awslabs

@awslabs source repo

73 skills
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Agent Advisor 2 · awslabs bundle
Unified entry point for AI-agent work on AWS: evaluate and pick a runtime, generate a full migration plan (for existing workloads), and build an executable POC — all in one flow. Triggers on: which runtime for my agent, AgentCore vs ECS vs EKS vs Lambda, AgentCore vs Lambda MicroVMs, deploy an AI agent on AWS, agent architecture on AWS, I have an agent idea what do I build, move my agents to AWS, migrate my agents to AWS with a plan, agent migration plan, add AgentCore services, add memory/gateway/identity/policy to my agent, enable AgentCore Memory, add observability to my agent, I'm already on AWS and want to add agent capabilities, migrate Temporal workers to AWS, Temporal to AWS, run Temporal on AWS, Temporal workers on AWS, we use Temporal and want to move to AWS, our service is orchestrated by Temporal, what do I build on AWS for my Temporal workers, move a Temporal-based service to AWS, Temporal Cloud or self-hosted on AWS. Runs a phased flow: Intake (entry point + technical background), Discover (ligh

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LLM To Bedrock 2 · awslabs bundle
Use when the user wants to migrate code that calls OpenAI, Gemini/Google AI, or the Anthropic API to Amazon Bedrock — a pure model/SDK rewrite. 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: agent runtime selection, agentic architecture decisions, or agent migration planning — use agent-advisor for those. Not for standalone Bedrock cost estimates or infrastructure-only migration. REQUIRES the gcp-to-aws skill installed alongside this one — Assess is delegated entirely to it via a cross-skill invocation, with no standalone fallback if gcp-to-aws is absent.

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Deploy · awslabs bundle
Deploy applications to AWS. Triggers on phrases like: deploy to AWS, host on AWS, run this on AWS, AWS architecture, estimate AWS cost, generate infrastructure. Analyzes any codebase and deploys to optimal AWS services.

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Planning · awslabs bundle
Discovers user intent and generates a structured, step-by-step plan for model customization workflows. This skill must always be activated alongside any other skill when the user's request relates to model customization — including fine-tuning, training, building, customizing, reviewing data, or getting advice on approach, regardless of domain. Do not skip this skill even if the immediate ask is narrow (e.g., reviewing data format or a single workflow step), because planning discovers the full scope of work needed. Also activate when the user wants to resume, continue, or modify an existing plan.

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Finetuning · awslabs bundle
Generates code that fine-tunes a base model using SageMaker serverless training jobs. Use when the user says "start training", "fine-tune my model", "I'm ready to train", or when the plan reaches the finetuning step. Supports SFT, DPO, RLVR, and RLAIF trainers, including RLVR Lambda reward function and RLAIF custom prompt creation.

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Model Selection · awslabs bundle
Selects a base model for the user's use case by querying SageMaker Hub. Use when the user asks which model to use, wants to select or change their base model, mentions a model name or family (e.g., "Llama", "Mistral", "Nova"), or wants to evaluate a base model — always activate even for known model names because the exact Hub model ID must be resolved. Queries available models, presents benchmarks and licenses, and confirms selection.

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AWS Architecture Diagram · awslabs bundle
Generate validated AWS architecture diagrams as draw.io XML using official AWS4 icon libraries. Use this skill whenever the user wants to create, generate, or design AWS architecture diagrams, cloud infrastructure diagrams, or system design visuals. Also triggers for requests to visualize existing infrastructure from CloudFormation, CDK, or Terraform code. Supports two modes: analyze an existing codebase to auto-generate diagrams, or brainstorm interactively from scratch. Exports .drawio files with optional PNG/SVG/PDF export via draw.io desktop CLI.

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Phase 5 Asset Flows · awslabs
Phase 5 Asset Flow Analysis guide. Use when identifying valuable assets, tracking data flows, or analyzing how sensitive data moves through the system.

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Phase 2 Architecture · awslabs
Phase 2 Architecture Analysis guide. Use when documenting system components, connections, data stores, or analyzing technical architecture for threat modeling.

