[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"org-aws-labs":3,"repo-skills-v-0-3-0":334},{"org":4,"repos":60},{"slug":5,"name":6,"logoUrl":7,"githubOrg":8,"website":9,"skillCount":10,"repoCount":11,"topRepos":12,"topTags":28,"lastUpdatedAt":59},"aws-labs","AWS Labs","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Faws-labs.png","awslabs","https:\u002F\u002Faws.amazon.com",150,11,[13,16,19,22,25],{"name":14,"skillCount":15},"awslabs\u002Fstartups",42,{"name":17,"skillCount":18},"awslabs\u002Fhcls-agent-skills",40,{"name":20,"skillCount":21},"awslabs\u002Fagent-plugins",33,{"name":23,"skillCount":24},"awslabs\u002Fcli-agent-orchestrator",16,{"name":26,"skillCount":27},"awslabs\u002Fmcp",7,[29,32,35,38,41,44,47,50,53,56],{"slug":30,"name":31},"aws","AWS",{"slug":33,"name":34},"healthcare","Healthcare",{"slug":36,"name":37},"life-sciences","Life Sciences",{"slug":39,"name":40},"deployment","Deployment",{"slug":42,"name":43},"infrastructure","Infrastructure",{"slug":45,"name":46},"agents","Agents",{"slug":48,"name":49},"architecture","Architecture",{"slug":51,"name":52},"bioinformatics","Bioinformatics",{"slug":54,"name":55},"data-analysis","Data Analysis",{"slug":57,"name":58},"engineering","Engineering","2026-08-01T05:43:53.724377",[61,79,108,131,157,189,224,246,267,284,311],{"name":62,"fullName":14,"repoUrl":63,"skillCount":15,"stars":64,"forks":65,"description":66,"topics":67,"topTags":68,"topTagCount":77,"lastUpdatedAt":78},"startups","https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fstartups",14,15,"Official AWS Startups repository that hosts plugins, skills, tools and resources to support startup builders on AWS",[],[69,70,71,72,73,76],{"slug":30,"name":31},{"slug":48,"name":49},{"slug":39,"name":40},{"slug":42,"name":43},{"slug":74,"name":75},"security","Security",{"slug":57,"name":58},57,"2026-07-25T05:32:20.160804",{"name":80,"fullName":17,"repoUrl":81,"skillCount":18,"stars":82,"forks":83,"description":84,"topics":85,"topTags":98,"topTagCount":21,"lastUpdatedAt":107},"hcls-agent-skills","https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fhcls-agent-skills",4,0,"Agent skills for healthcare and life sciences: genomics, imaging, claims, drug discovery, and more. Works with Amazon Quick, Kiro, Amazon AgentCore, AWS Strands SDK, Claude Code, Codex, and any Agent Skills-compatible platform.",[86,87,88,89,90,91,92,93,94,36,95,96,97],"agent-skills","agentcore","ai-agents","amazon-quick-desktop","claims-processing","drug-discovery","genomics","healthcare-ai","kiro","medical-imaging","risk-adjustment","strands-agents",[99,100,101,102,103,106],{"slug":36,"name":37},{"slug":33,"name":34},{"slug":51,"name":52},{"slug":54,"name":55},{"slug":104,"name":105},"research","Research",{"slug":30,"name":31},"2026-07-25T05:56:34.955181",{"name":109,"fullName":20,"repoUrl":110,"skillCount":21,"stars":111,"forks":112,"description":113,"topics":114,"topTags":117,"topTagCount":130,"lastUpdatedAt":59},"agent-plugins","https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fagent-plugins",831,127,"Agent Plugins for AWS equip AI coding agents with the skills to help you architect, deploy, and operate on AWS.",[109,86,45,30,115,116],"coding-agent-skills","coding-agents",[118,119,120,121,124,127],{"slug":30,"name":31},{"slug":39,"name":40},{"slug":42,"name":43},{"slug":122,"name":123},"debugging","Debugging",{"slug":125,"name":126},"llm","LLM",{"slug":128,"name":129},"observability","Observability",52,{"name":132,"fullName":23,"repoUrl":133,"skillCount":24,"stars":134,"forks":135,"description":136,"topics":137,"topTags":138,"topTagCount":155,"lastUpdatedAt":156},"cli-agent-orchestrator","https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fcli-agent-orchestrator",871,164,null,[],[139,140,143,146,149,152],{"slug":45,"name":46},{"slug":141,"name":142},"mcp","MCP",{"slug":144,"name":145},"automation","Automation",{"slug":147,"name":148},"api-development","API Development",{"slug":150,"name":151},"cli","CLI",{"slug":153,"name":154},"orchestration","Orchestration",24,"2026-07-29T06:00:28.147989",{"name":141,"fullName":26,"repoUrl":158,"skillCount":27,"stars":159,"forks":160,"description":161,"topics":162,"topTags":170,"topTagCount":187,"lastUpdatedAt":188},"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fmcp",9427,1634,"Open source MCP Servers for