
AWS Step Functions
OfficialFreeStreamline your workflow orchestration with ASL guidance.
Free · Opens the source repo
What AWS Step Functions does
AWS Step Functions is a powerful tool for defining state machines using Amazon States Language (ASL) in JSON format. This skill provides developers and designers with the necessary guidance to create and manage complex workflows, enabling the orchestration of microservices, automation of processes, and the building of data and machine learning pipelines. By leveraging ASL, users can define workflows that include various state types such as Task, Choice, Map, Parallel, and more, allowing for intricate control over the execution flow.
The skill covers essential aspects of ASL, including syntax, error handling, and service integrations. It provides detailed information on the eight available workflow states and offers examples for data transformation and architecture patterns. Additionally, it includes guidance on migrating from JSONPath to JSONata, which simplifies input and output handling within state machines. This makes it particularly useful for users who are looking to modernize their workflows or incorporate more advanced data manipulation techniques.
Whether you're building, debugging, or migrating a Step Functions state machine, this skill serves as a comprehensive reference. It is designed for developers who need to create multi-step workflows with features like branching, retries, and human-approval callbacks. The skill is also suitable for those looking to implement saga or compensation patterns in their applications, ensuring robust workflow management across various use cases.
While this skill is focused on AWS Step Functions, it is important to note that it should not be used for general Lambda function code or other AWS services like API Gateway or EventBridge. This specialization ensures that users receive targeted support for their state machine development needs, enhancing their ability to effectively utilize AWS services for workflow orchestration.
When to use it
Use this skill when developing, authoring, or debugging AWS Step Functions state machines or when orchestrating complex workflows that require branching and error handling.
When not to use it
This skill is not suitable for general-purpose AWS Lambda function code or for integrating with services like API Gateway or EventBridge.
What you can build with it
Building a Microservices Workflow
Use this skill to define a state machine that orchestrates multiple microservices, ensuring smooth communication and data flow between them.
Implementing Error Handling
Leverage the skill's guidance on error handling to create robust workflows that can gracefully handle failures and retries.
Migrating Existing Workflows
Utilize the migration guidance to transition your existing JSONPath-based workflows to the more efficient JSONata format.
How to install AWS Step Functions
View source1. Install with the skills CLI
npx skills add aws/agent-toolkit-for-aws/aws-step-functions --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by awsAWS Step Functions
Overview
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:
- ASL structure and JSONata expression syntax
- Details on the eight available workflow states
- The
$statesreserved variable - Workflow variables with
Assign - Error handling
- AWS Service integration patterns
- Example code for data transformation and architecture
- Validation and testing of state machines
- How to migrate from JSONPath to JSONata
The AWS MCP server is recommended for sandboxed execution and audit logging when following this skill, but all steps use AWS CLI syntax and work without it.
When to Load Reference Files
Load the appropriate reference file based on what the user is working on:
- ASL structure, state types, Task, Pass, Choice, Wait, Succeed, Fail, Parallel, Map → see
references/asl-state-types.md - Error handling, troubleshooting, Retry, Catch, fallback, error codes, States.Timeout, States.ALL → see
references/error-handling.md - Service integrations, Lambda invoke, DynamoDB, SNS, SQS, SDK integrations, Resource ARN, sync, async → see
references/service-integrations.md - Migrating from JSONPath to JSONata, migration, JSONPath to JSONata, InputPath, Parameters, ResultSelector, ResultPath, OutputPath, intrinsic functions, Iterator, payload template → see
references/migrating-from-jsonpath-to-jsonata.md - Validation, linting, testing, TestState, test state, mock, mocking, unit test, inspection level, DEBUG, TRACE, validate state, test in isolation → see
references/validation-and-testing.md - Architecture patterns, examples, polling, saga, compensation, scatter-gather, semaphore, lock, human-in-the-loop, escalation, Express to Standard → see
references/architecture-patterns.md - Data transformation, JSONata expressions, filtering, aggregation, string operations, $reduce, $lookup, $toMillis, $partition, $parse, $hash, $uuid → see
references/transforming-data.md - State input/output, $states, Assign, Output, Arguments, variable scope, variable limits, evaluation order, passing data between states → see
references/processing-state-inputs-and-outputs.md
Quick Reference
Standard vs Express Workflows
| 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.
When recommending Express, the single limitation you must always state — even for fire-and-forget / high-throughput pipelines — is that Express does NOT support
.syncor.waitForTaskToken(no callbacks, no nested.syncwaits, no human-approval or job-completion waits). Also note: 5-minute max duration, no queryable execution history (CloudWatch Logs only), and at-least-once (async) / at-most-once (sync) execution — so non-idempotent work can run twice. If any of these matter, choose Standard (exactly-once, up to 1 year, full history).
Setting the State Machine Query Language
JSONata is the 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 supported and is the default if QueryLanguage is omitted — existing state machines do not need to be migrated.
Field mapping (JSONPath → JSONata):
| JSONPath field | JSONata equivalent |
|---|---|
Parameters (keys use key.$) | Arguments — drop the .$ suffix and wrap each value in {% %} |
ResultSelector and OutputPath | Output (reference the raw result via $states.result) |
ResultPath | Assign (preferred) or Output |
InputPath | not needed — reference $states.input directly |
A state uses one query language, not both. Never mix JSONPath fields (
InputPath/Parameters/ResultSelector/ResultPath/OutputPath) with JSONata fields (Arguments/Output) in the same state — this is the most common migration error. Seereferences/migrating-from-jsonpath-to-jsonata.mdfor full details.
