[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XGDTQQ8DW1GC9SFSYSC2Z5":3,"skill-same:01M6XGDTQQ8DW1GC9SFSYSC2Z5":107},{"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":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":58,"commitSha":59,"skillDirectory":60,"forksCount":61,"repositoryHtmlUrl":62,"repositoryHomepageUrl":6,"ownerHtmlUrl":63,"ownerBlogUrl":6,"ownerDescription":64,"updatedAt":65,"install":66,"resources":79},"01M6XGDTQQ8DW1GC9SFSYSC2Z5","hipaa-compliance-auditor",null,"Automatically detect and de-identify PII (Personal Identifiable Information) and PHI (Protected Health Information) from clinical\u002Fmedical text to ensure HIPAA compliance. Trigger when processing medical records, patient data, clinical notes, insurance information, or any healthcare-related text containing potential patient identifiers.","FDU-INS\u002FInsurance-Skills","FDU-INS","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F167067594?v=4","01M6XEYMRS4CVJMZ834YWE66BV","FDU-Insurance","01M6XEYMXPDA34HFM0ZE6AY78G",82,"MIT","https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fhipaa-compliance-auditor\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000037","律师与法务","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"compliance","hipaa compliance auditor","auditor","compliance auditor","hipaa compliance","hipaa","healthcare","text","pii","medical","clinical","patient","de","data","processing","information","when","insurance","2026","notes","2026-11-10 10:12:30",false,"---\nname: hipaa-compliance-auditor\ndescription: Automatically detect and de-identify PII (Personal Identifiable Information)\n  and PHI (Protected Health Information) from clinical\u002Fmedical text to ensure HIPAA\n  compliance. Trigger when processing medical records, patient data, clinical notes,\n  insurance information, or any healthcare-related text containing potential patient\n  identifiers.\nversion: 1.0.0\ncategory: Clinical\ntags: []\nauthor: AIPOCH\nlicense: MIT\nstatus: Draft\nrisk_level: Medium\nskill_type: Tool\u002FScript\nowner: AIPOCH\nreviewer: ''\nlast_updated: '2026-02-06'\n---\n\n# HIPAA Compliance Auditor\n\nA clinical-grade PII\u002FPHI detection and de-identification tool for healthcare text data.\n\n## Overview\n\nThis skill analyzes text for HIPAA-protected identifiers and automatically redacts or anonymizes them. It uses a combination of regex patterns, NLP entity recognition, and contextual analysis to identify 18 HIPAA identifier categories.\n\n## Features\n\n- **18 HIPAA Identifiers Detection**: Names, dates, SSN, MRN, phone\u002Ffax, email, geographic data, etc.\n- **Automatic De-identification**: Replace PII with semantic tokens (e.g., `[PATIENT_NAME]`, `[DATE_1]`)\n- **Context-Aware Detection**: Distinguishes between similar patterns (dates vs. lab values)\n- **Audit Logging**: Track all redaction actions for compliance documentation\n- **Confidence Scoring**: Flag uncertain detections for manual review\n\n## Usage\n\n### Command Line\n```bash\npython scripts\u002Fmain.py --input \"patient_text.txt\" --output \"deidentified.txt\"\npython scripts\u002Fmain.py --text \"Patient John Doe, SSN 123-45-6789...\" --audit-log audit.json\n```\n\n### Python API\n```python\nfrom scripts.main import HIPAAAuditor\n\nauditor = HIPAAAuditor()\nresult = auditor.deidentify(\"Patient John Doe was admitted on 2024-01-15...\")\nprint(result.cleaned_text)  # De-identified output\nprint(result.detected_pii)  # List of found PII entities\n```\n\n## Parameters\n\n| Parameter | Type | Default | Required | Description |\n|-----------|------|---------|----------|-------------|\n| `--input`, `-i` | string | - | No | Path to input text file |\n| `--text` | string | - | No | Direct text input (alternative to file) |\n| `--output`, `-o` | string | - | No | Path for de-identified output file |\n| `--audit-log` | string | - | No | Path for JSON audit log |\n| `--confidence` | float | 0.7 | No | Minimum confidence threshold (0.0-1.0) |\n| `--preserve-structure` | bool | true | No | Maintain document structure |\n| `--custom-patterns` | string | - | No | Path to custom regex patterns JSON |\n\n## HIPAA Identifier Categories Detected\n\n1. Names (patient, relatives, employers)\n2. Geographic subdivisions smaller than state\n3. Dates (except year) related to individual\n4. Phone numbers\n5. Fax numbers\n6. Email addresses\n7. SSN\n8. Medical record numbers\n9. Health plan beneficiary numbers\n10. Account numbers\n11. Certificate\u002Flicense numbers\n12. Vehicle identifiers\n13. Device identifiers\n14. URLs\n15. IP addresses\n16. Biometric identifiers\n17. Full-face photos\n18. Any other unique identifying numbers\n\n## Output Format\n\n### De-identified Text\nOriginal identifiers replaced with semantic tags:\n- `[PATIENT_NAME_1]`, `[PATIENT_NAME_2]` ...