[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M713QF45VMV1685AFNF1GPMA":3,"skill-same:01M713QF45VMV1685AFNF1GPMA":141},{"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":31,"publishedAt":52,"linkOnly":53,"markdownContent":54,"markdownBody":55,"frontMatter":56,"refName":57,"commitSha":58,"skillDirectory":59,"forksCount":60,"repositoryHtmlUrl":61,"repositoryHomepageUrl":6,"ownerHtmlUrl":62,"ownerBlogUrl":6,"ownerDescription":63,"updatedAt":64,"install":65,"resources":78},"01M713QF45VMV1685AFNF1GPMA","weekly-kpi-report",null,"Generate McKinsey-style board presentation PPTs from weekly auto insurance data. Automatically calculates 16+ KPIs, creates executive-level slides with actionable insights, and supports week-over-week comparisons. Use when user uploads insurance cost data (Excel\u002FCSV) and requests board report, weekly presentation, executive briefing, or mentions keywords like 董事会汇报, 周报PPT, 经营分析演示, McKinsey-style reports.","FDU-INS\u002FInsurance-Skills","FDU-INS","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F167067594?v=4","01M6XEYMRS4CVJMZ834YWE66BV","FDU-Insurance","01M6XEYMXPDA34HFM0ZE6AY78G",82,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fweekly-kpi-report\u002FSKILL.md",[18,23,26],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000003","演示文稿","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000004","电子表格",{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000034","行政与办公人员","PROFESSION","适用职业",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51],"weekly kpi report","kpi","report","weekly","uploads","use","slides","presentation","data","演示","generate","reports","board report","when","auto","briefing","insights","actionable","insurance","xlsx","2026-11-10 10:12:30",false,"---\nname: weekly-kpi-report\ndescription: Generate McKinsey-style board presentation PPTs from weekly auto insurance data. Automatically calculates 16+ KPIs, creates executive-level slides with actionable insights, and supports week-over-week comparisons. Use when user uploads insurance cost data (Excel\u002FCSV) and requests board report, weekly presentation, executive briefing, or mentions keywords like 董事会汇报, 周报PPT, 经营分析演示, McKinsey-style reports.\n---\n\n# Weekly KPI Report Generator (McKinsey Style)\n\n## Purpose\n\nTransform weekly auto insurance policy cost data into executive-ready board presentation slides using McKinsey consulting design principles. Generate data-driven insights with conclusion-first structure, professional visualization, and actionable recommendations.\n\n## Quick Start\n\n### Three-Step Generation Process\n\n1. **Upload Data**: Provide weekly insurance cost data file (Excel\u002FCSV)\n2. **Automatic Processing**: Skill validates data, calculates KPIs, and generates insights\n3. **Download PPT**: Receive McKinsey-style board presentation ready for executive meeting\n\n### Basic Usage Example\n\n```\nUser: \"Generate board report from this week's insurance data\"\n\nAssistant (using this skill):\n1. Validates uploaded file and extracts week number\n2. Calculates 16+ KPIs (cost rates, premium progress, loss ratios)\n3. Generates 12-13 slide deck with:\n   - Executive summary with key insights\n   - Institutional and customer segment analysis\n   - Problem-oriented headlines with actionable recommendations\n4. Returns: \"{Organization}_Week{N}_McKinsey_Report.pptx\"\n```\n\n### Minimal Requirements\n\n- **Input**: Excel\u002FCSV file with insurance policy cost data\n- **Week Number**: Extracted from filename or user-provided\n- **Configuration** (optional): Custom thresholds in `references\u002Fconfig.json`\n- **Output**: Professional PPT with charts, insights, and recommendations\n\n## When to Use This Skill\n\nTrigger this skill when:\n\n- User uploads auto insurance weekly cost data (Excel\u002FCSV format) and requests board presentation\n- User mentions keywords: \"董事会汇报\", \"周报PPT\", \"经营分析演示\", \"board report\", \"executive briefing\"\n- User asks to generate presentation slides from insurance data\n- User requests McKinsey-style or consulting-style