- IamDesign and review AWS IAM configurations. Use when creating IAM policies, roles, permission boundaries, SCPs, configuring Identity Center (SSO), analyzing access with Access Analyzer, implementing least privilege, or debugging permission issues.awslabs/startups13
- IotDeep-dive into AWS IoT architecture, device connectivity, edge computing, and fleet management. This skill should be used when the user asks to "design an IoT solution", "connect devices to AWS", "set up MQTT messaging", "configure IoT rules", "provision a device fleet", "use Greengrass at the edge", "build a device shadow", "set up IoT security", "manage OTA updates", "store telemetry data", "create IoT topic rules", "configure fleet provisioning", or mentions IoT Core, MQTT, Greengrass, Deviceawslabs/startups13
- Knowledge Base For StartupsAWS Startups reference content — Activate FAQ, credits guide, programs, partner offers, sample architectures, and hundreds of learn articles spanning generative AI, cloud architecture, cost optimization, security, fundraising, go-to-market, and real-world startup case studies. Use when the user asks factual questions about AWS Activate (eligibility, credits, programs, providers), wants a sample architecture or solution guide, or needs an AWS-curated learn article on a specific startup topic. Forawslabs/startups13
- LambdaDesign, build, and optimize AWS Lambda functions. Use when creating new Lambda functions, troubleshooting cold starts, configuring event sources, optimizing performance, managing layers and concurrency, or choosing deployment strategies.awslabs/startups13
- Llm To BedrockUse when the user wants to migrate code that calls OpenAI, Gemini/Google AI, or the Anthropic API to Amazon Bedrock. End-to-end: assesses the codebase, then rewrites SDK calls, evaluates output quality against Bedrock, and delivers a ready-to-merge git branch. Not for standalone Bedrock cost estimates or infrastructure-only migration. Requires the migration-to-aws plugin for the Assess phase.awslabs/startups13
- MessagingDeep-dive into AWS messaging services including SQS, SNS, and EventBridge. Use when designing event-driven architectures, choosing between messaging services, configuring queues and topics, implementing fan-out patterns, setting up dead-letter queues, or troubleshooting message delivery issues.awslabs/startups13
- Migration Apprunner To Ecs ExpressGuided migration from AWS App Runner to Amazon ECS Express Mode. Covers IAM setup, deployment, custom domains, DNS cutover, cost comparison, and troubleshooting. Use when the user asks to "migrate from App Runner", "move to ECS Express Mode", "replace App Runner", or mentions App Runner deprecation.awslabs/startups13
- Migration Azure To AwsAzure to AWS migration guidance with service mappings, gotchas, and assessment. Use when migrating from Microsoft Azure, mapping Azure services to AWS equivalents, assessing Azure environments, or planning Azure-to-AWS migrations.awslabs/startups13
- Migration Gcp To AwsGCP to AWS migration guidance with service mappings, gotchas, and assessment. Use when migrating from Google Cloud Platform, mapping GCP services to AWS equivalents, assessing GCP environments, or planning GCP-to-AWS migrations.awslabs/startups13
- Migration To AwsMigrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workawslabs/startups13
- MlopsEnd-to-end MLOps guidance on AWS — platform selection, training, inference, pipelines, monitoring, and cost optimization. This skill should be used when the user asks to "build an ML pipeline", "deploy a model on SageMaker", "set up MLOps", "configure SageMaker Pipelines", "choose between SageMaker and Bedrock", "deploy ML models to production", "set up model monitoring", "use MLflow on AWS", "train a model with Spot instances", "configure inference endpoints", "set up distributed training", or awslabs/startups13
- NetworkingDesign and troubleshoot AWS networking. Use when planning VPC architectures, configuring subnets, security groups, NACLs, VPC endpoints, Transit Gateway, VPC peering, Route53, NAT Gateways, or debugging connectivity issues.awslabs/startups13
- ObservabilityDesign and implement AWS observability solutions. Use when configuring CloudWatch metrics, logs, alarms, dashboards, Logs Insights queries, X-Ray tracing, anomaly detection, or debugging monitoring gaps.awslabs/startups13