\n- `[DATE_1]`, `[DATE_2]` ...\n- `[SSN_1]`\n- `[PHONE_1]`, `[PHONE_2]` ...\n- `[EMAIL_1]`\n- `[MRN_1]` (Medical Record Number)\n- `[ADDRESS_1]`\n\n### Audit Log JSON\n```json\n{\n  \"timestamp\": \"2024-01-15T10:30:00Z\",\n  \"input_hash\": \"sha256:abc123...\",\n  \"detections\": [\n    {\n      \"type\": \"PATIENT_NAME\",\n      \"position\": [10, 18],\n      \"confidence\": 0.95,\n      \"replacement\": \"[PATIENT_NAME_1]\",\n      \"original_length\": 8\n    }\n  ],\n  \"statistics\": {\n    \"total_pii_found\": 5,\n    \"categories_detected\": [\"NAME\", \"DATE\", \"PHONE\", \"SSN\"]\n  }\n}\n```\n\n## Technical Architecture\n\n1. **Preprocessing**: Normalize text encoding, handle line breaks\n2. **Regex Engine**: Pattern matching for structured identifiers (SSN, phone, email, MRN)\n3. **NLP Pipeline**: spaCy NER for names, organizations, locations\n4. **Context Filter**: Remove false positives (e.g., \"Dr. Smith\" vs. \"smith fracture\")\n5. **Replacement Engine**: Sequential replacement with semantic tokens\n6. **Validation**: Ensure no original PII remains in output\n\n## Dependencies\n\n- Python 3.9+\n- spaCy (en_core_web_trf or en_core_web_lg)\n- regex (for advanced pattern matching)\n- Presidio (optional, for enhanced PII detection)\n\nSee `references\u002Frequirements.txt` for full dependency list.\n\n## Limitations & Warnings\n\n⚠️ **CRITICAL**: This tool is designed as a helper, not a replacement for human review.\n\n- Context-dependent PII (e.g., rare disease names + location) may not be fully detected\n- Unstructured narrative text may contain identifying information not caught by patterns\n- Always perform manual QA on output before HIPAA-compliant release\n- **AI Autonomous Acceptance Status**: 需人工检查 (Requires Manual Review)\n\n## References\n\n- `references\u002Fhipaa_safe_harbor_guide.pdf` - HIPAA Safe Harbor de-identification standards\n- `references\u002Fpii_patterns.json` - Complete regex pattern definitions\n- `references\u002Ftest_cases\u002F` - Sample clinical texts with expected outputs\n- `references\u002Frequirements.txt` - Python dependencies\n\n## Technical Difficulty: High\n\nComplex NLP pipelines, contextual disambiguation, regulatory compliance requirements.\n\n## Risk Assessment\n\n| Risk Indicator | Assessment | Level |\n|----------------|------------|-------|\n| Code Execution | Python\u002FR scripts executed locally | Medium |\n| Network Access | No external API calls | Low |\n| File System Access | Read input files, write output files | Medium |\n| Instruction Tampering | Standard prompt guidelines | Low |\n| Data Exposure | Output files saved to workspace | Low |\n\n## Security Checklist\n\n- [ ] No hardcoded credentials or API keys\n- [ ] No unauthorized file system access (..\u002F)\n- [ ] Output does not expose sensitive information\n- [ ] Prompt injection protections in place\n- [ ] Input file paths validated (no ..\u002F traversal)\n- [ ] Output directory restricted to workspace\n- [ ] Script execution in sandboxed environment\n- [ ] Error messages sanitized (no stack traces exposed)\n- [ ] Dependencies audited\n## Prerequisites\n\n```bash\n# Python dependencies\npip install -r requirements.txt\n```\n\n## Evaluation Criteria\n\n### Success Metrics\n- [ ] Successfully executes main functionality\n- [ ] Output meets quality standards\n- [ ] Handles edge cases gracefully\n- [ ] Performance is acceptable\n\n### Test Cases\n1. **Basic Functionality**: Standard input → Expected output\n2. **Edge Case**: Invalid input → Graceful error handling\n3. **Performance**: Large dataset → Acceptable processing time\n\n## Lifecycle Status\n\n- **Current Stage**: Draft\n- **Next Review Date**: 2026-03-06\n- **Known Issues**: None\n- **Planned Improvements**: \n  - Performance optimization\n  - Additional feature support\n","# HIPAA Compliance Auditor\n\nA clinical-grade PII\u002FPHI detection and de-identification tool for healthcare text data.