reports\n\n## Core Workflow\n\n### Step 1: Data Validation\n\nExecute the data validator to ensure data quality:\n\n```bash\npython scripts\u002Fdata_validator.py \u003Cuploaded_file_path>\n```\n\nThe validator checks:\n\n- Required field completeness (policy numbers, premium amounts, cost rates)\n- Data type correctness (numeric fields, date formats)\n- Week number extraction from filename (e.g., \"第45周\" → Week 45)\n- Record count and date range calculation\n\n### Step 2: KPI Calculation\n\nCalculate board-level KPIs (not raw data dumps):\n\n```bash\npython scripts\u002Fkpi_calculator.py \u003Cfile_path> \u003Cweek_number>\n```\n\n**Four KPI Categories:**\n\n1. **Business Scale**\n   - Weekly premium revenue and growth rate\n   - Policy count and average premium per policy\n   - Business type distribution (truck\u002Fpassenger\u002Fprivate)\n\n2. **Profitability**\n   - Combined ratio (loss ratio + expense ratio)\n   - Variable cost rate distribution and outliers\n   - Profitability comparison by customer segment\n\n3. **Business Structure**\n   - New energy vehicle (NEV) penetration rate and trend\n   - Renewal rate vs. new policy ratio\n   - Contribution by distribution channel\n\n4. **Risk Management**\n   - Claims frequency and high-risk business proportion\n   - Average claim amount changes\n   - Risk exposure in high-risk segments (e.g., highway freight)\n\n### Step 3: Generate McKinsey-Style PPT\n\nCreate presentation slides with consulting-grade design:\n\n```bash\npython scripts\u002Fboard_ppt_generator.py \u003Cweek_number> \u003Ckpi_data_json>\n```\n\n**Slide Structure (7 slides):**\n\n1. **Cover** - Title, date range, presenter\n2. **Executive Summary** - Core metrics with top 3 highlights\u002Frisks\n3. **Premium Analysis** - Revenue trends, business mix, YoY comparison\n4. **Profitability Analysis** - Combined ratio breakdown, cost rate by segment\n5. **NEV Business Focus** - NEV penetration, loss ratio comparison vs. traditional vehicles\n6. **Risk Management** - Claims frequency heatmap, high-risk business list\n7. **Action Items** - Auto-generated recommendations based on data patterns\n\nRefer to [references\u002Fmckinsey-style-guide.md](references\u002Fmckinsey-style-guide.md) for detailed design principles.\n\n### Step 4 (Optional): Week-over-Week Comparison\n\nWhen user provides data for two consecutive weeks:\n\n```bash\npython scripts\u002Foptional_modules\u002Fweek_comparator.py \u003Cweek1_kpis.json> \u003Cweek2_kpis.json>\n```\n\nGenerates additional comparison slide showing WoW changes in key metrics.\n\n## Design Principles\n\n**McKinsey Three Pillars:**\n\n1. **Conclusion-First Titles** - Every slide title answers \"So what?\"\n   - ❌ Wrong: \"Profitability Analysis\"\n   - ✅ Right: \"Profitability remains healthy with 83.9% combined ratio below industry benchmark\"\n\n2. **Minimalist Layout** - Less is more\n   - Large white space (0.8\" margins)\n   - Single red accent line at top\n   - No excessive decorations or logo stacking\n\n3. **Left-Aligned Structure** - Professional business style\n   - Title left-aligned (24pt, conclusion statement)\n   - Left column: bullet points\n   - Right column: supporting charts\n   - Bottom: italic recommendations (12pt)\n\n**Color Scheme:**\nUses client-specific colors extracted from corporate reports:\n\n- Primary: Deep Red (#a02724) - 60% usage for core messages\n- Alert: Bright Red (#c00000) - warnings and risks\n- Text: Black (#000000) - titles and important text\n- Background: White (#FFFFFF) - clean backdrop\n\nConfigure colors in [assets\u002Fmckinsey_config.json](assets\u002Fmckinsey_config.json).\n\n## Configuration\n\n### Alert Thresholds\n\nCustomize business rules in [config.json](config.json):\n\n```json\n{\n  \"预警阈值\": {\n    \"综合成本率_上限\": 95, \u002F\u002F Alert if combined ratio > 95%\n    \"新能源车赔付率差距\": 10 \u002F\u002F Alert if NEV loss ratio > traditional + 10pp\n  }\n}\n```\n\n### Display Parameters\n\n```json\n{\n  \"报表参数\": {\n    \"显示TOP业务类型数\": 5, \u002F\u002F Show top 5 business types\n    \"显示TOP机构数\": 5 \u002F\u002F Show top 5 distribution channels\n  }\n}\n```\n\nRefer to [references\u002Fconfig-guide.md](references\u002Fconfig-guide.md) for full configuration options.