AWS",[30,141,163,164,165,166,167,168,169],"mcp-client","mcp-clients","mcp-host","mcp-server","mcp-servers","mcp-tools","modelcontextprotocol",[171,172,175,178,181,184],{"slug":30,"name":31},{"slug":173,"name":174},"database","Database",{"slug":176,"name":177},"serverless","Serverless",{"slug":179,"name":180},"sql","SQL",{"slug":182,"name":183},"migration","Migration",{"slug":185,"name":186},"aurora","Aurora",10,"2026-07-12T08:37:22.601527",{"name":190,"fullName":191,"repoUrl":192,"skillCount":193,"stars":194,"forks":195,"description":196,"topics":197,"topTags":208,"topTagCount":65,"lastUpdatedAt":223},"agentcore-samples","awslabs\u002Fagentcore-samples","https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fagentcore-samples",6,3176,1233,"Amazon Bedrock Agentcore accelerates AI agents into production with the scale, reliability, and security, critical to real-world deployment.",[198,199,45,200,201,202,203,204,205,206,207],"agent","agentic-ai","authentication","bedrock","core","gateway","identity-management","memory-management","production-code","runtime",[209,212,215,218,221,222],{"slug":210,"name":211},"finance","Finance",{"slug":213,"name":214},"analytics","Analytics",{"slug":216,"name":217},"accounting","Accounting",{"slug":219,"name":220},"reporting","Reporting",{"slug":144,"name":145},{"slug":30,"name":31},"2026-07-12T08:41:44.135656",{"name":225,"fullName":226,"repoUrl":227,"skillCount":228,"stars":229,"forks":83,"description":230,"topics":231,"topTags":235,"topTagCount":244,"lastUpdatedAt":245},"codeknit","awslabs\u002Fcodeknit","https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fcodeknit",2,3,"Static code structure extractor that turns a codebase into a compact, LLM-friendly graph of functions, classes, and relationships for refactoring, duplicate detection, and analysis across 12 languages",[232,125,233,234],"code-analysis","refactoring","static-analysis",[236,238,239,240,241],{"slug":232,"name":237},"Code Analysis",{"slug":57,"name":58},{"slug":48,"name":49},{"slug":122,"name":123},{"slug":242,"name":243},"git","Git",5,"2026-07-12T08:38:40.01893",{"name":247,"fullName":248,"repoUrl":249,"skillCount":250,"stars":251,"forks":252,"description":136,"topics":253,"topTags":257,"topTagCount":82,"lastUpdatedAt":266},"aws-cloudformation-iam-policy-validator","awslabs\u002Faws-cloudformation-iam-policy-validator","https:\u002F\u002Fgithub.com\u002Fawslabs\u002Faws-cloudformation-iam-policy-validator",1,146,25,[30,254,255,256],"aws-iam","aws-iam-policies","cloudformation",[258,259,262,265],{"slug":30,"name":31},{"slug":260,"name":261},"compliance","Compliance",{"slug":263,"name":264},"infrastructure-as-code","Infrastructure as Code",{"slug":74,"name":75},"2026-07-12T08:37:13.052325",{"name":268,"fullName":269,"repoUrl":270,"skillCount":250,"stars":229,"forks":83,"description":136,"topics":271,"topTags":272,"topTagCount":82,"lastUpdatedAt":283},"managed-service-for-apache-flink-agent-steering-files","awslabs\u002Fmanaged-service-for-apache-flink-agent-steering-files","https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fmanaged-service-for-apache-flink-agent-steering-files",[],[273,274,277,280],{"slug":30,"name":31},{"slug":275,"name":276},"data-engineering","Data Engineering",{"slug":278,"name":279},"data-pipeline","Data Pipeline",{"slug":281,"name":282},"performance","Performance","2026-07-12T08:37:20.055112",{"name":285,"fullName":286,"repoUrl":287,"skillCount":250,"stars":288,"forks":289,"description":290,"topics":291,"topTags":304,"topTagCount":229,"lastUpdatedAt":310},"nx-plugin-for-aws","awslabs\u002Fnx-plugin-for-aws","https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fnx-plugin-for-aws",85,27,"The @aws\u002Fnx-plugin is a collection of code generators that automate the creation and configuration of cloud-native applications using AWS, TypeScript, Python and React within the Nx development ecosystem.",[30,292,293,294,295,296,141,297,298,299,300,301,302,303],"aws-cdk","cloudfront","cloudscape","fastapi","lambda","nx","productivity","python","react","tanstack","trpc","typescript",[305,306,309],{"slug":30,"name":31},{"slug":307,"name":308},"cloud","Cloud",{"slug":263,"name":264},"2026-07-29T05:39:03.760685",{"name":312,"fullName":313,"repoUrl":314,"skillCount":250,"stars":315,"forks":316,"description":317,"topics":318,"topTags":327,"topTagCount":82,"lastUpdatedAt":333},"threat-designer","awslabs\u002Fthreat-designer","https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fthreat-designer",267,46,"Threat Designer is a GenerativeAI application designed to automate and streamline the threat modeling process for secure system design.",[199,319,320,321,322,323,324,325,326],"appsec","cybersecurity","devsecops","generativeai","threat-modeling","threat-modeling-tool","threatmodeling","threatmodelling",[328,329,330,331],{"slug":30,"name":31},{"slug":232,"name":237},{"slug":74,"name":75},{"slug":323,"name":332},"Threat