[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6EB8V40JSF3RJW0V1F79K5X":3,"skill-same:01M6EB8V40JSF3RJW0V1F79K5X":128},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":54,"repositoryHtmlUrl":55,"repositoryHomepageUrl":6,"ownerHtmlUrl":56,"ownerBlogUrl":57,"ownerDescription":58,"updatedAt":59,"install":60,"resources":73},"01M6EB8V40JSF3RJW0V1F79K5X","customer-success-manager",null,"Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success. Use when analyzing customer accounts, reviewing retention metrics, scoring at-risk customers, or when the user mentions churn, customer health scores, upsell opportunities, expansion revenue, retention analysis, or customer analytics. Runs three Python CLI tools to produce deterministic health scores, churn risk tiers, and prioritized expansion recommendations across Enterprise, Mid-Market, and SMB segments.","levalencia\u002Fagent-god-mode","levalencia","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6962857?v=4","01M6EB0FZZ4BPQPC4HET4QY6NM","Luis Valencia ","01M6EB0G4HWHPWW0H9X7QKSAHP",3,"MIT","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002F.gemini-customer-success-manager\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"customer","customer success manager","churn","python","retention","opportunity","three","scoring","deterministic","Market","use","growth","customer-success","business","calculator","analysis","saas","risk","Enterprise","produce","2026-11-10 10:12:30",false,"---\nname: \"customer-success-manager\"\ndescription: Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success. Use when analyzing customer accounts, reviewing retention metrics, scoring at-risk customers, or when the user mentions churn, customer health scores, upsell opportunities, expansion revenue, retention analysis, or customer analytics. Runs three Python CLI tools to produce deterministic health scores, churn risk tiers, and prioritized expansion recommendations across Enterprise, Mid-Market, and SMB segments.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Alireza Rezvani\n  category: business-growth\n  domain: customer-success\n  updated: 2026-02-06\n  python-tools: health_score_calculator.py, churn_risk_analyzer.py, expansion_opportunity_scorer.py\n  tech-stack: customer-success, saas-metrics, health-scoring\n---\n\n# 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\n---\n\n## Table of Contents\n\n- [Input Requirements](#input-requirements)\n- [Output Formats](#output-formats)\n- [How to Use](#how-to-use)\n- [Scripts](#scripts)\n- [Reference Guides](#reference-guides)\n- [Templates](#templates)\n- [Best Practices](#best-practices)\n- [Limitations](#limitations)\n\n---\n\n## Input Requirements\n\nAll scripts accept a JSON file as positional input argument. See `assets\u002Fsample_customer_data.json` for complete schema examples and sample data.\n\n### Health 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\n### Churn 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\n### Expansion 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\n---\n\n## Output Formats\n\nAll scripts support two output formats via the `--format` flag:\n\n- **`text`** (default): Human-readable formatted output for terminal viewing\n- **`json`**: Machine-readable JSON output for integrations and pipelines\n\n---\n\n## How to Use\n\n### Quick Start\n\n```bash\n# Health scoring\npython scripts\u002Fhealth_score_calculator.py assets\u002Fsample_customer_data.json\npython scripts\u002Fhealth_score_calculator.py assets\u002Fsample_customer_data.json --format json\n\n# Churn risk analysis\npython scripts\u002Fchurn_risk_analyzer.py assets\u002Fsample_customer_data.json\npython scripts\u002Fchurn_risk_analyzer.py assets\u002Fsample_customer_data.json --format json\n\n# Expansion opportunity scoring\npython scripts\u002Fexpansion_opportunity_scorer.py assets\u002Fsample_customer_data.json\npython scripts\u002Fexpansion_opportunity_scorer.py assets\u002Fsample_customer_data.json --format json\n```\n\n### Workflow Integration\n\n```bash\n# 1. Score customer health across portfolio\npython scripts\u002Fhealth_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\u002Fchurn_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\u002Fexpansion_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\u002Fqbr_template.md\n```\n\n**Error handling:** If a script exits with an error, check that:\n- The input JSON matches the required schema for that script (see Input Requirements above)\n- All required fields are present and correctly typed\n- Python 3.7+ is being used (`python --version`)\n- Output files from prior steps are non-empty before piping into subsequent steps\n\n---\n\n## Scripts\n\n### 1. health_score_calculator.py\n\n**Purpose:** Multi-dimensional customer health scoring with trend analysis and segment-aware benchmarking.