\n\n## Usage Examples\n\n**Example 1: Basic Usage**\n\n```\nUser: 我上传了第45周的车险数据,帮我生成董事会汇报PPT\n\nExecution:\n1. Identify file: \"车险保单变动成本清单__第45周_.xlsx\"\n2. Run data_validator.py\n3. Run kpi_calculator.py with config.json thresholds\n4. Run board_ppt_generator.py using assets\u002Fmckinsey_board_template.pptx\n5. Output: \"华安车险周报_第45周_麦肯锡版.pptx\"\n6. Return download link with brief data summary\n```\n\n## Error Handling\n\n- **Missing week number in filename** → Prompt user to confirm week number\n- **Missing required fields** → List missing columns and ask whether to proceed\n- **All cost rates abnormal (>100%)** → Warning that data may be incorrect\n- **Invalid JSON config** → Use default values and notify user\n\n## Technical Stack\n\n- **Data processing:** pandas, numpy\n- **Visualization:** matplotlib (Chinese font handling), seaborn\n- **PPT generation:** python-pptx\n- **Template:** assets\u002Fmckinsey_board_template.pptx\n- **Field Mapping:** field_mapping.json (支持中英文字段自动适配)\n- **Supported Data Formats:**\n  - Excel files (.xlsx, .xls) with Chinese field names\n  - CSV files (.csv) with English field names (e.g., from transformed data)\n\n## Output Location\n\nGenerated PPT files saved to: `\u002Fmnt\u002Fuser-data\u002Foutputs\u002F`\n\nFilename format: `华安车险周报_第{week_number}周_麦肯锡版.pptx`\n\n## Version Information\n\n- **Version:** v2.0.0 (Field Mapping Support)\n- **Last Updated:** 2025-12-08\n- **Maintainer:** Alongor\n- **Data Source:** Hua'an Insurance Sichuan Branch weekly auto insurance reports\n- **Supported Formats:** Excel (.xlsx, .xls), CSV (.csv)\n- **Supported Field Names:** Chinese (跟单保费, 业务类型分类) and English (signed_premium_yuan, business_type_category)\n","# Weekly KPI Report Generator (McKinsey Style)\n\n## Purpose\n\nTransform weekly auto insurance policy cost data into executive-ready board presentation slides using McKinsey consulting design principles. Generate data-driven insights with conclusion-first structure, professional visualization, and actionable recommendations.\n\n## Quick Start\n\n### Three-Step Generation Process\n\n1. **Upload Data**: Provide weekly insurance cost data file (Excel\u002FCSV)\n2. **Automatic Processing**: Skill validates data, calculates KPIs, and generates insights\n3. **Download PPT**: Receive McKinsey-style board presentation ready for executive meeting\n\n### Basic Usage Example\n\n```\nUser: \"Generate board report from this week's insurance data\"\n\nAssistant (using this skill):\n1. Validates uploaded file and extracts week number\n2. Calculates 16+ KPIs (cost rates, premium progress, loss ratios)\n3. Generates 12-13 slide deck with:\n   - Executive summary with key insights\n   - Institutional and customer segment analysis\n   - Problem-oriented headlines with actionable recommendations\n4. Returns: \"{Organization}_Week{N}_McKinsey_Report.pptx\"\n```\n\n### Minimal Requirements\n\n- **Input**: Excel\u002FCSV file with insurance policy cost data\n- **Week Number**: Extracted from filename or user-provided\n- **Configuration** (optional): Custom thresholds in `references\u002Fconfig.json`\n- **Output**: Professional PPT with charts, insights, and recommendations\n\n## When to Use This Skill\n\nTrigger this skill when:\n\n- User uploads auto insurance weekly cost data (Excel\u002FCSV format) and requests board presentation\n- User mentions keywords: \"董事会汇报\", \"周报PPT\", \"经营分析演示\", \"board report\", \"executive briefing\"\n- User asks to generate presentation slides from insurance data\n- User requests McKinsey-style or consulting-style reports\n\n## Core Workflow\n\n### Step 1: Data Validation\n\nExecute the data validator to ensure data quality:\n\n```bash\npython scripts\u002Fdata_validator.py \u003Cuploaded_file_path>\n```\n\nThe validator checks:\n\n- Required field completeness (policy numbers, premium amounts, cost rates)\n- Data type correctness (numeric fields, date formats)\n- Week