- Prompt Library For StartupsAWS-curated copy-paste prompts for AI coding agents (MVP scaffolding, RAG chatbot with Claude on Bedrock, security baseline evaluation, cost anomaly detection, GPU quota requests, EKS deployment, Well-Architected review, etc.) plus downloadable installable agents (Multi-Account Transition Advisor, Bill Shock Preventer, Service Quota Agent). Use when the user asks for a prompt to do X on AWS, wants an installable agent for multi-account / cost monitoring / quota management, or asks how to use AWSawslabs/startups13
- Rds AuroraDeep-dive into Amazon RDS and Aurora database design, engine selection, high availability, and operations. This skill should be used when the user asks to "design an RDS database", "choose between RDS and Aurora", "configure Aurora Serverless", "set up read replicas", "plan a database migration", "configure RDS Proxy", "tune database parameters", "set up Multi-AZ", "plan blue/green deployments", or mentions RDS, Aurora, Aurora Serverless v2, database failover, or relational database design on AWawslabs/startups13
- Resolve Bedrock Model IdValidate a plan-supplied Bedrock model ID against live inference profiles in the target AWS account and correct it if stale. Load this when a plan's target_model_id needs to be used for the first time (ai-code-analyzer §10), or when a Bedrock call returns "invalid model identifier".awslabs/startups13
- Run Source Model BaselineRe-run a JSONL of golden prompts against the live source LLM provider (OpenAI, Anthropic, or Gemini) on the local host and write per-prompt responses to <repo>/.saws-migrate/eval-results/source_baselines.jsonl. Load this from ai-prompt-evaluator §9 when source_baseline_available == true and same_model_family == false; the produced JSONL gives the evaluator real source-vs-Bedrock side-by-side outputs instead of comparing Bedrock against agent-synthesized baselines.awslabs/startups13
- S3Deep-dive into Amazon S3 bucket configuration, storage optimization, and access control. Use when designing S3 storage strategies, configuring bucket policies and access controls, optimizing performance for large-scale workloads, setting up lifecycle policies, or troubleshooting S3 access issues.awslabs/startups13
- Security ReviewReview AWS infrastructure code and configurations for security issues. Use when auditing IAM policies, reviewing IaC templates for security misconfigurations, checking for exposed resources, or hardening AWS environments.awslabs/startups13
- Service MeshDesign, deploy, and operate a service mesh on Amazon EKS with Istio. Use when working with service mesh, mTLS between services, east-west traffic management, canary or blue/green traffic shifting, sidecar vs ambient data planes, or migrating off the deprecated AWS App Mesh.awslabs/startups13
- Start Building For StartupsInteractive discovery + implementation workflow that gathers requirements through picker-based questions (intent, scope, constraints, preferences), scans the codebase for what it can already infer, then writes an AWS architectural scaffold and implementation directly into the project. Use when the user wants to build a new app, scaffold a project, or expand/refactor an existing one on AWS — anything that calls for a structured discovery flow followed by code changes, not a one-off lookup. Do notawslabs/startups13
- Step FunctionsDesign and build AWS Step Functions workflows. Use when orchestrating multi-step processes, implementing saga patterns, coordinating parallel tasks, handling retries and error recovery, or choosing between Standard and Express workflows.awslabs/startups13
- Strands AgentScaffold and build AI agents using the Strands Agents SDK with Bedrock AgentCore. Use when creating new agent projects, building greenfield AgentCore applications, prototyping agents with Strands, or when asked about the Strands framework. Covers both TypeScript and Python.awslabs/startups13
- Well ArchitectedRun formal AWS Well-Architected Framework reviews against workloads. Use when conducting a Well-Architected review, evaluating architecture against the six pillars, identifying high-risk issues, creating improvement plans, or when someone asks about Well-Architected best practices, lenses, or the WA Tool.awslabs/startups13
- Eval JudgeLLM judge agent for grading AI agent eval transcripts. Checks deterministic assertions (transcript_contains, tool_called) and uses LLM reasoning for behavioral assertions (llm_judge). Returns structured JSON grades.awslabs/agent-builder-toolkit-aws-transform5
- Scenario RunnerSimulated human agent for eval scenarios. Interacts with the agent under test via the ACP bridge, following the scenario goal and guidance to respond to agent questions, approve tool calls, and drive the multi-turn flow to completion.awslabs/agent-builder-toolkit-aws-transform5