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Phase 3 Threat Actors · awslabs
Phase 3 Threat Actor Analysis guide. Use when identifying threat actors, setting relevance and priority, or analyzing who might attack the system.

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Phase 8 Residual Risk · awslabs
Phase 8 Residual Risk Analysis guide. Use when assessing remaining risk after mitigations and recording explicit risk decisions.

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Phase 1 Business Context · awslabs
Phase 1 Business Context Analysis guide. Use when starting a threat model, setting business context, or configuring business features like industry sector, data sensitivity, and regulatory requirements.

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Phase 4 Trust Boundaries · awslabs
Phase 4 Trust Boundary Analysis guide. Use when defining trust zones, crossing points, and security boundaries between architecture nodes.

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Phase 7 5 Code Validation · awslabs
Phase 7.5 Code Validation guide. Use when validating threats and mitigations against actual code or reviewing implementation evidence.

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Phase 9 Output Generation · awslabs
Phase 9 Output Generation guide with Threat Composer export reference. Use when generating final reports, exporting to JSON/Markdown, or completing the threat modeling process.

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Phase 7 Mitigation Planning · awslabs
Phase 7 Mitigation Planning guide. Use when creating mitigations, linking them to threats, validating coverage, or planning security controls.

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Phase 6 Threat Identification · awslabs
Phase 6 Threat Identification guide with STRIDE methodology reference. Use when identifying threats, categorizing security issues, applying STRIDE analysis, or assessing threat severity and likelihood.

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GCP To AWS · awslabs bundle
Migrate 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, migrate App Engine to Elastic Beanstalk, 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 workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model familie

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Agent Advisor · awslabs bundle
Unified entry point for AI-agent work on AWS: evaluate and pick a runtime, generate a full migration plan (for existing workloads), and build an executable POC — all in one flow. Triggers on: which runtime for my agent, AgentCore vs ECS vs EKS vs Lambda, AgentCore vs Lambda MicroVMs, deploy an AI agent on AWS, agent architecture on AWS, I have an agent idea what do I build, move my agents to AWS, migrate my agents to AWS with a plan, agent migration plan, add AgentCore services, add memory/gateway/identity/policy to my agent, enable AgentCore Memory, add observability to my agent, I'm already on AWS and want to add agent capabilities, migrate Temporal workers to AWS, Temporal to AWS, run Temporal on AWS, Temporal workers on AWS, we use Temporal and want to move to AWS, our service is orchestrated by Temporal, what do I build on AWS for my Temporal workers, move a Temporal-based service to AWS, Temporal Cloud or self-hosted on AWS. Runs a phased flow: Intake (entry point + technical background), Discover (ligh

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Heroku To AWS · awslabs bundle
Migrate workloads from Heroku to AWS. Triggers on: migrate from Heroku, Heroku to AWS, move off Heroku, migrate Heroku app, migrate Heroku Postgres to RDS, migrate Heroku Redis to ElastiCache, migrate Heroku Kafka to MSK, migrate dynos to Elastic Beanstalk, migrate dynos to Fargate, Heroku migration, move from Heroku to AWS, migrate Heroku Private Space, Heroku to Elastic Beanstalk, Heroku to ECS, Heroku to Fargate, leave Heroku, migrate off Heroku platform, what-if workshop, reprice Heroku migration, compare migration scenarios, workshop mode. Runs a 6-phase process: discover Heroku resources live via the authenticated Heroku CLI (read-only, consent-gated) and/or from Terraform files, Procfile/app.json, and optional billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. After Estimate, an optional what-if workshop can reprice region/HA/compute/Graviton scenarios without re-discovery. Clarify must finish before Des

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LLM To Bedrock · awslabs bundle
Use when the user wants to migrate code that calls OpenAI, Gemini/Google AI, or the Anthropic API to Amazon Bedrock — a pure model/SDK rewrite. 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: agent runtime selection, agentic architecture decisions, or agent migration planning — use agent-advisor for those. Not for standalone Bedrock cost estimates or infrastructure-only migration. REQUIRES the gcp-to-aws skill installed alongside this one — Assess is delegated entirely to it via a cross-skill invocation, with no standalone fallback if gcp-to-aws is absent.