number extraction from filename (e.g., \"第45周\" → Week 45)\n- Record count and date range calculation\n\n### Step 2: KPI Calculation\n\nCalculate board-level KPIs (not raw data dumps):\n\n```bash\npython scripts\u002Fkpi_calculator.py \u003Cfile_path> \u003Cweek_number>\n```\n\n**Four KPI Categories:**\n\n1. **Business Scale**\n   - Weekly premium revenue and growth rate\n   - Policy count and average premium per policy\n   - Business type distribution (truck\u002Fpassenger\u002Fprivate)\n\n2. **Profitability**\n   - Combined ratio (loss ratio + expense ratio)\n   - Variable cost rate distribution and outliers\n   - Profitability comparison by customer segment\n\n3. **Business Structure**\n   - New energy vehicle (NEV) penetration rate and trend\n   - Renewal rate vs. new policy ratio\n   - Contribution by distribution channel\n\n4. **Risk Management**\n   - Claims frequency and high-risk business proportion\n   - Average claim amount changes\n   - Risk exposure in high-risk segments (e.g., highway freight)\n\n### Step 3: Generate McKinsey-Style PPT\n\nCreate presentation slides with consulting-grade design:\n\n```bash\npython scripts\u002Fboard_ppt_generator.py \u003Cweek_number> \u003Ckpi_data_json>\n```\n\n**Slide Structure (7 slides):**\n\n1. **Cover** - Title, date range, presenter\n2. **Executive Summary** - Core metrics with top 3 highlights\u002Frisks\n3. **Premium Analysis** - Revenue trends, business mix, YoY comparison\n4. **Profitability Analysis** - Combined ratio breakdown, cost rate by segment\n5. **NEV Business Focus** - NEV penetration, loss ratio comparison vs. traditional vehicles\n6. **Risk Management** - Claims frequency heatmap, high-risk business list\n7. **Action Items** - Auto-generated recommendations based on data patterns\n\nRefer to [references\u002Fmckinsey-style-guide.md](references\u002Fmckinsey-style-guide.md) for detailed design principles.\n\n### Step 4 (Optional): Week-over-Week Comparison\n\nWhen user provides data for two consecutive weeks:\n\n```bash\npython scripts\u002Foptional_modules\u002Fweek_comparator.py \u003Cweek1_kpis.json> \u003Cweek2_kpis.json>\n```\n\nGenerates additional comparison slide showing WoW changes in key metrics.\n\n## Design Principles\n\n**McKinsey Three Pillars:**\n\n1. **Conclusion-First Titles** - Every slide title answers \"So what?\"\n   - ❌ Wrong: \"Profitability Analysis\"\n   - ✅ Right: \"Profitability remains healthy with 83.9% combined ratio below industry benchmark\"\n\n2. **Minimalist Layout** - Less is more\n   - Large white space (0.8\" margins)\n   - Single red accent line at top\n   - No excessive decorations or logo stacking\n\n3. **Left-Aligned Structure** - Professional business style\n   - Title left-aligned (24pt, conclusion statement)\n   - Left column: bullet points\n   - Right column: supporting charts\n   - Bottom: italic recommendations (12pt)\n\n**Color Scheme:**\nUses client-specific colors extracted from corporate reports:\n\n- Primary: Deep Red (#a02724) - 60% usage for core messages\n- Alert: Bright Red (#c00000) - warnings and risks\n- Text: Black (#000000) - titles and important text\n- Background: White (#FFFFFF) - clean backdrop\n\nConfigure colors in [assets\u002Fmckinsey_config.json](assets\u002Fmckinsey_config.json).\n\n## Configuration\n\n### Alert Thresholds\n\nCustomize business rules in [config.json](config.json):\n\n```json\n{\n  \"预警阈值\": {\n    \"综合成本率_上限\": 95, \u002F\u002F Alert if combined ratio > 95%\n    \"新能源车赔付率差距\": 10 \u002F\u002F Alert if NEV loss ratio > traditional + 10pp\n  }\n}\n```\n\n### Display Parameters\n\n```json\n{\n  \"报表参数\": {\n    \"显示TOP业务类型数\": 5, \u002F\u002F Show top 5 business types\n    \"显示TOP机构数\": 5 \u002F\u002F Show top 5 distribution channels\n  }\n}\n```\n\nRefer to [references\u002Fconfig-guide.md](references\u002Fconfig-guide.md) for full configuration options.