\n\n## Overview\n\nThis skill analyzes text for HIPAA-protected identifiers and automatically redacts or anonymizes them. It uses a combination of regex patterns, NLP entity recognition, and contextual analysis to identify 18 HIPAA identifier categories.\n\n## Features\n\n- **18 HIPAA Identifiers Detection**: Names, dates, SSN, MRN, phone\u002Ffax, email, geographic data, etc.\n- **Automatic De-identification**: Replace PII with semantic tokens (e.g., `[PATIENT_NAME]`, `[DATE_1]`)\n- **Context-Aware Detection**: Distinguishes between similar patterns (dates vs. lab values)\n- **Audit Logging**: Track all redaction actions for compliance documentation\n- **Confidence Scoring**: Flag uncertain detections for manual review\n\n## Usage\n\n### Command Line\n```bash\npython scripts\u002Fmain.py --input \"patient_text.txt\" --output \"deidentified.txt\"\npython scripts\u002Fmain.py --text \"Patient John Doe, SSN 123-45-6789...\" --audit-log audit.json\n```\n\n### Python API\n```python\nfrom scripts.main import HIPAAAuditor\n\nauditor = HIPAAAuditor()\nresult = auditor.deidentify(\"Patient John Doe was admitted on 2024-01-15...\")\nprint(result.cleaned_text)  # De-identified output\nprint(result.detected_pii)  # List of found PII entities\n```\n\n## Parameters\n\n| Parameter | Type | Default | Required | Description |\n|-----------|------|---------|----------|-------------|\n| `--input`, `-i` | string | - | No | Path to input text file |\n| `--text` | string | - | No | Direct text input (alternative to file) |\n| `--output`, `-o` | string | - | No | Path for de-identified output file |\n| `--audit-log` | string | - | No | Path for JSON audit log |\n| `--confidence` | float | 0.7 | No | Minimum confidence threshold (0.0-1.0) |\n| `--preserve-structure` | bool | true | No | Maintain document structure |\n| `--custom-patterns` | string | - | No | Path to custom regex patterns JSON |\n\n## HIPAA Identifier Categories Detected\n\n1. Names (patient, relatives, employers)\n2. Geographic subdivisions smaller than state\n3. Dates (except year) related to individual\n4. Phone numbers\n5. Fax numbers\n6. Email addresses\n7. SSN\n8. Medical record numbers\n9. Health plan beneficiary numbers\n10. Account numbers\n11. Certificate\u002Flicense numbers\n12. Vehicle identifiers\n13. Device identifiers\n14. URLs\n15. IP addresses\n16. Biometric identifiers\n17. Full-face photos\n18. Any other unique identifying numbers\n\n## Output Format\n\n### De-identified Text\nOriginal identifiers replaced with semantic tags:\n- `[PATIENT_NAME_1]`, `[PATIENT_NAME_2]` ...\n- `[DATE_1]`, `[DATE_2]` ...\n- `[SSN_1]`\n- `[PHONE_1]`, `[PHONE_2]` ...\n- `[EMAIL_1]`\n- `[MRN_1]` (Medical Record Number)\n- `[ADDRESS_1]`\n\n### Audit Log JSON\n```json\n{\n  \"timestamp\": \"2024-01-15T10:30:00Z\",\n  \"input_hash\": \"sha256:abc123...\",\n  \"detections\": [\n    {\n      \"type\": \"PATIENT_NAME\",\n      \"position\": [10, 18],\n      \"confidence\": 0.95,\n      \"replacement\": \"[PATIENT_NAME_1]\",\n      \"original_length\": 8\n    }\n  ],\n  \"statistics\": {\n    \"total_pii_found\": 5,\n    \"categories_detected\": [\"NAME\", \"DATE\", \"PHONE\", \"SSN\"]\n  }\n}\n```\n\n## Technical Architecture\n\n1. **Preprocessing**: Normalize text encoding, handle line breaks\n2. **Regex Engine**: Pattern matching for structured identifiers (SSN, phone, email, MRN)\n3. **NLP Pipeline**: spaCy NER for names, organizations, locations\n4. **Context Filter**: Remove false positives (e.g., \"Dr. Smith\" vs. \"smith fracture\")\n5. **Replacement Engine**: Sequential replacement with semantic tokens\n6. **Validation**: Ensure no original PII remains in output\n\n## Dependencies\n\n- Python 3.9+\n- spaCy (en_core_web_trf or en_core_web_lg)\n- regex (for advanced pattern matching)\n- Presidio (optional, for enhanced PII detection)\n\nSee `references\u002Frequirements.txt` for full dependency list.\n\n## Limitations & Warnings\n\n⚠️ **CRITICAL**: This tool is designed as a helper, not a replacement for human review.