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Tf Best Practices · awslabs bundle
Best-practice authoring guidance AND a read-only policy gate for AWS Terraform generated by a migration skill. Load during any phase that writes a terraform/ directory — first as the "what to emit" posture rules + security-baseline spec, then after writing as the deterministic policy verdict. Read-only: it reports whether the generated Terraform passes; it never edits .tf files, never touches .phase-status.json, and never decides phase completion. Complements (does not replace) terraform fmt/init/validate.

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Architect For Startups · awslabs bundle
Startup-tailored AWS architecture advice that adjusts recommendations to the company's stage (pre-revenue through Series B+), team size, runway, and available credits. Use when a founder wants guidance or a recommendation rather than code changes: which services to choose, how to plan or review an architecture, how to stretch credits and control cost, or how to prepare architecture for a fundraise or technical diligence. For an interactive discovery flow that scaffolds and writes the architecture into the codebase, use start-building-for-startups. For AI-agent runtime selection or agentic architecture recommendations specifically, use agent-advisor. Do not use for: writing or scaffolding code, factual AWS Activate / programs / credits lookups (see knowledge-base-for-startups), a single copy-paste prompt (see prompt-library-for-startups), or migration intent such as GCP-to-AWS or Heroku-to-AWS (see the migration skills: `gcp-to-aws`, `heroku-to-aws`, `llm-to-bedrock`).

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Knowledge Base For Startups · awslabs bundle
AWS 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. For copy-paste AI prompts (RAG chatbot, MVP scaffold, security baseline, GPU quota, etc.), see the prompt-library-for-startups skill. Do not use for: account-specific lookups (credits balance, Activate membership status, application status), real-time event listings beyond the events stub, or content not present in the bundled `references/` tree.

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Prompt Library For Startups · awslabs bundle
AWS-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, Bedrock Model Availability Agent, AWS DB Advisor). Use when the user asks for a prompt to do X on AWS, wants an installable agent for multi-account / cost monitoring / quota management / Bedrock model availability / database selection, or asks how to use AWS prompts. For migration intent (GCP to AWS, OpenAI/Gemini to Bedrock), route to the migration skills in this plugin (`gcp-to-aws`, `heroku-to-aws`, `llm-to-bedrock`). Do not use for: factual AWS Activate / programs / credits questions, learn articles, sample architectures, or for prompts that are not in the bundled `references/prompt-library/` tree.

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Start Building For Startups · awslabs
Interactive 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 not use for: factual lookups about AWS Activate / programs / credits, requests for a single copy-paste prompt, non-AWS architectural work, architecture advice/recommendations without code changes (see architect-for-startups), a new idea centered on an AI agent — runtime selection, agentic architecture, or an agent POC (see agent-advisor), or migrating an existing GCP/Heroku workload or an LLM SDK rewrite (see gcp-to-aws / heroku-to-aws / llm-to-bedrock).

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Agentcore Patterns · awslabs bundle
This skill should be used when an automated agent's output is published under its own identity and carries weight in someone else's work, such as a reviewer whose verdict lands on a pull request, and the open question is how much authority it has actually earned. Covers which conclusions such an agent may state as settled and which it must hand to a person, why a verdict that moves between runs on unchanged input cannot be allowed to decide anything, handing an uncertain item to a human rather than discarding it, grounding each claim in something computed rather than recalled, and the published-state and recall behaviour that decides whether people keep reading the output or learn to skim it. It should also be used when such an agent is being ignored, contradicts itself between runs, or reports a state that nothing current supports. Not for measuring or improving an agent's own output quality, which belongs to aws-agents:agents-optimize, nor for building, deploying, or hardening an agent.