\n\n## Usage Examples\n\n**Example 1: Basic Usage**\n\n```\nUser: 我上传了第45周的车险数据,帮我生成董事会汇报PPT\n\nExecution:\n1. Identify file: \"车险保单变动成本清单__第45周_.xlsx\"\n2. Run data_validator.py\n3. Run kpi_calculator.py with config.json thresholds\n4. Run board_ppt_generator.py using assets\u002Fmckinsey_board_template.pptx\n5. Output: \"华安车险周报_第45周_麦肯锡版.pptx\"\n6. Return download link with brief data summary\n```\n\n## Error Handling\n\n- **Missing week number in filename** → Prompt user to confirm week number\n- **Missing required fields** → List missing columns and ask whether to proceed\n- **All cost rates abnormal (>100%)** → Warning that data may be incorrect\n- **Invalid JSON config** → Use default values and notify user\n\n## Technical Stack\n\n- **Data processing:** pandas, numpy\n- **Visualization:** matplotlib (Chinese font handling), seaborn\n- **PPT generation:** python-pptx\n- **Template:** assets\u002Fmckinsey_board_template.pptx\n- **Field Mapping:** field_mapping.json (支持中英文字段自动适配)\n- **Supported Data Formats:**\n  - Excel files (.xlsx, .xls) with Chinese field names\n  - CSV files (.csv) with English field names (e.g., from transformed data)\n\n## Output Location\n\nGenerated PPT files saved to: `\u002Fmnt\u002Fuser-data\u002Foutputs\u002F`\n\nFilename format: `华安车险周报_第{week_number}周_麦肯锡版.pptx`\n\n## Version Information\n\n- **Version:** v2.0.0 (Field Mapping Support)\n- **Last Updated:** 2025-12-08\n- **Maintainer:** Alongor\n- **Data Source:** Hua'an Insurance Sichuan Branch weekly auto insurance reports\n- **Supported Formats:** Excel (.xlsx, .xls), CSV (.csv)\n- **Supported Field Names:** Chinese (跟单保费, 业务类型分类) and English (signed_premium_yuan, business_type_category)\n",{"name":5,"description":7},"main","b22eca2e974f918ce591c5b2bd829fb7510e0191","Skills\u002Fweekly-kpi-report",18,"https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills","https:\u002F\u002Fgithub.com\u002FFDU-INS","Fudan University Department of Risk Management and Insurance","2026-07-12 12:54:34",{"source":8,"skillName":5,"commands":66,"prompt":76,"zipAvailable":77},[67,70,73],{"tool":68,"command":69},"npx","npx skills add FDU-INS\u002FInsurance-Skills --skill weekly-kpi-report",{"tool":71,"command":72},"bunx","bunx skills add FDU-INS\u002FInsurance-Skills --skill weekly-kpi-report",{"tool":74,"command":75},"pnpm","pnpm dlx 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weekly-kpi-report","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fweekly-kpi-report\u002FSKILL.md",true,[79,85,89,93,97,103,107,111,115,119,123,127,133,137],{"path":80,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":83,"sourceHtmlUrl":84},"assets\u002Fhuaan_board_template.pptx","ASSET","资产文件",28616,"https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fweekly-kpi-report\u002Fassets\u002Fhuaan_board_template.pptx",{"path":86,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":87,"sourceHtmlUrl":88},"assets\u002Fmckinsey_board_template.pptx",31648,"https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fweekly-kpi-report\u002Fassets\u002Fmckinsey_board_template.pptx",{"path":90,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":91,"sourceHtmlUrl":92},"assets\u002Fmckinsey_config.json",6135,"https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fweekly-kpi-report\u002Fassets\u002Fmckinsey_config.json",{"path":94,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":95,"sourceHtmlUrl":96},"assets\u002Ftheme_config.json",2933,"https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fweekly-kpi-report\u002Fassets\u002Ftheme_config.json",{"path":98,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":101,"sourceHtmlUrl":102},"CHANGELOG_v2.0.md","REFERENCE","参考文档",7221,"https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fweekly-kpi-report\u002FCHANGELOG_v2.0.md",{"path":104,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":105,"sourceHtmlUrl":106},"COMPLIANCE_REPORT.md",15475,"https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fweekly-kpi-report\u002FCOMPLIANCE_REPORT.md",{"path":108,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":109,"sourceHtmlUrl":110},"config.json",1833,"https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fweekly-kpi-report\u002Fconfig.json",{"path":112,