\n\n**Dimensions and Weights:**\n| Dimension | Weight | Metrics |\n|-----------|--------|---------|\n| Usage | 30% | Login frequency, feature adoption, DAU\u002FMAU ratio |\n| Engagement | 25% | Support ticket volume, meeting attendance, NPS\u002FCSAT |\n| Support | 20% | Open tickets, escalation rate, avg resolution time |\n| Relationship | 25% | Executive sponsor engagement, multi-threading depth, renewal sentiment |\n\n**Classification:**\n- Green (75-100): Healthy -- customer achieving value\n- Yellow (50-74): Needs attention -- monitor closely\n- Red (0-49): At risk -- immediate intervention required\n\n**Usage:**\n```bash\npython scripts\u002Fhealth_score_calculator.py customer_data.json\npython scripts\u002Fhealth_score_calculator.py customer_data.json --format json\n```\n\n### 2. churn_risk_analyzer.py\n\n**Purpose:** Identify at-risk accounts with behavioral signal detection and tier-based intervention recommendations.\n\n**Risk Signal Weights:**\n| Signal Category | Weight | Indicators |\n|----------------|--------|------------|\n| Usage Decline | 30% | Login trend, feature adoption change, DAU\u002FMAU change |\n| Engagement Drop | 25% | Meeting cancellations, response time, NPS change |\n| Support Issues | 20% | Open escalations, unresolved critical, satisfaction trend |\n| Relationship Signals | 15% | Champion left, sponsor change, competitor mentions |\n| Commercial Factors | 10% | Contract type, pricing complaints, budget cuts |\n\n**Risk Tiers:**\n- Critical (80-100): Immediate executive escalation\n- High (60-79): Urgent CSM intervention\n- Medium (40-59): Proactive outreach\n- Low (0-39): Standard monitoring\n\n**Usage:**\n```bash\npython scripts\u002Fchurn_risk_analyzer.py customer_data.json\npython scripts\u002Fchurn_risk_analyzer.py customer_data.json --format json\n```\n\n### 3. expansion_opportunity_scorer.py\n\n**Purpose:** Identify upsell, cross-sell, and expansion opportunities with revenue estimation and priority ranking.\n\n**Expansion Types:**\n- **Upsell**: Upgrade to higher tier or more of existing product\n- **Cross-sell**: Add new product modules\n- **Expansion**: Additional seats or departments\n\n**Usage:**\n```bash\npython scripts\u002Fexpansion_opportunity_scorer.py customer_data.json\npython scripts\u002Fexpansion_opportunity_scorer.py customer_data.json --format json\n```\n\n---\n\n## Reference Guides\n\n| Reference | Description |\n|-----------|-------------|\n| `references\u002Fhealth-scoring-framework.md` | Complete health scoring methodology, dimension definitions, weighting rationale, threshold calibration |\n| `references\u002Fcs-playbooks.md` | Intervention playbooks for each risk tier, onboarding, renewal, expansion, and escalation procedures |\n| `references\u002Fcs-metrics-benchmarks.md` | Industry benchmarks for NRR, GRR, churn rates, health scores, expansion rates by segment and industry |\n\n---\n\n## Templates\n\n| Template | Purpose |\n|----------|---------|\n| `assets\u002Fqbr_template.md` | Quarterly Business Review presentation structure |\n| `assets\u002Fsuccess_plan_template.md` | Customer success plan with goals, milestones, and metrics |\n| `assets\u002Fonboarding_checklist_template.md` | 90-day onboarding checklist with phase gates |\n| `assets\u002Fexecutive_business_review_template.md` | Executive stakeholder review for strategic accounts |\n\n---\n\n## Best Practices\n\n1. **Combine signals**: Use all three scripts together for a complete customer picture\n2. **Act on trends, not snapshots**: A declining Green is more urgent than a stable Yellow\n3. **Calibrate thresholds**: Adjust segment benchmarks based on your product and industry per `references\u002Fhealth-scoring-framework.md`\n4. **Prepare with data**: Run scripts before every QBR and executive meeting; reference `references\u002Fcs-playbooks.md` for intervention guidance\n\n---\n\n## Limitations\n\n- **No real-time data**: Scripts analyze point-in-time snapshots from JSON input files\n- **No CRM integration**: