itsmostafa/aws-agent-skills/bedrock

Name: bedrock
Author: itsmostafa

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AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns.

Install

npx vskill@latest install itsmostafa/aws-agent-skills/bedrock

SKILL.md

AWS Bedrock

Amazon Bedrock provides access to foundation models (FMs) from AI companies through a unified API. Build generative AI applications with text generation, embeddings, and image generation capabilities.

Core Concepts
Common Patterns
CLI Reference
Best Practices
Troubleshooting
References

Core Concepts

Foundation Models

Pre-trained models available through Bedrock:

Claude (Anthropic): Text generation, analysis, coding
Nova / Titan (Amazon): Text, multimodal, embeddings
GPT / gpt-oss (OpenAI): Text generation, reasoning
Llama (Meta): Open-weight text generation
Mistral: Efficient text generation
Stable Image (Stability AI): Image generation and editing

Model Access

In commercial Regions, access to all serverless models is enabled by default (no console opt-in). In GovCloud (US), models are still enabled manually on the Model access page (third-party models also in the linked commercial account):

First invocation of a third-party model auto-subscribes via AWS Marketplace (up to 15 min); caller needs aws-marketplace:Subscribe, Unsubscribe, ViewSubscriptions
Anthropic models on bedrock-runtime need a one-time use case form per account/org (put-use-case-for-model-access)
Invoking implies EULA acceptance; to block a model, deny both bedrock:InvokeModel and bedrock:InvokeModelWithResponseStream on it (SCP/IAM); streaming APIs such as ConverseStream use the latter. Denying aws-marketplace:Subscribe alone does not block first use

Endpoints

Endpoint	APIs	Use for
`bedrock-runtime.{region}.amazonaws.com` (recommended)	InvokeModel, Converse, Anthropic Messages (`/anthropic`), OpenAI Responses/Chat Completions (`/openai/v1`)	Guardrails, cross-Region inference, prompt routing, application inference profiles
`bedrock-mantle.{region}.api.aws`	OpenAI Responses/Chat Completions (`/openai/v1`), Anthropic Messages	Server-side tools (Web Search), `background=true` async, Projects/Workspaces, single-Region access to CRIS-only models

Same per-token price on both; auth via SigV4 or Bedrock API key (AWS_BEARER_TOKEN_BEDROCK)
IAM: bedrock:InvokeModel (runtime) vs bedrock-mantle:CreateInference (mantle)
Responses API on bedrock-runtime is synchronous only and has no server-side tools

Inference Profiles and Model Lifecycle

Newer models (e.g. Claude Sonnet 5) have no in-Region on-demand ID on bedrock-runtime: use a geo (us., eu., au.) or global. inference profile ID as modelId
Lifecycle is Active -> Legacy -> EOL (see modelLifecycle in get-foundation-model). Legacy: no new Provisioned Throughput, fine-tuning, or quota increases; EOL: requests fail
Model cards list an "EOL no sooner than" date; check before pinning a model ID

Knowledge Bases and Agents

Managed knowledge bases (type: MANAGED): Bedrock runs storage, indexing, and retrieval. Only type that supports AgenticRetrieveStream (query decomposition, iterative retrieval, optional AgentCore Memory via memoryConfiguration)
Native multimodal managed KBs embed video/audio/image directly with TwelveLabs Marengo Embed 3.0 (twelvelabs.marengo-embed-3-0-v1:0); query with text via Retrieve only (no RetrieveAndGenerate)
Bedrock Agents Classic is in maintenance mode: closed to new accounts since July 30, 2026 (CreateAgent/InvokeInlineAgent return 403 without prior 12-month usage), model catalog frozen. Build new agents on Amazon Bedrock AgentCore

Inference Types

Type	Use Case	Pricing
On-Demand	Variable workloads	Per token
Provisioned Throughput	Consistent high-volume	Hourly commitment
Batch Inference	Async large-scale	Discounted per token

Common Patterns

Invoke Model (Text Generation)