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":113,"sourceHtmlUrl":114},"OPTIMIZATION_SUMMARY.md",12647,"https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fweekly-kpi-report\u002FOPTIMIZATION_SUMMARY.md",{"path":116,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":117,"sourceHtmlUrl":118},"README.md",8740,"https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fweekly-kpi-report\u002FREADME.md",{"path":120,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":121,"sourceHtmlUrl":122},"references\u002Fconfig-guide.md",5245,"https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fweekly-kpi-report\u002Freferences\u002Fconfig-guide.md",{"path":124,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":125,"sourceHtmlUrl":126},"references\u002Fmckinsey-style-guide.md",8137,"https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fweekly-kpi-report\u002Freferences\u002Fmckinsey-style-guide.md",{"path":128,"resourceType":129,"resourceTypeDesc":130,"sizeBytes":131,"sourceHtmlUrl":132},"scripts\u002Fboard_ppt_generator.py","SCRIPT","脚本",25217,"https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fweekly-kpi-report\u002Fscripts\u002Fboard_ppt_generator.py",{"path":134,"resourceType":129,"resourceTypeDesc":130,"sizeBytes":135,"sourceHtmlUrl":136},"scripts\u002Fkpi_calculator.py",14601,"https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fweekly-kpi-report\u002Fscripts\u002Fkpi_calculator.py",{"path":138,"resourceType":129,"resourceTypeDesc":130,"sizeBytes":139,"sourceHtmlUrl":140},"scripts\u002Foptional_modules\u002Fweek_comparator.py",7477,"https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fweekly-kpi-report\u002Fscripts\u002Foptional_modules\u002Fweek_comparator.py",[142,169,193,222,244,271],{"skillNo":143,"name":144,"nameZh":6,"description":145,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":146,"categories":147,"keywords":151,"publishedAt":52},"01M713QW6EGEG0YAD75440GSD0","로인장 (Roinjiang)","Activates when the user needs legal advice, contract review, insurance law consultation, startup legal support, or any legal\u002Fregulatory matter. She is a top-tier in-house lawyer with absolute loyalty to the CEO, specializing in insurance law, financial consumer protection, privacy, contracts, labor law, family law, civil and criminal matters.","https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002F로인장 (Roinjiang)\u002FSKILL.md",[148],{"categoryNo":149,"categoryName":150,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000037","律师与法务",[144,152,50,153,154,155,45,156,157,158,159,160,161,162,163,164,165,166,167,168],"insurance law","civil","contract","family","review","loyalty","with","top","contract review","ceo","support","advice","consultation","regulatory","privacy","protection","startup",{"skillNo":170,"name":171,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":173,"categories":174,"keywords":175,"publishedAt":52},"01M713QRN8VFMA9VKS6R4DJ2WS","zocdoc-doctor-finder","Use when a user needs to find a doctor, dentist, specialist, therapist, or healthcare provider — especially when they mention insurance, in-network, availability, location, appointments, or booking. Also use for browsing provider reviews, comparing doctors by specialty, or checking if a provider takes their insurance.","https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fzocdoc-doctor-finder\u002FSKILL.md",[],[176,177,178,179,50,180,181,45,182,183,184,185,186,187,188,189,190,191,37,192],"zocdoc doctor finder","doctor","healthcare","reviews","browser automation","access","browsing","browser","booking","network","therapist","computer","availability","for","provider","tool","automation",{"skillNo":194,"name":195,"nameZh":6,"description":196,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":197,"categories":198,"keywords":202,"publishedAt":52},"01M713QKGV3FTX7EEYWKDM8ENZ","zenmux-context","Answers any question about ZenMux by reading the latest official documentation. Use this skill whenever the user asks about ZenMux product features, API usage, configuration, integration, pricing, models, providers, routing, fallback, streaming, multimodal, structured