Data must be exported manually from your CRM\u002FCS platform\n- **Deterministic only**: No predictive ML -- scoring is algorithmic based on weighted signals\n- **Threshold tuning**: Default thresholds are industry-standard but may need calibration for your business\n- **Revenue estimates**: Expansion revenue estimates are approximations based on usage patterns\n\n---\n\n**Last Updated:** February 2026\n**Tools:** 3 Python CLI tools\n**Dependencies:** Python 3.7+ standard library only\n","# 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\n---\n\n## Table of Contents\n\n- [Input Requirements](#input-requirements)\n- [Output Formats](#output-formats)\n- [How to Use](#how-to-use)\n- [Scripts](#scripts)\n- [Reference Guides](#reference-guides)\n- [Templates](#templates)\n- [Best Practices](#best-practices)\n- [Limitations](#limitations)\n\n---\n\n## Input Requirements\n\nAll scripts accept a JSON file as positional input argument. See `assets\u002Fsample_customer_data.json` for complete schema examples and sample data.\n\n### Health 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\n### Churn 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\n### Expansion 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\n---\n\n## Output Formats\n\nAll scripts support two output formats via the `--format` flag:\n\n- **`text`** (default): Human-readable formatted output for terminal viewing\n- **`json`**: Machine-readable JSON output for integrations and pipelines\n\n---\n\n## How to Use\n\n### Quick Start\n\n```bash\n# Health scoring\npython scripts\u002Fhealth_score_calculator.py assets\u002Fsample_customer_data.json\npython scripts\u002Fhealth_score_calculator.py assets\u002Fsample_customer_data.json --format json\n\n# Churn risk analysis\npython scripts\u002Fchurn_risk_analyzer.py assets\u002Fsample_customer_data.json\npython scripts\u002Fchurn_risk_analyzer.py assets\u002Fsample_customer_data.json --format json\n\n# Expansion opportunity scoring\npython scripts\u002Fexpansion_opportunity_scorer.py assets\u002Fsample_customer_data.json\npython scripts\u002Fexpansion_opportunity_scorer.py assets\u002Fsample_customer_data.json --format json\n```\n\n### Workflow Integration\n\n```bash\n# 1. Score customer health across portfolio\npython scripts\u002Fhealth_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\u002Fchurn_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\u002Fexpansion_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\u002Fqbr_template.md\n```\n\n**Error handling:** If a script exits with an error, check that:\n- The input JSON matches the required schema for that script (see Input Requirements above)\n- All required fields are present and correctly typed\n- Python 3.7+ is being used (`python --version`)\n- Output files from prior steps are non-empty before piping into subsequent steps\n\n---\n\n## Scripts\n\n### 1. health_score_calculator.py\n\n**Purpose:** Multi-dimensional customer health scoring with trend analysis and segment-aware benchmarking.\n\n**Dimensions and Weights:**\n| Dimension | Weight | Metrics |\n|-----------|--------|---------|\n| Usage | 30% | Login frequency, feature adoption, DAU\u002FMAU ratio |\n| Engagement | 25% | Support ticket volume, meeting attendance, NPS\u002FCSAT |\n| Support | 20% | Open tickets, escalation rate, avg resolution time |\n| Relationship | 25% | Executive sponsor engagement, multi-threading depth, renewal sentiment |\n\n**Classification:**\n- Green (75-100): Healthy -- customer achieving value\n- Yellow (50-74): Needs attention -- monitor closely\n- Red (0-49): At risk -- immediate intervention required\n\n**Usage:**\n```bash\npython scripts\u002Fhealth_score_calculator.py customer_data.json\npython scripts\u002Fhealth_score_calculator.py customer_data.json --format json\n```\n\n### 2. churn_risk_analyzer.py\n\n**Purpose:** Identify at-risk accounts with behavioral signal detection and tier-based intervention recommendations.