How Assign and Output Are Evaluated (Parallel, Not Sequential)
Within a single state, Assign and Output are evaluated at the same time — in parallel — both reading the same data (the state input plus the task result). They are NOT evaluated one after the other. Because they run together, a variable you set in Assign is not visible in that same state's Output: there is no ordering in which Output could observe the just-assigned value. The assigned value becomes available only to subsequent states.
So if you set a variable in Assign and reference it in the same state's Output, you get the old/undefined value — not because Output runs "before" Assign, but because both evaluate concurrently from the same snapshot. To use the value immediately, reference it in the next state (variables persist across states); to shape the current state's output from the task result, use $states.result directly in Output.
Unit Testing a State with TestState
Test a single state without deploying the state machine or calling the real service using the TestState API (aws stepfunctions test-state) with --mock. A complete answer covers all four points:
- Mock the service response exactly — the
--mockresultMUST match the target AWS service's API response schema exactly (field names are case-sensitive). For a LambdainvokeTask that isStatusCodeandPayload:--mock '{"result":"{\"StatusCode\":200,\"Payload\":{...}}"}'. - All three inspection levels (
--inspection-level):INFO(default —output,status,nextState),DEBUG(adds data flow:afterArguments,result,variables— use to debug JSONata/data flow),TRACE(adds raw HTTPrequest/response, for HTTP Task). .syncand.waitForTaskTokenintegrations still require a mock — for.sync, mock the polling API (e.g.DescribeExecution, not the initial call); for.waitForTaskToken, also pass--context '{"Task":{"Token":"..."}}'.- No deployment or real invocation is needed — the state is tested in isolation.
See references/validation-and-testing.md for per-service mock structures and error/retry/Map/Parallel testing.
Best Practices
- Set
"QueryLanguage": "JSONata"at the top level for new state machines unless the user wants to use JSONPath - Keep
Outputminimal — only include what the state immediately after the current state needs - Use
Assignto store variables needed in later states instead of threading it through Output - Use
$states.inputto reference original state input AssignandOutputare evaluated in parallel from the state's entry data, NOT sequentially — a variable set inAssignis therefore NOT visible in the same state'sOutput(which still sees the pre-Assignvalues); the new value takes effect only in the next state.- All JSONata expressions must produce a defined value —
$data.nonExistentFieldthrowsStates.QueryEvaluationError - Use
$states.context.Execution.Inputto access the original workflow input from any state - Save state machine definitions with
.asl.jsonextension when working outside the console - Prefer the optimized Lambda integration (
arn:aws:states:::lambda:invoke) over the SDK integration
Troubleshooting
Common Errors
States.QueryEvaluationError— JSONata expression failed. Check for type errors, undefined fields, or out-of-range values.- Mixing JSONPath fields with JSONata fields in the same state.
- Using
$or$$at the top level of a JSONata expression — use$states.inputinstead. - Forgetting
{% %}delimiters around JSONata expressions — the string will be treated as a literal. - Assigning variables in
Assignand expecting them inOutputof the same state — new values only take effect in the next state. - Reference references/validation-and-testing.md and references/error-handling.md for detailed troubleshooting information.
Security Considerations
- Least-privilege execution role. Scope the state machine's IAM role to the specific resources and actions it invokes (specific Lambda/DynamoDB/SQS/SNS ARNs). Avoid
*FullAccesspolicies andservice:*wildcards. - Encryption. Recommend encryption at rest and in transit for every data store a workflow touches: KMS-encrypted DynamoDB tables, server-side encryption (
KmsMasterKeyId) on SQS queues and SNS topics, and TLS for HTTP Tasks. - Task tokens and message bodies are sensitive. A
.waitForTaskTokentoken is a credential — treat it as a secret. Do not place PII, financial data, or secrets in SQS/SNS message bodies or notifications; pass a reference ID and have recipients look up details through an authorized channel. - Validate input and fail fast. Validate required fields at the start of the workflow with a Choice (or Pass) state using
$exists()and$type(), and route invalid input to a Fail state so malformed data never reaches downstream states. Protect downstream services from bursts by settingMaxConcurrencyon Map states and throttling upstream (StartExecution rate limits or EventBridge). - Cross-account access. When using the
Credentialsfield to assume a role in another account, include condition keys such asaws:SourceArnoraws:SourceAccountin the target role's trust policy to prevent unintended assumption. - External secrets. For HTTP Tasks calling third-party APIs, store API keys and tokens in AWS Secrets Manager (referenced via an EventBridge connection), never embedded in the state machine definition.
- Observability. Enable CloudWatch Logs for executions (log level
ALLorERROR; required for Express workflows, which have no queryable execution history), enable CloudTrail to audit Step Functions API calls, and set CloudWatch Alarms on execution failures. Always encrypt the execution log group with a customer-managed KMS key, since state input/output routinely flows through execution logs.
Resources
Frequently asked questions about AWS Step Functions
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