[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DAWM3180PK17RA2F7C3GVX":3,"skill-same:01M6DAWM3180PK17RA2F7C3GVX":79},{"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":26,"linkOnly":27,"markdownContent":28,"markdownBody":29,"frontMatter":30,"refName":31,"commitSha":32,"skillDirectory":33,"forksCount":14,"repositoryHtmlUrl":34,"repositoryHomepageUrl":6,"ownerHtmlUrl":35,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":36,"install":37,"resources":50},"01M6DAWM3180PK17RA2F7C3GVX","tooluniverse-rare-disease-diagnosis",null,"Provide differential diagnosis for patients with suspected rare diseases based on phenotype and genetic data. Matches symptoms to HPO terms, identifies candidate diseases from Orphanet\u002FOMIM, prioritizes genes for testing, interprets variants of uncertain significance. Use when clinician asks about rare disease diagnosis, unexplained phenotypes, or genetic testing interpretation.","Prekzursil\u002Fagent-skills-toolchain","Prekzursil","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F54636077?v=4","01M6DACZ1PCQHD99SCR1YXG28B","Andrei Mihai Visalon","01M6DACZ670WZF535V3WYYSS4Z",0,"MIT","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-rare-disease-diagnosis\u002FSKILL.md",[],[19,20,21,22,23,24,25],"diagnosis","tooluniverse rare disease diagnosis","testing","interpretation","data","terms","use","2026-11-10 10:12:30",false,"---\nname: tooluniverse-rare-disease-diagnosis\ndescription: Provide differential diagnosis for patients with suspected rare diseases based on phenotype and genetic data. Matches symptoms to HPO terms, identifies candidate diseases from Orphanet\u002FOMIM, prioritizes genes for testing, interprets variants of uncertain significance. Use when clinician asks about rare disease diagnosis, unexplained phenotypes, or genetic testing interpretation.\n---\n\n# Rare Disease Diagnosis Advisor\n\nSystematic diagnosis support for rare diseases using phenotype matching, gene panel prioritization, and variant interpretation across Orphanet, OMIM, HPO, ClinVar, and structure-based analysis.\n\n**KEY PRINCIPLES**:\n1. **Report-first** - Create report file FIRST, update progressively\n2. **Phenotype-driven** - Convert symptoms to HPO terms before searching\n3. **Multi-database triangulation** - Cross-reference Orphanet, OMIM, OpenTargets\n4. **Evidence grading** - Grade diagnoses by supporting evidence strength\n5. **English-first queries** - Always use English terms in tool calls\n\n## LOOK UP, DON'T GUESS\nWhen uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory.\n\n---\n\n## COMPUTE, DON'T DESCRIBE\nWhen analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.\n\n## Clinical Reasoning Framework (BEFORE Tools)\n\nApply these strategies to form a 3-5 candidate differential, then use tools to confirm\u002Frefute:\n\n1. **Multi-system involvement** - Symptoms spanning 2+ organ systems = strongest rare disease signal. Ask: what single pathway explains ALL features?\n2. **Regression question** - Losing abilities vs never acquired? Regression = neurodegenerative\u002Fmetabolic storage. Stable = developmental\u002Fstructural.\n3. **Trigger question** - Episodic\u002Ftriggered (fasting, illness, exercise) = metabolic disorder (often treatable). Constitutive = structural\u002Fdegenerative.\n4. **Rarest feature first** - Build differential from most specific finding, not most prominent. Check remaining features for consistency.\n5. **Treatable-first** - Move treatable conditions to top for urgent workup (enzyme replacement, dietary, chelation, vitamin-responsive).\n6. **Occupational\u002Fenvironmental exposure** - Latency up to 50 years. Asbestos\u002Fsilica\u002Fheavy metals\u002Fsolvents\u002Ffarming. Always ask about PAST jobs.\n7. **Autoimmune differential** - Which joints? Symmetric? Extra-articular? Serologic pattern? Organ under attack?