Amazon Braket
Runs quantum computing workflows on AWS through Amazon Braket — discovering devices (QPUs and simulators) and their availability, building gate-model circuits and analog Hamiltonian programs, submitting quantum tasks, program sets and hybrid jobs, looking up prices, and capping spend with spending limits. Applies to any request about quantum computing, quantum hardware, quantum simulation, AHS, OpenQASM, or running a quantum algorithm on AWS.
- Skill ID
- aws/agent-toolkit-for-aws/amazon-braket
- Publisher
- aws
- Repository
- agent-toolkit-for-aws
- Installs
- 0
- Files
- 6
- Synced
- Sep 23, 2026
Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.
aws/agent-toolkit-for-aws/amazon-braketInstalls these files- SKILL.md
- references/devices.md
- references/hybrid-job.md
- references/pricing.md
- references/program-sets.md
- references/spending-limit.md
What this skill tells the agent
Amazon Braket
Primitives
The vocabulary of a Braket workflow, and which reference to open for each.
| Primitive | What it is | Related References | Open it when the request involves |
|---|---|---|---|
| Device | A simulator or QPU, identified by a region-scoped ARN | devices.md | anything about a device: what exists, discovering or filtering the fleet, availability and status (online, offline, retired), which region a device lives in, ARNs, choosing a device for a workload, qubit count, connectivity or topology, native gates, fidelities, calibration data, queue depth, shot and gate limits, paradigm (gate-model vs analog Hamiltonian simulation), whether a device supports program sets, pulse-level control, simulators and local emulators |
| Program | The workload/input — one executable (Circuit, AHS, OpenQASM). Which type is legal depends on the device's paradigm | - | - |
| Quantum task | One program + shots, run once (the atomic unit Braket meters) | - | - |
| Task batch | Many independent tasks — SDK-only fallback for when a program set does not fit; works on all devices | program-sets.md | see the Program set row; also running multiple programs |
| Program set | Many programs in one service-side task — preferred way to run multiple programs instead of task batch | program-sets.md | running more than one program: parameter sweeps, scanning parameter values, task batches, run_batch, several circuits submitted together, attaching observables across programs, and minimizing per-task fees when many programs run, program sets. Also getting started with program sets |
| Hybrid job | Managed classical-quantum loop that orchestrates many tasks | hybrid-job.md | hybrid jobs: @hybrid_job, algorithm scripts and source modules, entry_point, embedded simulators, BYOC and custom container images, CUDA-Q, job execution roles, hyperparameters, checkpoints, and retrieving job results |
| Spending limit | Service-side hard cap that rejects QPU tasks — the only true enforcement (not SDK) | spending-limit.md | capping or enforcing spend: spending limits (create, update, delete, search), and cost guardrails |
| Cost tracking | In-session cost estimate (not enforcement) | spending-limit.md | in-session cost tracking with Tracker |
Each reference carries the domain detail for its own area — field paths, key names, API shapes, and billing models.
Additional notes:
- Reservation — exclusive device access for a booked window. This skill covers reservations at pointer depth only: the billing model is in pricing.md, reservation-specific device limits such as
service.reservationShotsRangeare in devices.md, and the full model is in the reservations developer guide. - Gate calibrations / pulse control — access native gate calibrations on QPUs and attach custom pulse sequences at run time. See devices.md for detecting support, and the pulse control developer guide for the full model.
Critical Rules
These rules apply to every Braket request, whatever it involves.
- The Amazon Braket Python SDK (`pip install amazon-braket-sdk`, imported as `braket`) is the primary entry point. Prefer it for every operation including Braket API operations, and understand what it covers by reading the docs or inspecting the SDK's modules locally.
- Use local execution tools for running the Braket SDK, such as
shellwithpython3 -c "<code>".
- The AWS MCP server is recommended for executing any other AWS API calls in this skill, especially operations not present in the Python SDK, although not required. Note: the AWS MCP's
run_scripttool executes code in a minimal sandbox without Braket libraries, so prefer using other tools for code execution, especially when using the Braket SDK.
- A small set of primitives composes every workflow — see Primitives for the vocabulary and the reference for each.
- Devices, quantum tasks, and hybrid jobs are region-scoped, so fan out across every Braket region whenever you use the API, CLI, or boto3 to search for resources. The SDK handles fanout for you where it can —
AwsDevice.get_devicessearches QPUs in all regions.
Get the list of regions Braket supports from aws___get_regional_availability when the AWS MCP server is available, or from the supported devices and regions documentation. Resource ARNs containing a region may only be queried from that same region, otherwise you will see a ResourceNotFoundException.
- Open the matching reference before you write code or answer. Use the Primitives table to find and read references. Note: A request that asks for several things, e.g. executing a series of circuits and controlling the cost thereof, may require reading multiple reference files.
- Verify, never recall. Device ARNs and statuses, API signatures, supported features, and prices (and other values) all change and may post-date training data.
- Confirm an SDK signature before you write code that calls it. Read it from the SDK reference docs.
If no available tool can reach them, get it from the installed SDK with shell:
PAGER=cat python -c "import braket; help(braket)" # subpackages: ahs, circuits, pulse, program_sets, ...
python -c "import braket.ahs; print(dir(braket.ahs))" # names: DrivingField, AtomArrangement, ...
python -c "import inspect; from braket.ahs import DrivingField; \
print(inspect.signature(DrivingField.from_lists)); print(inspect.getdoc(DrivingField.from_lists))"- Attribute skill usage. In every Python script that uses the Braket SDK, insert this block before any Braket SDK code, including
import braket:
import botocore
def _braket_attribution(session):
session.user_agent_extra = f"{session.user_agent_extra} AWSSkill-Braket/1.0.0".strip()
botocore.register_initializer(_braket_attribution)If using the AWS CLI, prepend this skill's identifier to AWS_SDK_UA_APP_ID. The variable holds one value and other tools also write it, so always append the existing value instead of replacing it:
# sh, bash, zsh, ksh, fish 3.1+
AWS_SDK_UA_APP_ID="AWSSkill-Braket-$AWS_SDK_UA_APP_ID" aws braket ... # PowerShell — session-scoped
if ($env:AWS_SDK_UA_APP_ID -notlike 'AWSSkill-Braket*') {
$env:AWS_SDK_UA_APP_ID = "AWSSkill-Braket-$env:AWS_SDK_UA_APP_ID"
}This marks AWS calls as originating from use with this skill. This is not needed when using AWS MCP tools as the AWS MCP automatically attributes during tool calls.
Guardrail — where this skill's own files live (MCP vs local install)
This skill can be loaded two ways, and they resolve the skill's own bundled files from different places. Determine how the skill was loaded before reading a reference:
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Use this skill in a project
Open any RiverX project, search the Skills panel for aws/agent-toolkit-for-aws/amazon-braket, and install it in one click.
