Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/awslabs/agent-plugins/aws-step-functionsnpx skills add awslabs/agent-plugins --skill aws-step-functionsgit clone --depth 1 https://github.com/awslabs/agent-pluginsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00057 | $0.01617 |
| Opus 5 | $0.00028 | $0.00809 |
| Sonnet 5 | $0.00011 | $0.00323 |
| Haiku 4.5 | $0.00006 | $0.00162 |
Grade A, and why
aws-step-functions scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
$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
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
- Deployment, SAM, CloudFormation, IaC, DefinitionSubstitutions, X-Ray tracing, logging → see the aws-serverless-deployment skill or deploy-on-aws plugin
What ships with it
15 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/compensation-saga-pattern.asl.json 3.5 KB
- examples/express-standard-handoff.asl.json 2.2 KB
- examples/human-in-the-loop-with-timeout-escalation.asl.json 3.4 KB
- examples/nested-map-parallel-structures.asl.json 2.0 KB
- examples/polling-loop-wait-check-choice.asl.json 2.0 KB
- examples/scatter-gather-with-partial-results.asl.json 2.2 KB
- examples/semaphore-concurrency-lock.asl.json 3.3 KB
- references/architecture-patterns.md 3.8 KB
- references/asl-state-types.md 12 KB
- references/error-handling.md 6.9 KB
- references/migrating-from-jsonpath-to-jsonata.md 13 KB
- references/processing-state-inputs-and-outputs.md 11 KB
- references/service-integrations.md 6.4 KB
- references/transforming-data.md 6.2 KB
- references/validation-and-testing.md 11 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 103 lines · 57 tokens per session scan A 179fc933ff2b
aws-step-functions is a skill published in the GitHub repository awslabs/agent-plugins (876 stars, last pushed 6d ago), licensed Apache-2.0. It adds 57 tokens to every session and 1,617 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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