Modeling","2026-07-12T08:37:14.772466",{"items":335,"total":18},[336,349,360,374,387,400,413,423,436,449,462,472,487,501,512,522,533,547,561,572,582,593,604,620],{"slug":337,"name":337,"fn":338,"description":339,"org":340,"tags":341,"stars":82,"repoUrl":81,"updatedAt":348},"aws-genai-ml-architect","design AWS GenAI and ML architectures","Reasoning skill for designing AWS GenAI and ML architectures for healthcare and life sciences workloads. Use when the user asks to choose between SageMaker and Bedrock, design a RAG system over medical literature, architect clinical NLP or medical imaging inference, plan genomics or drug discovery pipelines on AWS, address HIPAA\u002FPHI compliance in ML systems, design MLOps for regulated clinical models, or optimize cost for HCLS ML workloads. Triggers include \"AWS architecture\", \"SageMaker vs Bedrock\", \"HIPAA ML\", \"clinical RAG\", \"medical imaging inference\", \"genomics on AWS\", \"PHI training\", \"MLOps healthcare\", \"Bedrock guardrails\", \"HealthLake\", \"HCLS cloud architecture\", \"BAA compliance\", \"SageMaker endpoint\", \"Bedrock knowledge base\", \"clinical NLP on AWS\", \"FDA SaMD on AWS\".",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[342,344,345,346,347],{"name":49,"slug":48,"type":343},"tag",{"name":31,"slug":30,"type":343},{"name":34,"slug":33,"type":343},{"name":37,"slug":36,"type":343},{"name":126,"slug":125,"type":343},"2026-07-12T08:38:07.975937",{"slug":350,"name":350,"fn":351,"description":352,"org":353,"tags":354,"stars":82,"repoUrl":81,"updatedAt":359},"biomarker-discovery","guide biomarker discovery and validation","Reason about biomarker discovery and validation in HCLS — classifying biomarker intent, choosing feature-selection and cross-validation strategies, avoiding leakage, and planning external replication. Use when the user asks to discover, develop, or validate a biomarker; select features from high-dimensional omics or clinical data; design a validation study; choose evaluation metrics; justify sample size; combine multi-omics signals; or assess clinical utility. Triggers include \"discover a biomarker\", \"validate biomarker\", \"prognostic vs predictive\", \"feature selection\", \"LASSO vs elastic net\", \"nested cross-validation\", \"data leakage\", \"C-index\", \"time-dependent AUC\", \"decision curve analysis\", \"external validation cohort\", \"events per variable\", \"optimism-corrected\", \"multi-omics integration\", \"clinical utility of a biomarker\", \"is this biomarker ready\".",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[355,356,357,358],{"name":31,"slug":30,"type":343},{"name":52,"slug":51,"type":343},{"name":37,"slug":36,"type":343},{"name":105,"slug":104,"type":343},"2026-07-12T08:37:49.295301",{"slug":361,"name":361,"fn":362,"description":363,"org":364,"tags":365,"stars":82,"repoUrl":81,"updatedAt":373},"cdisc-compliance","reason about CDISC SDTM and ADaM implementation","Reason about CDISC SDTM and ADaM implementation for regulatory submissions. Use when the user asks about SDTM domain mapping, ADaM dataset design, controlled terminology versioning, define.xml completeness, FDA or PMDA submission requirements, query prioritization by clinical impact, SUPPQUAL usage, or CDISC compliance review. Triggers include \"SDTM mapping\", \"ADaM dataset\", \"CDISC compliance\", \"controlled terminology\", \"define.xml\", \"FDA submission data\", \"PMDA submission\", \"SDTM domain\", \"ADSL\", \"ADAE\", \"ADLB\", \"BDS structure\", \"SUPPQUAL\", \"RELREC\", \"value-level metadata\", \"CDISC CT\", \"regulatory submission data standards\", \"eCTD datasets\", \"SDTM 3.3\", \"ADaM 1.1\", \"query prioritization\", \"clinical data review\".\n",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[366,369,370],{"name":367,"slug":368,"type":343},"Clinical Trials","clinical-trials",{"name":37,"slug":36,"type":343},{"name":371,"slug":372,"type":343},"Regulatory Compliance","regulatory-compliance","2026-07-12T08:37:33.35594",{"slug":375,"name":375,"fn":376,"description":377,"org":378,"tags":379,"stars":82,"repoUrl":81,"updatedAt":386},"cell-type-annotation","annotate single-cell RNA-seq clusters","Generate code to assign cell type labels to single-cell RNA-seq clusters using CellTypist, SingleR, marker-based annotation, or reference label transfer (scANVI\u002Fingest). Triggers on requests to \"annotate cell types\", \"label clusters\", \"run CellTypist\", \"SingleR annotation\", \"marker gene dotplot\", \"transfer labels from reference atlas\", \"cell identity\", \"automated annotation\", \"reference