\n\n**Risk Signal Weights:**\n| Signal Category | Weight | Indicators |\n|----------------|--------|------------|\n| Usage Decline | 30% | Login trend, feature adoption change, DAU\u002FMAU change |\n| Engagement Drop | 25% | Meeting cancellations, response time, NPS change |\n| Support Issues | 20% | Open escalations, unresolved critical, satisfaction trend |\n| Relationship Signals | 15% | Champion left, sponsor change, competitor mentions |\n| Commercial Factors | 10% | Contract type, pricing complaints, budget cuts |\n\n**Risk Tiers:**\n- Critical (80-100): Immediate executive escalation\n- High (60-79): Urgent CSM intervention\n- Medium (40-59): Proactive outreach\n- Low (0-39): Standard monitoring\n\n**Usage:**\n```bash\npython scripts\u002Fchurn_risk_analyzer.py customer_data.json\npython scripts\u002Fchurn_risk_analyzer.py customer_data.json --format json\n```\n\n### 3. expansion_opportunity_scorer.py\n\n**Purpose:** Identify upsell, cross-sell, and expansion opportunities with revenue estimation and priority ranking.\n\n**Expansion Types:**\n- **Upsell**: Upgrade to higher tier or more of existing product\n- **Cross-sell**: Add new product modules\n- **Expansion**: Additional seats or departments\n\n**Usage:**\n```bash\npython scripts\u002Fexpansion_opportunity_scorer.py customer_data.json\npython scripts\u002Fexpansion_opportunity_scorer.py customer_data.json --format json\n```\n\n---\n\n## Reference Guides\n\n| Reference | Description |\n|-----------|-------------|\n| `references\u002Fhealth-scoring-framework.md` | Complete health scoring methodology, dimension definitions, weighting rationale, threshold calibration |\n| `references\u002Fcs-playbooks.md` | Intervention playbooks for each risk tier, onboarding, renewal, expansion, and escalation procedures |\n| `references\u002Fcs-metrics-benchmarks.md` | Industry benchmarks for NRR, GRR, churn rates, health scores, expansion rates by segment and industry |\n\n---\n\n## Templates\n\n| Template | Purpose |\n|----------|---------|\n| `assets\u002Fqbr_template.md` | Quarterly Business Review presentation structure |\n| `assets\u002Fsuccess_plan_template.md` | Customer success plan with goals, milestones, and metrics |\n| `assets\u002Fonboarding_checklist_template.md` | 90-day onboarding checklist with phase gates |\n| `assets\u002Fexecutive_business_review_template.md` | Executive stakeholder review for strategic accounts |\n\n---\n\n## Best Practices\n\n1. **Combine signals**: Use all three scripts together for a complete customer picture\n2. **Act on trends, not snapshots**: A declining Green is more urgent than a stable Yellow\n3. **Calibrate thresholds**: Adjust segment benchmarks based on your product and industry per `references\u002Fhealth-scoring-framework.md`\n4. **Prepare with data**: Run scripts before every QBR and executive meeting; reference `references\u002Fcs-playbooks.md` for intervention guidance\n\n---\n\n## Limitations\n\n- **No real-time data**: Scripts analyze point-in-time snapshots from JSON input files\n- **No CRM integration**: Data must be exported manually from your CRM\u002FCS platform\n- **Deterministic only**: No predictive ML -- scoring is algorithmic based on weighted signals\n- **Threshold tuning**: Default thresholds are industry-standard but may need calibration for your business\n- **Revenue estimates**: Expansion revenue estimates are approximations based on usage patterns\n\n---\n\n**Last Updated:** February 2026\n**Tools:** 3 Python CLI tools\n**Dependencies:** Python 3.7+ standard library only\n",{"name":5,"license":15,"metadata":44,"description":7},{"author":45,"domain":31,"updated":46,"version":47,"category":48,"tech-stack":49,"python-tools":50},"Alireza Rezvani",1770336000000,"1.0.0","business-growth","customer-success, saas-metrics, health-scoring","health_score_calculator.py, churn_risk_analyzer.py, expansion_opportunity_scorer.py","main","fa638b97a18ff6d9f26cf0eaf3dfa123531bcc87","organized-skills\u002F.gemini-customer-success-manager",1,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode","https:\u002F\u002Fgithub.com\u002Flevalencia","https:\u002F\u002Fmedium.com\u002F@luisevalencia","Microsoft MVP - Artificial Intelligence\r\nMachine Learning Enthusiast\r\n","2026-05-02 06:55:36",{"source":8,"skillName":5,"commands":61,"prompt":71,"zipAvailable":72},[62,65,68],{"tool":63,"command":64},"npx","npx skills add levalencia\u002Fagent-god-mode --skill customer-success-manager",{"tool":66,"command":67},"bunx","bunx skills add levalencia\u002Fagent-god-mode --skill customer-success-manager",{"tool":69,"command":70},"pnpm","pnpm dlx skills add levalencia\u002Fagent-god-mode --skill