\n\n- Context-dependent PII (e.g., rare disease names + location) may not be fully detected\n- Unstructured narrative text may contain identifying information not caught by patterns\n- Always perform manual QA on output before HIPAA-compliant release\n- **AI Autonomous Acceptance Status**: 需人工检查 (Requires Manual Review)\n\n## References\n\n- `references\u002Fhipaa_safe_harbor_guide.pdf` - HIPAA Safe Harbor de-identification standards\n- `references\u002Fpii_patterns.json` - Complete regex pattern definitions\n- `references\u002Ftest_cases\u002F` - Sample clinical texts with expected outputs\n- `references\u002Frequirements.txt` - Python dependencies\n\n## Technical Difficulty: High\n\nComplex NLP pipelines, contextual disambiguation, regulatory compliance requirements.\n\n## Risk Assessment\n\n| Risk Indicator | Assessment | Level |\n|----------------|------------|-------|\n| Code Execution | Python\u002FR scripts executed locally | Medium |\n| Network Access | No external API calls | Low |\n| File System Access | Read input files, write output files | Medium |\n| Instruction Tampering | Standard prompt guidelines | Low |\n| Data Exposure | Output files saved to workspace | Low |\n\n## Security Checklist\n\n- [ ] No hardcoded credentials or API keys\n- [ ] No unauthorized file system access (..\u002F)\n- [ ] Output does not expose sensitive information\n- [ ] Prompt injection protections in place\n- [ ] Input file paths validated (no ..\u002F traversal)\n- [ ] Output directory restricted to workspace\n- [ ] Script execution in sandboxed environment\n- [ ] Error messages sanitized (no stack traces exposed)\n- [ ] Dependencies audited\n## Prerequisites\n\n```bash\n# Python dependencies\npip install -r requirements.txt\n```\n\n## Evaluation Criteria\n\n### Success Metrics\n- [ ] Successfully executes main functionality\n- [ ] Output meets quality standards\n- [ ] Handles edge cases gracefully\n- [ ] Performance is acceptable\n\n### Test Cases\n1. **Basic Functionality**: Standard input → Expected output\n2. **Edge Case**: Invalid input → Graceful error handling\n3. **Performance**: Large dataset → Acceptable processing time\n\n## Lifecycle Status\n\n- **Current Stage**: Draft\n- **Next Review Date**: 2026-03-06\n- **Known Issues**: None\n- **Planned Improvements**: \n  - Performance optimization\n  - Additional feature support\n",{"name":5,"tags":49,"owner":50,"author":50,"status":51,"license":15,"version":52,"category":53,"reviewer":54,"risk_level":55,"skill_type":56,"description":7,"last_updated":57},[],"AIPOCH","Draft","1.0.0","Clinical","","Medium","Tool\u002FScript","2026-02-06","main","b22eca2e974f918ce591c5b2bd829fb7510e0191","Skills\u002Fhipaa-compliance-auditor",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":67,"prompt":77,"zipAvailable":78},[68,71,74],{"tool":69,"command":70},"npx","npx skills add FDU-INS\u002FInsurance-Skills --skill hipaa-compliance-auditor",{"tool":72,"command":73},"bunx","bunx skills add FDU-INS\u002FInsurance-Skills --skill hipaa-compliance-auditor",{"tool":75,"command":76},"pnpm","pnpm dlx skills add FDU-INS\u002FInsurance-Skills --skill hipaa-compliance-auditor","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fhipaa-compliance-auditor\u002FSKILL.md",true,[80,86,90,94,98,101],{"path":81,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":84,"sourceHtmlUrl":85},"_meta.json","REFERENCE","参考文档",303,"https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fhipaa-compliance-auditor\u002F_meta.json",{"path":87,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":88,"sourceHtmlUrl":89},"references\u002Fhipaa_safe_harbor_guide.md",3058,"https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fhipaa-compliance-auditor\u002Freferences\u002Fhipaa_safe_harbor_guide.md",{"path":91,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":92,"sourceHtmlUrl":93},"references\u002Fpii_patterns.json",3624,"https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fhipaa-compliance-auditor\u002Freferences\u002Fpii_patterns.json",{"path":95,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":96,"sourceHtmlUrl":97},"references\u002Frequirements.txt",610,"https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fhipaa-compliance-auditor\u002Freferences\u002Frequirements.txt",{"path":99,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":61,"sourceHtmlUrl":100},"requirements.txt","https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fhipaa-compliance-auditor\u002Frequirements.txt",{"path":102,"resourceType":103,"resourceTypeDesc":104,"sizeBytes":105,"sourceHtmlUrl":106},"scripts\u002Fmain.py","SCRIPT","脚本",18398,"https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fhipaa-compliance-auditor\u002Fscripts\u002Fmain.py",[108,134,158,189,210,242],{"skillNo":109,"name":110,"nameZh":6,"description":111,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":112,"licenseFileUrl":6,"sourceHtmlUrl":113,"categories":114,"keywords":116,"publishedAt":44},"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.","Apache-2.0","https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002F로인장 (Roinjiang)\u002FSKILL.md",[115],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[110,117,41,118,119,120,40,121,122,123,124,125,126,127,128,129,130,131,132,133],"insurance law","civil","contract","family","review","loyalty","with","top","contract review","ceo","support","advice","consultation","regulatory","privacy","protection","startup",{"skillNo":135,"name":136,"nameZh":6,"description":137,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":112,"licenseFileUrl":6,"sourceHtmlUrl":138,"categories":139,"keywords":140,"publishedAt":44},"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",[],[141,142,30,143,41,144,145,40,146,147,148,149,150,151,152,153,154,155,156,157],"zocdoc doctor finder","doctor","reviews","browser automation","access","browsing","browser","booking","network","therapist","computer","availability","for","provider","tool","use","automation",{"skillNo":159,"name":160,"nameZh":6,"description":161,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":112,"licenseFileUrl":6,"sourceHtmlUrl":162,"categories":163,"keywords":169,"publishedAt":44},"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",[164],{"categoryNo":165,"categoryName":166,"taxonomyType":167,"taxonomyTypeDesc":168},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[170,171,172,173,41,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188],"context","zenmux context","Video","chinese","project","web search","translate","search","open","question","pay","image","you","activate","architecture","logs","streaming","cherry studio","how",{"skillNo":190,"name":191,"nameZh":6,"description":192,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":112,"licenseFileUrl":6,"sourceHtmlUrl":193,"categories":194,"keywords":195,"publishedAt":44},"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",[],[196,197,198,199,200,41,201,202,156,40,203,123,204,205,24,206,207,208,209],"workers","comp","workers comp","workers comp guide","guide","triggers","workers-comp","tasks","claims management","claims","help","compensation","management","user",{"skillNo":211,"name":212,"nameZh":6,"description":213,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":112,"licenseFileUrl":6,"sourceHtmlUrl":214,"categories":215,"keywords":225,"publishedAt":44},"01M713QF45VMV1685AFNF1GPMA","weekly-kpi-report","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.","https:\u002F\u002Fgithub.com\u002FFDU-INS\u002FInsurance-Skills\u002Fblob\u002Fmain\u002FSkills\u002Fweekly-kpi-report\u002FSKILL.md",[216,219,222],{"categoryNo":217,"categoryName":218,"taxonomyType":167,"taxonomyTypeDesc":168},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":220,"categoryName":221,"taxonomyType":167,"taxonomyTypeDesc":168},"01JBSEEDCAT000000000000004","电子表格",{"categoryNo":223,"categoryName":224,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000034","行政与办公人员",[226,227,228,229,230,41,231,232,233,234,40,235,236,237,238,37,239,240,156,241],"weekly","weekly kpi report","kpi","report","xlsx","actionable","insights","briefing","auto","board report","reports","generate","演示","presentation","slides","uploads",{"skillNo":243,"name":244,"nameZh":6,"description":245,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":112,"licenseFileUrl":6,"sourceHtmlUrl":246,"categories":247,"keywords":251,"publishedAt":44},"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",[248],{"categoryNo":249,"categoryName":250,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000040","财务",[252,253,41,254,255,40,183,256,257,258,259,260,261,262,263,264,265,266,267,268],"wealth advisory","advisory","stock","inheritance","conversion","indexing","plan","simulation","personal finance","rate","allocation","asset","direct","personal","investing","trust","planning"]