mapping\", \"scANVI label transfer\", \"canonical markers\", \"immune cell types\", \"hierarchical annotation\", \"majority voting CellTypist\", \"over-clustering annotation\".",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[380,381,382,383],{"name":52,"slug":51,"type":343},{"name":55,"slug":54,"type":343},{"name":37,"slug":36,"type":343},{"name":384,"slug":385,"type":343},"RNA-seq","rna-seq","2026-07-12T08:38:05.443454",{"slug":388,"name":388,"fn":389,"description":390,"org":391,"tags":392,"stars":82,"repoUrl":81,"updatedAt":399},"cheminformatics","calculate molecular properties with RDKit","Cheminformatics pipeline for small-molecule property calculation, filtering, and similarity analysis using RDKit. Use when the user asks to compute molecular descriptors, filter compounds by Lipinski or Veber rules, detect PAINS, calculate fingerprint similarity, run matched molecular pair analysis, generate ADMET descriptors, or process SMILES. Triggers include \"RDKit\", \"molecular descriptors\", \"Lipinski\", \"rule of five\", \"Veber\", \"PAINS\", \"pan-assay interference\", \"Morgan fingerprint\", \"Tanimoto\", \"fingerprint similarity\", \"matched molecular pair\", \"MMP\", \"mmpdb\", \"ADMET\", \"druglikeness\", \"SMILES\", \"cheminformatics\", \"compound filtering\", \"chemical similarity\".",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[393,394,397,398],{"name":52,"slug":51,"type":343},{"name":395,"slug":396,"type":343},"Chemistry","chemistry",{"name":55,"slug":54,"type":343},{"name":105,"slug":104,"type":343},"2026-07-12T08:37:28.334619",{"slug":401,"name":401,"fn":402,"description":403,"org":404,"tags":405,"stars":82,"repoUrl":81,"updatedAt":412},"claims-analytics","analyze and parse healthcare claims data","Pipeline skill for healthcare claims data parsing, analysis, and fraud detection. Use when the user asks to parse X12 837 or 835 claim files, manipulate ICD-10 CPT or HCPCS codes, detect billing pattern anomalies, profile providers against specialty peers, identify outlier billing behavior, validate NCCI edits programmatically, detect duplicate claims, run Benford's law analysis on charges, build claims data pipelines, or analyze E&M code distributions. Triggers include \"parse X12 837\", \"parse 835\", \"claims SQL\", \"ICD-10 manipulation\", \"CPT code analysis\", \"provider profiling\", \"billing outlier\", \"NCCI validation code\", \"duplicate claim detection\", \"Benford's law charges\", \"claims ETL\", \"E&M distribution analysis\", \"claims analytics pipeline\".\n",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[406,407,408,411],{"name":55,"slug":54,"type":343},{"name":34,"slug":33,"type":343},{"name":409,"slug":410,"type":343},"Insurance","insurance",{"name":37,"slug":36,"type":343},"2026-07-12T08:37:34.815088",{"slug":414,"name":414,"fn":415,"description":416,"org":417,"tags":418,"stars":82,"repoUrl":81,"updatedAt":422},"claims-billing-rules","analyze healthcare claims billing rules","Reasoning skill for healthcare claims billing rules and fraud detection logic. Use when the user asks about CMS billing rules, place of service codes, global surgery periods, modifier usage (25 59 76 77), NCCI edit logic, column 1 column 2 code pairs, mutually exclusive procedures, modifier indicators, fraud waste and abuse patterns, E&M upcoding, unbundling, phantom billing, impossible day detection, coding error versus fraud distinction, FWA investigation methodology, or claims audit logic. Triggers include \"CMS billing rules\", \"NCCI edits\", \"modifier 25\", \"modifier 59\", \"global surgery period\", \"upcoding\", \"unbundling\", \"phantom billing\", \"impossible day\", \"FWA\", \"fraud waste abuse\", \"coding error vs fraud\", \"claims audit\", \"billing compliance\", \"E&M level selection\".\n",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[419,420,421],{"name":34,"slug":33,"type":343},{"name":409,"slug":410,"type":343},{"name":371,"slug":372,"type":343},"2026-07-12T08:38:28.210856",{"slug":424,"name":424,"fn":425,"description":426,"org":427,"tags":428,"stars":82,"repoUrl":81,"updatedAt":435},"clinical-data-standards","reason about clinical data terminology standards","Reason about clinical data terminology standards — MedDRA hierarchy (LLT→PT→HLT→HLGT→SOC), ICD-10 code structure and grouping, SNOMED CT concept model, LOINC panel relationships, and mapping decisions between systems. Use when the user asks to code adverse events, map diagnoses to ICD-10, choose a coding granularity level, group AEs by SOC or PT, interpret SNOMED CT relationships, select LOINC codes for lab panels, convert between