Aws Step Functions logo

Aws Step Functions

Organization
awslabs
aws-step-functions

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.

Overview

Publisherawslabs
Repositoryagent-plugins
Skill nameaws-step-functions
Stars
917
Forks
162
Bundled files
15
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 15 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by awslabs on GitHub. Read the source before you install it.

Installation

Install the Aws Step Functions AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/awslabs/agent-plugins/tree/main/plugins/aws-serverless/skills/aws-step-functions
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/awslabs/agent-plugins.git /tmp/agent-plugins
mkdir -p .claude/skills
cp -r /tmp/agent-plugins/plugins/aws-serverless/skills/aws-step-functions .claude/skills/aws-step-functions
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aws Step Functions in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Aws Step Functions on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Aws Step Functions is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AWS 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 $states reserved 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

When to Load Reference Files

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

Quick Reference

Standard vs Express Workflows

StandardExpress
Max duration1 year5 minutes
Execution semanticsExactly-onceAt-least-once (async) / At-most-once (sync)
Execution historyRetained 90 days, queryable via APICloudWatch Logs only
Max throughput2,000 exec/sec100,000 exec/sec
Pricing modelPer state transitionPer execution count + duration
.sync / .waitForTaskTokenSupportedNot supported
Best forAuditable, non-idempotent operationsHigh-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.

Setting the State Machine Query Language

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:

json
{ "QueryLanguage": "JSONata", "StartAt": "...", "States": {...} }

Or per-state to migrate from JSONPath incrementally:

json
{ "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.

Best Practices

  • Set "QueryLanguage": "JSONata" at the top level for new state machines unless the user wants to use JSONPath
  • Keep Output minimal — only include what the state immediately after the current state needs
  • Use Assign to store variables needed in later states instead of threading it through Output
  • Use $states.input to reference original state input
  • Remember: Assign and Output are evaluated in parallel — variable assignments in Assign are NOT available in Output of the same state
  • All JSONata expressions must produce a defined value — $data.nonExistentField throws States.QueryEvaluationError
  • Use $states.context.Execution.Input to access the original workflow input from any state
  • Save state machine definitions with .asl.json extension 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.input instead.
  • Forgetting {% %} delimiters around JSONata expressions — the string will be treated as a literal.
  • Assigning variables in Assign and expecting them in Output of 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.

Resources

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Aws Step Functions AI skill do?

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.

Why use Aws Step Functions on TypingMind?

Because you install it once and use it with any model. Aws Step Functions is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Aws Step Functions in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/awslabs/agent-plugins/tree/main/plugins/aws-serverless/skills/aws-step-functions. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Aws Step Functions?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Aws Step Functions?

As many as you like. As long as a model supports skills, you can use Aws Step Functions with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Aws Step Functions AI skill free?

Yes. It is published on GitHub by awslabs under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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