AWS CLI:

bash

# Invoke Claude
aws bedrock-runtime invoke-model \
  --model-id us.anthropic.claude-sonnet-5 \
  --content-type application/json \
  --accept application/json \
  --cli-binary-format raw-in-base64-out \
  --body '{
    "anthropic_version": "bedrock-2023-05-31",
    "max_tokens": 4096,
    "messages": [
      {"role": "user", "content": "Explain AWS Lambda in 3 sentences."}
    ]
  }' \
  response.json
 
# Claude Sonnet 5/Opus 5 think by default: content may start with a thinking block
cat response.json | jq -r '.content[] | select(.type=="text") | .text'

boto3:

python

import boto3
import json
bedrock = boto3.client('bedrock-runtime')
def invoke_claude(prompt, max_tokens=4096):
    response = bedrock.invoke_model(
        modelId='us.anthropic.claude-sonnet-5',
        contentType='application/json',
        accept='application/json',
        body=json.dumps({
            'anthropic_version': 'bedrock-2023-05-31',
            'max_tokens': max_tokens,
            'messages': [
                {'role': 'user', 'content': prompt}
            ]
        })
    )
    result = json.loads(response['body'].read())
    # Skip thinking blocks (adaptive thinking is on by default for Sonnet 5).
    # max_tokens caps thinking + text, so a truncated response may have no text block.
    if result['stop_reason'] == 'max_tokens':
        print('Truncated at max_tokens: raise it or lower output_config.effort')
    return next((b['text'] for b in result['content'] if b['type'] == 'text'), '')
# Usage
response = invoke_claude('What is Amazon S3?')
print(response)

Streaming Response

python

import boto3
import json
bedrock = boto3.client('bedrock-runtime')
def stream_claude(prompt):
    response = bedrock.invoke_model_with_response_stream(
        modelId='us.anthropic.claude-sonnet-5',
        contentType='application/json',
        accept='application/json',
        body=json.dumps({
            'anthropic_version': 'bedrock-2023-05-31',
            'max_tokens': 4096,
            'messages': [
                {'role': 'user', 'content': prompt}
            ]
        })
    )
    for event in response['body']:
        chunk = json.loads(event['chunk']['bytes'])
        if chunk['type'] == 'content_block_delta':
            yield chunk['delta'].get('text', '')
# Usage
for text in stream_claude('Write a haiku about cloud computing.'):
    print(text, end='', flush=True)

Generate Embeddings

python

import boto3
import json
bedrock = boto3.client('bedrock-runtime')
def get_embedding(text):
    response = bedrock.invoke_model(
        modelId='amazon.titan-embed-text-v2:0',
        contentType='application/json',
        accept='application/json',
        body=json.dumps({
            'inputText': text,
            'dimensions': 1024,
            'normalize': True
        })
    )
    result = json.loads(response['body'].read())
    return result['embedding']
# Usage
embedding = get_embedding('AWS Lambda is a serverless compute service.')
print(f'Embedding dimension: {len(embedding)}')

Conversation with History

python

import boto3
import json
bedrock = boto3.client('bedrock-runtime')
class Conversation:
    def __init__(self, system_prompt=None):
        self.messages = []
        self.system = system_prompt
    def chat(self, user_message):
        self.messages.append({
            'role': 'user',
            'content': user_message
        })
        body = {
            'anthropic_version': 'bedrock-2023-05-31',
            'max_tokens': 4096,
            'messages': self.messages
        }
        if self.system:
            body['system'] = self.system
        response = bedrock.invoke_model(
            modelId='us.anthropic.claude-sonnet-5',
            contentType='application/json',
            accept='application/json',
            body=json.dumps(body)
        )
        result = json.loads(response['body'].read())
        if result['stop_reason'] == 'max_tokens':
            # max_tokens caps thinking + text; don't store a truncated/empty turn
            self.messages.pop()
            raise RuntimeError('Truncated at max_tokens: raise it or lower output_config.effort')
        assistant_message = next(
            (b['text'] for b in result['content'] if b['type'] == 'text'), ''
        )
        self.messages.append({
            'role': 'assistant',
            'content': assistant_message
        })
        return assistant_message
# Usage
conv = Conversation(system_prompt='You are an AWS solutions architect.')
print(conv.chat('What database should I use for a chat application?'))
print(conv.chat('What about for time-series data?'))