output, tool calling, reasoning, prompt caching, image generation, video generation, web search, long context, observability, logs, cost tracking, usage analytics, insurance, subscription, pay-as-you-go, invoices, best practices for tools like Claude Code, Cursor, Cline, CodeX, Gemini CLI, opencode, Cherry Studio, Obsidian, Sider, Open-WebUI, Dify, Neovate, GitHub Copilot, OpenClaw, CC-Switch, Immersive Translate, Studio Chat, architecture, benchmarks, FAQ, contact, privacy, terms of service, compliance, or any other ZenMux-related topic. Also trigger when the user writes in Chinese about ZenMux: 文档, 使用指南, 快速开始, API 参考, 最佳实践, 模型路由, 供应商路由, 订阅, 按量计费, 可观测性, 帮助, 常见问题, 接入指南, 配置. Trigger on phrases like \"how to use ZenMux\", \"ZenMux docs\", \"ZenMux API\", \"ZenMux integration\", \"ZenMux 怎么用\", \"ZenMux 接入\", \"ZenMux 配置\". Even if the user doesn't say \"ZenMux\" explicitly but asks about LLM API aggregation, model routing, provider fallback in a context where ZenMux is the project being worked on, this skill should activate.","https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fzenmux-context\u002FSKILL.md",[199],{"categoryNo":200,"categoryName":201,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",[203,204,205,206,50,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221],"context","zenmux context","Video","chinese","project","web search","translate","search","open","question","pay","image","you","activate","architecture","logs","streaming","cherry studio","how",{"skillNo":223,"name":224,"nameZh":6,"description":225,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":226,"categories":227,"keywords":228,"publishedAt":52},"01M713QHWTBG634BCBV7NBJME1","workers-comp-guide","Guide workers compensation compliance and claims management.\nTRIGGERS - Use when user needs help with workers-comp-guide related tasks.","https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fworkers-comp-guide\u002FSKILL.md",[],[229,230,231,232,233,50,234,235,37,45,236,158,237,238,239,240,241,242,243],"workers","comp","workers comp","workers comp guide","guide","triggers","workers-comp","tasks","claims management","claims","compliance","help","compensation","management","user",{"skillNo":245,"name":246,"nameZh":6,"description":247,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":248,"categories":249,"keywords":253,"publishedAt":52},"01M713QA9WJDHMNVTTY1ENRKVB","wealth-advisory","Wealth management and financial planning for HNW and UHNW clients. Activate when the user\nmentions wealth management, financial planning, retirement, estate planning, tax planning,\nGRAT, IDGT, trust, asset allocation, withdrawal rate, Monte Carlo simulation, goals-based\ninvesting, tax-loss harvesting, Roth conversion, insurance, annuity, mortgage strategy,\nconcentrated stock, family office, direct indexing, 529 plan, charitable giving, donor-advised\nfund, private foundation, or asks about personal finance strategy, inheritance, long-term\nfinancial planning, or UHNW advisory.","https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fwealth-advisory\u002FSKILL.md",[250],{"categoryNo":251,"categoryName":252,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000040","财务",[254,255,50,256,257,45,216,258,259,260,261,262,263,264,265,266,267,268,269,270],"wealth advisory","advisory","stock","inheritance","conversion","indexing","plan","simulation","personal finance","rate","allocation","asset","direct","personal","investing","trust","planning",{"skillNo":272,"name":273,"nameZh":6,"description":274,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":275,"categories":276,"keywords":277,"publishedAt":52},"01M713Q86HM43G76S2H9A7JKCS","vn-payroll","Calculates Net Income, Personal Income Tax (PIT), and Social Insurance (BHXH) based on Vietnam's progressive tax brackets.","https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fvn-payroll\u002FSKILL.md",[],[278,279,280,281,282,283,50,284,267,285],"vn payroll","payroll","tax","progressive","income","social insurance","brackets","social"]