\n8. **Rare syndrome signals** - Named triads, common diagnoses failing to explain ALL findings, failed standard treatment, unusual lab findings.\n9. **Tools verify, not generate** - Form hypothesis first, then use databases to confirm.\n\n**Common pitfalls**: Felty's (RA+splenomegaly+neutropenia) mimics infection; SLE nephritis mimics PSGN (check ASO); occupational exposures trigger autoimmunity (silica→scleroderma\u002FRA\u002FSLE).\n\n---\n\n## Tool Parameter Corrections\n\n| Tool | WRONG | CORRECT |\n|------|-------|---------|\n| `OpenTargets_get_associated_drugs_by_target_ensemblID` | `ensemblID` | `ensemblId` |\n| `ClinVar_get_variant_details` | `variant_id` | `id` |\n| `MyGene_query_genes` | `gene` | `q` |\n| `gnomad_get_variant` | `variant` | `variant_id` |\n\n---\n\n## Workflow\n\n```\nPhase 0: Clinical Reasoning → 3-5 candidate differential\nPhase 1: Phenotype → HPO terms (HPO_search_terms), core vs variable, onset, family history\nPhase 2: Disease Matching → Orphanet_search_diseases, OMIM_search, DisGeNET_search_gene\nPhase 3: Gene Panel → ClinGen validation, GTEx expression, prioritization scoring\nPhase 3.5: Expression Context → CELLxGENE, ChIPAtlas for tissue\u002Fcell-type confirmation\nPhase 3.6: Pathway Analysis → KEGG, IntAct for convergent pathways\nPhase 4: Variant Interpretation → ClinVar, gnomAD frequency, CADD\u002FAlphaMissense\u002FEVE\u002FSpliceAI, ACMG criteria\nPhase 5: Structure Analysis → AlphaFold2, InterPro domains (for VUS)\nPhase 6: Literature → PubMed, BioRxiv\u002FMedRxiv, OpenAlex\nPhase 7: Report Synthesis → Prioritized differential with next steps\n```\n\n### Key Phase Details\n\n**Phase 2 - Disease Matching**: `Orphanet_search_diseases(operation=\"search_diseases\", query=keyword)` then `Orphanet_get_genes(operation=\"get_genes\", orpha_code=code)`. Score overlap: Excellent >80%, Good 60-80%, Possible 40-60%.\n\n**Phase 3 - Gene Panel**: ClinGen classification drives inclusion (Definitive\u002FStrong\u002FModerate = include; Limited = flag; Disputed\u002FRefuted = exclude). Scoring: Tier 1 (top disease gene +5), Tier 2 (multi-disease +3), Tier 3 (ClinGen Definitive +3), Tier 4 (tissue expression +2), Tier 5 (pLI >0.9 +1).\n\n**Phase 4 - Variants**: gnomAD frequency classes: ultra-rare \u003C0.00001, rare \u003C0.0001, low-freq \u003C0.01. ACMG: PVS1 (null), PS1 (same AA), PM2 (absent pop), PP3 (computational), BA1 (>5% AF). 2+ concordant predictors strengthen PP3.\n\n---\n\n## Evidence Grading\n\n| Tier | Criteria |\n|------|----------|\n| **T1** (High) | Phenotype match >80% + gene match |\n| **T2** (Medium-High) | Phenotype match 60-80% OR likely pathogenic variant |\n| **T3** (Medium) | Phenotype match 40-60% OR VUS in candidate gene |\n| **T4** (Low) | Phenotype \u003C40% OR uncertain gene |\n\n---\n\n## Fallback Chains\n\n| Primary | Fallback 1 | Fallback 2 |\n|---------|------------|------------|\n| `get_joint_associated_diseases_by_HPO_ID_list` | `Orphanet_search_diseases` | PubMed phenotype search |\n| `ClinVar_get_variant_details` | `gnomad_get_variant` | VEP annotation |\n| `GTEx_get_expression_summary` | `HPA_search_genes_by_query` | Tissue-specific literature |\n\n---\n\n## Reference Files\n\n- [DIAGNOSTIC_WORKFLOW.md](DIAGNOSTIC_WORKFLOW.md) - Code examples and algorithms per phase\n- [REPORT_TEMPLATE.md](REPORT_TEMPLATE.md) - Report template and examples\n- [CHECKLIST.md](CHECKLIST.md) - Interactive completeness checklist\n- `scripts\u002Fclinical_patterns.py` - Clinical pattern lookup (syndromes, differentials, red flags, occupational exposures)\n","# Rare Disease Diagnosis Advisor\n\nSystematic diagnosis support for rare diseases using phenotype matching, gene panel prioritization, and variant interpretation across Orphanet, OMIM, HPO, ClinVar, and structure-based analysis.