terminology systems, or decide when to aggregate at HLT vs PT level. Triggers include \"MedDRA coding\", \"ICD-10 grouping\", \"SNOMED CT\", \"LOINC panel\", \"adverse event coding\", \"terminology mapping\", \"SOC table\", \"preferred term\", \"code hierarchy\", \"clinical coding\", \"AE frequency table\", \"diagnosis grouping\", \"lab code selection\", \"cross-walk between terminologies\".",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[429,430,431,434],{"name":367,"slug":368,"type":343},{"name":34,"slug":33,"type":343},{"name":432,"slug":433,"type":343},"Interoperability","interoperability",{"name":37,"slug":36,"type":343},"2026-07-12T08:38:12.348925",{"slug":437,"name":437,"fn":438,"description":439,"org":440,"tags":441,"stars":82,"repoUrl":81,"updatedAt":448},"dicom-processing","process medical images with DICOM pipelines","DICOM and NIfTI medical image processing pipeline. Triggers on DICOM, NIfTI, dcm2niix, de-identification, pydicom, DICOM header, conversion, anonymization, BIDS, DICOM tags, medical image format conversion, \"DICOM to NIfTI\", \"burned-in PHI\", \"SeriesInstanceUID\", \"nibabel\", \"DICOM anonymization\".",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[442,443,444,445],{"name":31,"slug":30,"type":343},{"name":279,"slug":278,"type":343},{"name":34,"slug":33,"type":343},{"name":446,"slug":447,"type":343},"Imaging","imaging","2026-07-12T08:37:32.100909",{"slug":450,"name":450,"fn":451,"description":452,"org":453,"tags":454,"stars":82,"repoUrl":81,"updatedAt":461},"digital-pathology","analyze whole-slide images with foundation models","Generate correct code for whole-slide image (WSI) analysis using TIAToolbox and foundation models (H-optimus-0, UNI, Prov-GigaPath). Triggers on requests involving whole-slide images, WSI, digital pathology, histopathology, SVS\u002FNDPI\u002Fpyramidal TIFF, tissue segmentation, patch extraction, stain normalization, H-optimus-0, TIAToolbox, CAMELYON16\u002F17, SlideGraph, MIL aggregation, HoVer-Net, PanNuke, or SageMaker deployment of pathology models. Produces deterministic commands and Python snippets for slide-info inspection, tissue masking, tile extraction at specified mpp, foundation-model feature embedding, slide-level aggregation, and regulated cloud inference.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[455,458,459,460],{"name":456,"slug":457,"type":343},"Computer Vision","computer-vision",{"name":34,"slug":33,"type":343},{"name":37,"slug":36,"type":343},{"name":105,"slug":104,"type":343},"2026-07-12T08:37:40.246467",{"slug":463,"name":463,"fn":464,"description":465,"org":466,"tags":467,"stars":82,"repoUrl":81,"updatedAt":471},"drug-repurposing","evaluate drug repurposing strategies","Reason about drug repurposing strategies in HCLS — choosing between target-based and phenotype-based approaches, evaluating mechanism-of-action overlap, querying drug-gene interaction databases, assessing clinical translatability, and ranking candidates by evidence strength. Use when the user asks to repurpose a drug, find approved drugs for a new indication, evaluate a repurposing candidate, query DGIdb or OpenTargets, assess drug-target interactions, design a repurposing study, rank repurposing evidence, or evaluate translatability of a candidate. Triggers include \"drug repurposing\", \"repurpose\", \"repositioning\", \"new indication\", \"off-label use\", \"target-based repurposing\", \"phenotype-based repurposing\", \"CMap\", \"L1000\", \"DGIdb\", \"OpenTargets\", \"DrugBank\", \"ChEMBL\", \"mechanism of action overlap\", \"drug-gene interaction\", \"translatability\", \"existing safety data\", \"repurposing evidence hierarchy\".",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[468,469,470],{"name":34,"slug":33,"type":343},{"name":37,"slug":36,"type":343},{"name":105,"slug":104,"type":343},"2026-07-12T08:37:38.401073",{"slug":473,"name":473,"fn":474,"description":475,"org":476,"tags":477,"stars":82,"repoUrl":81,"updatedAt":486},"edc-data-validation","validate clinical data exports","Generate code for EDC export validation, clinical data range checks, cross-form consistency checks, SDTM structure validation, controlled terminology verification, and define.xml generation. Use when the user asks to validate clinical trial data exports, check vital sign or lab value ranges, verify AE date consistency, validate SDTM datasets against CDISC rules, generate define.xml, or build an automated data review pipeline. Triggers include \"EDC validation\", \"range check clinical data\", \"cross-form consistency\", \"SDTM validation\", \"controlled terminology check\", \"define.xml generation\", \"Medidata Rave export\", \"Oracle InForm\", \"Veeva Vault CDMS\", \"clinical data cleaning\", \"edit check\", \"data query\", \"SDTM structure check\", \"lab range check\", \"vital signs validation\", \"AE date check\", \"protocol deviation detection\", \"OpenCDISC\", \"Pinnacles 21\", \"P21 validation\".