List Available Models

bash

# List all foundation models
aws bedrock list-foundation-models \
  --query 'modelSummaries[*].[modelId,modelName,providerName]' \
  --output table
 
# Filter by provider
aws bedrock list-foundation-models \
  --by-provider anthropic \
  --query 'modelSummaries[*].modelId'
 
# Get model details (includes modelLifecycle.status)
aws bedrock get-foundation-model \
  --model-identifier anthropic.claude-sonnet-5

Check Model Access

bash

# agreementAvailability.status AVAILABLE / NOT_AVAILABLE, authorizationStatus
aws bedrock get-foundation-model-availability \
  --model-id anthropic.claude-sonnet-5
 
# Anthropic one-time use case form (base64-encoded JSON:
# companyName, companyWebsite, intendedUsers, industryOption, otherIndustryOption, useCases)
aws bedrock put-use-case-for-model-access --form-data <base64-json>
 
# Programmatic agreement for third-party models
aws bedrock list-foundation-model-agreement-offers --model-id <model-id>
aws bedrock create-foundation-model-agreement --model-id <model-id> --offer-token <token>

Count Tokens

bash

# Free; returns inputTokens. Not supported for every model (e.g. CRIS-only Claude models)
aws bedrock-runtime count-tokens \
  --model-id anthropic.claude-3-5-haiku-20241022-v1:0 \
  --input '{"converse": {"messages": [{"role": "user", "content": [{"text": "Hello"}]}]}}'

CLI Reference

Bedrock (Control Plane)

Command	Description
`aws bedrock list-foundation-models`	List available models
`aws bedrock get-foundation-model`	Get model details
`aws bedrock list-custom-models`	List fine-tuned models
`aws bedrock create-model-customization-job`	Start fine-tuning
`aws bedrock list-provisioned-model-throughputs`	List provisioned capacity
`aws bedrock get-foundation-model-availability`	Check access/agreement status for a model
`aws bedrock put-use-case-for-model-access`	Submit Anthropic first-time use case form
`aws bedrock list-inference-profiles`	List system/application inference profiles
`aws bedrock create-model-invocation-job`	Start batch job (`--model-invocation-type InvokeModel\	Converse`)

Bedrock Runtime (Data Plane)

Command	Description
`aws bedrock-runtime invoke-model`	Invoke model synchronously
`aws bedrock-runtime converse`	Multi-turn conversation API
`aws bedrock-runtime count-tokens`	Count input tokens (`--input` with `invokeModel` or `converse`)
`aws bedrock-runtime apply-guardrail`	Evaluate content against a guardrail

InvokeModelWithResponseStream and ConverseStream are SDK-only (not in AWS CLI v2).

Bedrock Agent Runtime

Command	Description
`aws bedrock-agent-runtime retrieve`	Query knowledge base
`aws bedrock-agent-runtime retrieve-and-generate`	RAG query

InvokeAgent, RetrieveAndGenerateStream, and AgenticRetrieveStream are SDK-only (event streams).

Best Practices

Cost Optimization

This is the start of the file. Read the full SKILL.md on GitHub.

Security

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Model reviewPassed

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Quality evaluation

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Source

SKILL.md in the repositoryCheckingskills/bedrock/SKILL.md

Popularity

Repository stars: 1,096
Repository forks: 440
7-day trend score: 35

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 13 May 2026, 5 months ago.

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Install

SKILL.md

AWS Bedrock

Table of Contents

Core Concepts

Foundation Models

Model Access

Endpoints

Inference Profiles and Model Lifecycle

Knowledge Bases and Agents

Inference Types

Common Patterns

Invoke Model (Text Generation)

Streaming Response

Generate Embeddings

Conversation with History

List Available Models

Check Model Access

Count Tokens

CLI Reference

Bedrock (Control Plane)

Bedrock Runtime (Data Plane)

Bedrock Agent Runtime

Best Practices

Cost Optimization

Security

Quality evaluation

Source

Popularity

Works with