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Multi Tenant Isolation · awslabs
This skill should be used when designing or fixing tenant isolation for a multi-tenant SaaS on AWS, where a team with no dedicated security engineer must make a cross-tenant leak structurally impossible rather than a code-review responsibility. Covers choosing a silo, pool, or bridge model per layer rather than per application, enforcing isolation in IAM session policies and the database so a forgotten predicate fails closed, partitioning data across DynamoDB, Postgres, and S3, containing noisy neighbors, and attributing cost per tenant in a pooled fleet. It should also be used when hardening an existing pooled deployment against cross-tenant access, or when one large customer demands dedicated infrastructure mid-deal. Not for single-tenant architecture or general IAM policy authoring, which belong to the aws-core skills upstream.

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Ml Researcher · awslabs bundle
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/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 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".

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Cheminformatics · awslabs
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".

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Risk Adjustment · awslabs
Pipeline 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", "hierarchy resolution code", "coding gap detection", "Rx proxy gap", "lab proxy gap", "risk score projection", "HCC pipeline", "member risk report", "risk adjustment Python", "risk adjustment SQL", "RAF calculation code", "CMS-HCC pipeline".

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Variant Calling · awslabs bundle
Germline and somatic short-variant calling pipeline for Illumina short-read data. Use when the user mentions BWA, BWA-MEM2, GATK, HaplotypeCaller, Mutect2, VCF, GVCF, VQSR, variant calling, germline, somatic, SNV, or indel calling from FASTQ/BAM.

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Cdisc Compliance · awslabs
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".

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Claims Analytics · awslabs
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".

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Dicom Processing · awslabs
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".

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Drug Repurposing · awslabs
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".

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Ehr Data Parsing · awslabs
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".

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Quality Measures · awslabs
Pipeline 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 claimant", "Charlson score", "LACE score", "claims analysis", "quality measure SQL".

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Rna Seq Analysis · awslabs bundle
Bulk RNA-seq analysis pipeline covering alignment (STAR), quantification (Salmon, featureCounts), and differential expression (DESeq2). Triggers on RNA-seq, STAR, Salmon, DESeq2, differential expression, gene expression, tximport, featureCounts, transcriptomics, "bulk RNA-seq", "STAR alignment", "Salmon quantification", "gene counts", "DESeq2 results", "shrinkage estimator", "volcano plot RNA-seq".

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Digital Pathology · awslabs bundle
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/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 slide-info inspection, tissue masking, tile extraction at specified mpp, foundation-model feature embedding, slide-level aggregation, and regulated cloud inference.

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Molecular Docking · awslabs
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".

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Pa Clinical Policy · awslabs
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/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", "clinical criteria", "PA denial", "drug authorization", "utilization management".

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Scrna Seq Pipeline · awslabs
Scanpy-based single-cell RNA-seq analysis pipeline covering loading (10X, h5ad), QC, normalization, HVG selection, PCA/UMAP, Leiden clustering, differential expression, and batch correction (Harmony, scVI). Use when the user mentions single-cell, scRNA-seq, Scanpy, AnnData, UMAP, clustering, 10X, h5ad, leiden, highly variable genes, Harmony, scVI, cluster cells, find marker genes, filter low-quality cells, gene expression matrix, doublet removal, normalize counts, dimensionality reduction, cell clustering, QC single-cell, filtered_feature_bc_matrix, cellranger output, 10X h5, scrublet, cell ranger, count matrix, find cell types, batch effect, integration, neighborhood graph, or differential expression genes.

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Biomarker Discovery · awslabs
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".

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Edc Data Validation · awslabs
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".

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Ngs Quality Control · awslabs bundle
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/WES coverage analysis.

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Rwd Cohort Analysis · awslabs
Real-world data cohort analysis pipeline for claims and EHR data — cohort identification with ICD-10, NDC, and CPT codes, medication adherence metrics (PDC/MPR), propensity score estimation and balance diagnostics, Kaplan-Meier survival curves, and Cox proportional hazards models. Use when the user mentions claims data, cohort identification, ICD-10 codes, NDC codes, CPT codes, medication adherence, PDC, MPR, propensity score, SMD balance, Kaplan-Meier, Cox model, Schoenfeld residuals, survival analysis on claims, or real-world evidence pipeline.

