mcpbeat Sign in

AWS Lambda Durable Functions Agent Skill

>

31k tokens
context cost
the whole folder, loaded on every use
12
files
instructions only
0
copies elsewhere
how many repositories repackaged it
850
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/awslabs/agent-plugins --skill aws-lambda-durable-functions

The instruction itself

21 sections, as written by the author

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.

Onboarding

Step 1: Validate Prerequisites

Before using AWS Lambda durable functions, verify:

  • AWS CLI is installed (2.33.22 or higher) and configured:
   aws --version
   aws sts get-caller-identity
  • Runtime environment is ready:
  • For TypeScript/JavaScript: Node.js 22+ (node --version)
  • For Python: Python 3.11+ (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.)
  • Deployment capability exists (one of):
  • AWS SAM CLI (sam --version) 1.153.1 or higher
  • AWS CDK (cdk --version) v2.237.1 or higher
  • Direct Lambda deployment access

Step 2: Select language and IaC framework

Language Selection

Default: TypeScript

Override syntax:

  • "use Python" → Generate Python code
  • "use JavaScript" → Generate JavaScript code

When not specified, ALWAYS use TypeScript

IaC framework selection

Default: CDK

Override syntax:

  • "use CloudFormation" → Generate YAML templates
  • "use SAM" → Generate YAML templates

When not specified, ALWAYS use CDK

Error Scenarios

Unsupported Language
  • List detected language
  • State: "Durable Execution SDK is not yet available for [framework]"
  • Suggest supported languages as alternatives
Unsupported IaC Framework
  • List detected framework
  • State: "[framework] might not support Lambda durable functions yet"
  • Suggest supported frameworks as alternatives

Serverless MCP Server Unavailable

  • Inform user: "AWS Serverless MCP not responding"
  • Ask: "Proceed without MCP support?"
  • DO NOT continue without user confirmation

Step 3: Install SDK

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

When to Load Reference Files

Load the appropriate reference file based on what the user is working on:

  • Getting started, basic setup, example, ESLint, or Jest setup -> see getting-started.md
  • Understanding replay model, determinism, or non-deterministic errors -> see replay-model-rules.md
  • Creating steps, atomic operations, or retry logic -> see step-operations.md
  • Waiting, delays, callbacks, external systems, or polling -> see wait-operations.md
  • Parallel execution, map operations, batch processing, or concurrency -> see concurrent-operations.md
  • Error handling, retry strategies, saga pattern, or compensating transactions -> see error-handling.md
  • Advanced error handling, timeout handling, circuit breakers, or conditional retries -> see advanced-error-handling.md
  • Testing, local testing, cloud testing, test runner, or flaky tests -> see testing-patterns.md
  • Deployment, CloudFormation, CDK, SAM, log groups, deploy, or infrastructure -> see deployment-iac.md
  • Advanced patterns, GenAI agents, completion policies, step semantics, or custom serialization -> see advanced-patterns.md
  • troubleshooting, stuck execution, failed execution, debug execution ID, execution history, execution error, why did my execution fail, execution timed out, callback not received, diagnose execution, or root cause execution -> see troubleshooting-executions.md

Quick Reference

Basic Handler Pattern

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

Critical Rules

  • All non-deterministic code MUST be in steps (Date.now, Math.random, API calls)
  • Cannot nest durable operations - use runInChildContext to group operations
  • Closure mutations are lost on replay - return values from steps
  • Side effects outside steps repeat - use context.logger (replay-aware)

Python API Differences

The Python SDK differs from TypeScript in several key areas:

  • Steps: Use @durable_step decorator + context.step(my_step(args)), or inline context.step(lambda _: ..., name='...'). Prefer the decorator for automatic step naming.
  • Wait: context.wait(duration=Duration.from_seconds(n), name='...')
  • Exceptions: ExecutionError (permanent), InvocationError (transient), CallbackError (callback failures)
  • Testing: Use DurableFunctionTestRunner class directly - instantiate with handler, use context manager, call run(input=...)

Invocation Requirements

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

IAM Permissions

Your Lambda execution role MUST have the AWSLambdaBasicDurableExecutionRolePolicy managed policy attached. This includes:

  • lambda:CheckpointDurableExecution - Persist execution state
  • lambda:GetDurableExecutionState - Retrieve execution state
  • CloudWatch Logs permissions

Additional permissions needed for:

  • Durable invokes: lambda:InvokeFunction on target function ARNs
  • External callbacks: Systems need lambda:SendDurableExecutionCallbackSuccess and lambda:SendDurableExecutionCallbackFailure

Validation Guidelines

When writing or reviewing durable function code, ALWAYS check for these replay model violations:

  • Non-deterministic code outside steps: Date.now(), Math.random(), UUID generation, API calls, database queries must all be inside steps
  • Nested durable operations in step functions: Cannot call context.step(), context.wait(), or context.invoke() inside a step function — use context.runInChildContext() instead
  • Closure mutations that won't persist: Variables mutated inside steps are NOT preserved across replays — return values from steps instead
  • Side effects outside steps that repeat on replay: Use context.logger for logging (it is replay-aware and deduplicates automatically)

When implementing or modifying tests for durable functions, ALWAYS verify:

  • All operations have descriptive names
  • Tests get operations by NAME, never by index
  • Replay behavior is tested with multiple invocations
  • Use LocalDurableTestRunner for local testing

MCP Server Configuration

Write 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.

Resources

Other skills for the same job

different authors, same section of the catalogue
Azure Kubernetes Automatic Readiness
by microsoft
vendor ×3

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.

13k tokens
Capacity
by microsoft
vendor ×3

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.

6k tokens scripts
Customize
by microsoft
vendor ×3

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).

8k tokens
Deploy Model
by microsoft
vendor ×3

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).

26k tokens scripts
Preset
by microsoft
vendor ×3

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).

9k tokens
Lamindb
by christophacham
×3

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.

22k tokens
Latchbio Integration
by christophacham
×3

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

12k tokens
Modal
by christophacham
×3

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.

17k tokens

How to use it

Copy the folder

Take awslabs/aws-lambda-durable-functions from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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.

Install what it needs

The instructions reference pip, npm. Without those the skill loads but fails at the first command.