customer-success-manager","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002F.gemini-customer-success-manager\u002FSKILL.md",true,[74,80,84,88,92,96,100,106,110,114,120,124],{"path":75,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":78,"sourceHtmlUrl":79},"assets\u002Fexecutive_business_review_template.md","ASSET","资产文件",6951,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002F.gemini-customer-success-manager\u002Fassets\u002Fexecutive_business_review_template.md",{"path":81,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":82,"sourceHtmlUrl":83},"assets\u002Fexpected_output.json",4280,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002F.gemini-customer-success-manager\u002Fassets\u002Fexpected_output.json",{"path":85,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":86,"sourceHtmlUrl":87},"assets\u002Fonboarding_checklist_template.md",7353,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002F.gemini-customer-success-manager\u002Fassets\u002Fonboarding_checklist_template.md",{"path":89,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":90,"sourceHtmlUrl":91},"assets\u002Fqbr_template.md",4711,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002F.gemini-customer-success-manager\u002Fassets\u002Fqbr_template.md",{"path":93,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":94,"sourceHtmlUrl":95},"assets\u002Fsample_customer_data.json",9231,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002F.gemini-customer-success-manager\u002Fassets\u002Fsample_customer_data.json",{"path":97,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":98,"sourceHtmlUrl":99},"assets\u002Fsuccess_plan_template.md",5889,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002F.gemini-customer-success-manager\u002Fassets\u002Fsuccess_plan_template.md",{"path":101,"resourceType":102,"resourceTypeDesc":103,"sizeBytes":104,"sourceHtmlUrl":105},"references\u002Fcs-metrics-benchmarks.md","REFERENCE","参考文档",9116,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002F.gemini-customer-success-manager\u002Freferences\u002Fcs-metrics-benchmarks.md",{"path":107,"resourceType":102,"resourceTypeDesc":103,"sizeBytes":108,"sourceHtmlUrl":109},"references\u002Fcs-playbooks.md",10656,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002F.gemini-customer-success-manager\u002Freferences\u002Fcs-playbooks.md",{"path":111,"resourceType":102,"resourceTypeDesc":103,"sizeBytes":112,"sourceHtmlUrl":113},"references\u002Fhealth-scoring-framework.md",8540,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002F.gemini-customer-success-manager\u002Freferences\u002Fhealth-scoring-framework.md",{"path":115,"resourceType":116,"resourceTypeDesc":117,"sizeBytes":118,"sourceHtmlUrl":119},"scripts\u002Fchurn_risk_analyzer.py","SCRIPT","脚本",19540,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002F.gemini-customer-success-manager\u002Fscripts\u002Fchurn_risk_analyzer.py",{"path":121,"resourceType":116,"resourceTypeDesc":117,"sizeBytes":122,"sourceHtmlUrl":123},"scripts\u002Fexpansion_opportunity_scorer.py",15734,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002F.gemini-customer-success-manager\u002Fscripts\u002Fexpansion_opportunity_scorer.py",{"path":125,"resourceType":116,"resourceTypeDesc":117,"sizeBytes":126,"sourceHtmlUrl":127},"scripts\u002Fhealth_score_calculator.py",16651,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002F.gemini-customer-success-manager\u002Fscripts\u002Fhealth_score_calculator.py",[129,150,187,213,237,265],{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":133,"categories":134,"keywords":135,"publishedAt":39},"01M6EM8ES5M9J4S8N78NJST0YC","zarr-python","Chunked N-D arrays for cloud storage. Compressed arrays, parallel I\u002FO, S3\u002FGCS integration, NumPy\u002FDask\u002FXarray compatible, for large-scale scientific computing pipelines.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fzarr-python\u002FSKILL.md",[],[22,136,137,138,139,140,141,142,143,144,145,146,147,148,149],"zarr python","storage","integration","s3","gcs","cloud","dask","parallel","pipelines","arrays","numpy","scientific computing","scale","scientific",{"skillNo":151,"name":152,"nameZh":6,"description":153,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":154,"categories":155,"keywords":169,"publishedAt":39},"01M6EM8AR6ZFKZ9YVW8TTG850Z","zabbix","Zabbix monitoring system automation via API and Python. Use when: (1) Managing hosts, templates, items, triggers, or host groups, (2) Automating monitoring configuration, (3) Sending data via Zabbix trapper\u002Fsender, (4) Querying historical data or events, (5) Bulk operations on