\n\n**KEY PRINCIPLES**:\n1. **Report-first** - Create report file FIRST, update progressively\n2. **Phenotype-driven** - Convert symptoms to HPO terms before searching\n3. **Multi-database triangulation** - Cross-reference Orphanet, OMIM, OpenTargets\n4. **Evidence grading** - Grade diagnoses by supporting evidence strength\n5. **English-first queries** - Always use English terms in tool calls\n\n## LOOK UP, DON'T GUESS\nWhen uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory.\n\n---\n\n## COMPUTE, DON'T DESCRIBE\nWhen analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.\n\n## Clinical Reasoning Framework (BEFORE Tools)\n\nApply these strategies to form a 3-5 candidate differential, then use tools to confirm\u002Frefute:\n\n1. **Multi-system involvement** - Symptoms spanning 2+ organ systems = strongest rare disease signal. Ask: what single pathway explains ALL features?\n2. **Regression question** - Losing abilities vs never acquired? Regression = neurodegenerative\u002Fmetabolic storage. Stable = developmental\u002Fstructural.\n3. **Trigger question** - Episodic\u002Ftriggered (fasting, illness, exercise) = metabolic disorder (often treatable). Constitutive = structural\u002Fdegenerative.\n4. **Rarest feature first** - Build differential from most specific finding, not most prominent. Check remaining features for consistency.\n5. **Treatable-first** - Move treatable conditions to top for urgent workup (enzyme replacement, dietary, chelation, vitamin-responsive).\n6. **Occupational\u002Fenvironmental exposure** - Latency up to 50 years. Asbestos\u002Fsilica\u002Fheavy metals\u002Fsolvents\u002Ffarming. Always ask about PAST jobs.\n7. **Autoimmune differential** - Which joints? Symmetric? Extra-articular? Serologic pattern? Organ under attack?\n8. **Rare syndrome signals** - Named triads, common diagnoses failing to explain ALL findings, failed standard treatment, unusual lab findings.\n9. **Tools verify, not generate** - Form hypothesis first, then use databases to confirm.\n\n**Common pitfalls**: Felty's (RA+splenomegaly+neutropenia) mimics infection; SLE nephritis mimics PSGN (check ASO); occupational exposures trigger autoimmunity (silica→scleroderma\u002FRA\u002FSLE).\n\n---\n\n## Tool Parameter Corrections\n\n| Tool | WRONG | CORRECT |\n|------|-------|---------|\n| `OpenTargets_get_associated_drugs_by_target_ensemblID` | `ensemblID` | `ensemblId` |\n| `ClinVar_get_variant_details` | `variant_id` | `id` |\n| `MyGene_query_genes` | `gene` | `q` |\n| `gnomad_get_variant` | `variant` | `variant_id` |\n\n---\n\n## Workflow\n\n```\nPhase 0: Clinical Reasoning → 3-5 candidate differential\nPhase 1: Phenotype → HPO terms (HPO_search_terms), core vs variable, onset, family history\nPhase 2: Disease Matching → Orphanet_search_diseases, OMIM_search, DisGeNET_search_gene\nPhase 3: Gene Panel → ClinGen validation, GTEx expression, prioritization scoring\nPhase 3.5: Expression Context → CELLxGENE, ChIPAtlas for tissue\u002Fcell-type confirmation\nPhase 3.6: Pathway Analysis → KEGG, IntAct for convergent pathways\nPhase 4: Variant Interpretation → ClinVar, gnomAD frequency, CADD\u002FAlphaMissense\u002FEVE\u002FSpliceAI, ACMG criteria\nPhase 5: Structure Analysis → AlphaFold2, InterPro domains (for VUS)\nPhase 6: Literature → PubMed, BioRxiv\u002FMedRxiv, OpenAlex\nPhase 7: Report Synthesis → Prioritized differential with next steps\n```\n\n### Key Phase Details\n\n**Phase 2 - Disease Matching**: `Orphanet_search_diseases(operation=\"search_diseases\", query=keyword)` then `Orphanet_get_genes(operation=\"get_genes\", orpha_code=code)`. Score overlap: Excellent >80%, Good 60-80%, Possible 40-60%.\n\n**Phase 3 - Gene Panel**: ClinGen classification drives inclusion (Definitive\u002FStrong\u002FModerate = include; Limited = flag; Disputed\u002FRefuted = exclude). Scoring: Tier 1 (top disease gene +5), Tier 2 (multi-disease +3), Tier 3 (ClinGen Definitive +3), Tier 4 (tissue expression +2), Tier 5 (pLI >0.9 +1).\n\n**Phase 4 - Variants**: gnomAD frequency classes: ultra-rare \u003C0.00001, rare \u003C0.0001, low-freq \u003C0.01. ACMG: PVS1 (null), PS1 (same AA), PM2 (absent pop), PP3 (computational), BA1 (>5% AF). 