- Codeknit FingerprintDetects duplicate and near-duplicate code across a codebase using fuzzy hashing with codeknit. Use when finding copy-paste, refactoring candidates, merging similar implementations, auditing for DRY violations, or locating semantically equivalent code across different files or languages. Supports C, C++, C#, Go, Java, JavaScript, PHP, Python, Ruby, Rust, Scala, and TypeScript.awslabs/codeknit3
- Codeknit ParseExtracts code structure (functions, classes, methods, variables) and relationships (calls, inheritance, containment) from source files into compact .skt or JSON output using codeknit. Use when analyzing a codebase, understanding code structure, mapping dependencies, or preparing context for code review and refactoring. Supports C, C++, C#, Go, Java, JavaScript, PHP, Python, Ruby, Rust, Scala, and TypeScript.awslabs/codeknit3
- FlinkComprehensive guidance for developing, optimizing, and operating Apache Flink applications on Amazon Managed Service for Apache Flink. Use when working with Apache Flink, Managed Service for Apache Flink, streaming, stream-processing, real-time data, Kafka, or Kinesis.awslabs/managed-service-for-apache-flink-agent-steering-files3
- Aws Genai Ml ArchitectReasoning 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/PHI compliance in ML systems, design MLOps for regulated clinical models, or optimize cost for HCLS ML workloads. Triggers include "AWS architecture", "SageMaker vs Bedrawslabs/hcls-agent-skills2
- Biomarker DiscoveryReason 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 biomarawslabs/hcls-agent-skills2
- Cdisc ComplianceReason 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", "ADSLawslabs/hcls-agent-skills2
- Cell Type AnnotationGenerate code to assign cell type labels to single-cell RNA-seq clusters using CellTypist, SingleR, marker-based annotation, or reference label transfer (scANVI/ingest). 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", "majorawslabs/hcls-agent-skills2
- CheminformaticsCheminformatics 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", "Tanimoawslabs/hcls-agent-skills2
- Claims AnalyticsPipeline 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 8awslabs/hcls-agent-skills2
- Claims Billing RulesReasoning 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. Trawslabs/hcls-agent-skills2
- Clinical Data StandardsReason 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 levawslabs/hcls-agent-skills2
- Dicom ProcessingDICOM 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".awslabs/hcls-agent-skills2
- Digital PathologyGenerate 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/NDPI/pyramidal TIFF, tissue segmentation, patch extraction, stain normalization, H-optimus-0, TIAToolbox, CAMELYON16/17, SlideGraph, MIL aggregation, HoVer-Net, PanNuke, or SageMaker deployment of pathology models. Produces deterministic commands and Python snippets for sawslabs/hcls-agent-skills2
- Drug RepurposingReason 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 repurposinawslabs/hcls-agent-skills2
- Edc Data ValidationGenerate 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", "crossawslabs/hcls-agent-skills2
- Ehr Data ParsingParse 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", "PIawslabs/hcls-agent-skills2
- Genomic Variant InterpretationReason about germline and somatic variant classification using ACMG/AMP 2015 and AMP/ASCO/CAP 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/CADD/SpliceAI scores, decide whether PVS1 applies, or assess gene-disease validity before reporting. Triggers include "ACMG", "variant classification", "pathogenic", "likely pathogenic", "VUS", "benign", "Cliawslabs/hcls-agent-skills2
- Hedis Measure SpecificationReasoning 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/numerator/exclusion logic, continuous enrollment rules, Star Rating impact, or care gap closure strategies.awslabs/hcls-agent-skills2
- Imaging Study DesignReasoning 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 spacawslabs/hcls-agent-skills2
- Ml ResearcherReason 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/biomedical 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 AUPawslabs/hcls-agent-skills2
- Molecular DockingMolecular 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".awslabs/hcls-agent-skills2
- Multi Omics IntegrationReasoning 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 incawslabs/hcls-agent-skills2