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Trajectory Analysis · awslabs
Single-cell trajectory inference pipeline covering diffusion pseudotime (DPT), PAGA, RNA velocity with scVelo, and fate mapping with CellRank. Use when the user mentions pseudotime, trajectory, lineage, differentiation, RNA velocity, scVelo, CellRank, PAGA, diffusion map, fate probabilities, terminal states, spliced/unspliced, velocyto, order cells by development, cell differentiation path, cell fate, branching analysis, monocle, developmental trajectory, stem cell differentiation, progenitor to mature, how do cells differentiate, cell lineage tree, velocity arrows, fate mapping, transition probabilities, root cells, terminal states, absorption probabilities, or latent time.

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Cell Type Annotation · awslabs
Generate 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", "majority voting CellTypist", "over-clustering annotation".

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Claims Billing Rules · awslabs
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".

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Imaging Study Design · awslabs
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".

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Multi Omics Pipeline · awslabs
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".

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Pharmacoepidemiology · awslabs
Reason 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 marginal structural models, assess unmeasured confounding with E-values, select an active comparator, define a new-user cohort, or evaluate a pharmacoepidemiology study design. Triggers include "active comparator", "new-user design", "target trial emulation", "immortal time bias", "propensity score matching", "IPTW", "marginal structural model", "confounding by indication", "E-value", "negative control outcomes", "quantitative bias analysis", "time-varying confounding", "prevalent user bias", "washout period", "landmark

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AWS Genai Ml Architect · awslabs bundle
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/PHI 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".

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Pa Decision Automation · awslabs
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".

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Provider Denial Workup · awslabs
Reasoning skill for healthcare revenue cycle denial management. Use when the user needs to appeal a claim denial, draft a payer-ready denial appeal letter, analyze a denial reason code, or determine whether a denied claim should be overturned, pended, or accepted. Triggers include "appeal a claim denial", "draft denial appeal", "write appeal letter", "CO-50 denial", "medical necessity denial", "CARC code appeal", "RARC code", "overturn denial", "prior auth denial", "timely filing denial", "denial workup", "payer denial response", "claim denial overturn", "CO-96 denial", "CO-97 denial", "denial letter", "denial management", "remittance advice denial", "835 denial", "authorization required denial", "ERISA denial", "Medicaid managed care denial", "visit limit denial", "benefit limit denial", "inpatient denial", "observation downgrade".

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Translational Research · awslabs bundle
Reason about translational research problems in HCLS — especially neurology and hypothesis validation. Use when the user asks to design a validation study, evaluate a target, qualify a biomarker, choose endpoints, critique a preclinical-to-clinical plan, pick a trial design (basket/umbrella/platform), plan multimodal data integration, or assess whether a hypothesis is ready to advance. Triggers include phrases like "validate target", "biomarker context of use", "bench to bedside", "T0/T1/T2/T3/T4", "proof of mechanism", "target engagement", "design a trial for", "preclinical model translates", "imaging-genetics", "is this ready for Phase 2", "mouse results to humans", "GWAS/eQTL/MR", "endpoint selection", "drug repurposing rationale", "fail fast".

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Clinical Data Standards · awslabs
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".

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Multi Omics Integration · awslabs
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".

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Quantitative Proteomics · awslabs
Reason 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 imputation", "MNAR", "MinProb", "QRILC", "kNN imputation", "VSN normalization", "median centering", "quantile normalization", "limma proteomics", "ratio compression", "proteomics study design", "label-free quantification", "tandem mass tag", "data-independent acquisition", "DIA-NN", "Spectronaut", "MaxQuant LFQ", "proteomics differential expression", "empirical Bayes proteomics", "proteinGroups.txt", "MSFragger output", "TMT normalization code", "LFQ analysis", "proteomics pipeline R", "MaxQuant output".

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Radiology Preprocessing · awslabs
Structural 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.

