plugins/aws-serverless/skills/aws-step-functions/SKILL.md
Build workflows with AWS Step Functions state machines using the JSONata query language. Covers Amazon States Language (ASL) structure, state types, variables, data transformation, error handling, AWS service integration, and migrating from the JSONPath to the JSONata query language.
npx skillsauth add awslabs/agent-plugins aws-step-functionsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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AWS Step Functions uses Amazon States Language (ASL) to define state machines as JSON. With AWS Step Functions, you can create workflows, also called State machines, to build distributed applications, automate processes, orchestrate microservices, and create data and machine learning pipelines.
This skill provides comprehensive guidance for writing state machines in ASL, covering:
$states reserved variableAssignLoad the appropriate reference file based on what the user is working on:
| | Standard | Express |
| --------------------------------- | ------------------------------------ | ------------------------------------------- |
| Max duration | 1 year | 5 minutes |
| Execution semantics | Exactly-once | At-least-once (async) / At-most-once (sync) |
| Execution history | Retained 90 days, queryable via API | CloudWatch Logs only |
| Max throughput | 2,000 exec/sec | 100,000 exec/sec |
| Pricing model | Per state transition | Per execution count + duration |
| .sync / .waitForTaskToken | Supported | Not supported |
| Best for | Auditable, non-idempotent operations | High-volume, idempotent event processing |
Choose Standard for: payment processing, order fulfillment, compliance workflows, anything that must never execute twice.
Choose Express for: IoT data ingestion, streaming transformations, mobile backends, high-throughput short-lived processing.
JSONata is the modern, preferred way to reference and transform data in ASL. It replaces the five JSONPath I/O fields (InputPath, Parameters, ResultSelector, ResultPath, OutputPath) with just two: Arguments (inputs) and Output.
Enable at the top level to apply to all states:
{ "QueryLanguage": "JSONata", "StartAt": "...", "States": {...} }
Or per-state to migrate from JSONPath incrementally:
{ "Type": "Task", "QueryLanguage": "JSONata", ... }
JSONPath is still supported and is the default if QueryLanguage is omitted — existing state machines do not need to be migrated.
"QueryLanguage": "JSONata" at the top level for new state machines unless the user wants to use JSONPathOutput minimal — only include what the state immediately after the current state needsAssign to store variables needed in later states instead of threading it through Output$states.input to reference original state inputAssign and Output are evaluated in parallel — variable assignments in Assign are NOT available in Output of the same state$data.nonExistentField throws States.QueryEvaluationError$states.context.Execution.Input to access the original workflow input from any state.asl.json extension when working outside the consolearn:aws:states:::lambda:invoke) over the SDK integrationStates.QueryEvaluationError — JSONata expression failed. Check for type errors, undefined fields, or out-of-range values.$ or $$ at the top level of a JSONata expression — use $states.input instead.{% %} delimiters around JSONata expressions — the string will be treated as a literal.Assign and expecting them in Output of the same state — new values only take effect in the next state.tools
Design, build, deploy, test, and debug serverless applications with AWS Lambda. Triggers on phrases like: Lambda function, event source, serverless application, API Gateway, EventBridge, Step Functions, serverless API, event-driven architecture, Lambda trigger. For deploying non-serverless apps to AWS, use deploy-on-aws plugin instead.
development
Validates the user's environment for SageMaker AI operations — checks SDK version, AWS region, and execution role. Use when the user says "set up", "getting started", "check my environment", "configure SDK", or as the first step in any plan involving SageMaker/Bedrock training, evaluation, or deployment.
data-ai
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.
testing
Selects a fine-tuning technique (SFT, DPO, RLVR, or RLAIF) for the user's use case and validates it against the selected model's available recipes. Use when the user has decided to finetune and needs to choose a technique, or when the technique needs to be validated against a model. Requires a base model to already be selected (via model-selection skill).