Use all three scripts together for a complete customer picture\nAct on trends, not snapshots: A declining Green is more urgent than a stable Yellow\nCalibrate thresholds: Adjust segment benchmarks based on your product and industry per references/health-scoring-framework.md\nPrepare with data: Run scripts before every QBR and executive meeting; reference references/cs-playbooks.md for intervention guidance\nLimitations\nNo real-time data: Scripts analyze point-in-time snapshots from JSON input files\nNo CRM integration: Data must be exported manually from your CRM/CS platform\nDeterministic only: No predictive ML -- scoring is algorithmic based on weighted signals\nThreshold tuning: Default thresholds are industry-standard but may need calibration for your business\nRevenue estimates: Expansion revenue estimates are approximations based on usage patterns\n\nLast Updated: February 2026 Tools: 3 Python CLI tools Dependencies: Python 3.7+ standard library only"
  },
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    "sourceLabel": "tencent",
    "provenanceUrl": "https://clawhub.ai/alirezarezvani/customer-success-manager",
    "publisherUrl": "https://clawhub.ai/alirezarezvani/customer-success-manager",
    "owner": "alirezarezvani",
    "version": "2.1.1",
    "license": null,
    "verificationStatus": "Indexed source record"
  },
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