- Multi Omics PipelinePipeline 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. Triggeawslabs/hcls-agent-skills2
- Ngs Quality ControlNGS 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/WES coverage analysis.awslabs/hcls-agent-skills2
- Pa Clinical PolicyReasoning skill for prior authorization clinical policy evaluation. Use when the user asks about payer clinical criteria, step therapy requirements, medical necessity definitions, CMS LCD/NCD 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", awslabs/hcls-agent-skills2
- Pa Decision AutomationPipeline 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 awslabs/hcls-agent-skills2
- PharmacoepidemiologyReason about pharmacoepidemiologic study design for causal inference from real-world data — choosing active comparator new-user designs, emulating target trials, avoiding immortal time bias, handling time-varying confounding with marginal structural models, and selecting propensity score methods. Use when the user asks to design a drug safety or effectiveness study, choose between propensity score matching vs weighting vs stratification, emulate a target trial, handle immortal time bias, apply mawslabs/hcls-agent-skills2
- Protein Structure AnalysisPipeline skill for protein structure analysis covering PDB/mmCIF parsing, RMSD superposition, Ramachandran/dihedral analysis, binding pocket detection, contact maps, B-factor flexibility, DSSP secondary structure, and format conversion. Triggers on PDB, protein structure, RMSD, Ramachandran, binding pocket, Biopython, PyMOL, pocket detection, fpocket, DSSP, PDBQT, structural alignment, superposition.awslabs/hcls-agent-skills2
- Quality MeasuresPipeline skill for computing HEDIS quality measures from claims and clinical data. Use when the user asks to calculate HEDIS measure rates, check continuous enrollment, build denominator/numerator logic, detect care gaps, compute utilization rates, identify high-cost claimants, or score risk stratification indices. Triggers include "calculate HEDIS", "measure rate", "continuous enrollment check", "care gap detection", "denominator query", "numerator logic", "utilization rate", "high-cost claimanawslabs/hcls-agent-skills2
- Quantitative ProteomicsReason about quantitative proteomics experiment design and data analysis strategy. Use when the user asks to choose between LFQ, TMT, and DIA quantification; select an imputation method for missing values; pick a normalization strategy; interpret differential expression results from proteomics data; evaluate ratio compression; or design a proteomics study for biomarker discovery or validation. Triggers include "LFQ vs TMT", "DIA quantification", "proteomics normalization", "missing value imputatawslabs/hcls-agent-skills2
- Radiology PreprocessingStructural MRI/CT preprocessing pipeline for radiology workflows covering skull stripping, bias field correction, registration, and intensity normalization. Triggers on skull stripping, bias correction, registration, ANTs, FSL, HD-BET, N4, brain extraction, normalization, FLIRT, FNIRT, SyN, fslreorient2std, MNI registration, T1 preprocessing.awslabs/hcls-agent-skills2
- Risk AdjustmentPipeline skill for CMS-HCC risk adjustment calculation and coding gap identification. Use when the user asks to apply the ICD-10-to-HCC crosswalk, calculate RAF scores, resolve disease hierarchies programmatically, identify coding gaps from Rx or lab proxies, project member-level risk scores, build a risk adjustment data pipeline, compute HCC coefficients, run hierarchy resolution code, or generate member risk score reports. Triggers include "calculate RAF score", "ICD-10 to HCC crosswalk", "hieawslabs/hcls-agent-skills2
- Risk Adjustment StrategyReasoning skill for CMS-HCC risk adjustment strategy and methodology. Use when the user asks about CMS-HCC model versions V24 or V28, blended transition methodology, ICD-10-to-HCC mapping logic, disease interaction hierarchies, RAF score methodology, risk adjustment factor calculation, coding gap identification, audit-defensible documentation, HCC recapture strategy, prospective vs retrospective risk adjustment, or Medicare Advantage risk scoring. Triggers include "CMS-HCC", "V24", "V28", "blendawslabs/hcls-agent-skills2
- Risk Stratification IndicesReasoning skill for clinical risk stratification index selection and interpretation. Use when the user asks about LACE scores, Charlson Comorbidity Index, Elixhauser Index, readmission risk scoring, comorbidity weighting, SDOH Z-codes, Area Deprivation Index, or population health stratification methods.awslabs/hcls-agent-skills2