2+ concordant predictors strengthen PP3.\n\n---\n\n## Evidence Grading\n\n| Tier | Criteria |\n|------|----------|\n| **T1** (High) | Phenotype match >80% + gene match |\n| **T2** (Medium-High) | Phenotype match 60-80% OR likely pathogenic variant |\n| **T3** (Medium) | Phenotype match 40-60% OR VUS in candidate gene |\n| **T4** (Low) | Phenotype \u003C40% OR uncertain gene |\n\n---\n\n## Fallback Chains\n\n| Primary | Fallback 1 | Fallback 2 |\n|---------|------------|------------|\n| `get_joint_associated_diseases_by_HPO_ID_list` | `Orphanet_search_diseases` | PubMed phenotype search |\n| `ClinVar_get_variant_details` | `gnomad_get_variant` | VEP annotation |\n| `GTEx_get_expression_summary` | `HPA_search_genes_by_query` | Tissue-specific literature |\n\n---\n\n## Reference Files\n\n- [DIAGNOSTIC_WORKFLOW.md](DIAGNOSTIC_WORKFLOW.md) - Code examples and algorithms per phase\n- [REPORT_TEMPLATE.md](REPORT_TEMPLATE.md) - Report template and examples\n- [CHECKLIST.md](CHECKLIST.md) - Interactive completeness checklist\n- `scripts\u002Fclinical_patterns.py` - Clinical pattern lookup (syndromes, differentials, red flags, occupational exposures)\n",{"name":5,"description":7},"main","a298c929b6cbd662e1fce54302ce520d5ceea1a5",".agents\u002Flibrary\u002Fskills\u002Ftooluniverse-rare-disease-diagnosis","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain","https:\u002F\u002Fgithub.com\u002FPrekzursil","2026-11-02 14:30:57",{"source":8,"skillName":5,"commands":38,"prompt":48,"zipAvailable":49},[39,42,45],{"tool":40,"command":41},"npx","npx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-rare-disease-diagnosis",{"tool":43,"command":44},"bunx","bunx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-rare-disease-diagnosis",{"tool":46,"command":47},"pnpm","pnpm dlx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-rare-disease-diagnosis","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-rare-disease-diagnosis\u002FSKILL.md",true,[51,57,61,65,69,75],{"path":52,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":55,"sourceHtmlUrl":56},"CHECKLIST.md","REFERENCE","参考文档",6320,"https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-rare-disease-diagnosis\u002FCHECKLIST.md",{"path":58,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":59,"sourceHtmlUrl":60},"DIAGNOSTIC_WORKFLOW.md",20040,"https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-rare-disease-diagnosis\u002FDIAGNOSTIC_WORKFLOW.md",{"path":62,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":63,"sourceHtmlUrl":64},"EXAMPLES.md",13697,"https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-rare-disease-diagnosis\u002FEXAMPLES.md",{"path":66,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":67,"sourceHtmlUrl":68},"REPORT_TEMPLATE.md",11129,"https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-rare-disease-diagnosis\u002FREPORT_TEMPLATE.md",{"path":70,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":73,"sourceHtmlUrl":74},"scripts\u002Fclinical_patterns.py","SCRIPT","脚本",36160,"https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-rare-disease-diagnosis\u002Fscripts\u002Fclinical_patterns.py",{"path":76,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":77,"sourceHtmlUrl":78},"TOOLS_REFERENCE.md",25255,"https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-rare-disease-diagnosis\u002FTOOLS_REFERENCE.md",[80,101,132,159,173,189],{"skillNo":81,"name":82,"nameZh":6,"description":83,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":84,"categories":85,"keywords":86,"publishedAt":26},"01M6DF7D7478R55P46REYH4QAC","clarify-requirements","Resolve genuine pre-execution ambiguity by scanning repo context, generating 1-6 grounded options, and asking 1-3 focused questions via AskUserQuestion. 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