\n",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[478,481,482,485],{"name":479,"slug":480,"type":343},"Audit","audit",{"name":367,"slug":368,"type":343},{"name":483,"slug":484,"type":343},"Data Quality","data-quality",{"name":34,"slug":33,"type":343},"2026-07-12T08:37:58.940653",{"slug":488,"name":488,"fn":489,"description":490,"org":491,"tags":492,"stars":82,"repoUrl":81,"updatedAt":500},"ehr-data-parsing","parse and extract clinical data","Parse and extract clinical data from HL7v2 messages and FHIR R4 resources using Python. Use when the user mentions HL7v2, HL7, FHIR, PID segment, OBX segment, MSH segment, Patient resource, Observation resource, Condition resource, MedicationRequest, EHR data extraction, clinical message parsing, FHIR bundle, HL7 to FHIR conversion, ADT message, ORU message, lab result extraction, or clinical data quality checks. Triggers include \"parse HL7\", \"extract FHIR\", \"HL7v2 message\", \"FHIR resource\", \"PID segment\", \"OBX segment\", \"Patient resource\", \"Observation resource\", \"EHR parsing\", \"clinical data extraction\", \"HL7 to CSV\", \"FHIR to DataFrame\".",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[493,496,497,498],{"name":494,"slug":495,"type":343},"FHIR","fhir",{"name":34,"slug":33,"type":343},{"name":432,"slug":433,"type":343},{"name":499,"slug":299,"type":343},"Python","2026-07-12T08:37:36.137905",{"slug":502,"name":502,"fn":503,"description":504,"org":505,"tags":506,"stars":82,"repoUrl":81,"updatedAt":511},"genomic-variant-interpretation","interpret genomic variants","Reason about germline and somatic variant classification using ACMG\u002FAMP 2015 and AMP\u002FASCO\u002FCAP frameworks. Use when the user asks to classify a variant, interpret a VCF annotation, resolve a VUS, apply ACMG criteria, weigh ClinVar evidence, evaluate gnomAD allele frequencies, interpret REVEL\u002FCADD\u002FSpliceAI scores, decide whether PVS1 applies, or assess gene-disease validity before reporting. Triggers include \"ACMG\", \"variant classification\", \"pathogenic\", \"likely pathogenic\", \"VUS\", \"benign\", \"ClinVar\", \"gnomAD\", \"REVEL\", \"CADD\", \"SpliceAI\", \"PVS1\", \"loss of function\", \"nonsense variant\", \"missense interpretation\", \"splice variant\", \"filtering allele frequency\", \"ClinGen\", \"somatic variant tier\".",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[507,508,510],{"name":52,"slug":51,"type":343},{"name":509,"slug":92,"type":343},"Genomics",{"name":37,"slug":36,"type":343},"2026-07-12T08:38:36.80494",{"slug":513,"name":513,"fn":514,"description":515,"org":516,"tags":517,"stars":82,"repoUrl":81,"updatedAt":521},"hedis-measure-specification","specify HEDIS measures and care gaps","Reasoning skill for HEDIS measure specification, enrollment logic, exclusion evaluation, NCQA audit requirements, and care gap prioritization. Use when the user asks about HEDIS measure definitions, denominator\u002Fnumerator\u002Fexclusion logic, continuous enrollment rules, Star Rating impact, or care gap closure strategies.\n",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[518,519,520],{"name":479,"slug":480,"type":343},{"name":34,"slug":33,"type":343},{"name":371,"slug":372,"type":343},"2026-07-12T08:38:35.547597",{"slug":523,"name":523,"fn":524,"description":525,"org":526,"tags":527,"stars":82,"repoUrl":81,"updatedAt":532},"imaging-study-design","design medical imaging studies and biomarkers","Reasoning skill for medical imaging study design and biomarker selection. Use when the user asks to plan an imaging study, choose a preprocessing strategy, select an imaging biomarker, design a radiomics pipeline, handle DICOM de-identification, plan longitudinal imaging analysis, or pick a registration target. Triggers include \"imaging study\", \"preprocessing strategy\", \"DICOM de-identification\", \"imaging biomarker\", \"radiomics\", \"longitudinal imaging\", \"registration target\", \"MNI vs native space\", \"scanner harmonization\", \"ComBat\", \"IBSI\", \"multi-site imaging\", \"burned-in PHI\", \"test-retest reliability\", \"volumetric biomarker\", \"diffusion MRI\", \"perfusion imaging\", \"fMRI study design\", \"spectroscopy