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npx skills add https://github.com/awslabs/agent-plugins --skill aws-lambda-durable-functions
Build resilient multi-step applications and AI workflows that can execute for up to 1 year while maintaining reliable progress despite interruptions.
Before using AWS Lambda durable functions, verify:
aws --version
aws sts get-caller-identity
node --version)python --version. Note that currently only Lambda runtime environments 3.13+ come with the Durable Execution SDK pre-installed. 3.11 is the min supported Python version by the Durable SDK itself, however, you could use OCI to bring your own container image with your own Python runtime + Durable SDK.)sam --version) 1.153.1 or highercdk --version) v2.237.1 or higherDefault: TypeScript
Override syntax:
When not specified, ALWAYS use TypeScript
Default: CDK
Override syntax:
When not specified, ALWAYS use CDK
For TypeScript/JavaScript:
npm install @aws/durable-execution-sdk-js
npm install --save-dev @aws/durable-execution-sdk-js-testing
For Python:
pip install aws-durable-execution-sdk-python
pip install aws-durable-execution-sdk-python-testing
Load the appropriate reference file based on what the user is working on:
TypeScript:
import { withDurableExecution, DurableContext } from '@aws/durable-execution-sdk-js';
export const handler = withDurableExecution(async (event, context: DurableContext) => {
const result = await context.step('process', async () => processData(event));
return result;
});
Python:
from aws_durable_execution_sdk_python import durable_execution, DurableContext
@durable_execution
def handler(event: dict, context: DurableContext) -> dict:
result = context.step(lambda _: process_data(event), name='process')
return result
runInChildContext to group operationscontext.logger (replay-aware)The Python SDK differs from TypeScript in several key areas:
@durable_step decorator + context.step(my_step(args)), or inline context.step(lambda _: ..., name='...'). Prefer the decorator for automatic step naming.context.wait(duration=Duration.from_seconds(n), name='...')ExecutionError (permanent), InvocationError (transient), CallbackError (callback failures)DurableFunctionTestRunner class directly - instantiate with handler, use context manager, call run(input=...)Durable functions require qualified ARNs (version, alias, or $LATEST):
# Valid
aws lambda invoke --function-name my-function:1 output.json
aws lambda invoke --function-name my-function:prod output.json
# Invalid - will fail
aws lambda invoke --function-name my-function output.json
Your Lambda execution role MUST have the AWSLambdaBasicDurableExecutionRolePolicy managed policy attached. This includes:
lambda:CheckpointDurableExecution - Persist execution statelambda:GetDurableExecutionState - Retrieve execution stateAdditional permissions needed for:
lambda:InvokeFunction on target function ARNslambda:SendDurableExecutionCallbackSuccess and lambda:SendDurableExecutionCallbackFailureWhen writing or reviewing durable function code, ALWAYS check for these replay model violations:
Date.now(), Math.random(), UUID generation, API calls, database queries must all be inside stepscontext.step(), context.wait(), or context.invoke() inside a step function — use context.runInChildContext() insteadcontext.logger for logging (it is replay-aware and deduplicates automatically)When implementing or modifying tests for durable functions, ALWAYS verify:
LocalDurableTestRunner for local testingWrite access is enabled by default. The plugin ships with --allow-write in .mcp.json, so the MCP server can create projects, generate IaC, and deploy on behalf of the user.
Access to sensitive data (like Lambda and API Gateway logs) is not enabled by default. To grant it, add --allow-sensitive-data-access to .mcp.json.
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take awslabs/aws-lambda-durable-functions from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip, npm.
Without those the skill loads but fails at the first command.