Zabbix objects, (6) Maintenance window management, (7) User\u002Fpermission management","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fzabbix-api-skill\u002FSKILL.md",[156,161,164],{"categoryNo":157,"categoryName":158,"taxonomyType":159,"taxonomyTypeDesc":160},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":162,"categoryName":163,"taxonomyType":159,"taxonomyTypeDesc":160},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":165,"categoryName":166,"taxonomyType":167,"taxonomyTypeDesc":168},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[152,22,170,171,172,173,174,175,176,177,29,178,179,180,181,182,183,184,185,186],"automation","window management","monitoring","data","templates","api","system","operations","maintenance","objects","permission","bulk operations","triggers","host","events","via","bulk",{"skillNo":188,"name":189,"nameZh":6,"description":190,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":191,"categories":192,"keywords":193,"publishedAt":39},"01M6EM6Q9JG846ECQY9A210A26","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fx-twitter-growth\u002FSKILL.md",[],[194,195,30,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212],"twitter","x twitter growth","competitive intelligence","search","analyzing","plan","marketing","web search","competitors","analyze","write","optimize","social","intelligence","viral","growth engine","mechanics","grow","competitive",{"skillNo":214,"name":215,"nameZh":6,"description":216,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":217,"categories":218,"keywords":219,"publishedAt":39},"01M6EM3FQNPYNKPC4CV9TCVNGD","what-if-oracle","Run structured What-If scenario analysis with multi-branch possibility exploration. Use this skill when the user asks speculative questions like \"what if...\", \"what would happen if...\", \"what are the possibilities\", \"explore scenarios\", \"scenario analysis\", \"possibility space\", \"what could go wrong\", \"best case \u002F worst case\", \"risk analysis\", \"contingency planning\", \"strategic options\", or any question about uncertain futures. Also trigger when the user faces a fork-in-the-road decision, wants to stress-test an idea, or needs to think through consequences before committing.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fwhat-if-oracle\u002FSKILL.md",[],[220,221,222,223,224,225,36,34,226,227,228,229,29,230,231,232,233,234,235,236],"oracle","what if oracle","test","planning","ux","futures","explore","decision","branch","question","exploration","options","stress-test","strategies","trigger","case","multi",{"skillNo":238,"name":239,"nameZh":6,"description":240,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":241,"categories":242,"keywords":246,"publishedAt":39},"01M6EM08C06RGDYCJ86HAPK0FF","vercel-react-best-practices","React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React\u002FNext.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fvercel-react-best-practices\u002FSKILL.md",[243],{"categoryNo":244,"categoryName":245,"taxonomyType":159,"taxonomyTypeDesc":160},"01JBSEEDCAT000000000000009","前端开发",[247,248,249,250,251,252,253,254,255,256,257,258,259,173,260,261,262,263,264,182],"react","vercel react best practices","best practices","react best practices","vercel","writing","performance","components","react components","guidelines","tasks","code","Optimization","Next.js","refactoring","performance optimization","next","pages",{"skillNo":266,"name":267,"nameZh":6,"description":268,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":269,"categories":270,"keywords":281,"publishedAt":39},"01M6EM00S13326QM08DTM94J6Z","venue-templates","Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fvenue-templates\u002FSKILL.md",[271,274,277,278],{"categoryNo":272,"categoryName":273,"taxonomyType":159,"taxonomyTypeDesc":160},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":275,"categoryName":276,"taxonomyType":159,"taxonomyTypeDesc":160},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":162,"categoryName":163,"taxonomyType":159,"taxonomyTypeDesc":160},{"categoryNo":279,"categoryName":280,"taxonomyType":167,"taxonomyTypeDesc":168},"01JBSEEDCAT000000000000036","内容创作者",[174,282,283,256,284,285,286,287,288,289,290,204,291,292,293,294,295,296,149,297],"venue templates","research","NeurIPS","ICML","LaTeX","science","Bash","access","read","edit","proposals","Academic","requirements","formatting","journal","chi"]