biomarker\".",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[528,529,530,531],{"name":367,"slug":368,"type":343},{"name":34,"slug":33,"type":343},{"name":37,"slug":36,"type":343},{"name":105,"slug":104,"type":343},"2026-07-12T08:38:01.637878",{"slug":534,"name":534,"fn":535,"description":536,"org":537,"tags":538,"stars":82,"repoUrl":81,"updatedAt":107},"medical-device-software-compliance","assess software for medical device compliance","Reason about SaMD (Software as a Medical Device) regulatory compliance for FDA, EU MDR, and global submissions. Use when the user asks about IEC 62304 safety classification, ISO 14971 risk management, 21 CFR 820\u002FQMSR quality system requirements, Design History File structure, 510(k)\u002FDe Novo\u002FPMA pathway selection, SOUP\u002FOTS assessment, verification and validation planning, cybersecurity for medical devices, PCCP for AI\u002FML devices, design reviews, traceability matrices, or post-market surveillance. Triggers include \"SaMD\", \"medical device software\", \"IEC 62304\", \"ISO 14971\", \"ISO 13485\", \"21 CFR 820\", \"QMSR\", \"510(k)\", \"De Novo\", \"PMA\", \"design history file\", \"DHF\", \"software safety classification\", \"SOUP list\", \"risk management\", \"hazard analysis\", \"design review\", \"V&V protocol\", \"FDA submission\", \"EU MDR\", \"clinical evaluation\", \"PCCP\", \"predetermined change control\", \"GMLP\", \"cybersecurity premarket\", \"Part 11\", \"design controls\", \"design inputs\", \"design outputs\", \"traceability matrix\", \"post-market surveillance\", \"CAPA\", \"Class II device\", \"Class III device\", \"regulatory pathway\", \"predicate device\", \"substantial equivalence\".",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[539,542,543,544],{"name":540,"slug":541,"type":343},"FDA","fda",{"name":34,"slug":33,"type":343},{"name":371,"slug":372,"type":343},{"name":545,"slug":546,"type":343},"Risk Assessment","risk-assessment",{"slug":548,"name":548,"fn":549,"description":550,"org":551,"tags":552,"stars":82,"repoUrl":81,"updatedAt":560},"ml-researcher","design ML studies for biomedical data","Reason about ML experiment design for healthcare and life sciences data. Use when the user asks to design an ML study, choose a model for clinical\u002Fbiomedical data, set up cross-validation, pick evaluation metrics, audit fairness, plan a regulatory submission, or critique an ML pipeline on EHR, medical imaging, genomics, molecules, or clinical text. Triggers include \"design an ML experiment\", \"which model for this clinical data\", \"how should I split\", \"nested CV\", \"class imbalance\", \"AUROC vs AUPRC\", \"calibration\", \"decision curve\", \"net benefit\", \"TRIPOD+AI\", \"PROBAST\", \"CLAIM\", \"FDA SaMD\", \"PCCP\", \"GMLP\", \"site generalization\", \"temporal leakage\", \"scaffold split\", \"foundation model evaluation\", \"subgroup fairness\", \"is this model ready for deployment\".",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[553,554,555,556,557],{"name":55,"slug":54,"type":343},{"name":37,"slug":36,"type":343},{"name":126,"slug":125,"type":343},{"name":105,"slug":104,"type":343},{"name":558,"slug":559,"type":343},"Statistics","statistics","2026-07-12T08:37:25.382474",{"slug":562,"name":562,"fn":563,"description":564,"org":565,"tags":566,"stars":82,"repoUrl":81,"updatedAt":571},"molecular-docking","run molecular docking pipelines","Molecular docking pipeline using AutoDock Vina for structure-based drug discovery. Triggers on docking, AutoDock Vina, receptor preparation, ligand preparation, PDBQT, grid box, virtual screening, binding affinity, pose prediction, structure-based virtual screening, \"redocking RMSD\", \"Vina score\", \"docking pose\", \"prepare receptor\", \"ligand library screening\".",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[567,568,569,570],{"name":52,"slug":51,"type":343},{"name":395,"slug":396,"type":343},{"name":37,"slug":36,"type":343},{"name":105,"slug":104,"type":343},"2026-07-12T08:37:44.528258",{"slug":573,"name":573,"fn":574,"description":575,"org":576,"tags":577,"stars":82,"repoUrl":81,"updatedAt":581},"multi-omics-integration","integrate multi-omics data","Reasoning skill for multi-omics data integration strategy selection. Use when the user asks to integrate transcriptomics with proteomics, combine multi-omic layers, choose between early intermediate or late integration, apply batch correction across omics, handle partial sample overlap, run MOFA+ or iCluster, interpret multi-omic factors, select enrichment methods for multi-omic signatures, or decide how to merge genomics epigenomics transcriptomics proteomics and metabolomics data. Triggers include \"multi-omics integration\", \"combine omics layers\", \"early vs late fusion\", \"MOFA+\", \"iCluster\", \"batch correction across omics\", \"partial overlap\", \"multi-omic enrichment\", \"kernel integration\", \"concatenation vs stacking\", \"SNF\", \"similarity network fusion\", \"intermediate integration\", \"multi-omic factor analysis\".