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Coordination Of Benefits · awslabs bundle
Reasoning skill for Coordination of Benefits (COB) determination in healthcare claims. Use when a claim involves multiple insurers and the user needs to know which plan pays first, how to sequence billing, or why a COB-related denial or underpayment occurred. Covers Medicare Secondary Payer (MSP) rules and NAIC commercial COB order-of-benefits logic. Triggers include "coordination of benefits", "COB", "Medicare secondary payer", "MSP", "which insurance is primary", "birthday rule", "primary payer", "secondary payer", "COB denial", "working aged", "ESRD coordination", "disability MSP", "workers comp primary", "no-fault primary", "auto insurance primary", "COB order of benefits", "dual coverage", "double coverage", "two insurances", "secondary billing", "crossover claim", "COB adjustment", "non-duplication", "benefits coordination error".

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Risk Adjustment Strategy · awslabs
Reasoning 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", "blended transition", "HCC mapping", "disease hierarchy", "RAF score", "risk adjustment", "coding gap", "HCC recapture", "chart review", "audit defensible", "risk score methodology", "Medicare Advantage risk", "capitation revenue", "hierarchical condition category".

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Protein Structure Analysis · awslabs
Pipeline 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.

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Hedis Measure Specification · awslabs
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/numerator/exclusion logic, continuous enrollment rules, Star Rating impact, or care gap closure strategies.

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Risk Stratification Indices · awslabs
Reasoning 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.

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Structure Based Drug Design · awslabs
Reasoning skill for structure-based drug design strategy. Use when the user asks to assess target druggability, choose a docking strategy, select a scoring function, define a binding site, interpret docking results, plan molecular dynamics or free energy perturbation (FEP), design a virtual screening cascade, or decide between fragment-based and HTS screening. Triggers include "drug design", "druggability", "docking strategy", "scoring function", "binding pocket", "molecular dynamics", "FEP", "SAR", "virtual screening", "induced fit", "allosteric site", "fragment-based drug discovery", "lead optimization", "MM-GBSA", "pharmacophore".

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Genomic Variant Interpretation · awslabs
Reason 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", "ClinVar", "gnomAD", "REVEL", "CADD", "SpliceAI", "PVS1", "loss of function", "nonsense variant", "missense interpretation", "splice variant", "filtering allele frequency", "ClinGen", "somatic variant tier".

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Medical Device Software Compliance · awslabs
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/QMSR quality system requirements, Design History File structure, 510(k)/De Novo/PMA pathway selection, SOUP/OTS assessment, verification and validation planning, cybersecurity for medical devices, PCCP for AI/ML 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 surveil

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Agent Squad Typescript · awslabs bundle
Use when building or modifying a Node.js / TypeScript app that uses the agent-squad npm package — multi-agent orchestration: orchestrator, agents (all built-in types + GroundedAgent), classifier routing (Bedrock / Anthropic / OpenAI), storage (in-memory / DynamoDB / SQL), retrievers (Amazon KB / Dakera), and tools (AgentTools + MCPToolProvider).

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Agent Squad Python · awslabs bundle
Use when building or modifying a Python app that uses the agent-squad Python package — async multi-agent orchestration for Python 3.11+: orchestrator, agents (BedrockLLMAgent, AnthropicAgent, OpenAIAgent, SupervisorAgent, GroundedAgent, ChainAgent, and more), classifier routing (Bedrock, Anthropic, OpenAI), storage (in-memory, DynamoDB, SQL/Turso), retrievers (Amazon KB, Dakera), tools (AgentTools, MCPToolProvider), and custom implementations.

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Agent Squad Swift · awslabs bundle
Use when building or modifying a Swift app that uses the AgentSquad Swift framework — on-device multi-agent orchestration for iOS 16+ / macOS 14+: orchestrator, agents (Agent, GroundedAgent), classifier routing, LLM clients (OpenAI-compatible), tools (native + MCP), tool UIs/widgets, on-device storage, tracing, and realtime voice — built-in types and custom implementations.

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Dsql · awslabs
Deprecated compatibility redirect for Aurora DSQL guidance. Use when a request concerns DSQL, Aurora DSQL, distributed SQL, DSQL schemas, migrations, queries, authentication, performance, or application development.

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73 published skills