\n",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[578,579,580],{"name":52,"slug":51,"type":343},{"name":276,"slug":275,"type":343},{"name":37,"slug":36,"type":343},"2026-07-12T08:38:19.20019",{"slug":583,"name":583,"fn":584,"description":585,"org":586,"tags":587,"stars":82,"repoUrl":81,"updatedAt":592},"multi-omics-pipeline","process and integrate multi-omics data","Pipeline skill for multi-omics data processing and integration. Use when the user asks to map gene IDs between HGNC Ensembl and UniProt, convert between omic data formats, run batch correction with ComBat or ComBat-seq, perform GSEA or over-representation analysis on multi-omic results, run consensus clustering on integrated data, execute MOFA2 in R or mofapy2 in Python, build a multi-omics ETL pipeline, harmonize feature identifiers across omics layers, or run clusterProfiler enrichment. Triggers include \"map Ensembl to HGNC\", \"ID mapping omics\", \"ComBat code\", \"run MOFA2\", \"mofapy2\", \"GSEA Python\", \"fgsea R\", \"consensus clustering\", \"multi-omics pipeline\", \"gseapy\", \"biomaRt\", \"clusterProfiler\", \"mixOmics DIABLO\".\n",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[588,589,590,591],{"name":52,"slug":51,"type":343},{"name":276,"slug":275,"type":343},{"name":279,"slug":278,"type":343},{"name":37,"slug":36,"type":343},"2026-07-12T08:38:00.363983",{"slug":594,"name":594,"fn":595,"description":596,"org":597,"tags":598,"stars":82,"repoUrl":81,"updatedAt":603},"ngs-quality-control","run NGS quality control pipelines","NGS quality control pipeline for short-read sequencing data. Triggers on FastQC, QC, quality control, adapter trimming, coverage, mosdepth, Picard metrics, fastp, MultiQC, sequencing QC, BAM QC, WGS\u002FWES coverage analysis.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[599,600,601,602],{"name":31,"slug":30,"type":343},{"name":52,"slug":51,"type":343},{"name":279,"slug":278,"type":343},{"name":509,"slug":92,"type":343},"2026-07-12T08:37:50.555186",{"slug":605,"name":605,"fn":606,"description":607,"org":608,"tags":609,"stars":82,"repoUrl":81,"updatedAt":619},"pa-clinical-policy","evaluate clinical prior authorization policies","Reasoning skill for prior authorization clinical policy evaluation. Use when the user asks about payer clinical criteria, step therapy requirements, medical necessity definitions, CMS LCD\u002FNCD coverage rules, appeals documentation strategy, formulary tier implications, or FHIR Da Vinci PAS implementation guidance. Triggers include \"prior auth policy\", \"step therapy\", \"medical necessity\", \"coverage determination\", \"LCD\", \"NCD\", \"formulary tier\", \"PA appeal\", \"peer-to-peer review\", \"Da Vinci PAS\", \"clinical criteria\", \"PA denial\", \"drug authorization\", \"utilization management\".\n",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[610,611,612,613,616],{"name":261,"slug":260,"type":343},{"name":34,"slug":33,"type":343},{"name":409,"slug":410,"type":343},{"name":614,"slug":615,"type":343},"Medical Necessity","medical-necessity",{"name":617,"slug":618,"type":343},"Policy","policy","2026-07-12T08:37:46.137142",{"slug":621,"name":621,"fn":622,"description":623,"org":624,"tags":625,"stars":82,"repoUrl":81,"updatedAt":631},"pa-decision-automation","automate prior authorization decision workflows","Pipeline skill for automating prior authorization decision workflows. Use when the user asks to parse PA request data (X12 278 or FHIR PAS bundles), extract clinical features for adjudication, build rules-based PA decision engines, train ML classifiers on historical PA decisions, analyze denial patterns, or generate SHAP explanations for PA outcomes. Triggers include \"parse 278\", \"FHIR PAS bundle\", \"PA automation\", \"adjudication logic\", \"PA classifier\", \"denial analysis\", \"prior auth ML\", \"SHAP explainability\", \"PA feature extraction\", \"rules engine PA\", \"PA decision pipeline\", \"authorization workflow\", \"clinical criteria extraction\", \"denial pattern mining\", \"PA turnaround time\".\n",{"slug":5,"name":6,"logoUrl":7,"githubOrg":8},[626,627,628,629,630],{"name":145,"slug":144,"type":343},{"name":31,"slug":30,"type":343},{"name":494,"slug":495,"type":343},{"name":34,"slug":33,"type":343},{"name":409,"slug":410,"type":343},"2026-07-12T08:38:10.555568"]